Illuminating the blind spot of diagnostic error and improving diagnosis in health care will require a significant reenvisioning of the diagnostic process and widespread commitment to change. Diagnostic error is a complex and multifaceted problem; there is no single solution that is likely to achieve the changes that are needed. To address this challenge and to improve diagnosis for patients and their families, the committee makes eight recommendations. This chapter highlights the overarching conclusions from the committee’s deliberations and presents these recommendations.
Several major conclusions emerged from the committee’s discussions. The first conclusion is that urgent change is needed to address the issue of diagnostic error, which poses a major challenge to health care quality. Diagnostic errors persist throughout all settings of care, involve common and rare diseases, and continue to harm an unacceptable number of patients. Yet, diagnosis—and, in particular, the occurrence of diagnostic errors—is not a major focus in health care practice or research. The result of this inattention is significant: It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences.
The committee drew this conclusion based on its collective assessment of the available evidence describing the epidemiology of diagnostic errors. In every research area that the committee evaluated, diagnostic er-
rors were a consistent quality and safety challenge. For example, a recent study estimated that 5 percent of U.S. adults who seek outpatient care experience a diagnostic error, and the researchers who conducted the study noted that this is likely a conservative estimate (Singh et al., 2014). Postmortem examination research that spans several decades has consistently shown that diagnostic errors contribute to around 10 percent of patient deaths (Shojania et al., 2002, 2003). The Harvard Medical Practice Study, which reviewed medical records, found diagnostic errors in 17 percent of the adverse events occurring in hospitalized patients (Leape et al., 1991), and a more recent study in the Netherlands found that diagnostic errors comprised 6.4 percent of hospital adverse events (Zwaan et al., 2010). Analyses of malpractice claims data indicate that diagnostic errors are the leading type of paid claims, represent the highest proportion of total payments, and are almost twice as likely to have resulted in the patient’s death compared to other claims (Tehrani et al., 2013).
However, the committee concluded that the available research estimates were not adequate to extrapolate a specific estimate or range of the incidence of diagnostic errors within clinical practice today. There is even less information available with which to assess the frequency and severity of harm related to diagnostic errors. Part of the challenge is the variety of settings in which these errors can occur, including hospitals, emergency departments, a variety of outpatient settings (such as primary and specialty care settings and retail clinics), and long-term care settings (such as nursing homes and rehabilitation centers), combined with the complexity of the diagnostic process itself. Although there are more data available to examine diagnostic errors in some of these settings, there are wide gaps in the information and great variability in the amount and quality of information available. In addition, aggregating data from various research methods—such as postmortem examinations, medical record reviews, and malpractice claims—is problematic. Each method captures information about different subgroups in the population, different dimensions of the problem, and different insights into the frequency and causes of diagnostic error. Nonetheless, the committee concluded that, taken together, the evidence suggests that diagnostic errors are a significant and common challenge in health care necessitating urgent attention.
The second conclusion is that it is very important to consider diagnosis from a patient-centered perspective, as patients bear the ultimate risk of harm from diagnostic errors. Thus, patients should be recognized as vital partners in the diagnostic process, and the health care system needs to encourage and support their engagement and to facilitate respectful learning from diagnostic errors. The committee’s definition of diagnostic error—the failure to (a) establish an accurate and timely explanation of the patient’s health problem(s) or (b) communicate that explanation to
the patient—reflects a patient-centered approach and highlights the key role of communication among the patient and the health care professionals involved in the diagnostic process. The term “explanation” is included in the definition to highlight the manner in which a diagnosis is conveyed to a patient such that it facilitates patient understanding and aligns with a patient’s level of health literacy.
The committee concluded that a sole focus on reducing diagnostic errors will not achieve the extensive change that is needed. Reducing diagnostic errors will require a broader focus on improving diagnosis in health care. This conclusion reflects the input provided to the committee by Gary Klein, a senior scientist at MacroCognition, who argued that improvements in diagnosis will require balancing two interdependent efforts: reducing diagnostic errors and improving diagnostic performance (Klein, 2014). Related input from David Newman-Toker, an associate professor at Johns Hopkins University, suggested that improving diagnostic performance will require addressing both diagnostic quality and efficiency in order to achieve high-value diagnostic performance (Newman-Toker, 2014; Newman-Toker et al., 2013). Thus, many of the recommendations focus on improving diagnosis and the diagnostic process as well on the identification and mitigation of diagnostic errors.
To provide a framework for this dual focus, the committee developed a conceptual model to articulate the diagnostic process, identify the factors that influence this process, and identify opportunities to improve the diagnostic process and outcomes. This conceptual model highlights the committee’s conclusion that diagnosis is a team-based process that occurs within the context of a broader system. This system involves the dynamic interaction of the participants in the diagnostic process (which are influenced by the participants’ cognitive, perceptual, and affective factors), the tasks that they perform, the technology and tools they utilize, the organization and physical environment in which diagnosis takes place, and the external environmental factors involved, such as oversight processes, error reporting, medical liability, and the payment and care delivery environment.
The committee’s recommendations focus on achieving eight goals to improve diagnosis and reduce diagnostic error (see Box 9-1). These recommendations are meant to be applicable to all diagnostic team members and settings of care; thus, some of the committee’s recommendations are intentionally broad. Given the early state of the field, the committee also sought to develop recommendations that were not overly proscriptive. Importantly, the evidence base for some recommendations stems from
- Facilitate more effective teamwork in the diagnostic process among health care professionals, patients, and their families
- Enhance health care professional education and training in the diagnostic process
- Ensure that health information technologies support patients and health care professionals in the diagnostic process
- Develop and deploy approaches to identify, learn from, and reduce diagnostic errors and near misses in clinical practice
- Establish a work system and culture that supports the diagnostic process and improvements in diagnostic performance
- Develop a reporting environment and medical liability system that facilitates improved diagnosis by learning from diagnostic errors and near misses
- Design a payment and care delivery environment that supports the diagnostic process
- Provide dedicated funding for research on the diagnostic process and diagnostic errors
the broader patient safety and quality improvement literature. Making connections to previous efforts is important, given the limited focus on diagnosis and its relevance to overall health care quality. Patients and patient advocates have much to offer on how to implement the committee’s recommendations. Leveraging the expertise, power, and influence of the patient community will help spur progress.
Facilitate More Effective Teamwork in the Diagnostic Process Among Health Care Professionals, Patients, and Their Families
The diagnostic process is a collaborative activity. Making accurate and timely diagnoses requires teamwork among health care professionals, patients, and their family members. The committee’s focus on teamwork in diagnosis grew out of the recognition that too often diagnosis is characterized as a solitary activity, taking place exclusively within an individual physician’s mind. While the task of integrating relevant information and communicating a diagnosis to a patient is often the responsibility of an individual clinician, the diagnostic process ideally involves collaboration among multiple health care professionals, the patient, and the patient’s family. Consistent with the committee’s conclusion, recent reports in the
literature make the case that the diagnostic process is a team-based endeavor (Graedon and Graedon, 2014; Haskell, 2014; Henriksen and Brady, 2013; McDonald, 2014; Schiff, 2014a). For example, Schiff noted that the new paradigm for diagnosis is that it is carried out by a well-coordinated team of people working together through reliable processes; in this view, diagnosis is the collective work of the team of health care professionals and the patient and his or her family (Schiff, 2014a).
Patients and their families are critical partners in the diagnostic process. The goal of patient engagement in diagnosis is to improve patient care and outcomes by enabling patients and their families to contribute valuable input that will facilitate an accurate and timely diagnosis and improve shared decision making about the path of care. There are indications, however, that patients and families are not routinely engaged as true partners in the diagnostic process and that they face challenges in engaging in the diagnostic process (Haskell, 2014; Julavits, 2014; McDonald, 2014). Two of the more significant challenges involve unfamiliarity with the diagnostic process and health care environments that are not supportive of patient engagement.
The committee identified several opportunities to improve patient and family engagement in the diagnostic process. First, patients and their families could benefit from having a better overall understanding of the diagnostic process. Learning opportunities that describe what to expect during this process, the roles of specific diagnostic team members, and materials that facilitate patient and family participation in the process could all be helpful. For example, the National Patient Safety Foundation, the Society to Improve Diagnosis in Medicine, and Kaiser Permanente have developed resources to help patients partner with their clinicians to receive a correct diagnosis (Kaiser Permanente, 2012; NPSF and SIDM, 2014). Health care organizations and health care professionals have the responsibility to create environments that are receptive to and supportive of patient engagement in the diagnostic process. This includes recognizing that patients and their families have varying needs, values, and preferences in regard to engagement and being responsive to the desired level of involvement. Furthermore, the health care environments need to encourage patients and families to share feedback about their experiences with diagnosis and their concerns about diagnostic errors and near misses. Although there are limited systematic mechanisms for patients to provide feedback to health care professionals about the accuracy of their diagnoses, establishing opportunities to provide patient feedback is critical to improving diagnostic performance (Schiff, 2008). This feedback could also become a routine aspect of assessing patient satisfaction.
An important opportunity to improve engagement is through the use of health information technology (health IT) tools that make a patient’s
health information more accessible and transparent, including clinical notes and diagnostic testing results. The Office of the National Coordinator for Health Information Technology’s Meaningful Use 2 requirements include patient’s having access to their electronic health information, such as medication lists, diagnostic test results, allergies, and clinical problem lists; organizations have begun to employ patient portals in order to provide patients with access to this information (Adler-Milstein et al., 2014; Bruno et al., 2014; Furukawa et al., 2014; HealthIT.gov, 2015). The OpenNotes initiative, which is available to almost 5 million patients, has promoted even greater transparency of a patient’s health information by inviting patients to view the notes recorded by health care professionals during the patients’ clinical visit. Initiatives like OpenNotes may promote patient engagement in the diagnostic process and also serve as a mechanism for patients and their families to identify and avert diagnostic errors (Bell et al., 2014; Delbanco et al., 2010, 2012).
Health care professionals and organizations can also involve patients and their families in organizational learning efforts aimed at analyzing the causes of diagnostic errors and identifying interventions that could improve the diagnostic process. Patients and their families have unique insight into the diagnostic process, their outcomes, and the occurrence of diagnostic errors; thus, their perspectives are critical to improving the diagnostic process (Etchegaray et al., 2014; Gertler et al., 2014; Weingart et al., 2005). When a diagnostic error occurs, health care organizations can identify opportunities to involve a patient and his or her family in efforts to learn from the error, using mechanisms such as root cause analyses, morbidity and mortality conferences, and patient and family advisory councils (AHRQ, 2014c; Gertler et al., 2014; Zimmerman and Amori, 2007).
In addition to patient engagement, the committee highlighted the roles of health care professionals in the diagnostic process and the need for improved intra- and interprofessional collaboration. Depending on a patient’s health problem, the diagnostic process can involve various types of health care professionals, such as primary care clinicians (physicians, advance practice nurses [APNs], physician assistants [PAs]), physicians in a broad range of specialties (including radiology, pathology, and other disease-focused areas), nurses, technologists, therapists, social workers, pharmacists, and patient navigators. For simplicity, the committee’s conceptual model articulates two main types of health care professionals: diagnosticians, or those who make diagnoses, such as physicians, APNs, and PAs; and the health care professionals who support the diagnostic process. Inadequate teamwork and communication are major contributors to medical errors, including diagnostic errors (Baker et al., 2006; CRICO, 2014; Dingley et al., 2008; Singh et al., 2008). Because a patient’s diagnosis can hinge on the successful collaboration among these health care profes-
sionals, it is important that all health care professionals are well-prepared and supported to engage in diagnostic teamwork.
Recognition that interprofessional education and training is critical to the delivery of high-quality care has been gaining widespread traction; however, health care professionals are still not adequately prepared for this team-based practice (IOM, 2014; Patel et al., 2009; Pecukonis et al., 2008; Schmitt et al., 2011). Opportunities for interprofessional training have been slow to materialize because of a host of different issues, including logistical challenges, deep-rooted cultural differences among the health care professions, differences in educational curricula and trajectory, and costs (Josiah Macy Jr. Foundation and Carnegie Foundation for the Advancement of Teaching, 2010). Furthermore, intraprofessional collaboration can be difficult to achieve in practice, and the way that physicians are prepared today may be hindering their ability to engage in teamwork and cooperation (Hughes and Salas, 2013). For example, the traditional hierarchy among medical students, residents, and experienced physicians may prevent the more junior clinicians from speaking up about a potential error (Sorra et al., 2014).
In addition, the roles of some health care professionals who participate in the diagnostic process have been insufficiently recognized in current practice. For example, the fields of pathology and radiology are critical to diagnosis, but these health care professionals have sometimes been referred to as ancillary services and are not always engaged as full members of the diagnostic team despite their significant contributions to diagnosis. Enhanced collaboration has the potential to improve all aspects of the diagnostic testing process, including test ordering, analysis and interpretation, reporting and communicating the results, and subsequent decision making (Allen and Thorwarth, 2014; Epner, 2015; Kroft, 2014). One opportunity to better integrate these health care professionals into the diagnostic process is the diagnostic management team model; these integrated teams feature collaboration among pathologists, radiologists, and the treating health care professionals in order to ensure that the correct diagnostic tests are ordered and that the results are correctly interpreted and acted upon (Govern, 2013).1
In addition, nurses are often not recognized as collaborators in the diagnostic process, despite their critical roles in ensuring proper communication and care coordination among the health care professionals and between the professionals and the patient and his or her family; monitoring the patient’s condition over time to see if the patient’s course of treatment aligns with the working diagnosis; and identifying and preventing potential diagnostic errors. Depending on a particular patient’s needs,
1 Personal communication, M. Laposata, August 8, 2014.
many other health care professionals can play key roles in the diagnostic process, and they also need to be engaged to improve diagnosis.
Goal 1: Facilitate more effective teamwork in the diagnostic process among health care professionals, patients, and their families
Recommendation 1a: In recognition that the diagnostic process is a dynamic team-based activity, health care organizations should ensure that health care professionals have the appropriate knowledge, skills, resources, and support to engage in teamwork in the diagnostic process. To accomplish this, they should facilitate and support:
- Intra- and interprofessional teamwork in the diagnostic process.
- Collaboration among pathologists, radiologists, other diagnosticians, and treating health care professionals to improve diagnostic testing processes.
Recommendation 1b: Health care professionals and organizations should partner with patients and their families as diagnostic team members and facilitate patient and family engagement in the diagnostic process, aligned with their needs, values, and preferences. To accomplish this, they should:
- Provide patients with opportunities to learn about the diagnostic process.
- Create environments in which patients and their families are comfortable engaging in the diagnostic process and sharing feedback and concerns about diagnostic errors and near misses.
- Ensure patient access to electronic health records (EHRs), including clinical notes and diagnostic testing results, to facilitate patient engagement in the diagnostic process and patient review of health records for accuracy.
- Identify opportunities to include patients and their families in efforts to improve the diagnostic process by learning from diagnostic errors and near misses.
Enhance Health Care Professional Education and Training in the Diagnostic Process
Getting the right diagnosis depends on all health care professionals receiving appropriate education and training. There are indications, however, that health care professionals, including diagnosticians, are not prepared to function optimally in the diagnostic process (Brush,
2014; Dhaliwal, 2014; Durning, 2014; Richardson, 2007; ten Cate, 2014; Trowbridge et al., 2013). Education and training-related challenges include methods that have not kept pace with advances in the learning sciences2 and have an insufficient focus on areas critical to the diagnostic process. Numerous experts in health care professional education provided input to the committee; a common theme of this input was that health care professional education and training is not adequately preparing individuals to become skilled diagnosticians. One of the criticisms is that current approaches to education do not take advantage of advances in the learning sciences, which have found that learners need to develop a deep conceptual understanding of their content area and to have opportunities to reflect on their knowledge; furthermore, educators need to consider factors such as the learning environment, building on prior knowledge, and focusing on learning in addition to teaching. The lack of feedback—or information on the accuracy of a clinician’s diagnosis—in the current training environment can result in few opportunities to reflect on one’s state of knowledge. This can lead to poorly calibrated clinicians who are unaware of their diagnostic performance and overly confident in their diagnoses (Berner and Graber, 2008). In addition, the authenticity of the learning environment can affect the acquisition of diagnostic skills, and a better alignment of training environments with clinical practice can improve the development of diagnostic skills. For example, clinicians often learn from case studies that reflect prototypical cases, but they are faced with the complexities of real patient cases in their clinical practice (Papa, 2014).
It was not within the committee’s charge to define the specific curriculum for all health care professionals; the content of the curriculum and training will need to be tailored to the needs of specific health care professionals. However, the committee highlighted several areas that are important to the diagnostic process. Opportunities to improve the content of health care professional education and training in the diagnostic process include placing a greater emphasis on teamwork and communication with patients, their families, and other health care professionals; providing more training in the ordering of diagnostic testing and in the application of these results to subsequent decision making; and offering more training in the use of health IT. In addition, current health care professional education and training underemphasizes clinical reasoning, including critical thinking skills and decision making in the diagnostic process (Brush, 2014; Durning, 2014; Richardson, 2014; ten Cate, 2014). Although diagnosticians are trained to make diagnoses, few programs
2 The learning sciences study how people learn in order to optimize education and training.
feature explicit training in various aspects of clinical reasoning, such as the dual process theory, heuristics, and biases. This lack of focus on clinical reasoning and on understanding the cognitive contributions to decision making represents a major gap in health care professional education for all diagnostic team members. Among the strategies proposed to improve clinical reasoning education and training are instruction and practice on generating and refining a differential diagnosis; developing an appreciation of how diagnostic errors occur and of the strategies to mitigate them; engaging in metacognition and debiasing strategies; and fostering intuition and progressive problem solving (Eva and Norman, 2005; Gigerenzer, 2000; Gigerenzer and Goldstein, 1996; Hirt and Markman, 1995; Hodges et al., 2001; Marewski and Gigerenzer, 2012; Mumma and Steven, 1995; Mussweiler et al., 2000; Redelmeier, 2005; Trowbridge et al., 2013; Wegwarth et al., 2009).
Oversight processes, such as education and training program accreditation, licensure, and certification, can help ensure that health care professionals achieve and maintain competency in the diagnostic process. Many accreditation organizations already include skills important for diagnostic performance in their accreditation requirements, but diagnostic competencies need to be a larger priority within those requirements. Organizations responsible for health care professional licensure and certification can help ensure that individual health care professionals have achieved and maintain competency in the skills essential for diagnosis. For example, the American Board of Medical Specialties, which grants board certification in more than 150 medical specialties and subspecialties, could use its certification processes to assess competencies in the diagnostic process both in initial board certification and in maintenance of certification efforts.
Goal 2: Enhance health care professional education and training in the diagnostic process
Recommendation 2a: Educators should ensure that curricula and training programs across the career trajectory:
- Address performance in the diagnostic process, including areas such as clinical reasoning; teamwork; communication with patients, their families, and other health care professionals; appropriate use of diagnostic tests and the application of these results on subsequent decision making; and use of health information technology.
- Employ educational approaches that are aligned with evidence from the learning sciences.
Recommendation 2b: Health care professional certification and accreditation organizations should ensure that health care professionals have and maintain the competencies needed for effective performance in the diagnostic process, including the areas listed above.
Ensure That Health Information Technologies Support Patients and Health Care Professionals in the Diagnostic Process
Health IT plays a critical role in the diagnostic process and includes such technologies as electronic health records (EHRs), health information exchanges, laboratory and medical imaging information systems, clinical decision support, patient engagement tools, computerized provider order entry, and medical devices. When health IT tools support the diagnostic team members and tasks in the diagnostic process and reflect human-centered design principles, health IT has the potential to improve diagnosis and reduce diagnostic errors. For example, health IT can facilitate timely access to information; improve communication among health care professionals, patients, and their families; aid in clinical reasoning and decision making; and help provide feedback and follow-up in the diagnostic process (El-Kareh et al., 2013; Schiff and Bates, 2010). Despite this potential, there have been few demonstrations that health IT improved diagnosis in clinical practice. Indeed, many experts are concerned that current health IT tools are not effectively facilitating the diagnostic process and that they may even be contributing to diagnostic errors (Basch, 2014; Berenson et al., 2011; El-Kareh et al., 2013; Kuhn et al., 2015; Ober, 2015; ONC, 2014; Schiff and Bates, 2010; Verghese, 2008).
The major challenges of health IT in the diagnostic process include problems with the usefulness and usability of health IT tools, poor integration into clinical workflow, difficulty sharing information among diagnostic team members and settings, limitations in supporting clinical reasoning in the diagnostic process, and a lack of opportunities to measure diagnostic errors through health IT tools. In particular, clinicians have expressed concern that clinical documentation in EHRs is not promoting high-quality diagnosis, but is instead aimed at meeting billing and legal requirements, forcing clinicians to “focus on ticking boxes rather than on thoughtfully documenting their clinical thinking” (Schiff and Bates, 2010, p. 1066) (see also Recommendation 7). Collaboration among health IT vendors, the Office of the National Coordinator for Health Information Technology (ONC), and users is warranted to ensure that health IT tools are better aligned with the diagnostic process.
Another health IT–related challenge in the diagnostic process is the lack of interoperability, or the inability for different IT systems and soft-
ware applications to communicate, exchange data, and use information effectively (Basch, 2014; CHCF, 2014; HIMSS, 2014). Because the diagnostic process occurs over time and can involve multiple health care professionals across different care settings, the free flow of information is critical. In order for health care professionals to develop a complete picture of a patient’s health problem, it is crucial that all relevant health information is available and easily accessible. However, progress toward achieving health interoperability has been slow (CHCF, 2014). Only 30 percent of clinicians and hospitals are able to exchange clinical data with other clinicians electronically (Adler-Milstein and Jha, 2014). Similarly, a recent survey of office-based physicians found that while 67 percent were able to view lab results electronically, only 42 percent were able to incorporate lab results into their EHR, and only 31 percent of the physicians exchanged patient clinical summaries with other clinicians (Patel et al., 2013). Challenges to interoperability include the inconsistent and slow adoption of standards, particularly among organizations that are not subject to EHR certification programs, as well as a lack of incentives, such as a business model that generates revenue for health IT vendors via fees associated with transmitting and receiving data (Adler-Milstein, 2015; CHCF, 2014).
Among the federal efforts to improve interoperability are programs to support the development of flexible interoperability standards and meaningful use incentives. Given the importance of interoperability to diagnosis, ONC can play a critical role in accelerating progress toward interoperability by ensuring that health IT vendors meet these requirements by 2018. This recommendation is in line with the recent legislation that repealed the sustainable growth rate, which included a provision that declared it a national objective to “achieve widespread exchange of health information through interoperable certified electronic health records technology nationwide by December 31, 2018.”3
Improving interoperability across different health care organizations as well as across laboratory and radiology information systems will be critical to improving the diagnostic process. One challenge will be specifying the scope of interoperable information. For example, the interface between EHRs and laboratory and radiology information systems typically has limited clinical information, and the lack of sufficient patient information makes it difficult for a pathologist or radiologist to determine whether diagnostic testing is appropriate or to understand the context for interpreting findings (Epner, 2014, 2015). Another emerging challenge is establishing interoperability between EHRs and patient-facing health IT, including health-related mobile health applications such as those that
3 Medicare Access and CHIP Reauthorization Act of 2015. P.L. 114-10. (April 16, 2015).
keep track of physical activity and glucose levels (Dehling et al., 2015; Marceglia et al., 2015; Otte-Trojel et al., 2014).
Patient safety risks in the diagnostic process related to the use of health IT are another important concern because there is growing recognition that the use of health IT can result in adverse events (IOM, 2012; ONC, 2014). Health IT safety risks have been identified in the context of the sociotechnical system (including technology, people, workflow, organizational factors, and external environment) that can dynamically interact and contribute to adverse events (IOM, 2012; Sittig and Singh, 2010). A number of health IT–related patient safety risks may affect the occurrence of diagnostic errors. For example, two areas of increased concern are clinical documentation and the use of the copy and paste functionality of EHRs. While the use of copy and paste functionality may increase efficiency by saving time spent retyping or reentering information, it carries with it a number of risks, including redundancy that contributes to lengthy notes and cognitive overload as well as the propagation of inaccurate, outdated, or incomprehensible information (AHIMA, 2014; The Joint Commission, 2015; Kuhn et al., 2015).
Unfortunately, contractual provisions, designed to protect vendors’ intellectual property interests and liability from unsafe use of health IT products end up limiting the free exchange of information about health IT–related patient safety risks (IOM, 2012). Specifically, “some vendors require contract clauses that force [health IT] system purchasers to adopt vendor-defined policies that prevent the disclosure of errors, bugs, design flaws, and other [health IT] software-related hazards” (Goodman et al., 2011, p. 77). These contractual barriers among health IT vendors and users may propagate safety risks and pose significant challenges to the use of data for future patient safety and quality improvement research (IOM, 2012). Thus, the Institute of Medicine (IOM) report Health IT and Patient Safety recommended that “the Secretary of the Department of Health and Human Services [HHS] should ensure insofar as possible that health IT vendors support the free exchange of information about health IT experiences and issues and not prohibit sharing of such information, including details (e.g., screenshots) relating to patient safety” (IOM, 2012, pp. 7 and 128). The committee endorses this recommendation and adds that the Secretary of HHS should require health IT vendors to permit and support the free exchange of information on users’ experiences with health IT design and implementation that contribute to adverse effects on the diagnostic process. Health IT users can discuss these patient safety concerns in appropriate forums, such as the forthcoming ONC National Patient Safety Center or patient safety organizations (PSOs) (RTI International, 2014; Sittig et al., 2015). The Agency for Healthcare Research and Quality (AHRQ) has developed a Common Format reporting form for health IT
adverse events, and HHS is beginning to evaluate patient safety events related to health IT (ONC, 2014; RTI International, 2014).
Because the safety of health IT is critical for improvements to the diagnostic process, health IT vendors need to proactively monitor their products in order to identify potential adverse events, which could contribute to diagnostic errors and challenges in the diagnostic process (Carayon et al., 2011). To ensure that these vendors’ products are unlikely to contribute to diagnostic errors and adverse events, independent, routine third-party evaluations of health IT products used in the diagnostic process need to be performed. If health IT products have the potential to contribute to diagnostic errors or have other adverse effects on the diagnostic process, health IT vendors have a responsibility to communicate this information to their users in a timely manner.
Goal 3: Ensure that health information technologies support patients and health care professionals in the diagnostic process
Recommendation 3a: Health information technology (health IT) vendors and the Office of the National Coordinator for Health Information Technology (ONC) should work together with users to ensure that health IT used in the diagnostic process demonstrates usability, incorporates human factors knowledge, integrates measurement capability, fits well within clinical workflow, provides clinical decision support, and facilitates the timely flow of information among patients and health care professionals involved in the diagnostic process.
Recommendation 3b: ONC should require health IT vendors to meet standards for interoperability among different health IT systems to support effective, efficient, and structured flow of patient information across care settings to facilitate the diagnostic process by 2018.
Recommendation 3c: The Secretary of the Department of Health and Human Services should require health IT vendors to:
- Routinely submit their products for independent evaluation and notify users about potential adverse effects on the diagnostic process related to the use of their products.
- Permit and support the free exchange of information about real-time user experiences with health IT design and implementation that adversely affect the diagnostic process.
Develop and Deploy Approaches to Identify, Learn from, and Reduce Diagnostic Errors and Near Misses in Clinical Practice
Diagnostic errors are an understudied and underappreciated quality challenge in health care organizations (Graber, 2005; Wachter, 2010). Very few health care organizations have focused on the identification of diagnostic errors and near misses in clinical practice (Graber et al., 2014; Kanter, 2014; Singh, 2014; Trowbridge, 2014). In a presentation to the committee, Paul Epner reported that the Society to Improve Diagnosis in Medicine “know[s] of no effort initiated in any health system to routinely and effectively assess diagnostic performance” (Epner, 2014). Thus, “the true prevalence of diagnostic error is unknown” (Singh et al., 2008, p. 489). The paucity of attention on diagnostic errors in clinical practice has been attributed to a number of factors. Two major contributors are the lack of effective measurement of diagnostic error and the difficulty in detecting these errors in clinical practice (Berenson et al., 2014; Graber et al., 2012; Singh and Sittig, 2015). Additional factors may include a health care organization’s competing priorities in patient safety and quality improvement, the perception that diagnostic errors are inevitable or that they are too difficult to address, and the lack of financial resources to address this problem (Croskerry, 2003; Graber, 2005; Graber et al., 2014; Henriksen, 2014; Singh and Sittig, 2015). These challenges make it difficult to identify, analyze, and learn from diagnostic errors in clinical practice.
Compared to diagnostic errors, other types of medical errors—including medication errors, surgical errors, and health care–acquired infections—have historically received more attention within health care organizations (Graber et al., 2014; Kanter, 2014). This is partly attributable to the lack of focus on diagnostic errors within national patient safety and quality improvement efforts. For example, AHRQ’s Patient Safety Indicators and The Joint Commission’s list of specific sentinel events do not focus on diagnostic errors (The Joint Commission, 2014; Schiff et al., 2005). The National Quality Forum’s Serious Reportable Events include 29 endorsed events, but only one of those is closely tied to diagnostic error: “Patient death or serious injury resulting from a failure to follow up or communicate laboratory, pathology, or radiology test results” (NQF, 2011, p. 10). The neglect of diagnostic performance measures for accountability purposes means that hospitals today could meet standards for high-quality care and be rewarded through public reporting and pay-for-performance initiatives even if they have major challenges with diagnostic accuracy (Wachter, 2010).
Identifying diagnostic errors within clinical practice is critical to improving the quality of diagnosis for patients; however, measurement has become an “unavoidable obstacle to progress” (Singh, 2013, p. 789). The
lack of comprehensive information on diagnostic errors within clinical practice perpetuates the belief that these errors are uncommon or unavoidable and impedes progress on reducing diagnostic errors. Improving diagnosis will likely require a concerted effort among all health care organizations and across all settings of care to better identify diagnostic errors and near misses, to learn from them, and, ultimately, to take steps to improve the diagnostic process. In addition to identifying near misses and errors, health care organizations can also benefit from evaluating factors that are contributing to improved diagnostic performance.
Given the nascent field of measurement of the diagnostic process, bottom-up experimentation will be necessary to develop approaches for monitoring the diagnostic process and identifying diagnostic errors and near misses. It is unlikely that any one specific method will be successful at identifying all diagnostic errors and near misses; some approaches may be more appropriate than others for specific organizational settings, types of diagnostic errors, or for identifying factors that contributed to these errors. It may be necessary for health care organizations to use a variety of methods to develop a better sense of their diagnostic performance (Shojania, 2010). Medical record reviews, medical malpractice claims analysis, health insurance claims analysis, and second reviews in diagnostic testing may be more pragmatic approaches for health care organizations because they leverage readily available data sources. Patient surveys may also be an important mechanism for health care organizations to consider; this is in line with the committee’s recommendation to create environments in which patients and their families feel comfortable sharing their feedback and concerns about diagnostic error. It is important to note that many of these methods are just beginning to be applied to diagnostic error detection in clinical practice; very few are validated or available for widespread use in clinical practice (Bhise and Singh, 2015; Graber, 2013; Singh and Sittig, 2015).
Beyond identifying diagnostic errors and near misses, organizational learning to improve diagnostic performance and reduce diagnostic errors will require a focus on understanding where in the diagnostic process these errors occurred, the work system factors that contributed to their occurrence, what the outcomes were, and how these errors may be prevented or mitigated. Health care organizations can employ formal error analysis and other risk assessment methods to understand the work system factors that underlie these events, including analytical methods employed in human factors and ergonomics research. Once health care organizations have a better understanding of diagnostic errors within their organization, they will need to implement and evaluate interventions to prevent or mitigate these errors.
Accreditation organizations and Medicare conditions of participation should ensure that health care organizations’ programs are achieving improvements in the quality and safety of diagnosis, including appropriate monitoring, careful analysis of diagnostic errors, and system changes in response to these errors and near misses.
Postmortem examinations are an important method for identifying diagnostic errors because these examinations can, in many cases, determine the cause of death and reveal discrepancies between premortem and postmortem clinical findings (Shojania et al., 2002). However, the number of postmortem examinations performed in the United States has declined substantially since the 1960s because of a range of medical, legal, social, and economic factors (Lundberg, 1998; Shojania et al., 2002).
The committee concluded that a new approach to increasing the use of postmortem examinations is warranted. The committee weighed the relative merits of increasing the number of postmortem examinations conducted throughout the United States versus a more targeted approach. The current requirements for postmortem examinations under the Medicare conditions of participation already state that postmortem examinations should be performed when there is an unusual death or a death of medical-legal and educational interest, and the committee concluded that health care organizations should continue to perform the examinations in these circumstances. In addition, the committee concluded that it is appropriate to have a limited number of highly qualified health care systems participate in conducting routine postmortem exams that produce research-quality information about the incidence and nature of diagnostic errors. To accomplish this, a subset of health care systems that reflect a broad array of different settings of care could receive funding to perform postmortem examinations in a representative sample of patient deaths.4 This approach will likely provide better epidemiologic data and represent an advance over current selection methods for performing postmortem examinations, because clinicians do not seem to be able to predict cases in which diagnostic errors will be found (Shojania et al., 2002, 2003). The committee recognizes that the data collected from health care systems that are highly qualified to conduct routine postmortem examinations may not be representative of all systems of care. However, the committee concluded that this approach is more feasible given the financial and workforce demands of conducting postmortem examinations.
These health care organizations could also investigate how new, minimally invasive postmortem approaches compare with full-body postmor-
4 Not all patients’ next of kin will consent to the performance of a postmortem examination; these systems can characterize the frequency with which the request for a postmortem examination is refused and better describe the risk of response bias in results.
tem examinations. Less invasive approaches include medical imaging, laparoscopy, biopsy, histology, and cytology. Given the advances in molecular diagnostics and advanced imaging techniques, these new approaches could provide useful insights on diagnostic error and may be more acceptable options for patients’ next of kin. Further understanding the benefits and limitations of minimally invasive approaches may provide critical information moving forward. If successful approaches to minimally invasive postmortem examinations are found, they could play a role in reestablishing the practice of routine postmortem investigation in medicine.
Health care organizations can also implement mechanisms that improve systematic feedback at all levels. Feedback entails informing individuals, teams, or organizations about their diagnostic performance, including their successes, near misses, and diagnostic errors. The committee received substantial input indicating that there are limited opportunities for feedback on diagnostic performance. Feedback can help clinicians assess how well they are performing in the diagnostic process, correct overconfidence, identify when remediation efforts are needed, and reduce the likelihood of repeated mistakes. Feedback on diagnostic performance can also provide opportunities for organizational learning and improvements to the work system of health care organizations. Characteristics of effective feedback mechanisms include being actionable, timely, individualized, and nonpunitive (Hysong et al., 2006). Health care organizations also need to be aware of the factors that can impede the provision of feedback, such as the fragmentation of the health care system, resistance to critical feedback from clinicians, and the lack of time for follow-up (Schiff, 2008).
There are many opportunities to provide feedback in clinical practice. Methods to monitor the diagnostic process and identify diagnostic errors and near misses can be leveraged as mechanisms to provide feedback. Feedback opportunities include disseminating postmortem examination results to clinicians who were involved in the patient’s care; sharing the results of patient surveys, medical record reviews, or information gained through follow-up with the health care professionals; using patient-actors or simulated care scenarios to assess and inform health care professionals’ diagnostic performance; and others. Because patients and their families have unique insights into the diagnostic process and the occurrence of diagnostic error, following up with patients and their families about their experiences and outcomes will be an important source of feedback (Schiff, 2008). Another example of feedback is RADPEER, a program developed by the American College of Radiology that allows anonymous peer review of previous image interpretations to be conducted during the interpretation of current images. Summary statistics of these reviews
are made available to participating groups, and they can be used as feedback to improve individual and group practice performance (Allen and Thorwarth, 2014). Morbidity and mortality conferences, root cause analyses, departmental meetings, and WalkRounds provide additional opportunities for feedback to different groups in health care.
There is also an opportunity to improve diagnosis by engaging health care professional societies in identifying areas within their specialties to reduce diagnostic errors and improve diagnostic performance. This can facilitate improvements in diagnosis based on intrinsic motivation and professionalism rather than other incentives or disincentives. Efforts to improve diagnosis can include both improving the quality and safety of diagnosis and increasing efficiency and value by minimizing inappropriate diagnostic testing. This effort could be modeled on Choosing Wisely, which was initiated by the American Board of Internal Medicine Foundation to encourage patient and health care professional communication as a means to ensure high-quality, high-value care. The initiative invited each health care professional society to identify a list of five services (i.e., tests, treatments, procedures) that are commonly used in practice but may be unnecessary or not supported by the evidence as improving patient care. These lists were made publicly available as a way of encouraging discussions about appropriate care between patients and health care professionals. Choosing Wisely received widespread national media attention and engaged more than 50 health care professional societies (Choosing Wisely, 2014). A major lesson from the Choosing Wisely initiative was the importance of beginning with a small group of founding organizations and then expanding membership. Engaging consumer groups as the initiative progressed was also an important component. Another factor in the initiative’s success was that it allowed flexibility within limits; participating health care professional societies and boards were given flexibility in identifying their “Top 5” services, but items on each list had to be evidence-based and within the purview of that particular society.
A similar effort engaging health care professional societies could focus on prioritizing diagnostic errors. Early efforts on prioritization could focus on identifying the most common diagnostic errors and “don’t miss” health conditions, such as those that present the greatest likelihood for diagnostic errors and harm (Newman-Toker, 2014; Newman-Toker et al., 2013). Each organization could be asked to identify five high-priority areas to improve diagnosis. These groups could be given latitude in how they chose to identify their targets, as in Choosing Wisely. Efforts to improve diagnosis can include both improving the quality and safety of diagnosis and increasing efficiency and value, such as identifying inappropriate diagnostic testing. Another approach may be for societies to
identify “low-hanging fruit,” or targets that are easily remediable, as a high priority. This strategy could increase the likelihood of creating early wins that may contribute to the long-term success of this type of effort. Some groups might identify particular actions, tools, or approaches to reduce diagnostic errors with a particular diagnosis within their specialties (such as checklists, second reviews, or decision support tools).
This could also be an opportunity for health care professional societies to collaborate, especially on diagnoses that may be missed due to an inappropriate isolation of symptoms. For example, urologists, primary care clinicians, and neurologists could collaborate to make the diagnosis of normal pressure hydrocephalus (whose symptoms include frequent urination, balance problems, and memory loss) a “not to be missed” diagnosis (McDonald, 2014).
Goal 4: Develop and deploy approaches to identify, learn from, and reduce diagnostic errors and near misses in clinical practice
Recommendation 4a: Accreditation organizations and the Medicare conditions of participation should require that health care organizations have programs in place to monitor the diagnostic process and identify, learn from, and reduce diagnostic errors and near misses in a timely fashion. Proven approaches should be incorporated into updates of these requirements.
Recommendation 4b: Health care organizations should:
- Monitor the diagnostic process and identify, learn from, and reduce diagnostic errors and near misses as a component of their research, quality improvement, and patient safety programs.
- Implement procedures and practices to provide systematic feedback on diagnostic performance to individual health care professionals, care teams, and clinical and organizational leaders.
Recommendation 4c: The Department of Health and Human Services should provide funding for a designated subset of health care systems to conduct routine postmortem examinations on a representative sample of patient deaths.
Recommendation 4d: Health care professional societies should identify opportunities to improve accurate and timely diagnoses and reduce diagnostic errors in their specialties.
Establish a Work System and Culture That Supports the Diagnostic Process and Improvements in Diagnostic Performance
Testimony to the committee indicated that the work systems of many health care organizations could do a better job of supporting the diagnostic process (Gandhi, 2014; Kanter, 2014; Sarter, 2014; Schiff, 2014b). Health care organizations influence the work system in which diagnosis occurs and play a role in implementing changes to improve diagnosis and avert diagnostic errors.
The committee focused on organizational culture as well as the leadership and management of an organization as key characteristics for ensuring continuous learning and improvements to the diagnostic process. Organizational culture refers to an organization’s norms of behavior and the shared basic assumptions and values that sustain those norms (Kotter, 2012; Schein, 2004). Health care organizations are responsible for developing a culture that promotes a safe place for all health care professionals to identify and learn from diagnostic errors. Organizational leaders and managers can facilitate this culture and set the priorities to achieve progress in improving diagnostic performance and reducing the occurrence of diagnostic errors.
Some aspects of culture in health care organizations, such as an emphasis on quality, safety, professionalism, and the intrinsic motivation of health care professionals, promote diagnostic performance. There are other aspects of culture that do not promote improved diagnostic performance, such as an emphasis on blame and punishment and a lack of emphasis on team-based care. A recent survey of more than 400,000 staff at 653 hospitals found that fewer than half of all surveyed staff members perceived that their organization had a nonpunitive response to error (AHRQ, 2014a). A culture that emphasizes discipline and punishment for those who make mistakes presents a significant barrier to the reporting of errors, which in turn thwarts the learning process. Cultural taboos on providing feedback to colleagues can further hinder efforts to identify and learn from diagnostic errors. To improve diagnosis, health care organizations need to develop nonpunitive cultures that promote open discussion and feedback on diagnostic performance (Gandhi, 2014; Kanter, 2014; Thomas, 2014). Organizations can support learning and continual improvement in diagnostic performance by implementing a just culture (Kanter, 2014; Khatri et al., 2009; Larson, 2002; Marx, 2001; Milstead, 2005) or by adapting the traits of high reliability organizations, which operate in high-stakes conditions but maintain high safety levels (such as those found in nuclear power and aviation industries) (Chassin and Loeb, 2011; Singh, 2014; Thomas, 2014; Weick and Sutcliffe, 2011). The involvement of supportive and committed leadership is another component of successful
attempts to improve culture (Chassin, 2013; Hines et al., 2008; IOM, 2013; Kotter, 1995, 2012).
Collaboration among organizational leaders is critical to achieving a health care organization’s quality goals, as well as successful change initiatives (Dixon-Woods et al., 2011; Firth-Cozens, 2004; Gandhi, 2014; Kotter, 1995; Larson, 2002; Moran and Brightman, 2000; Silow-Carroll et al., 2007). Leaders communicate the priorities of the organization, set expectations, and ensure that the rules, policies, and resources encourage and support the improvement of diagnostic performance. In many health care organizations, organizational leaders have not yet focused significant attention on improving diagnosis and reducing diagnostic errors (Gandhi, 2014; Graber, 2005; Henriksen, 2014; Wachter, 2010; Zwaan et al., 2013). However, facilitating change will require their support and involvement, and it may also include prioritizing diagnosis and supporting senior managers in implementing policies and practices that support continued learning and improved diagnostic performance, adopting a continuously learning culture, raising awareness of the quality and safety challenges related to diagnostic error, and dispelling the myth that diagnostic errors are inevitable (Leape, 2010; Wachter, 2010).
Many components of the work system are under the purview of health care organizations. Thus, organizations can implement changes that ensure a work system that supports the diagnostic process. One principle that health care organizations can apply to the design of the diagnostic work system is “error recovery,” which refers to the early identification of an error so that actions can be taken to mitigate or avert negative effects resulting from the error (IOM, 2000). There are a variety of opportunities for health care organizations to improve error recovery and resiliency in the diagnostic process. For example, improved patient access to clinical notes and diagnostic testing results is one form of error recovery; this access gives patients the opportunity to identify and correct errors in their medical records that could lead to a diagnostic error, potentially before any harm results. Thoughtful use of redundancies, such as second reviews of anatomic pathology specimens and medical images, consultations, and second opinions in challenging cases or complex care environments, is also a form of error recovery that health care organizations can consider.
In addition, organizations can ensure that workforce staffing and supervision policies support human performance and address patient safety risks caused by fatigue (including decision fatigue), sleep deprivation, and sleep debt (IOM, 2009). Health care organizations can also focus on improvements in workflow design, care transitions, and settings of care that are prone to diagnostic errors (such as emergency departments and
outpatient settings). Technologies that support the diagnostic process, such as clinical decision support, can also be considered.
Health care organizations can ensure that the design and characteristics of the physical environments in which diagnosis takes place support the diagnostic process. Elements of the physical environment, including layout, distractions, noise, and lighting, can have an impact on human performance and, thereby, the quality and safety of care (Carayon, 2012; Hogarth, 2010; Reiling et al., 2008). Although the impact of the physical environment on diagnostic error has not been well studied, there are indications that it may be an important contributor to diagnostic performance. For example, the emergency department has been described as a challenging environment in which to make accurate and timely diagnoses because of the presence of high-acuity illness, incomplete information, time constraints, and frequent interruptions and distractions (Croskerry and Sinclair, 2001). Cognitive performance is vulnerable to distractions and interruptions, which influence the likelihood of error. Other physical environment factors that are likely to influence the diagnostic process include the placement of health IT used in the diagnostic process, the presence of noise that interferes with clinical reasoning and communication among the diagnostic team, and the amount of space available for team members to complete tasks related to the diagnostic process.
Health care organizations can also make concerted efforts to address diagnostic challenges related to fragmentation within the broader health care system. Although improved teamwork and interoperability will help with systemic fragmentation in health care, organizations need to recognize that patients may traverse organizational boundaries, and this has the potential to contribute to diagnostic errors and failures to learn from them. It is important to develop approaches within health care organizations to identify potential vulnerabilities to fragmentation. For example, the committee heard testimony that one important area to address is strengthening communication with pathologists and radiologists to improve diagnostic test selection and result interpretation. Closed-loop reporting systems that ensure test results or specialist findings are reported back to the treating health care professional in a timely manner are one mechanism that can be used to reduce diagnostic errors (Lacson et al., 2014).
Goal 5: Establish a work system and culture that supports the diagnostic process and improvements in diagnostic performance
Recommendation 5: Health care organizations should:
- Adopt policies and practices that promote a nonpunitive culture that values open discussion and feedback on diagnostic performance.
- Design the work system in which the diagnostic process occurs to support the work and activities of patients, their families, and health care professionals and to facilitate accurate and timely diagnoses.
- Develop and implement processes to ensure effective and timely communication between diagnostic testing health care professionals and treating health care professionals across all health care delivery settings.
Develop a Reporting Environment and Medical Liability System That Facilitates Improved Diagnosis by Learning from Diagnostic Errors and Near Misses
The committee concluded that there is a need for safe places where health care organizations and professionals can share and learn from their experiences with diagnostic errors, near misses, and adverse events. Performing analyses of these events presents the best opportunity to learn from such experiences and to implement changes to improve the diagnostic process. To Err Is Human (2000) recommended that reporting systems be used to collect this information. Various groups, including individual states, The Joint Commission, the Department of Veterans Affairs, and AHRQ have developed a number of reporting systems, which collect different types of information for different purposes. Characteristics of successful reporting systems include: “reporting is safe for the individuals who report, reporting leads to a constructive response, expertise and adequate financial resources are available to allow for meaningful analysis of reports, and the reporting system must be capable of disseminating information on hazards and recommendations for changes” (WHO, 2005, p. 49; see also Barach and Small, 2000). In contrast, systems that focus on punishing individuals will prevent people from reporting because they fear that their reports may be used as evidence of fault, could precipitate lawsuits, or could result in disciplinary action by state medical boards and employers (IOM, 2012; WHO, 2005). Thus, there is a need for safe environments, without the threat of legal discovery or disciplinary action, where diagnostic errors, adverse events, and near misses can be analyzed and learned from in order to improve the quality of diagnosis and prevent future diagnostic errors.
It is often difficult to create environments where diagnostic errors, near misses, and adverse events can be shared and discussed. Health care organizations and clinicians have been challenged by the limitations of the inconsistent and individual peer review processes that have been enacted by various states for the protection of information relating to adverse events and medical errors, for the external use of such information, and for the benefits they receive from reporting. In response to this challenge, To Err Is Human recommended that “Congress should pass legislation to extend peer review protections to data related to patient safety and quality improvement that are collected and analyzed by health care organizations for internal use or shared with others solely for purposes of improving safety and quality” (IOM, 2000, p. 10). In 2005, the Patient Safety and Quality Improvement Act (PSQIA) was passed by Congress; it provides privilege and confidentiality protections to health care organizations that share specific patient safety information with federally listed patient safety organizations (PSOs) (HHS, 2015). The PSO program, which is overseen by AHRQ, is an important national tool for increasing voluntary error reporting and analysis.
The PSO program enables public or private organizations to be listed as PSOs provided they meet certain qualifications articulated in the Patient Safety Rule (AHRQ, 2015d). If health care organizations or health care professionals join a specific PSO, they can then voluntarily send patient safety data to the PSO for analysis and feedback on how to improve care. Additionally, PSOs can send de-identified patient safety data to patient safety databases overseen by AHRQ. The intent of the program is for AHRQ to analyze the aggregated data and to publish reports based on those analyses (GAO, 2010).
Progress in implementing the PSO program has been slow, and there is very limited information about the impact of PSOs on learning about and improving the quality and safety of care. The Government Accountability Office concluded in 2010 that it was too early to evaluate the effectiveness of the program (GAO, 2010). Currently, there are more than 80 PSOs (AHRQ, 2015c), and the PSO network is active, as evidenced by the PSOs’ websites, which share information with their members about strategies to mitigate patient safety events. A provision in the Affordable Care Act will likely increase the number of hospitals that join PSOs; hospitals with more than 50 beds will be required to join a PSO by January 2017 in order to contract with health plans in insurance exchanges (CFPS, 2015; CMS, 2013).
AHRQ has developed Common Formats, or generic- and event-specific forms, to encourage standardized event reporting among PSOs (AHRQ, 2015b). However, these formats are voluntary, and some organizations are implementing them variably or using legacy reporting formats
(ONC, 2014). In addition, there are no common formats for diagnostic errors (PSO Privacy Protection Center, 2014); in order to promote voluntary reporting efforts, common formats for diagnostic errors and near misses are needed. AHRQ could begin with common formats for high-priority areas such as the most frequent diagnostic errors and “don’t miss” health conditions that may result in significant patient harm, such as stroke, acute myocardial infarction, and pulmonary embolism.
In 2015, AHRQ noted that no data were submitted to the network of patient safety databases for aggregation and analysis because the data have not been of sufficient quality or volume to ensure accuracy and deidentification. In addition, fewer than half of PSOs (27 of 76) signed data use agreements with AHRQ by the end of 2014; signing a user agreement is a requirement for sending data to be aggregated for analysis (AHRQ, 2015a). There are also concerns that the federal privilege protections extended by PSQIA are not shielding organizations from state reporting requirements. A recent ruling by the Kentucky Supreme Court found that information that a hospital is required to generate under state law is not protected by PSQIA, even if it is shared with a PSO.5 This type of court decision could undermine the creation of a safe environment to share this information and prevent voluntary submissions to PSOs.
Given that the PSO program has potential to improve learning about diagnostic errors and to expedite the implementation of solutions and adoption of best practices, it is important to evaluate whether the program is meeting the statutory objectives of PSQIA, namely, that the PSO program is creating opportunities to examine and learn from medical errors, including diagnostic errors.
The committee is concerned that a number of challenges that the current program is facing may limit its ability to facilitate much-needed voluntary reporting, analysis, and learning from diagnostic errors and near misses. Because of this concern, the committee recognizes that additional federal efforts across HHS—including AHRQ—as well as the involvement of other independent entities may need to be considered in order to prioritize voluntary event reporting for diagnostic errors and near misses. For example, the IOM report Health IT and Patient Safety: Building Safer Systems for Better Care made a recommendation for a new entity, akin to the National Transportation Safety Board, that could investigate patient deaths, serious injuries, and potentially unsafe conditions, and report the results of these activities (IOM, 2012).
Smaller-scale or more localized efforts to encourage voluntary reporting of diagnostic errors and near misses could also be implemented. For example, at the level of a health care organization, quality and patient
5Tibbs v. Bunnel, Ky., 2012-SC-000603-MR (August 21, 2014).
safety committees can analyze and learn from diagnostic errors, as these activities may be protected from disclosure by state statutes.
The two core functions of the medical liability system are to compensate negligently injured patients and to promote quality by encouraging clinicians and organizations to avoid medical errors. Although the medical liability environment may act as a generalized deterrent to medical errors, it is not well aligned with the promotion of high-quality, safe care. Concerns over medical liability prevent clinicians from disclosing medical errors to patients and their families despite calls from numerous groups about the ethical necessity of full disclosure and a requirement for The Joint Commission accreditation (Hendrich et al., 2014; Sage et al., 2014). In spite of this, clinicians often struggle to fulfill this responsibility: There is limited guidance for clinicians concerning how to disclose this information effectively; a number of factors, including embarrassment, inexperience, lack of confidence, and mixed messages from risk managers and health care organizations, can discourage clinicians from making disclosures to patients and their families (Gallagher et al., 2007, 2013; The Joint Commission, 2005).
The current tort-based system for resolving medical liability disputes sets up barriers to improvements in quality and patient safety and stifles continuous learning. Medical malpractice reform could be designed to permit patients and health care professionals to become allies in trying to make health care safer by encouraging transparency with regard to errors. Such an approach would allow patients to be promptly and fairly compensated for any injuries that were avoidable, while turning errors into lessons to improve subsequent performance (AHRQ, 2014b; Berenson, 2005; Kachalia and Mello, 2011).
Diagnostic errors are a leading type of malpractice claim, and these claims are more likely to be associated with patient deaths than other types of medical errors (Tehrani et al., 2013). Reforming the medical liability system, therefore, has the potential to improve learning from diagnostic errors and increase the disclosure of diagnostic errors to patients and their families as well as to lead to fairer treatment in the medical injury resolution processes. There have been many calls for changes to the medical liability system. Traditional mechanisms to reform the liability system—such as imposing barriers to bringing lawsuits, limiting compensation, and changing the way that damage awards are paid—have not contributed to improvements in either compensating negligently injured patients or deterring unsafe care (Mello et al., 2014). Thus, the committee concluded that stakeholders need to consider alternative approaches to
improving the legal environment and promoting learning from diagnostic errors. Similarly, To Err Is Human concluded that alternative approaches to the resolution of medical injuries could resolve the incentive to hide medical injuries, and in 2002, the IOM proposed state-level demonstration projects to explore alternative approaches to the current liability system that are patient-centered and focused on patient safety (IOM, 2000, 2002).
Although enthusiasm for alternative approaches to the current medical liability system is growing, in general progress has been slow, especially toward more fundamental changes to the medical liability system. Thus, the committee took both a pragmatic and an aspirational approach to considering changes to medical liability that would promote the improved disclosure of diagnostic errors as well as opportunities to learn from these errors. A number of alternative approaches to the current medical liability system were evaluated, and the committee concluded that the most promising approaches include communication and resolution programs (CRPs), the use of evidence-based clinical practice guidelines as safe harbors, and administrative health courts. CRPs represent a more pragmatic approach in that they are the more likely to be implemented in the current medical liability climate, and they have a strong focus on improving patient safety as well as reducing litigation. States, in collaboration with other stakeholders, should encourage the adoption of CRPs with legal protections for disclosures and apologies under state laws. Safe harbors for adherence to evidence-based clinical practice guidelines and administrative health courts are challenging in regard to implementation, and more information is needed about their impact on improving diagnosis. Thus, further demonstrations of these two approaches are warranted.
CRPs are principled comprehensive patient safety programs in which health care professionals and organizations openly discuss adverse outcomes with patients and proactively seek resolution while promoting patient-centeredness, learning, and quality improvement. CRPs rely on creating transparent health care cultures in which the early reporting of adverse events is the norm and is coupled with systems-based event analysis that is designed to understand the root causes of adverse events and to develop plans for preventing recurrences. Improved transparency surrounding diagnostic errors can help foster an improved culture of reporting, which can in turn promote learning about and identification of interventions to improve the safety and quality of diagnosis (Mello et al., 2014). Although CRPs do not require legislative changes, CRP adoption could be facilitated through changes to state laws, such as laws protecting disclosures and apologies (Sage et al., 2014). In addition, a national collaborative of CRPs could help accelerate the spread of CRPs and widely disseminate learning from these programs.
Safe harbors for following evidence-based clinical guidelines have
the potential to raise the quality of health care by creating an incentive (liability protection) for clinicians to follow evidence-based clinical practice guidelines. Unlike the case with other approaches to improving the medical liability environment, input to the committee suggested that safe harbors would offer direct opportunities to improve diagnosis (Kachalia, 2014). However, there are few clinical practice guidelines available for diagnosis, and implementing safe harbors for adherence to these guidelines is administratively complex.
Administrative health courts have been proposed as a way to provide injured patients with expedited compensation decisions for certain types of medical errors and to promote the disclosure of medical errors (such as diagnostic errors). Administrative health courts provide a nonjudicial process of handling medical injuries in which cases are filed through an administrative process. The goal in using these courts is to quickly and equitably compensate patients who have experienced avoidable injuries without requiring the patients to prove negligence in an adversarial proceeding (Berenson, 2005). The establishment of these courts would represent a fundamental change that would promote a more open environment for identifying, studying, and learning from errors. However, implementing administrative health courts would pose a number of challenges, including the need for legislative action, the courts’ operational complexity, and resistance from stakeholders who are strongly committed to preserving the current tort-based system.
Professional liability insurance carriers and health care organizations that participate in a captive insurance program or other self-insurance arrangement have a vested interest in improving diagnosis. Many of these carriers and organizations are actively exploring opportunities to improve diagnosis and reduce diagnostic errors. Given their expertise in understanding the contributors to diagnostic errors, they bring an important perspective to efforts to improve diagnosis, both those focused on individual health care professionals and those focused on the work system components that may contribute to diagnostic errors. The expertise of health professional liability insurance carriers needs to be leveraged to improve the diagnostic process. Improved collaboration between health professional liability insurance carriers and health care professionals and organizations could help to identify resources, prioritize areas of concern, and devise interventions. Collaboration among health care professional educators and professional liability insurance carriers could also be helpful in developing interventions for trainees.
Goal 6: Develop a reporting environment and medical liability system that facilitates improved diagnosis by learning from diagnostic errors and near misses
Recommendation 6a: The Agency for Healthcare Research and Quality (AHRQ) or other appropriate agencies or independent entities should encourage and facilitate the voluntary reporting of diagnostic errors and near misses.
Recommendation 6b: AHRQ should evaluate the effectiveness of patient safety organizations (PSOs) as a major mechanism for voluntary reporting and learning from these events and modify the PSO Common Formats for reporting of patient safety events to include diagnostic errors and near misses.
Recommendation 6c: States, in collaboration with other stakeholders (health care organizations, professional liability insurance carriers, state and federal policy makers, patient advocacy groups, and medical malpractice plaintiff and defense attorneys), should promote a legal environment that facilitates the timely identification, disclosure, and learning from diagnostic errors. Specifically, they should:
- Encourage the adoption of communication and resolution programs with legal protections for disclosures and apologies under state laws.
- Conduct demonstration projects of alternative approaches to the resolution of medical injuries, including administrative health courts and safe harbors for adherence to evidence-based clinical practice guidelines.
Recommendation 6d: Professional liability insurance carriers and captive insurers should collaborate with health care professionals on opportunities to improve diagnostic performance through education, training, and practice improvement approaches and increase participation in such programs.
Design a Payment and Care Delivery Environment That Supports the Diagnostic Process
Fee-for-service (FFS) payment, the predominant form of payment for health care services in the United States, pays health care professionals for each service they provide. FFS payment has long been recognized for its inability to incentivize well-coordinated, high-quality, and efficient health
care (Council of Economic Advisors, 2009; IOM, 2001, 2013; National Commission on Physician Payment Reform, 2013). There is relatively little information about the impact of payment on the diagnostic process. However, the committee concluded that it is likely to have an impact, and several payment experts who provided input to the committee helped elaborate on some of the likely consequences (Miller, 2014; Rosenthal, 2014; Wennberg, 2014).
In general, FFS payment may not incentivize a high-quality, efficient diagnostic process because the more services the diagnostic process takes, the more remuneration will result. There is no disincentive for ordering unnecessary diagnostic testing that could lead to false positive results and diagnostic errors (Miller, 2014; Wennberg, 2014). There is also a financial incentive to provide treatment to patients rather than determining that a patient does not have a health problem; thus, inappropriate diagnoses are better compensated than determining that a patient does not have a health problem. Likewise, accuracy in the diagnostic process is not incentivized by FFS payment: Clinicians who interpret diagnostic tests or provide a diagnosis during a patient visit receive payment regardless of whether the work was done adequately to support accurate interpretation and diagnosis and regardless of whether the interpretations and diagnoses were accurate (Miller, 2014).
Given the importance of team-based care in the diagnostic process, the lack of financial incentives in FFS payment to coordinate care can contribute to challenges in diagnosis and diagnostic errors, particularly delays in diagnosis (Allen and Thorwarth, 2014; Kroft, 2014; Miller, 2014; Rosenthal, 2014). FFS Medicare and most commercial payers do not pay for a clinician’s time spent contacting other clinicians by phone or e-mail to facilitate the diagnostic process, for example, by helping determine the appropriate diagnostic testing procedures for a patient. In addition, clinicians are not reimbursed for proactive outreach to patients to obtain diagnostic testing, schedule visits with specialists, or make follow-up appointments (Miller, 2014). To improve teamwork and care coordination in the diagnostic process, new current procedural terminology (CPT) codes can be developed and compensated, such as codes for communication among treating clinicians, pathologists, and radiologists about diagnostic testing ordering, interpretation, and the subsequent decision making. These codes could be modeled on existing Medicare codes that compensate clinicians’ time for coordination and planning activities, such as the codes for radiation therapy planning, post-discharge transitional care coordination, and complex chronic care coordination (ASTRO, 2014; Bendix, 2013; CMS, 2014b; Edwards and Landon, 2014).
The Medicare physician fee schedule sets payment rates based on relative value units that are meant to reflect the level of time, effort,
skill, and stress associated with providing each service (MedPAC, 2014). Fee schedule services can include evaluation and management services (“E&M services,” such as office, inpatient, or emergency department visits), diagnostic testing, and other procedures. For all medical specialties, there are well-documented fee schedule distortions that result in more generous payments (in relation to the costs of production) for procedures and also for diagnostic testing interpretations compared to E&M services (Berenson, 2010; National Commission on Physician Payment Reform, 2013). These distortions have coincided with a large growth in diagnostic testing in health care: For example, the percent of patients presenting to the emergency department with dizziness who underwent computed tomography scans rose from 9 percent in 1995 to 40 percent in 2013, although there has been no increase in diagnoses of stroke or other neurologic diseases (Iglehart, 2009; Newman-Toker et al., 2013).
The lower relative value afforded to E&M services versus procedure-oriented care is an obstacle to improved diagnostic performance. E&M services reflect the cognitive expertise and skills that all clinicians have and use in the diagnostic process, and the distortions may be diverting attention and time from important tasks in the diagnostic process, such as performing a patient’s clinical history and interview, conducting a physical exam, and thoughtful decision making in the diagnostic process. Realigning relative value fees to better compensate clinicians for the cognitive work in the diagnostic process has the potential to improve accuracy in diagnosis while reducing the incentives that drive inappropriate utilization of diagnostic testing in the diagnostic process.
E&M payment policies and documentation guidelines are also misaligned with the goal of accurate and timely diagnosis. E&M payments penalize clinicians for spending extra time on the diagnostic process for individual patients. There are different levels of E&M visits based on time and complexity, and clinicians receive better compensation if they see more patients with shorter appointment lengths. For example, in Medicare if a clinician spends 20 minutes instead of 15 minutes with a patient billed as a level 3 E&M visit, the clinician will receive 25 percent less revenue per hour; if a clinician spends 25 minutes for a level 4 E&M visit instead of 15 minutes for a level 3 visit, the clinician will receive 11 percent less revenue per hour (Miller, 2014). Time pressures in clinical visits can contribute to challenges in clinical reasoning and to the occurrence of errors (Durning, 2014; Evans and Kim, 2006; Kostis et al., 2007; Sarkar et al., 2012, 2014; Schiff et al., 2009; Singh et al., 2013). Documentation guidelines for E&M services were created to ensure that the services performed were consistent with the insurance coverage; to validate specific information, such as the site of service, the appropriateness of the care, and the accuracy of the reported information; and to prevent fraud
and abuse (Berenson, 1999; CMS, 2014a). Documentation guidelines also specify the extent of a patient’s clinical history and physical exam and the complexity of the medical decision making involved in the E&M visit (Berenson et al., 2011; HHS, 2010). There are a number of criticisms of the documentation guidelines. The primary criticism is that the level of detail required is onerous, often irrelevant to patient care, and shifts the purpose of the medical record toward billing rather than on facilitating clinical reasoning (Berenson et al., 2011; Brett, 1998; Kassirer and Angell, 1998; Kuhn et al., 2015; Schiff and Bates, 2010).
The documentation guidelines have become an even greater concern with the broad implementation of EHRs because EHR design emphasizes fulfilling documentation and legal requirements rather than facilitating the diagnostic process (Berenson et al., 2011; Schiff and Bates, 2010). The orientation of EHRs to documentation, their overreliance on templates, and their copy and paste functionalities have resulted in “EHR-generated data dumps, including repetitive documentation of elements of patients’ histories and physical examinations, that merely result in electronic versions of clinically cumbersome, uninformative patient records” (Berenson et al., 2011, p. 1894). Generating documentation to support E&M coding (or assigning higher levels of E&M coding than warranted—known as “upcoding”) can result in inaccuracies in the patient’s EHR that can contribute to diagnostic errors.
A number of payment and care delivery reforms to counter the limitations of the FFS payment system are now actively being considered, implemented, and evaluated. These include capitation/global payments, shared savings, bundled episodes of care, accountable care organizations, patient-centered medical homes, and pay for performance (which in Medicare is labeled “value-based purchasing”). The Centers for Medicare & Medicaid Services (CMS) recently announced that it plans “to have 30 percent of Medicare fee-for-service payments tied to quality or value through alternative payment models by the end of 2016, and 50 percent of payments by the end of 2018” (Burwell, 2015). Legislation that repealed the sustainable growth rate also continues down the path toward alternative payment models, particularly for the payment of Medicare clinicians.6 There is very limited evidence concerning the impact of payment and delivery models on the diagnostic process and the accuracy of diagnosis, and this represents a fundamental research need. Assessing the impact of payment and care delivery models, including FFS, on the diagnostic process, diagnostic errors, and learning are critical areas of focus as these models are evaluated.
The committee asked for input from payment and delivery experts
6 Medicare Access and CHIP Reauthorization Act of 2015. P.L. 114-10. (April 16, 2015).
about the potential effects of new models on diagnosis and diagnostic error. Rosenthal (2014) suggested that global payment and meaningful use incentives have the potential to improve diagnosis by promoting the adoption of diagnostic test and referral tracking systems that better connect health care professionals throughout the continuum of care. Miller (2014) suggested that the development of measures for diagnostic accuracy could be used to also provide feedback and reward clinicians for diagnostic accuracy. Wennberg (2014) suggested that population-based payment models, including capitation and global budgets, have the greatest potential to reduce diagnostic errors. While new payment models have the potential to reduce diagnostic errors, these models may also create incentives for clinicians and health care organizations that could reduce use of appropriate testing and clinician services (e.g., specialty consultations) that may inadvertently lead to greater diagnostic errors. Thus, research in this area will be helpful in assessing the impact of payment and care delivery models on diagnosis.
Even when alternate payment and care delivery approaches to FFS are employed, they are often based on or influenced by existing coding and payment rules (Berenson et al., 2011). For example, bundled payments are combinations of current codes. Thus, the current distortions in the fee schedule and other volume-based payment approaches, such as diagnosis-related group coding, will remain a dominant component of payment and care delivery models in the near future and need to be addressed.
Goal 7: Design a payment and care delivery environment that supports the diagnostic process
Recommendation 7a: As long as fee schedules remain a predominant mechanism for determining clinician payment, the Centers for Medicare & Medicaid Services (CMS) and other payers should:
- Create current procedural terminology codes and provide coverage for additional evaluation and management activities not currently coded or covered, including time spent by pathologists, radiologists, and other clinicians in advising ordering clinicians on the selection, use, and interpretation of diagnostic testing for specific patients.
- Reorient relative value fees to more appropriately value the time spent with patients in evaluation and management activities.
- Modify documentation guidelines for evaluation and management services to improve the accuracy of information in
the electronic health record and to support decision making in the diagnostic process.
Recommendation 7b: CMS and other payers should assess the impact of payment and care delivery models on the diagnostic process, the occurrence of diagnostic errors, and learning from these errors.
Provide Dedicated Funding for Research on the Diagnostic Process and Diagnostic Errors
The diagnostic process and the challenge of diagnostic errors have been neglected within the national health care research agenda (Berenson et al., 2014; Wachter, 2010; Zwaan et al., 2013). Input provided to the committee concluded that “although correct treatment presumes a correct diagnosis, federal resources devoted to diagnostic research are vastly eclipsed by those devoted to treatment” (Newman-Toker, 2014, p. 12). There are a number of reasons why diagnosis and diagnostic errors may be underrepresented in current research activities, including a lack of awareness or the perceived inevitability of the problem, attitudes and a culture that encourage inaction and tolerance of errors, poorly understood characteristics of the diagnostic and clinical reasoning processes, and the lack of financial and other resources needed to address the problem (Berenson et al., 2014; Croskerry, 2012).
A major barrier to research on diagnosis and diagnostic error is the current disease-focused approach to medical research funding. For example, the structure and funding mechanisms of the National Institutes of Health (NIH) are often organized by disease or organ systems, which facilitates the study of these specific areas but impedes research efforts that seek to provide a more comprehensive understanding of diagnosis as a distinct research area. Newman-Toker (2014, p. 12) asserted that diagnostic research “invariably falls between rather than within individual Institute missions.” As such, the topic of diagnosis, which cuts across various diseases and body parts, is not centralized within the NIH research portfolio, and available research funding for diagnosis often targets specific diseases but not diagnosis as a whole or the diagnosis of several diseases with similar presentations. Diagnosis and diagnostic error are not a focus of federal health services’ research efforts, with the exception of two special emphasis notices from AHRQ for diagnostic error in 2007 and 2013, as well as 2015 grant opportunities (AHRQ, 2007, 2013, 2015e,f). AHRQ posted an R01 grant opportunity for “understanding and improving diagnostic safety in ambulatory care: incidence and contributing factors” (AHRQ, 2015e) and an R18 grant opportunity for identifying
strategies and interventions to improve diagnostic safety in ambulatory care (AHRQ, 2015f).
Although these initial steps are promising, the committee concluded that there is an urgent need for dedicated, coordinated federal funding for research on diagnosis and diagnostic error. Given the potential for federal research for diagnosis and diagnostic error to fall between institutional missions, federal agencies need to collaborate to develop a national research agenda that addresses diagnosis and diagnostic error by 2016. Zwaan and colleagues (2013) outlined potential research opportunities that were broadly classified into three categories: the epidemiology of diagnostic errors, the causes of diagnostic error, and error prevention strategies. The Society to Improve Diagnosis in Medicine has formed a research committee to bring together multidisciplinary perspectives to advance a research agenda derived from critical gaps in the evidence base. Building on this work, the committee identified additional areas of research that could help shape a national research agenda on diagnosis and diagnostic error (see Chapter 8).
The federal government should commit dedicated funding to implementing this research agenda. Because federal investments in biomedical and health services research are declining (Moses et al., 2015), the committee recognizes that funding for diagnosis and diagnostic error will likely draw federal resources away from other important priorities. However, given the consistent lack of resources for research on diagnosis and the potential for diagnostic errors to contribute significant patient harm, the committee concluded that this is necessary for broader improvements to the quality and safety of health care. In addition, improving diagnosis could also lead to potential cost savings by preventing diagnostic errors, inappropriate treatment, and related adverse events.
In addition to federal-level research on diagnosis and diagnostic errors, there is an important role for public–private collaboration and coordination among the federal government, foundations, industry, and other organizations. Collaborative funding efforts help extend the existing financial resources and reduce duplications in research efforts. Interested parties can unite around mutual interests and spearhead progress toward a specific cause. Foundations and industry can make important contributions—financially and within their areas of expertise—to the field of diagnosis and diagnostic errors that can enhance the medical community’s knowledge in this area. Various types of collaborative models that have been employed to share information, resources, and capabilities have been described in the literature (Altshuler et al., 2010; Portilla and Alving, 2010).
Goal 8: Provide dedicated funding for research on the diagnostic process and diagnostic errors
Recommendation 8a: Federal agencies, including the Department of Health and Human Services, the Department of Veterans Affairs, and the Department of Defense, should:
- Develop a coordinated research agenda on the diagnostic process and diagnostic errors by the end of 2016.
- Commit dedicated funding to implementing this research agenda.
Recommendation 8b: The federal government should pursue and encourage opportunities for public–private partnerships among a broad range of stakeholders, such as the Patient-Centered Outcomes Research Institute, foundations, the diagnostic testing and health information technology industries, health care organizations, and professional liability insurers to support research on the diagnostic process and diagnostic errors.
Adler-Milstein, J. 2015. America’s health IT transformation: Translating the promise of electronic health records into better care. Paper presented at U.S. Senate Committee on Health, Education, Labor and Pensions, March 17. www.help.senate.gov/imo/media/doc/Adler-Milstein.pdf (accessed June 5, 2015).
Adler-Milstein, J., and A. Jha. 2014. Health information exchange among U.S. hospitals: Who’s in, who’s out and why? Healthcare 2(1):26–32.
Adler-Milstein, J., C. M. DesRoches, M. F. Furukawa, C. Worzala, D. Charles, P. Kralovec, S. Stalley, and A. K. Jha. 2014. More than half of U.S. hospitals have at least a basic EHR, but stage 2 criteria remain challenging for most. Health Affairs (Millwood) 33(9):1664–1671.
AHIMA (American Health Information Management Association). 2014. Appropriate use of the copy and paste functionality in electronic health records. www.ahima.org/topics/ehr (accessed March 27, 2015).
AHRQ (Agency for Healthcare Research and Quality). 2007. Special emphasis notice (SEN): AHRQ announces interest in research on diagnostic errors in ambulatory care settings. http://grants.nih.gov/grants/guide/notice-files/NOT-HS-08-002.html (accessed May 5, 2015).
AHRQ. 2013. AHRQ announces interest in research to improve diagnostic performance in ambulatory care settings. http://grants.nih.gov/grants/guide/notice-files/NOTHS-13-009.html (accessed February 4, 2015).
AHRQ. 2014a. Hospital survey on patient safety culture: 2014 user comparative database report: Chapter 5. Overall results. www.ahrq.gov/professionals/quality-patient-safety/patientsafetyculture/hospital/2014/hosp14ch5.html (accessed February 25, 2014).
AHRQ. 2014b. Medical Liability Reform & Patient Safety Initiative. www.ahrq.gov/professionals/quality-patient-safety/patient-safety-resources/resources/liability (accessed April 9, 2015).
AHRQ. 2014c. Patient and family advisory councils. https://cahps.ahrq.gov/quality-improvement/improvement-guide/browse-interventions/Customer-Service/Listening-Posts/Advisory-Councils.html (accessed May 26, 2015).
AHRQ. 2015a. Agency for Healthcare Research and Quality: Justification of estimates for appropriations committees. www.ahrq.gov/sites/default/files/wysiwyg/cpi/about/mission/budget/2016/cj2016.pdf (accessed May 3, 2015).
AHRQ. 2015b. Common formats. www.pso.ahrq.gov/common (accessed March 28, 2015).
AHRQ. 2015c. Federally-listed PSOs. www.pso.ahrq.gov/listed (accessed May 3, 2015).
AHRQ. 2015d. Patient Safety Organization (PSO) Program: Frequently asked questions. www.pso.ahrq.gov/faq#WhatisaPSO (accessed May 3, 2015).
AHRQ. 2015e. Understanding and improving diagnostic safety in ambulatory care: Incidence and contributing factors (R01). http://grants.nih.gov/grants/guide/pa-files/PA-15-180.html (accessed May 3, 2015).
AHRQ. 2015f. Understanding and improving diagnostic safety in ambulatory care: Strategies and interventions (R18). http://grants.nih.gov/grants/guide/pa-files/PA-15-179.html (accessed May 3, 2015).
Allen, B. and W. T. Thorworth. 2014. Input submitted to the Committee on Diagnostic Error in Health Care, November 5 and December 29, 2014, Washington, DC.
Altshuler, J. S., E. Balogh, A. D. Barker, S. L. Eck, S. H. Friend, G. S. Ginsburg, R. S. Herbst, S. J. Nass, C. M. Streeter, and J. A. Wagner. 2010. Opening up to precompetitive collaboration. Science Translational Medicine 2(52):52cm26.
ASTRO (American Society for Radiation Oncology). 2014. Basics of RO coding. www.astro.org/Practice-Management/Reimbursement/Basics-of-RO-Coding.aspx (accessed March 26, 2015).
Baker, D. P., R. Day, and E. Salas. 2006. Teamwork as an essential component of high-reliability organizations. Health Services Research 41(4p2):1576–1598.
Barach, P., and S. D. Small. 2000. Reporting and preventing medical mishaps: Lessons from non-medical near miss reporting systems. BMJ 320(7237):759–763.
Basch, P. 2014. ONC’s 10-year roadmap towards interoperability requires changes to the meaningful use program. http://healthaffairs.org/blog/2014/11/03/oncs-10-year-roadmap-towards-interoperability-requires-changes-to-the-meaningful-use-program (accessed March 27, 2015).
Bell, S., M. Anselmo, J. Walker, and T. Delbanco. 2014. Information on OpenNotes. Input submitted to the Committee on Diagnostic Error in Health Care, December 2, 2014, Washington, DC.
Bendix, J. 2013. Making sense of the new transitional care codes. http://medicaleconomics.modernmedicine.com/medical-economics/news/user-defined-tags/99495/making-sense-new-transitional-care-codes?page=full (accessed March 26, 2015).
Berenson, R. A. 1999. Evaluation and management guidelines. New England Journal of Medicine 340(11):889; author reply 890–891.
Berenson, R. A. 2005. Malpractice makes perfect. The New Republic, October 10. www.newrepublic.com/article/health-care-malpractice-bush-frist (accessed May 26, 2015).
Berenson, R. A. 2010. Out of whack: Pricing distortions in the Medicare physician fee schedule. Expert Voices, September. www.nihcm.org/pdf/NIHCM-EV-Berenson_FINAL.pdf (accessed June 8, 2015).
Berenson, R. A., P. Basch, and A. Sussex. 2011. Revisiting E&M visit guidelines—A missing piece of payment reform. New England Journal of Medicine 364(20):1892–1895.
Berenson, R. A., D. K. Upadhyay, and D. R. Kaye. 2014. Placing diagnosis errors on the policy agenda. Washington, DC: Urban Institute. www.urban.org/research/publication/placing-diagnosis-errors-policy-agenda (accessed June 7, 2015).
Berner, E. S., and M. L. Graber. 2008. Overconfidence as a cause of diagnostic error in medicine. American Journal of Medicine 121(5):S2–S23.
Bhise, V., and H. Singh. 2015. Measuring diagnostic safety of inpatients: Time to set sail in uncharted waters. Diagnosis 2(1):1–2.
Brett, A. S. 1998. New guidelines for coding physicians’ services—A step backward. New England Journal of Medicine 339(23):1705–1708.
Bruno, M. A., J. M. Petscavage-Thomas, M. J. Mohr, S. K. Bell, and S. D. Brown. 2014. The “open letter”: Radiologists’ reports in the era of patient web portals. Journal of the American College of Radiology 11(9):863–867.
Brush, J. E. 2014. Forming good habits to decrease diagnostic error: A case for likelihood ratios. Input submitted to the Committee on Diagnostic Error in Health Care, October 21, 2014, Washington, DC.
Burwell, S. M. 2015. Setting value-based payment goals—HHS efforts to improve U.S. health care. New England Journal of Medicine 372(10):897–899.
Carayon, P. 2012. The physical environment in health care. In C. J. Alvarado (ed.), Handbook of human factors and ergonomics in health care and patient safety (pp. 215–234). Boca Raton, FL: Taylor & Francis Group.
Carayon, P., H. Faye, A. S. Hundt, B.–T. Karsh, and T. Wetterneck, T. 2011. Patient safety and proactive risk assessment. In Y. Yuehwern (ed.), Handbook of Healthcare Delivery Systems (pp. 12–1–12–15.). Boca Raton, FL: Taylor & Francis.
CFPS (Center for Patient Safety). 2015. National health reform provisions and PSOs. www.centerforpatientsafety.org/nationalhealthreformprovisionsandpsos (accessed November 7, 2015).
Chassin, M. R. 2013. Improving the quality of health care: What’s taking so long? Health Affairs (Millwood) 32(10):1761–1765.
Chassin, M. R., and J. M. Loeb. 2011. The ongoing quality improvement journey: Next stop, high reliability. Health Affairs (Millwood) 30(4):559–568.
CHCF (California HealthCare Foundation). 2014. Ten years in: Charting the progress of health information exchange in the U.S. www.chcf.org/~/media/MEDIA%20LIBRARY%20Files/PDF/T/PDF%20TenYearsProgressHIE.pdf (accessed February 9, 2015).
Choosing Wisely. 2014. Lists. www.choosingwisely.org/doctor-patient-lists (accessed May 26, 2015).
CMS (Centers for Medicare & Medicaid Services). 2013. Patient Protection and Affordable Care Act; HHS Notice of Benefit and Payment Parameters for 2015. Federal Register. www.federalregister.gov/articles/2013/12/02/2013-28610/patient-protection-and-affordable-care-act-hhs-notice-of-benefit-and-payment-parameters-for-2015 (accessed November 12, 2015).
CMS. 2014a. Evaluation and management services guide: Official CMS information for Medicare fee-for-service providers. Washington, DC: CMS.
CMS. 2014b. Policy and payment changes to the Medicare physician fee schedule for 2015. www.cms.gov/newsroom/mediareleasedatabase/fact-sheets/2014-Fact-sheets-items/2014-10-31-7.html (accessed March 26, 2015).
Council of Economic Advisors. 2009. The economic case for health care reform. www.whitehouse.gov/assets/documents/CEA_Health_Care_Report.pdf (accessed March 17, 2015).
CRICO. 2014. Analysis of Diagnosis-Related Medical Malpractice Claims. Input submitted to the Committee on Diagnostic Error in Health Care, August 4, 2014, Washington, DC.
Croskerry, P. 2003. The importance of cognitive errors in diagnosis and strategies to minimize them. Academic Medicine 78(8):775–780.
Croskerry, P. 2012. Perspectives on diagnostic failure and patient safety. Healthcare Quarterly 15(Special issue):50–56.
Croskerry, P., and D. Sinclair. 2001. Emergency medicine: A practice prone to error. Canadian Journal of Emergency Medicine 3(4):271–276.
Dehling, T., F. Gao, S. Schneider, and A. Sunyaev. 2015. Exploring the far side of mobile health: Information security and privacy of mobile health apps on iOS and Android. JMIR mHealth and uHealth 3(1):e8.
Delbanco, T., J. Walker, J. D. Darer, J. G. Elmore, H. J. Feldman, S. G. Leveille, J. D. Ralston, S. E. Ross, E. Vodicka, and V. D. Weber. 2010. Open notes: doctors and patients signing on. Annals of Internal Medicine 153(2):121–125.
Delbanco, T., J. Walker, S. K. Bell, J. D. Darer, J. G. Elmore, N. Farag, H. J. Feldman, R. Mejilla, L. Ngo, J. D. Ralston, S. E. Ross, N. Trivedi, E. Vodicka, and S. G. Leveille. 2012. Inviting patients to read their doctors’ notes: A quasi-experimental study and a look ahead. Annals of Internal Medicine 157(7):461–470.
Dhaliwal, G. 2014. Blueprint for diagnostic excellence. Presentation to the Committee on Diagnostic Error in Health Care, November 21, 2014, Washington, DC.
Dingley, C., K. Daugherty, M. K. Derieg, and R. Persing. 2008. Improving patient safety through provider communication strategy enhancements. In Advances in Patient Safety: New Directions and Alternative Approaches (Vol. 3: Performance and Tools). Rockville, MD: Agency for Healthcare Research and Quality. www.ahrq.gov/professionals/quality-patient-safety/patient-safety-resources/resources/advances-in-patient-safety-2/vol3/Advances-Dingley_14.pdf (accessed June 11, 2015).
Dixon-Woods, M., C. Bosk, E. Aveling, C. Goeschel, and P. Pronovost. 2011. Explaining Michigan: Developing an ex-post theory of a quality improvement program. Milbank Quarterly 89(2):167–205.
Durning, S. J. 2014. Submitted input. Input submitted to the Committee on Diagnostic Error in Health Care, October 24, 2014, Washington, DC.
Edwards, S. T., and B. E. Landon. 2014. Medicare’s chronic care management payment—Payment reform for primary care. New England Journal of Medicine 371(22):2049–2051.
El-Kareh, R., O. Hasan, and G. Schiff. 2013. Use of health information technology to reduce diagnostic error. BMJ Quality and Safety 22(Suppl 2):ii40–ii44.
Epner, P. 2014. An Overview of Diagnostic Error in Medicine. Presentation to the Committee on Diagnostic Error in Health Care, April 28, 2014, Washington, DC.
Epner, P. 2015. Written input. Input submitted to the Committee on Diagnostic Error in Health Care, January 13, 2015, Washington, DC.
Etchegaray, J. M., M. J. Ottosen, L. Burress, W. M. Sage, S. K. Bell, T. H. Gallagher, and E. J. Thomas. 2014. Structuring patient and family involvement in medical error event disclosure and analysis. Health Affairs (Millwood) 33(1):46–52.
Eva, K. W., and G. R. Norman. 2005. Heuristics and biases—A biased perspective on clinical reasoning. Medical Education 39(9):870–872.
Evans, W. N., and B. Kim. 2006. Patient outcomes when hospitals experience a surge in admissions. Journal of Health Economics 25(2):365–388.
Firth-Cozens, J. 2004. Organisational trust: The keystone to patient safety. Quality & Safety in Health Care 13(1):56–61.
Furukawa, M. F., J. King, V. Patel, C. J. Hsiao, J. Adler-Milstein, and A. K. Jha. 2014. Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings. Health Affairs (Millwood) 33(9):1672–1679.
Gallagher, T., C. Denham, L. Leape, G. Amori, and W. Levinson. 2007. Disclosing unanticipated outcomes to patients: The art and practice. Journal of Patient Safety 3:158–165.
Gallagher, T. H., M. M. Mello, W. Levinson, M. K. Wynia, A. K. Sachdeva, L. Snyder Sulmasy, R. D. Truog, J. Conway, K. Mazor, A. Lembitz, S. K. Bell, L. Sokol-Hessner, J. Shapiro, A.-L. Puopolo, and R. Arnold. 2013. Talking with patients about other clinicians’ errors. New England Journal of Medicine 369(18):1752–1757.
Gandhi, T. 2014. Focus on diagnostic errors: understanding and prevention. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
GAO (Government Accountability Office). 2010. Patient Safety Act: HHS is in the process of implementing the act, so its effectiveness cannot yet be evaluated. GAO 10-281. Washington, DC: Government Accountability Office.
Gertler, S. A., Z. Coralic, A. Lopez, J. C. Stein, and U. Sarkar. 2014. Root cause analysis of ambulatory adverse drug events that present to the emergency department. Journal of Patient Safety. February 27 [Epub ahead of print].
Gigerenzer, G. 2000. Adaptive thinking: Rationality in the real world. New York: Oxford University Press.
Gigerenzer, G., and D. G. Goldstein. 1996. Reasoning the fast and frugal way: Models of bounded rationality. Psychology Review 103:650–669.
Goodman, K. W., E. S. Berner, M. A. Dente, B. Kaplan, R. Koppel, D. Rucker, D. Z. Sands, and P. Winkelstein. 2011. Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: A report of an AMIA special task force. Journal of the American Medical Informatics Association 18(1):77–81.
Govern, P. 2013. Diagnostic management efforts thrive on teamwork. http://news.vanderbilt.edu/2013/03/diagnostic-management-efforts-thrive-on-teamwork (accessed May 26, 2015).
Graber, M. L. 2005. Diagnostic errors in medicine: A case of neglect. Joint Commission Journal on Quality and Patient Safety 31(2):106–113.
Graber, M. L. 2013. The incidence of diagnostic error in medicine. BMJ Quality and Safety 22(Suppl 2):ii21–ii27.
Graber, M. L., R. M. Wachter, and C. K. Cassel. 2012. Bringing diagnosis into the quality and safety equations. JAMA 308(12):1211–1212.
Graber, M., R. Trowbridge, J. Myers, C. A. Umscheid, W. Strull, and M. Kanter. 2014. The next organizational challenge: Finding and addressing diagnostic error. Joint Commission Journal on Quality and Patient Safety 40(3):102–110.
Graedon, T., and J. Graedon. 2014. Let patients help with diagnosis. Diagnosis 1(1):49–51.
Haskell, H. 2014. What’s in a story? Lessons from patients who have suffered diagnostic failure. Diagnosis 1(1):53–54.
HealthIT.gov. 2015. Patient ability to electronically view, download & transmit (VDT) health information. www.healthit.gov/providers-professionals/achieve-meaningful-use/core-measures-2/patient-ability-electronically-view-download-transmit-vdt-healthinformation (accessed March 15, 2015).
Hendrich, A., C. K. McCoy, J. Gale, L. Sparkman, and P. Santos. 2014. Ascension health’s demonstration of full disclosure protocol for unexpected events during labor and delivery shows promise. Health Affairs (Millwood) 33(1):39–45.
Henriksen, K. 2014. Improving diagnostic performance: some unrecognized obstacles. Diagnosis 1(1):35–38.
Henriksen, K., and J. Brady. 2013. The pursuit of better diagnostic performance: A human factors perspective. BMJ Quality & Safety 22(Suppl 2):ii1–ii5.
HHS (Department of Health and Human Services). 2010. Improper payments for evaluation and management services cost Medicare billions in 2010. Washington, DC: HHS Office of Inspector General.
HHS. 2015. Patient Safety and Quality Improvement Act of 2005 Statute and Rule. www.hhs.gov/ocr/privacy/psa/regulation (accessed March 29, 2015).
HIMSS (Healthcare Information and Management Systems Society). 2014. What is interoperability? www.himss.org/library/interoperability-standards/what-is-interoperability (accessed February 9, 2015).
Hines, S., K. Luna, J. Lofthus, M. Marquardt, and D. Stelmokas. 2008. Becoming a high reliability organization: Operational advice for hospital leaders. Rockville, MD: Agency for Healthcare Research and Quality.
Hirt, E., and K. Markman. 1995. Multiple explanation: A consider-an-alternative strategy for debiasing judgments. Journal of Personality and Social Psychology 69:1069–1086.
Hodges, B., G. Regehr, and D. Martin. 2001. Difficulties in recognizing one’s own incompetence: Novice physicians who are unskilled and unaware of it. Academic Medicine 76(10 Suppl):S87–S89.
Hogarth, R. 2010. On the learning of intuition. In H. Plessner, C. Betsch, and T. Betsch (eds.), Intuition in judgment and decision making (pp. 91–105). New York: Taylor & Francis.
Hughes, A. M., and E. Salas. 2013. Hierarchical medical teams and the science of teamwork. AMA Journal of Ethics 15(6):529–533. http://virtualmentor.ama-assn.org/2013/06/msoc1-1306.html (accessed May 26, 2015).
Hysong, S. J., R. G. Best, and J. A. Pugh. 2006. Audit and feedback and clinical practice guideline adherence: Making feedback actionable. Implementation Science 1(9). www.implementationscience.com/content/pdf/1748-5908-1-9.pdf (accessed June 10, 2015).
Iglehart, J. K. 2009. Health insurers and medical-imaging policy—A Work in Progress. New England Journal of Medicine 360(10):1030–1037.
IOM (Institute of Medicine). 2000. To err is human: Building a safer health system. Washington, DC: National Academy Press.
IOM. 2001. Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press.
IOM. 2002. Fostering rapid advances in health care: Learning from system demonstrations. Washington, DC: The National Academies Press.
IOM. 2009. Resident duty hours: Enhancing sleep, supervision, and safety. Washington, DC: The National Academies Press.
IOM. 2012. Health IT and patient safety: Building safer systems for better care. Washington, DC: The National Academies Press.
IOM. 2013. Best care at lower cost: The path to continuously learning health care in America. Washington, DC: The National Academies Press.
IOM. 2014. Graduate medical education that meets the nation’s health needs. Washington, DC: The National Academies Press.
The Joint Commission. 2005. Health care at the crossroads: Strategies for improving the medical liability system and preventing patient injury. The Joint Commission. www.jointcommission.org/assets/1/18/Medical_Liability.pdf (accessed April 9, 2015).
The Joint Commission. 2014. Sentinel event policy and procedures. www.jointcommission.org/Sentinel_Event_Policy_and_Procedures (accessed June 11, 2015).
The Joint Commission. 2015. Preventing copy-and-paste errors in the EHR. www.jointcommission.org/issues/article.aspx?Article=bj%2B%2F2w37MuZrouWveszI1weWZ7ufX%2FP4tLrLI85oCi0%3D (accessed March 27, 2015).
Josiah Macy Jr. Foundation and Carnegie Foundation for the Advancement of Teaching. 2010. Educating nurses and physicians: Towards new horizons. Advancing inter-professional education in academic health centers, conference summary. June 16–18, 2010, Palo Alto, California.
Julavits, H. 2014. Diagnose this! How to be your own best doctor. Harper’s April:25–35.
Kachalia, A. 2014. Legal issues in diagnostic error. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Kachalia, A., and M. M. Mello. 2011. New directions in medical liability reform. New England Journal of Medicine 364(16):1564–1572.
Kaiser Permanente. 2012. Smart Partners About Your Health, edited by Kaiser Permanente.
Kanter, M. 2014. Diagnostic errors—Patient safety. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Kassirer, J. P., and M. Angell. 1998. Evaluation and management guidelines—Fatally flawed. New England Journal of Medicine 339(23):1697–1698.
Khatri, N., G. D. Brown, and L. L. Hicks. 2009. From a blame culture to a just culture in health care. Health Care Management Review 34(4):312–322.
Klein, G. 2014. A naturalistic perspective. Input submitted to the Committee on Diagnostic Error in Health Care, December 20, 2014, Washington, DC.
Kostis, W. J., K. Demissie, S. W. Marcella, Y. H. Shao, A. C. Wilson, and A. E. Moreyra. 2007. Weekend versus weekday admission and mortality from myocardial infarction. New England Journal of Medicine 356(11):1099–1109.
Kotter, J. 1995. Leading change: Why transformation efforts fail. Harvard Business Review 73(2):59-67.
Kotter, J. 2012. The key to changing organizational culture. Forbes, September 27. www.forbes.com/sites/johnkotter/2012/09/27/the-key-to-changing-organizational-culture (accessed March 9, 2015).
Kroft, S. H. 2014. Statement of Steven H. Kroft, MD, FASCP, American Society for Clinical Pathology (ASCP). Presentation to the Committee on Diagnostic Error in Health Care, April 28, 2014, Washington, DC.
Kuhn, T., P. Basch, M. Barr, and T. Yackel. 2015. Clinical documentation in the 21st century: Executive summary of a policy position paper from the American College of Physicians. Annals of Internal Medicine 162(4):301–303.
Lacson, R., L. M. Prevedello, K. P. Andriole, S. D. O’Connor, C. Roy, T. Gandhi, A. K. Dalal, L. Sato, and R. Khorasani. 2014. Four-year impact of an alert notification system on closed-loop communication of critical test results. American Journal of Roentgenology 203(5):933–938.
Larson, E. B. 2002. Measuring, monitoring, and reducing medical harm from a systems perspective: A medical director’s personal reflections. Academic Medicine 77(10):993–1000.
Leape., L. L. 2010. Q&A with Lucian Leape, M.D., adjunct professor of health policy, Harvard University. www.commonwealthfund.org/publications/newsletters/statesin-action/2010/jan/january-february-2010/ask-the-expert/ask-the-expert (accessed September 23, 2014).
Leape, L. L., T. A. Brennan, N. Laird, A. G. Lawthers, A. R. Localio, B. A. Barnes, L. Hebert, J. P. Newhouse, P. C. Weiler, and H. Hiatt. 1991. The nature of adverse events in hospitalized patients: Results of the Harvard Medical Practice Study II. New England Journal of Medicine 324(6):377–384.
Lundberg, G. D. 1998. Low-tech autopsies in the era of high-tech medicine: Continued value for quality assurance and patient safety. JAMA 280(14):1273–1274.
Marceglia, S., P. Fontelo, and M. J. Ackerman. 2015. Transforming consumer health informatics: Connecting CHI applications to the health-IT ecosystem. Journal of the American Medical Informatics Association 22(e1):e210–e212.
Marewski, J. N., and G. Gigerenzer. 2012. Heuristic decision making in medicine. Dialogues Clinical Neuroscience 14(1):77–89.
Marx, D. A. 2001. Patient safety and the “just culture”: A primer for health care executives. Medical Event Reporting System–Transfusion Medicine. www.safer.healthcare.ucla.edu/safer/archive/ahrq/FinalPrimerDoc.pdf (accessed June 7, 2015).
McDonald, K. M. 2014. The diagnostic field’s players and interactions: From the inside out. Diagnosis 1(1):55–58.
MedPAC (Medicare Payment Advisory Commission). 2014. Physician and other health professional payment system. www.medpac.gov/-documents-/payment-basics/page/2 (accessed March 17, 2015).
Mello, M. M., D. M. Studdert, and A. Kachalia. 2014. The medical liability climate and prospects for reform. JAMA 312(20):2146–2155.
Miller, H. D. 2014. How healthcare payment systems and benefit designs can support more accurate diagnosis. Input submitted to the Committee on Diagnostic Error in Health Care, December 29, 2014, Washington, DC.
Milstead, J. A. 2005. The culture of safety. Policy, Politics, & Nursing Practice 6(1):51–54.
Moran, J. W., and B. K. Brightman. 2000. Leading organizational change. Journal of Workplace Learning 12(2):66–74.
Moses, H., 3rd, D. H. Matheson, S. Cairns-Smith, B. P. George, C. Palisch, and E. R. Dorsey. 2015. The anatomy of medical research: U.S. and international comparisons. JAMA 313(2):174–189.
Mumma, G., and W. Steven. 1995. Procedural debiasing of primary/anchoring effects in clinical-like judgments. Journal of Clinical Psychology 51:841–853.
Mussweiler, T., F. Strack, and T. Pfeiffer. 2000. Overcoming the inevitable anchoring effect: Considering the opposite compensates for selective accessibility. Personality and Social Psychology Bulletin 26(9):1142–1150.
National Commission on Physician Payment Reform. 2013. Report of the National Commission on Physician Payment Reform. Washington, DC: National Commission on Physician Payment Reform. http://physicianpaymentcommission.org/report (accessed March 17, 2015).
Newman-Toker, D. 2014. Prioritization of diagnostic error problems and solutions: Concepts, economic modeling, and action plan. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Newman-Toker, D. E., K. M. McDonald, and D. O. Meltzer. 2013. How much diagnostic safety can we afford, and how should we decide? A health economics perspective. BMJ Quality and Safety 22(Suppl 2):ii11–ii20.
NPSF (National Patient Safety Foundation) and SIDM (Society to Improve Diagnosis in Medicine). 2014. Checklist for getting the right diagnosis. www.npsf.org/?page=rightdiagnosis and http://c.ymcdn.com/sites/www.npsf.org/resource/collection/930A0426-5BAC-4827-AF94-1CE1624CBE67/Checklist-for-Getting-the-Right-Diagnosis.pdf (accessed June 26, 2015).
NQF (National Quality Forum.) 2011. Serious reportable events in healthcare—2011 update: A consensus report. Washington, DC: National Quality Forum. www.qualityforum.org/projects/hacs_and_sres.aspx (accessed June 11, 2015).
Ober, K. P. 2015. The electronic health record: Are we the tools of our tools? The Pharos 78(1):8–14.
ONC (Office of the National Coordinator for Health Information Technology). 2014. Health information technology adverse event reporting: Analysis of two databases. Washington, DC: Office of the National Coordinator for Health Information Technology.
Otte-Trojel, T., A. de Bont, J. van de Klundert, and T. G. Rundall. 2014. Characteristics of patient portals developed in the context of health information exchanges: Early policy effects of incentives in the meaningful use program in the United States. Journal of Medical Internet Research 16(11):e258.
Papa, F. 2014. A response to the IOM’s ad hoc committee on Diagnostic Error in Health Care. Input submitted to the Committee on Diagnostic Error in Health Care, October 24, 2014, Washington, DC.
Patel, V., N. Yoskowitz, and J. Arocha. 2009. Towards effective evaluation and reform in medical education: A cognitive and learning sciences perspective. Advances in Health Sciences Education 14(5):791–812.
Patel, V., M. J. Swain, J. King, and M. F. Furukawa. 2013. Physician capability to electronically exchange clinical information, 2011. American Journal of Managed Care 19(10):835–843.
Pecukonis, E., O. Doyle, and D. L. Bliss. 2008. Reducing barriers to interprofessional training: Promoting interprofessional cultural competence. Journal of Interprofessional Care 22(4):417–428.
Portilla, L. M., and B. Alving. 2010. Reaping the benefits of biomedical research: Partnerships required. Science Translational Medicine 2(35):35cm17.
PSO (Patient Safety Organization) Privacy Protection Center. 2014. AHRQ Common Formats. www.psoppc.org/web/patientsafety (accessed August 10, 2015).
Redelmeier, D. A. 2005. Improving patient care. The cognitive psychology of missed diagnoses. Annals of Internal Medicine 142(2):115–120.
Reiling, J., G. Hughes, and M. Murphy. 2008. Chapter 28: The impact of facility design on patient safety. In R. G. Hughes (ed.), Patient safety and quality: An evidence-based handbook for nurses (pp. 700–725). Rockville, MD: Agency for Healthcare Research and Quality.
Richardson, W. S. 2007. We should overcome the barriers to evidence-based clinical diagnosis! Journal of Clinical Epidemiology 60(3):217–227.
Richardson, W. S. 2014. Twenty suggestions that could improve clinical diagnosis and reduce diagnostic error. Input submitted to the Committee on Diagnostic Error in Health Care, October 23, 2014, Washington, DC.
Rosenthal, M. 2014. Comments to the Institute of Medicine Committee on Diagnostic Error in Health Care. Input submitted to the Committee on Diagnostic Error in Health Care, December 29, 2014, Washington, DC.
RTI International. 2014. RTI International to develop road map for health IT safety center. www.rti.org/newsroom/news.cfm?obj=FCC8767E-C2DA-EB8B-AD7E2F778E6CB91A (accessed March 27, 2015).
Sage, W. M., T. H. Gallagher, S. Armstrong, J. S. Cohn, T. McDonald, J. Gale, A. C. Woodward, and M. M. Mello. 2014. How policy makers can smooth the way for communication-and-resolution programs. Health Affairs (Millwood) 33(1):11–19.
Sarkar, U., D. Bonacum, W. Strull, C. Spitzmueller, N. Jin, A. Lopez, T. D. Giardina, A. N. Meyer, and H. Singh. 2012. Challenges of making a diagnosis in the outpatient setting: A multi-site survey of primary care physicians. BMJ Quality and Safety 21(8):641–648.
Sarkar, U., B. Simchowitz, D. Bonacum, W. Strull, A. Lopez, L. Rotteau, and K. G. Shojania. 2014. A qualitative analysis of physician perspectives on missed and delayed outpatient diagnosis: The focus on system-related factors. Joint Commission Journal on Quality and Patient Safety 40(10):461–470.
Sarter, N. 2014. Use(r)-centered design of health IT: Challenges and lessons learned. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Schein, E. H. 2004. Organizational culture and leadership, 3rd ed. San Francisco, CA: John Wiley & Sons.
Schiff, G. D. 2008. Minimizing diagnostic error: The importance of follow-up and feedback. American Journal of Medicine 121(5):S38–S42.
Schiff, G. D. 2014a. Diagnosis and diagnostic errors: Time for a new paradigm. BMJ Quality and Safety 23(1):1–3.
Schiff, G. D. 2014b. Presentation to IOM Committee on Diagnostic Error in Health Care. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Schiff, G. D., and D. W. Bates. 2010. Can electronic clinical documentation help prevent diagnostic errors? New England Journal of Medicine 362(12):1066–1069.
Schiff, G. D., S. Kim, R. Abrams, K. Cosby, A. S. Elstein, S. Hasler, N. Krosnjar, R. Odwanzy, M. F. Wisniewsky, and R. A. McNutt. 2005. Diagnosing diagnosis errors: Lessons from a multi-institutional collaborative project for the diagnostic error evaluation and research project investigators. Rockville, MD: Agency for Healthcare Research and Quality. www.ahrq.gov/qual/advances (accessed June 10, 2015).
Schiff, G. D., O. Hasan, S. Kim, R. Abrams, K. Cosby, B. L. Lambert, A. S. Elstein, S. Hasler, M. L. Kabongo, N. Krosnjar, R. Odwazny, M. F. Wisniewski, and R. A. McNutt. 2009. Diagnostic error in medicine: Analysis of 583 physician-reported errors. Archives of Internal Medicine 169(20):1881–1887.
Schmitt, M., A. Blue, C. A. Aschenbrener, and T. R. Viggiano. 2011. Core competencies for interprofessional collaborative practice: reforming health care by transforming health professionals’ education. Academic Medicine 86(11):1351.
Shojania, K. G. 2010. The elephant of patient safety: What you see depends on how you look. Joint Commission Journal of Quality and Patient Safety 36(9):399–401.
Shojania, K. G., E. C. Burton, K. M. McDonald, and L. Goldman. 2002. Autopsy as an outcome and performance measure. Rockville, MD: Agency for Healthcare Research and Quality.
Shojania, K. G., E. C. Burton, K. M. McDonald, and L. Goldman. 2003. Changes in rates of autopsy-detected diagnostic errors over time: A systematic review. JAMA 289(21):2849–2856.
Silow-Carroll, S., T. Alteras, and J. A. Meyer. 2007. Hospital quality improvement: Strategies and lessons from U.S. hospitals. www.commonwealthfund.org/publications/fund-reports/2007/apr/hospital-quality-improvement--strategies-and-lessons-from-u-s--hospitals (accessed June 7, 2015).
Singh, H. 2013. Diagnostic errors: Moving beyond “no respect” and getting ready for prime time. BMJ Quality & Safety 22(10):789–792.
Singh, H. 2014. Building a robust conceptual foundation for defining and measuring diagnostic errors. Presentation to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Singh, H., and D. F. Sittig. 2015. Advancing the science of measurement of diagnostic errors in healthcare: The Safer Dx framework. BMJ Quality & Safety 24:103–110.
Singh, H., A. D. Naik, R. Rao, and L. A. Petersen. 2008. Reducing diagnostic errors through effective communication: Harnessing the power of information technology. Journal of General Internal Medicine 23(4):489–494.
Singh, H., T. D. Giardina, A. N. D. Meyer, S. N. Forjuoh, M. D. Reis, and E. J. Thomas. 2013. Types and origins of diagnostic errors in primary care settings. JAMA Internal Medicine 173(6):418–425.
Singh, H., A. N. D. Meyer, and E. J. Thomas. 2014. The frequency of diagnostic errors in outpatient care: Estimations from three large observational studies involving US adult populations. BMJ Quality & Safety 23(9). doi: 10.1136/bmjqs-2013-002627.
Sittig, D. F., and H. Singh. 2010. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality and Safety in Health Care 19(Suppl 3):i68–i74.
Sittig, D. F., D. C. Classen, and H. Singh. 2015. Patient safety goals for the proposed Federal Health Information Technology Safety Center. Journal of the American Medical Informatics Association 22(2):472–478.
Sorra, J., T. Famolaro, N. D. Yount, S. A. Smith, S. Wilson, and H. Liu. 2014. Hospital Survey on Patient Safety Culture—2014 user comparative database report. AHRQ Publication No. 14-0019-EF. Rockville, MD: Agency for Healthcare Research and Quality.
Tehrani, A., H. Lee, S. Mathews, A. Shore, M. Makary, P. Pronovost, and D. Newman-Toker. 2013. 25-year summary of U.S. malpractice claims for diagnostic errors 1986–2010: An analysis from the National Practitioner Data Bank. BMJ Quality and Safety 22:672–680.
ten Cate, O. 2014. Advice to the Institute of Medicine Committee on Diagnostic Error. Input submitted to the Committee on Diagnostic Error in Health Care, November 28, 2014, Washington, DC.
Thomas, E. J. 2014. Safety culture and diagnostic error: A rising tide lifts all boats. Presentation to the Committee on Diagnostic Error in Health Care, November 5, 2014, Washington, DC.
Trowbridge, R. 2014. Diagnostic performance: Measurement and feedback. Paper presented to the Committee on Diagnostic Error in Health Care, August 7, 2014, Washington, DC.
Trowbridge, R., G. Dhaliwal, and K. Cosby. 2013. Educational agenda for diagnostic error reduction. BMJ Quality and Safety 22(Suppl 2):ii28–ii32.
Verghese, A. 2008. Culture shock—patient as icon, icon as patient. New England Journal of Medicine 359(26):2748–2751.
Wachter, R. M. 2010. Why diagnostic errors don’t get any respect—And what can be done about them. Health Affairs (Millwood) 29(9):1605–1610.
Wegwarth, O., W. Gaissmaier, and G. Gigerenzer. 2009. Smart strategies for doctors and doctors-in-training: Heuristics in medicine. Medical Education 43(8):721–728.
Weick, K. E., and K. M. Sutcliffe. 2011. Business organizations must learn to operate “mindfully” to ensure high performance. www.bus.umich.edu/FacultyResearch/Research/ManagingUnexpected.htm (accessed May 26, 2015).
Weingart, S. N., O. Pagovich, D. Z. Sands, J. M. Li, M. D. Aronson, R. B. Davis, D. W. Bates, and R. S. Phillips. 2005. What can hospitalized patients tell us about adverse events? Learning from patient-reported incidents. Journal of General Internal Medicine 20(9):830–836.
Wennberg, D. 2014. Comments for the Institute of Medicine’s Committee on Diagnostic Error in Health Care. Input submitted to the Committee on Diagnostic Error in Health Care, December 29, 2014, Washington, DC.
WHO (World Health Organization). 2005. WHO draft guidelines for adverse event reporting and learning systems. Geneva, Switzerland: WHO.
Zimmerman, T. M., and G. Amori. 2007. Including patients in root cause and system failure analysis: Legal and psychological implications. Journal of Healthcare Risk Management 27(2):27–34.
Zwaan, L., M. de Bruijne, C. Wagner, A. Thijs, M. Smits, G. van der Wal, and D. R. Timmermans. 2010. Patient record review of the incidence, consequences, and causes of diagnostic adverse events. Archives of Internal Medicine 170(12):1015–1021.
Zwaan, L., G. D. Schiff, and H. Singh. 2013. Advancing the research agenda for diagnostic error reduction. BMJ Quality & Safety 22(Suppl 2):ii52–ii57.
This page intentionally left blank.