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Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health (2017)

Chapter: 3 Patient Taxonomy and Implications for Care Delivery

« Previous: 2 Key Characteristics of High-Need Patients
Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
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3

PATIENT TAXONOMY AND IMPLICATIONS FOR CARE DELIVERY

Fictional patient vignette: Sarah is a 26-year-old woman who was recently involved in a car accident that left her paralyzed from the waist down. She was having a lot of trouble not only adjusting to her new reality but also navigating all of her new health care needs. Sarah had been a regular runner before the accident, and she had always been in good health, so she was largely unfamiliar with the ins and outs of doctors’ offices. She turned to Nora for advice because it seemed as if this family friend was always either coming from or going to one doctor or another. Nora was in her mid-sixties and had been living with diabetes and heart disease for almost 20 years. Nora talked about how her nutritionist had helped her manage her diet, and how helpful her general practitioner was. Sarah was really hoping Nora would be able to help her understand how to navigate appointments with specialists and to recommend a way to get mental health care that wasn’t readily covered by insurance. Even though Nora had tried to help, Sarah left their conversation feeling more confused. It was apparent that even though she and Nora each had a severe illness, their health care needs were incredibly different.

The 12 million high-need patients in the United States are members of a diverse group of individuals affected by a range of medical, behavioral, and functional conditions and limitations (Hayes et al., 2016). Adding a layer of complexity to the effective care of high-need patients is the disproportionate impact of social circumstances—isolation, unemployment, lack of permanent or safe housing, and food insecurity, for example—on this population’s health and well-being. Because of the varying needs and preferences of high-need patients, multiple tools and approaches are necessary to ensure that they receive the most appropriate care, with individual patient characteristics and preferences informing

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

selection from among care models. Therefore, serving this heterogeneous population more effectively and efficiently requires construction of a taxonomy that has groupings based on shared characteristics and functional needs.

Drawing from discussions and common themes throughout the workshop series and the published evidence, this chapter reports on current approaches in—and evidence for—the application of taxonomies to the management of high-need patients as a means of improving their care. In particular, it provides an overview of the taxonomies used by two organizations, the Harvard T.H. Chan School of Public Health and The Commonwealth Fund, and guidance on the adoption and application of their key elements in practice. Given the profound role of social risk and behavioral health factors on the health of high-need patients, the intersection of these factors with the clinical domain receives particular attention. This chapter has been informed by two main sources: the insights gleaned from the workshop series presentations and discussions, and the assessment of an expert group of researchers, clinicians, and policy experts on the state of the evidence around the use of a patient taxonomy and their insights on how to advance its utility and adoption.

PURPOSE AND OPERATION OF PATIENT SEGMENTATION

Segmenting target populations is not a novel concept. Marketing agencies divide populations and target potential strategies based on shared characteristics. In health care, triage has long been a foundational concept for ensuring that patients with the most urgent needs are given priority for treatment (Robertson Steel, 2006), and it is an increasingly common protocol to sort cancer patients, for example, based on genomic characterization and various molecular markers to better inform therapeutic strategies (Konecny et al., 2016; Wang et al., 2014). Health system leaders can use a taxonomy to better understand their systems’ patient populations and inform program planning, care team compositions and work flow, training, and infrastructure investments—leading to improved health and well-being outcomes and reduced costs.

Patient stratification strategies can take several forms. For instance, whole population risk stratification segments a health care system’s entire patient population based on a projected risk of requiring care. Health systems create these risk profiles using various risk prediction algorithms that group their patients according to their utilization of services or specific health conditions, such as diabetes or high blood pressure. Health systems have developed whole population risk stratification methods to predict the anticipated costs for their specific patient populations. This approach, however, captures only a small fraction of

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

the patients who could benefit from greater oversight or help in managing their conditions (Kansagara et al., 2011), in part because any technique based on the presumption of homogeneity is structurally limiting, and in part because it does not account for the socioeconomic characteristics and behaviors that affect health outcomes. For example, patients with diabetes have highly varied treatment requirements, and those with social challenges face still other requirements (Hostetter and Klein, 2015).

One of the earliest stratification systems was developed by Kaiser Permanente’s cofounder Sidney Garfield, whose parsimonious categorization system comprised four groups for all patients: sick, well, worried well, and early sick (Garfield, 1970). These categories have since been revised: no chronic conditions, one or more chronic conditions, advanced illness, and extremely frail and near end of life (Zhou et al., 2014). The “Bridges to Health” model, first proposed by Lynn and colleagues at Centers for Medicare & Medicaid Services, divides the entire population into eight groups, from healthy to failing health near death (Lynn et al., 2007).

Patient segmentation using a taxonomy of the sort described in this chapter is driven by the goal of grouping the individuals in a health system’s population by the care they need as well as how often they might need it. Segmentation involves separating the highest-risk patients (as determined using whole population risk stratification) into subgroups with common needs. A key operational concept for a useful taxonomy for patient segmentation is that it should account for the unique factors that drive an individual’s health care needs.

Patient targeting goes one step further by looking within each segment to identify which patients need the highest intensity of complex care management. Both the literature and discussions with providers indicate that most successful care models, such as those discussed in Chapter 4, use targeting to refine further how they allocate resources more efficiently among their high-need patients.

DEVELOPING A TAXONOMY

The need for greater precision is a natural product of the move toward value-based care, the emphasis on patient-engaged care, and the better insights emerging on what works best under different circumstances. While a general consensus exists on the benefits of segmenting high-need patients to target care (Vuik et al., 2016), work is still in progress on the optimal definitions of patient groups. For high-need patients in particular, we know that any taxonomy must take into account social risk and behavioral health factors at play—areas that need much elaboration (Johnson et al., 2015a; Kansagara et al., 2011).

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

Developing and implementing any taxonomy to guide service delivery to high-need patients requires solving numerous challenges. Segmenting high-need patients into meaningful subgroups requires access to information about their physical and behavioral conditions, their care utilization, and their social challenges. Most health information technology systems, however, do not support this type of integrated and streamlined data collection. The most readily available source of information is claims-based data, but these data offer a limited, condition-based perspective of patients and are not available in real time. Electronic health records (EHRs) can serve as a key source of data, but the design of many EHR systems does not enable them to collect data on behavioral issues, social challenges, or functional limitations (Institute of Medicine, 2014a, 2014b). The burden on health systems to collect, store, and properly use data are additional practical and logistical considerations.

A patient taxonomy that is effective in driving more productive treatment strategies for the high-need patient pool requires a delicate balance between precision and generalization. It is impractical to assume that every relevant feature can be captured and characterized for each patient. Although defining patient subgroups and sub-subgroups introduces more precision into categorizing patients, a taxonomy that contains too many subgroups is not feasible to implement. On the other hand, having too few groups is an oversimplification and does not meaningfully inform care planning and management. In addition, multiple payers and varied benefits packages pose administrative and operational hurdles for the implementation of any taxonomy. Medicaid is of particular concern because a disproportionate number of high-need patients are covered—at least in part—by the program, yet coverage varies widely from state to state. Chapter 5 covers this subject in more detail.

IDENTIFYING SEGMENTS

To address the challenge of creating an actionable stratifying tool, the taxonomy workgroup developed a conceptual starter taxonomy. In the third workshop, Melinda Abrams, vice president for delivery system reform at The Commonwealth Fund and chair of the taxonomy workgroup, explained that the medical aspects of this taxonomy build largely on the work of the Harvard T.H. Chan School of Public Health group, led by Ashish Jha and Jose Figueroa.

Jha, Figueroa, and colleagues conducted a set of analyses of Massachusetts claims data to empirically derive mutually exclusive subpopulations of high-need patients in three distinct populations: the non-Medicare population under age 65, the Medicare population, and the dual-eligible population (Joynt et al.,

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

2017). While claims data are often maligned, said Jha in the second workshop, in his opinion they are currently the best way to draw a picture of high-need, high-cost individuals in the United States. Through a yearlong iterative process, with input from clinical leaders and working closely with a group led by Gerard Anderson at Johns Hopkins University, the Harvard team defined the subpopulations with a noniterative, hierarchical categorization that assigned patients to groups of increasing complexity. The resulting six subpopulations, in the order in which individuals are classified, are listed as follows: under-65 disabled who are not included in the non-Medicare under-65 population; frail, with two or more frailty indicators; major complex chronic, with two or more chronic conditions from a list of nine major chronic diseases that account for the majority of spending and morbidity; minor complex chronic, with one chronic condition from the list of nine major chronic diseases; simple chronic, which includes less severe conditions such as hyperlipidemia; and relatively healthy. Individuals are assigned to no more than one of these groups by first determining whether the patient is under 65 or 65 or older. Individuals under 65 are assigned to the first category. Of those individuals age 65 or older, those with two or more frailty indicators are assigned to the frail elderly group. Last, the remaining individuals are assigned to one of the final four categories based on the number of chronic conditions they have (Joynt et al., 2016).

Jha noted that this may not be the ideal way to segment the population, but he believes it is a reasonable approach. One limitation is that it does not specifically address patients with advanced illness or those patients at the end of life. Jha added that it would be important to examine other populations, particularly children, and try to understand the characteristics of providers that do better with one subpopulation as compared to another.

Building on the Harvard group’s work and an analysis of Medical Expenditure Panel Survey (MEPS) data by Anderson and colleagues at Johns Hopkins (Roberts and Anderson, 2014), Abrams and collaborators at The Commonwealth Fund looked at how to characterize some of the issues and challenges facing high-need and high-cost patients. As explained by Melinda Abrams during the second workshop, the Commonwealth Fund team examined segmentation and programmatic literature, such as program evaluations and case studies, as a “reverse engineering” strategy to identify patient groups based on how existing programs identified and segmented patients. The team also conducted interviews with health system leaders, program experts, and payers, and they collaborated with an advisory group to define 11 specific patient groups, including a standalone segment for individuals with social risk and behavioral health factors. After further consideration and analysis, Abrams and colleagues merged some of these

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

segments into six subpopulations: under-65 disabled, advancing illness, frail elderly, complex chronic conditions, multiple chronic conditions, and children with complex needs.6 At any given time, patients are assigned to just one of these six segments and their designation is determined by their medical needs that are driving their health care costs. For example, a frail elderly individual with multiple chronic conditions would be assigned to the frail elderly segment because the frailty indicators are what is driving medical needs and ultimately costs. However, over time, as their medical needs change, patients may shift between segments.

In her presentation at the second workshop, Abrams explained some of the logic behind merging categories and settling on these six subpopulations. For example, for people with functional limitations, it did not matter whether they were under or over age 65. The two larger subcategories that made more sense practically were under-65 disabled and frail elderly. With regard to Jha’s subcategories of major complex chronic, minor complex chronic, and simple chronic, Abrams said those were based on elegant work, but for practical purposes, those were too finely divided. As a result, The Commonwealth Fund team merged them into two categories: complex chronic conditions and multiple chronic conditions. Additionally, the stand-alone category of patients with social risk and behavioral health factors actually spanned all of the medical categories. Abrams noted that while the segmentation literature is small and greatly variable in terms of quality and rigor, it did suggest some additional segments beyond Anderson’s and Jha’s work, including advanced illness, end-of-life, and children with complex conditions (Lynn et al., 2007; Zhou et al., 2014).

Addressing some of the limitations of this work, Abrams said there are multiple plausible segmentation strategies, and the approach taken depends on the audience and the purpose. In addition, this work was based on limited data sources. “We need more information from patients, social services agencies, and interoperable systems,” said Abrams during the second workshop. She noted, too, that segmentation is, at this stage, inherently imprecise, and she emphasized the need for more comprehensive data on patients that would be more informative than claims data, as was stated in a 2014 Institute of Medicine report (Institute of Medicine, 2014a).

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6 This taxonomy was presented by Abrams at the second workshop. More information can be found at http://www.bettercareplaybook.org/resources/overview-segmentation-high-need-high-cost-patient-population (accessed on March 29, 2017).

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
Image
FIGURE 3–1 | A conceptual model of a starter taxonomy for high-need patients.
NOTE: For this taxonomy, functional impairments are intrinsically tied to the clinical segments.
SOURCE: Adapted from Abrams presentation, October 21, 2016

A CONCEPTUAL “STARTER” TAXONOMY

While still theoretical, taxonomies such as the ones Jha and Abrams laid out are medically oriented approaches. Given the availability of data, grouping patients according to medical characteristics is a feasible starting point for most health systems: the patient groups are clinically meaningful and carry implications for care delivery, and health systems can access information needed to identify and assign patients to groups via claims and EHR data. Assigning a patient to one of these groups tells only part of the patient story, however, and may neglect other characteristics and factors that are key drivers of functional limitations and health care spending. Here, the taxonomy workgroup offers a conceptual “starter” taxonomy for high-need patients (see Figure 3–1) that builds on the ones Jha and Abrams described to illustrate the incorporation of functional, social, and behavioral factors into a medically oriented taxonomy, not as independent segments but as factors that influence the care model or care team composition most likely to benefit a particular patient in one of the segments.

Fundamentally, this starter taxonomy aims to be actionable to inform care and workforce decisions and to reflect the reality of the data that are available to health system leaders. Table 3–1 describes the criteria for each group.

Because the segments were based largely on the work of both the Harvard and The Commonwealth Fund teams, there are limitations to clinical grouping that arise from the fact that the categorization was informed by the structure of limited datasets. For example, while children with complex needs are included, other high-risk groups worth further consideration, such as high-risk pregnancies, adolescents, and those who have suffered a traumatic event such as a brain or

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

spinal injury, were not specifically designated as a segment. In addition, because identification of functional impairment is intrinsically tied to the clinical segments, the segments may not capture the complete diversity of functional limitations.

TABLE 3–1 | Clinical Group Features

CLINICAL GROUP FEATURES
Children with complex needs Have sustained severe impairment in at least four categories together with enteral/parenteral feeding or sustained severe impairment in at least two categories and requiring ventilation or continuous positive airway pressureA
Non-elderly disabled Under 65 years and with end-stage renal disease or disability based on receiving Supplemental Security Income
Multiple chronic Only one complex condition and/or between one and five noncomplex conditionsB,C
Major complex chronic Two or more complex conditions or at least six noncomplex conditionsB,C
Frail elderly Over 65 years and with two or more frailty indicatorsD
Advancing illness Other terminal illness, or end of life

A Categories for children with complex needs are: learning and mental functions, communication, motor skills, self-care, hearing, vision

B Complex conditions, as defined in (Joynt et al., 2016), are listed in Table 2–1.

C Noncomplex conditions as defined in (Joynt et al. 2016) are listed in Table 2–1.

D Frailty indicators, as defined in (Joynt et al., 2016), are gait abnormality, malnutrition, failure to thrive, cachexia, debility, difficulty walking, history of fall, muscle wasting, muscle weakness, decubitus ulcer, senility, or durable medical equipment use.

This starter taxonomy can, however, provide guidance for health system leaders and payers on how to embed social risk factors, behavioral health factors, and functional limitations in a taxonomy for high-need patients. Patients would first be assigned to one clinical segment based on what medical needs are driving their health care costs, with follow-on assessment of behavioral health issues and social services needs to determine the specific type of services an individual requires. For example, the major complex chronic conditions patient segment would include patients who simultaneously have diabetes, heart disease, and kidney disease, suggesting that a care team should include a complex care manager. If some of the patients also have severe depression, bipolar illness, or other behavioral health conditions, their care team would require someone with training in behavioral health issues. If the patient subpopulation also has unstable housing and sources of food, the care team would require personnel with expertise in addressing housing and food security. The model also assumes that the medical,

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

behavioral, and social needs of patients will change. For example, an individual patient could move from frail elderly to advancing illness, which would suggest shifting resources from medical care to hospice care.

HIGH-IMPACT SOCIAL RISK AND BEHAVIORAL HEALTH VARIABLES

Two important components of this starter taxonomy are the social risk and behavioral health factors that affect a patient’s health and influence the specific needs of each individual in a particular segment defined by medical and functional status. A review of the literature on social domains that affect care, insights from planning committee members and outside experts, and a survey of available resources (such as the National Association of Community Health Center’s Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences [PRAPARE], a tool for assessing their patients’ social determinants of health),7 produced a list of four high-impact variables in the social services domain which were determined to be the most likely to affect care delivery decisions (see Table 3–2).

TABLE 3–2 | High-Impact Social Variables

VARIABLE CRITERIA/MEASUREMENT SOURCES
  1. Low socioeconomic status
Income and/or education Adler et al., 1994; Bengle et al., 2010; Bisgaier and Rhodes, 2011; Metallinos-Katsaras et al., 2012; Vijayaraghavan et al., 2011
  1. Social isolation
Marital/relationship status and whether living alone Ennis et al., 2014; House, 2001; Seeman, 1996
  1. Community deprivation
Median household income by census tract; proximity to pharmacies and other health care services Cutts et al., 2011; Wang et al., 2013; Bartley et al., 2003
  1. Housing insecurity
Homelessness; recent eviction Cutts et al., 2011; Schanzer et al., 2007

An analysis of MEPS data conducted by Claudia Salzberg at Johns Hopkins University for The Commonwealth Fund (Hayes et al., 2016b) shows the importance of behavioral health factors, as she found that 56 percent of high-need adults, or approximately 6.7 million people, have a behavioral health condition (such as depression, anxiety, or alcohol- or substance-related disorders) or a severe mental

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7 For more information, see http://nachc.org/research-and-data/prapare/toolkit (accessed on March 9, 2017).

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

illness (such as schizophrenia) as one of their three or more chronic conditions. Salzberg also found that high-need individuals with behavioral health conditions made 27 percent more visits to hospital emergency departments, used 35 percent more home health care days, were more likely to have unmet medical needs, and were less likely to have easy access to specialists or have good patient-provider communication compared to high-need individuals who did not have a behavioral health condition. Moreover, 34 percent of high-need adults with a behavioral health condition remained in the top 10 percent of spending over a 2-year period compared to 23 percent of high-need adults without a behavioral health condition.

The subpopulation of high-need adults with a behavioral health condition is relatively younger; is more likely to be white, female, and less educated; is more likely to have lower income and fair or poor health status; and is more likely to be insured by Medicaid, either alone or in combination with Medicare. A list of four high-impact behavioral variables, which were determined to be the most likely to affect care delivery decisions (see Table 3–3), was developed by a review of the literature, insights from planning committee members and outside experts, and a survey of available resources.

TABLE 3–3 | High-Impact Behavioral Variables

VARIABLE CRITERIA/MEASUREMENT SOURCES
  1. Substance abuse
Excessive alcohol, tobacco, prescription and/or illegal drug use Doll et al., 2004; Eisenhauer et al., 2011; Fagerstrom, 2002; Lai and Huang, 2009; Makela et al., 1997; Ryan, 1995
  1. Serious mental illness
Schizophrenia and other psychotic disorders, bipolar, major depression De Hert et al., 2011; Katon, 2003
  1. Cognitive decline
Dementia disorders (Alzheimer’s, Parkinson’s, vascular dementia) Schulz and Sherwood, 2008; Zeisel et al., 2003
  1. Chronic toxic stress
Functionally impairing psychological disorders or conditions (e.g., PTSD, ACE, anxiety) Brunner, 1997; Cohen et al., 2007; King and Chassin, 2008; Kivimaki et al., 2002; Schnurr and Green, 2004; Stansfeld et al., 2002; Taft et al., 2007
NOTE: ACE = Adverse Childhood Experiences; PTSD = Post-Traumatic Stress Disorder

For both lists of variables, social risk and behavioral health, the criteria for being “high-impact” included whether a variable had the potential for impact on both health and the type of care delivered, whether adding the variable would capture an otherwise missed patient population, and whether the variable would alter a person’s status in the taxonomy in a manner that would be linked

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

readily to clinical care. Some variables, such as race and ethnicity (Jackson et al., 2016; Larney et al., 2016; Morton et al., 2016; Segal et al., 2016) and incarceration (Wang et al., 2013), can affect health but are rooted in deeper systemic issues that are beyond the scope or purpose of this taxonomy. A variable such as health literacy can have a significant effect on health (Baker et al., 2007; Bennett et al., 2009; Institute of Medicine, 2004; Schillinger et al., 2002; Taylor et al., 2016), but the inventory of effective care models discussed in Chapter 4 does not directly address health literacy. As Abrams explained, the committee thought about the process of selecting the four social and the four behavioral health variables in terms of the taxonomy and its ability to match with the care model exemplars.

Image
FIGURE 3–2 | A framework for health with all of the factors that would go into an ideal taxonomy.
NOTE: SES = Socioeconomic status.
SOURCE: Reproduced from Abrams presentation, October 21, 2016.

ADVANCING THE USE OF A TAXONOMY

Categorizing high-need patients into smaller groups around which the delivery system can shape appropriate resources and strategies is sensible, given their heterogeneous medical needs, the varying impact of behavioral health issues and social factors on their functional abilities, and the high cost of caring for these

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

individuals, as described in Chapter 2 (Boyd et al., 2010; Cohen and Uberoi, 2013; Stanton and Rutherford, 2006). In the third workshop, Abrams described an ideal patient taxonomy—one not yet achieved—that could provide a holistic assessment of how care should be targeted and delivered to improve the health of high-need individuals (see assessment of a patient’s medical, behavioral, functional, and social characteristics to inform Figure 3–2). Developing such an approach for each patient segment, however, requires the integration of systems that capture physical, behavioral, and social information. Currently, this level of systems integration is only just starting to take place.

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FIGURE 3–3 | Differences in the proportion of high-cost patients in six patient categories for three distinct payer groups.
SOURCE: Adapted from Jha presentation, January 19, 2016

Even with the proposed conceptual models, though, it is possible for health system leaders and payers to determine practical information about their high-need population segments. In the second workshop, Jha provided an example

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

of the type of useful indicators a medically grounded taxonomy could produce. When Jha, Figueroa, and colleagues analyzed spending patterns among the three payer groups and six subpopulations of patients used in their taxonomy, the analysis revealed some surprises (see Figure 3–3), Jha said. For example, in the commercially insured, under-65 non-Medicare population, the majority of spending is by individuals in the minor complex chronic and simple chronic segments. Spending in the Medicare population differs greatly, he noted, with the frail and under-65 disabled accounting for the bulk of the high-cost patients. In the dual-eligible population, the under-65 disabled segment accounts for nearly half of the high-cost patients.

Image
FIGURE 3–4 | Preventable spending by patient group in the Medicare population.
SOURCE: Reproduced from Jha presentation, January 19, 2016.

The Harvard team also examined preventable spending among all of the Medicare patients included in the Massachusetts dataset (see Figure 3–4). For a definition of preventable, they looked at ambulatory care-sensitive conditions. For ambulatory care-sensitive conditions, most of the spending is by the frail elderly, who account for 10 percent of the total Medicare population and 45 percent of all hospitalizations for ambulatory care-sensitive conditions.

Jha discussed another analysis showing the mean distributional spending among high-cost patients (see Figure 3–5). For example, average annual inpatient

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

spending by a high-cost under-65 disabled individual is $15,947, and outpatient spending accounts for another $13,344, but the biggest cost for these individuals is Medicare Part D spending on drugs, which is $23,003 (Joynt et al., 2016). In contrast, Part D spending by the frail elderly represents a small proportion of total spending, with inpatient care and postacute care and long-term care being the big-ticket items for this group (Joynt et al., 2016).

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FIGURE 3–5 | High-cost Medicare patients’ distributional mean spending by patient category.
NOTE: DME = Durable Medical Equipment; PAC = Post-Acute Care; LTC = Long-Term Care
SOURCE: Adapted from Joynt et al., 2016.

This sort of distributional analysis, Jha explained, highlights the different spending profiles of the subpopulations and the need for health system leaders and payers to think carefully about how to address the expense of caring for these different types of high-cost patients. Segmentation offers opportunities for payers to more effectively target finite resources and improve outcomes, which ideally will reduce the total cost of care.

In this way, a formal taxonomy can ideally inform the development of care plans and the allocation of resources to the interventions, assisting in a threefold aim to improve the care match with patient goals, improve patient outcomes, and improve the efficiency of care delivery. Highlighting the needs and use profiles

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

of the various subpopulations, a taxonomy can help health care system leaders and payers make informed investments in a program, care team composition, work flow, training, and infrastructure. In Chapter 4, we discuss some models—many focused on specific segments of the high-need population—that health care system leaders can implement or look to for best practices. For a taxonomy to serve those purposes, however, it is necessary to align efforts across health systems and payers to ensure that payment structures incentivize, rather than hinder, effective care—a subject discussed in more detail in Chapter 5.

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Baker, D. W., M. S. Wolf, J. Feinglass, J. A. Thompson, J. A. Gazmararian, and J. Huang. 2007. Health literacy and mortality among elderly persons. Archives of Internal Medicine 167(14):1503–1509.

Bartley, M., D. Blane, E. Brunner, D. Dorling, J. Ferrie, M. Jarvis, M. Marmot, M. McCarthy, M. Shaw, A. Sheiham, S. Stansfeld, M. Wadsworth, and R. Wilkinson. 2003. Social Determinants of Health: The Solid Facts Second Edition. The World Health Organization: Copenhagen, Denmark.

Bengle, R., S. Sinnett, T. Johnson, M. A. Johnson, A. Brown, and J. S. Lee. 2010. Food insecurity is associated with cost-related medication non-adherence in community-dwelling, low-income older adults in Georgia. Journal of Nutrition for the Elderly 29(2):170–191.

Bennett, I. M., J. Chen, J. S. Soroui, and S. White. 2009. The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults. Annals of Family Medicine 7(3):204–211.

Bisgaier J., and K. V. Rhodes. 2011. Cumulative adverse financial circumstances: associations with patient health status and behaviors. Health and Social Work 26(2) 129–137.

Boyd, C., B. Leff, C. Weiss, J. Wolff, A. Hamblin, and L. Martin. 2010. Clarifying multimorbidity patterns to improve targeting and delivery of clinical services for Medicaid populations. Hamilton, NJ: Center for Health Care Strategies.

Brunner E. J. 1997. Stress and the biology of inequality. British Medical Journal 314:1472–1476.

Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×

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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
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×
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×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
×
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Suggested Citation:"3 Patient Taxonomy and Implications for Care Delivery." National Academy of Medicine. 2017. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health. Washington, DC: The National Academies Press. doi: 10.17226/27115.
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To advance insights and perspectives on how to better manage the care of the high-need patient population, the National Academy of Medicine, with guidance from an expert planning committee, was tasked with convening three workshops held between July 2015 and October 2016. The resulting special publication, Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value, and Health, summarizes the presentations, discussions, and relevant literature.

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