Skip to main content

Currently Skimming:

4 Researcher Perspectives on Data Needs
Pages 41-56

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 41...
... best positioned to address in the context of its public mission, authorities, programs, and data resources? David Meltzer, University of Chicago, discussed the use of existing data, including PCOR datasets, to conduct research on whether vitamin D could reduce the burden of COVID-19.
From page 42...
... COVID cases o Secure computing environment, collaborative ecosystem o  Sample size/coverage to allow analysis of levels, seasonality, location, racial subgroups marizes Meltzer's observations related to the usefulness of the PCOR data based on this work. Andrew Bazemore, American Board of Family Medicine, noted that although we live in a time of unprecedented health-data availability, there are also some structural blind spots in the U.S.
From page 43...
... . NOTE: COVID = coronavirus disease 2019; EHR = electronic health records; NCATS = National Center for Advancing Translational Sciences; PCOR = patient-centered outcomes research; PRO = patient-reported outcome; RCT = randomized controlled trial; Rx = prescrip tion; VA = U.S.
From page 44...
... Among the data sources that are missing from the current PCOR data infrastructure, according to Bazemore, are not only primary care practices themselves but also two other data sources: primary care registries and health information exchanges focused on primary care and the safety net. On the analytical side, Bazemore noted that a major concern is that new technologies such as artificial intelligence and machine learning do not typically involve primary care patients in their algorithm development.
From page 45...
... It also provides opportunities to support those on the front lines trying to contextualize care, as well as to support relationshipcentered care. Concerning the question of who is poorly served by the current PCOR data infrastructure, Stange underscored previous points about the limitations of the data on people living with multiple chronic conditions and various disadvantaged groups.
From page 46...
... For additional data that could be incorporated into the PCOR data infrastructure, Stange suggested the Person-Centered Primary Care Measures, which he and his coauthors developed based on what patients, clinicians, and (to a lesser extent) payors said was important to them in health care.3 Box 4-2 shows these measures, which Stange said are widely used and are also pending endorsement by the National Quality Forum and the Centers for Medicare & Medicaid Services (CMS)
From page 47...
... While this is at the core of the terminology comparative effectiveness, the data available and associated context are not enabling researchers to design and conduct crisp, reliable comparative effectiveness studies. Califf pointed out the important role the pragmatic randomized trial played in providing answers related to the COVID-19 pandemic.
From page 48...
... There are rising health concerns specific to both of these populations, and therefore there is a need to develop approaches that would address the data limitations associated with these groups. David Cella, Northwestern University, summarized key data needs as a common data model for patient-reported outcomes; common data elements for patient-reported outcomes; comparative effectiveness metrics across conditions; and medical and nonmedical cost data.
From page 49...
... The broadened statutory scope of PCOR also provides ingredients for a learning health systems approach across provider organizations. Concerning the questions that cannot be answered with the current PCOR infrastructure, Cella said it continues to be difficult to answer crosscutting comparative effectiveness research questions as they relate to patient-reported outcomes.
From page 50...
... For example, an inability to disaggregate data to compare Filipino health care workers to other Asians and Pacific Islanders could mean missing a disproportionate impact of the COVID-19 pandemic on Filipino nurses and nurses of Filipino descent. Corbie-Smith said there has been growing momentum to understand the social determinants of health and that some information of this kind is being captured in electronic medical records.
From page 51...
... Corbie-Smith also highlighted the opportunities presented by including networks of community service providers in the research. These stakeholders include not only federally qualified community health centers but also community service providers that are providing a matrix of care.
From page 52...
... HICOR's community engagement program shares data about clinic performance and costs across a common population-based data platform. The network includes all of the 28 oncology practices in Washington State, five of the state's major health insurance providers, representatives from local and state government, and a number of patient advocacy groups and patient advocates, including those who represent typically underrepresented minorities.
From page 53...
... However, it is becoming increasingly clear to the cancer care community that social determinants of health may play a bigger role than any other factor in the observed differences in outcomes among cancer patients. Social determinants of health influence patients over a lifetime, but to date little has been done to understand this at a level of specificity that can address policy.
From page 54...
... Ramsey argued that HHS does not necessarily need to create new data. Instead, he said, the agency is best positioned to facilitate access to existing data that currently live in the private sector; create regulations that foster interoperability; establish privacy safeguards; and improve timeliness of databases, particularly in areas such as cancer care, which is quickly evolving.
From page 55...
... Researchers echoed the need to make PCOR data more widely available to empower patients and communities to use this information. Efforts to reduce disparities, in particular, cannot be accomplished by research alone.


This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.