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3 Methods for Patient-Centered Outcomes Research
Pages 33-44

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From page 33...
... Shah, Stanford University, argued that to make better use of patient-centered outcomes data, it would be useful to adopt a patient timeline view of the data. Typically, data are thought of as residing in tables, text files, images, and so on, but health care happens over time, and it is useful to think of the data in those terms, as events occurring over time.
From page 34...
... To be able to make full use of timeline data, it is also necessary to have tools that can perform interval algebra. The next step after performing interval algebra would be navigating knowledge graphs.
From page 35...
... , a search ­engine he and his colleagues developed for patient data.1 The search e­ ngine consists of a persistent in-memory database of patient objects and a temporal query language, both optimized for fast search, and a flexible application programming interface to access and retrieve data. Researchers can quickly find patients by searching across diagnosis and procedure codes, concepts extracted from clinical notes, laboratory test results, or vital signs, as well as by visit types and duration of inpatient stays.
From page 36...
... Normand pointed at missing data as another challenge for PCOR. While missing data has always posed difficulties for statisticians, it is important to understand what this means specifically for electronic health records and to consider solutions for irregularly spaced data.
From page 37...
... Sherri Rose, Stanford University, began by talking about data transfor mations in cases where it might be necessary to intervene if the information available represents structural biases in the collection of those data. Rose mentioned a recent paper she coauthored with colleagues on the ethical use of machine learning in health care.2 The paper discusses the potential for data tools to exacerbate existing health disparities, along the different steps in the process from problem selection, through data collection and outcome definitions, and finally to algorithm development and potential postdeployment activities.
From page 38...
... In connection with building tools for a data infrastructure, she named several social impact principles, including responsibility, explainability, accuracy, auditability, and fairness. Nirosha Mahendraratnam Lederer, of Aetion, said that the company's Aetion Evidence Platform puts real-world data on a patient timeline, and uses transparent and scientifically validated workflows to analyze the data to generate real-world evidence.
From page 39...
... Lederer suggested that identifying a minimum set of core data elements to collect in routine clinical care can enable more meaningful research and facilitate data linkages. She added that building on existing tools and initiatives instead of creating new programs might be most practical.
From page 40...
... Food and Drug Administration (FDA) in collaboration with Friends of Cancer Research's COVID-19 Evidence Accelerator, which convenes health care stakeholders to use a common data shell and protocol to run analyses for high-priority research questions for COVID-19.
From page 41...
... She said that transparency could be accelerated through incentives, such as tying access to federal data sets or federal funding to the registration of the studies and the publication of the research protocols and results. Lara Mangravite, Sage Bionetworks, focused on the governance component of the data infrastructure, discussing issues related to the governance structures used to enable research that typically involves data from multiple sources.
From page 42...
... In summary, Mangravite highlighted the need for integration of data across systems, and the integration of participants into the research life cycle, as two of the areas that need the most attention in terms of the data infrastructure. To integrate participants, her specific suggestions were to focus on enabling richer understanding of lived experiences outside of the medical system, support the alignment of research questions with commu nity needs, and support capacity building for translating research outcomes into action.
From page 43...
... A theme that had been explored in detail in the committee's first workshop and was revisited by the participants in this one was the need to broaden research perspectives from the patient to the person in a broader sense, bringing in additional data on factors that are outside of the health care provider system. Integrating relevant data that go beyond provider databases represents its own challenges, but a timeline view that expands beyond a person's experiences within the health care system would greatly increase our ability to understand, for example, chronic diseases.
From page 44...
... CONCLUSION 3-2: Observing scientific best practices, including those of transparency and ethical use of data, is essential to generate trust in patient-centered outcomes research among all stakeholders, including the public and researchers. This is important both for observational data and for emerging data sources and methods.


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