Preparing healthcare delivery organizations for managing computable knowledge

IntroductionThe growth of data science has led to an explosion in new knowledge alongside various approaches to representing and sharing biomedical knowledge in computable form. These changes have not been matched by an understanding of what healthcare delivery organizations need to do to adapt and continuously deploy computable knowledge. It is therefore important to begin to conceptualize such changes in order to facilitate routine and systematic application of knowledge that improves the health of individuals and populations.MethodsAn AHRQâ funded conference convened a group of experts from a range of fields to analyze the current state of knowledge management in healthcare delivery organizations and describe how it needs to evolve to enable computable knowledge management. Presentations and discussions were recorded and analyzed by the author team to identify foundational concepts and new domains of healthcare delivery organization knowledge management capabilities.ResultsThree foundational concepts include 1) the current state of knowledge management in healthcare delivery organizations relies on an outdated biomedical library model, and only a small number of organizations have developed enterpriseâ scale knowledge management approaches that â pushâ knowledge in computable form to frontline decisions, 2) the concept of Learning Health Systems creates an imperative for scalable computable knowledge management approaches, and 3) the ability to represent data science discoveries in computable form that is FAIR (findable, accessible, interoperable, reusable) is fundamental to spread knowledge at scale. For healthcare delivery organizations to engage with computable knowledge management at scale, they will need new organizational capabilities across three domains: policies and processes, technology, and people. Examples of specific capabilities were developed.ConclusionsHealthcare delivery organizations need to substantially scale up and retool their knowledge management approaches in order to benefit from computable biomedical knowledge.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149202/1/lrh210070.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149202/2/lrh210070_am.pd

Predictive Technologies in Healthcare: Public Perspectives and Health System Governance in the Context of Structural Inequity

The health data ecosystem is increasingly focused on the design and implementation predictions in the form of AI-enabled clinical decision support, risk calculation, and resource allocation. This system of prediction in healthcare is developing rapidly in the context of limited regulation and structural inequity. The stakes for patients and health systems are high as predictive models are deployed more widely, affecting multiple aspects of care from appointment wait times to treatment for sepsis. Risks of racism, bias, and other inequities in the data used to build these models are increasingly recognized. However, public perspectives and values related to predictive modeling in healthcare have not yet been studied at the national level. It is also unclear how health systems are currently governing prediction, especially in the context of structural inequity. In this dissertation, I analyze an original national survey of the public to understand their perspectives on prediction in healthcare. I also analyze qualitative in-depth interviews with health system leadership to examine their governance strategies for predictive models. This approach treats both health system leadership and members of the public as key stakeholders engaged in and affected by the sociotechnical system of prediction. In the first study, I analyze public comfort with data use for prediction using survey responses from a national sample of US adults. I identify that the public differentiates between the use of various data types for prediction and observe higher comfort among 1) white respondents and 2) those who have not experienced discrimination while seeking healthcare. In the second study of a national sample of US adults, I identify misalignment between public perspectives and current regulatory frameworks. Analyzing original survey measures of comfort with six specific predictive models in healthcare, I find that the public is less comfortable with administrative applications of prediction (e.g., predicting missed appointments) than with clinical applications (e.g., predicting stroke). The third study presents findings from qualitative interviews with leadership from academic medical centers across the country about how they manage and design governance processes. This project focuses on understanding how predictive models are currently governed, how regulation shapes that governance, and whether equity is a consideration in health system governance processes. I identify variation among academic medical centers in their governance structures and the degree to which they consider equity when evaluating predictive models. I also find that current regulation is ambiguous for these decision-makers and could be strengthened to provide important guidance for health system policy. As patients are increasingly exposed to predictive technologies and healthcare systems are expected to govern them, there is a critical need for empirical evidence on both stakeholders’ needs, perspectives, and expectations. Policymakers, model developers, and health system leadership have roles to play in leveraging this evidence to design more responsive and equitable predictive systems in healthcare.PhDHealth Services Organization & PolicyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/177847/1/ptassie_1.pd

Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice

To date, attempts to address racially discriminatory clinical algorithms have largely focused on fairness and the development of models that “do no harm.” While the push for fairness is rooted in a desire to avoid or ameliorate health disparities, it generally neglects the role of racism in shaping health outcomes and does little to repair harm to patients. These limitations necessitate reconceptualizing how clinical algorithms should be designed and employed in pursuit of racial justice and health equity. A useful lens for this work is bounded justice, a concept and research analytic proposed by Melissa Creary to guide multidisciplinary health equity interventions. We describe how bounded justice offers a lens for (1) articulating the deep injustices embedded in the datasets, methodologies, and sociotechnical infrastructure underlying design and implementation of clinical algorithms and (2) envisioning how these algorithms can be redesigned to contribute to larger efforts that not only address current inequities, but to redress the historical mistreatment of communities of color by biomedical institutions. Thus, the aim of this article is two-fold. First, we apply the bounded justice analytic to fairness and clinical algorithms by describing structural constraints on health equity efforts such as medical device regulatory frameworks, race-based medicine, and racism in data. We then reimagine how clinical algorithms could function as a reparative technology to support justice and empower patients in the healthcare system

Fifty Years of Trust Research in Health Care: A Synthetic Review

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/176087/1/milq12598_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/176087/2/milq12598.pd

Learning about COVID-19: sources of information, public trust, and contact tracing during the pandemic

Abstract Objective To assess the association between public attitudes, beliefs, and information seeking about the COVID-19 pandemic and willingness to participate in contact tracing in Michigan. Methods Using data from the quarterly Michigan State of the State survey conducted in May 2020 (n = 1000), we conducted multiple regression analyses to identify factors associated with willingness to participate in COVID-19 contact tracing efforts. Results Perceived threat of the pandemic to personal health (B = 0.59, p = <.00, Ref = No threat) and general trust in the health system (B = 0.17, p < 0.001), were the strongest positive predictors of willingness to participate in contact tracing. Concern about misinformation was also positively associated with willingness to participate in contact tracing (B = 0.30, p < 0.001; Ref = No concern). Trust in information from public health institutions was positively associated with willingness to participate in contact tracing, although these institutions were not necessarily the main sources of information about COVID-19. Conclusion Policy makers can enhance willingness to participate in public health efforts such as contact tracing during infectious disease outbreaks by helping the public appreciate the seriousness of the public health threat and communicating trustworthy information through accessible channels.http://deepblue.lib.umich.edu/bitstream/2027.42/173517/1/12889_2022_Article_13731.pd

Do people have an ethical obligation to share their health information? Comparing narratives of altruism and health information sharing in a nationally representative sample.

BackgroundWith the emergence of new health information technologies, health information can be shared across networks, with or without patients' awareness and/or their consent. It is often argued that there can be an ethical obligation to participate in biomedical research, motivated by altruism, particularly when risks are low. In this study, we explore whether altruism contributes to the belief that there is an ethical obligation to share information about one's health as well as how other health care experiences, perceptions, and concerns might be related to belief in such an obligation.MethodsWe conducted an online survey using the National Opinion Research Center's (NORC) probability-based, nationally representative sample of U.S. adults. Our final analytic sample included complete responses from 2069 participants. We used multivariable logistic regression to examine how altruism, together with other knowledge, attitudes, and experiences contribute to the belief in an ethical obligation to allow health information to be used for research.ResultsWe find in multivariable regression that general altruism is associated with a higher likelihood of belief in an ethical obligation to allow one's health information to be used for research (OR = 1.22, SE = 0.14, p = 0.078). Trust in the health system and in care providers are both associated with a significantly higher likelihood of believing there is an ethical obligation to allow health information to be used (OR = 1.48, SE = 0.76, pConclusionsBelief that there is an ethical obligation to allow one's health information to be used for research is shaped by altruism and by one's experience with, and perceptions of, health care and by general concerns about the use of personal information. Altruism cannot be assumed and researchers must recognize the ways encounters with the health care system influence (un)willingness to share one's health information