Minakshi Raj, PhD, MPH

As predictive analytics are increasingly used and developed by health care systems, recognition of the threat posed by bias has grown along with concerns about how providers can make informed decisions related to predictive models. To facilitate informed decision-making around the use of these models and limit the reification of bias, this study aimed to (1) identify user requirements for informed decision-making and utilization of predictive models and (2) anticipate and reflect equity concerns in the information provided about models.

 Qualitative analysis of user-centered design (n = 46) and expert interviews (n = 10).

 We conducted a user-centered design study at an academic medical center with clinicians and stakeholders to identify informational elements required for decision-making related to predictive models with a product information label prototype. We also conducted equity-focused interviews with experts to extend the user design study and anticipate the ways in which models could interact with or reflect structural inequity.

 Four key informational elements were reported as necessary for informed decision-making and confidence in the use of predictive models: information on (1) model developers and users, (2) methodology, (3) peer review and model updates, and (4) population validation. In subsequent expert interviews, equity-related concerns included the purpose or application of a model and its relationship to structural inequity.

Health systems should provide key information about predictive models to clinicians and other users to facilitate informed decision-making about the use of these models. Implementation efforts should also expand to routinely incorporate equity considerations from inception through the model development process.

Communicating effectively about predictive models and their equity implications is critical for (1) informed decision-making and (2) preventing potential harms to patients.

In this article we identify provider and administrator informational needs for making informed decision-making about the use of predictive models. We provide a prototype product information label for predictive tools and incorporate equity concerns.

As the health care system works toward evidence-based medicine, the evidence on bias and inequity must be integrated into decision-making around predictive analytics. When this evidence is treated as secondary and unrelated to model development, the negative consequences of predictive models will remain “unintended” but preventable. Designing policy around model transparency, communicating key informational elements, and analyzing equity implications are important steps that health systems can take to facilitate informed decision-making and to prevent harm to patients.

Predictive models continue to gain attention and investment in the US health care system.

 By leveraging large quantities of data from electronic health records (EHRs), clinical research, and other sources, these models can be used to anticipate patient outcomes such as disease progression, predict health service utilization, or identify appropriate treatments.

 In addition to commercial vendors, health systems are building predictive analytics tools and using them for clinical decision support and administrative purposes.

 Although some promising applications of predictive models have been identified, such as sepsis prediction,

 the evidence base remains fragmented and marked by both methodological and implementation issues.

 Furthermore, regulation of predictive models is minimal, with only limited oversight of particular diagnostic models by the FDA.

 In the context of little regulatory oversight and a highly varied body of evidence, the risk of exacerbating existing inequalities or creating new types of inequity is high.

Because predictive models are built on historic data from the health care system, they reflect and can intensify long-standing racism and inequities in health care.

 Bias in these models has been documented, discussed, and conceptualized in a growing body of literature.

 In a well-known example, an algorithm used to allocate care management resources systematically discriminated against Black patients. The model used health expenditures as a proxy for health conditions. At any given risk score used to indicate predicted need, Black patients were sicker than their White counterparts who received the same score, resulting in comparatively healthier White patients receiving additional resources.

 There are many historical and contemporary racial inequities in health care expenditures, quality of care, and aggressive interventions that disadvantage Black patients.

 Reflecting these inequities, the algorithm underestimated the health needs of Black patients. Similar patterns have been identified in predictions related to kidney disease, whereby some models have contributed to or worsened racial inequity by building barriers to treatment for Black patients.

 These examples highlight cases in which harms have been identified post facto, in part because they did not account for inequity prior to model development. They also demonstrate how high the stakes of predictive models in health care are.

Models used to manage many health conditions are similarly reflective of social context and inequity. For example, hepatitis C is curable but very costly to treat. Although predictive models can be helpful in anticipating disease progression and targeting financial resources, there are serious equity concerns related to access to treatment.

 Hepatitis C disproportionately affects racial and ethnic minorities, and White patients are more likely to receive treatment.

 Given this context, it is important to vet models and account for existing inequity prior to their use.

Model auditing and analysis of potentially unequal outcomes have been recommended as potential safeguards against these harms.

 Although such steps could be highly useful, they generally take place and identify bias post implementation.

 To anticipate bias and inequity before they cause harm, social context and equity need to inform predictive models from inception through utilization.

 Traditional approaches to building and using predictive analytics are limited in this capacity. These efforts tend to fall into silos of expertise such that experts in equity or social sciences, who might anticipate the “unintended consequences” of predictive models, are not regularly included in their development.

It is critically important that inequity is considered from inception and that clinicians are provided necessary information about predictive models to make informed decisions about their use.

 Much of the work on this topic to date has been focused on concepts and commentary. Although this literature is foundational to our understanding of predictive models, there is a dearth of related empirical analysis in health care. This exploratory study aimed to contribute to developing the literature by understanding how stakeholders assess the value of these models, identifying ways to facilitate informed decision-making about their use, and building equity into basic communication about the models from inception. To accomplish this, we applied user-centered design to develop a product information label that would help clinicians and administrators evaluate predictive models. We conducted follow-up expert interviews to investigate the equity implications inherent in the development and use of predictive analytics and to offer an alternative to the current approach to model development.

Phase 1: Eliciting User Needs Related to Predictive Model Use

In phase 1 of this project, our design team followed the double diamond method to identify the needs and priorities of predictive model users. This is a type of user-centered design method widely deployed for information technology (IT) development that is characterized by iterative development cycles that produce a solution or prototype that responds to user needs.

 In this study, the double diamond process was used to (1) define problems faced by clinicians and administrators in the use of predictive models, (2) develop a prototype product information label for a predictive model, and (3) elicit reactions from users.

The first step of the double diamond method was a needs assessment.

 Interviews were conducted with 46 clinicians, administrators, model developers, IT staff, and project managers at a large academic medical institution with predictive analytics infrastructure. We interviewed clinicians from a variety of specialties, including emergency medicine, pediatrics, general medicine, oncology, and primary care. The semistructured interviews sought to understand participants’ needs, concerns, and preferences related to a hypothetical hepatitis C model prototype that predicted serious illness to allocate treatment resources. A prototype product information label was developed and updated iteratively through rapid prototyping as interviews were conducted.

 Participant needs and concerns informed the prototype design, which was then used to validate and expand on the findings from earlier interviews.

 Participant quotes were also used to construct analytic matrices for validation and comparison of interviewees’ priorities and concerns.

Equity and bias were raised in these interviews, although they were not central to the priorities of interviewed stakeholders. It remained unclear how communication about predictive models could better incorporate information related to equity. To better understand how equity could be incorporated and addressed in a product information label, we continued the double diamond method with a second phase of the research focused specifically on these topics.

Phase 2: Expert Interviews on Equity Implications

Building on the findings in phase 1 and recognizing the pressing concern of inequity in health care, the second phase of the study included 10 interviews with investigators and clinicians specializing in predictive models, clinical care, and health equity at the same institution who did not participate in phase 1 interviews. Using the hypothetical hepatitis C case developed in phase 1, these semistructured interviews focused on predictive models, high-cost treatment, and equity to better understand the ways that predictive models can either exacerbate or reflect inequity. All interviews were recorded and transcribed professionally. The rigorous and accelerated data reduction (RADaR) technique was used to produce concise data tables and identify themes.

 Using this approach, interview content is reduced through successive iterations of coding to identify and refine codes and produce core insights from interview content. One author (P.N.) conducted the RADaR process with regular review, input, discussion, and oversight from the senior author (J.P.). Text reduction was reviewed and any thematic coding issues were resolved during regular analysis meetings. Phase 2 interviews informed an updated product information label that communicated key equity concerns to potential users.

Trust in predictive models and willingness to use them depend on characteristics of and communication about the models.

Four key informational elements required for informed decision-making emerged from the phase 1 interviews with clinicians, administrators, and model developers (

). These were (1) information about the developers and users of a given model, (2) the methodology with which it was developed, (3) the extent to which it has been peer reviewed and updated, and (4) the populations in which it has been validated. These types of information were necessary for evaluation and confidence in a given predictive model, especially among clinicians.

The informational elements required for informed engagement with a model, and potential use, are included in the product information label prototype (

When viewing the product information label prototype, interviewees searched for the developer and other users of a model. Specific trusted institutions, such as leading academic medical centers or professional societies, were critical for trust in a predictive model. Utilization of the model by well-known institutions was seen as evidence that it was vetted and reliable. Relatedly, peer review was an important mechanism of verification. Because administrators and clinicians expressed that they might not have the time to evaluate specific statistical methods used in a model, they required evidence that trusted institutions, experts, or organizations had approved it.

Although nondevelopers expressed that they did not have the time or specialized knowledge necessary to evaluate specific methodological decisions like the use of random forest models, key methodological information was required for model evaluation. The data used to create the model, specific evidence underlying its function, and its original purpose were of particular interest. Clinicians were especially concerned that a given model was validated on their patient population, or a very similar one, to ensure that the model was appropriate for their patients.

 includes the equity implications of phase 1 key informational elements, as well as 2 additional informational elements that emerged from the interviews: (1) purpose and application and (2) relationship to structural inequity.

Implications of Phase 1 Informational Elements

Experts reported that ethics and equity were often treated as separate from model development processes, which prioritized pragmatism and speed over ethical concerns. This separation of model development from the consideration of social context meant that equity was largely absent from predictive model development and implementation. Experts emphasized the risks to marginalized patients, including exacerbating existing inequities and building barriers to care or treatment. Accounting for and communicating these risks in model development and implementation were critically important, although there was considerable variation in opinions about where and with whom this responsibility should lie.

The lack of standards and safeguards against bias in predictive analytics was also a key threat to equity. Without clear requirements for updating and modifying a model, for example, bias cannot be systematically identified. Model development, methodology, and peer review/updates were especially affected by this lack of standards (

). The implications include harms to marginalized patients, inability to systematically identify bias, and inadequate population validation on minority populations. Validation and calibration are not currently standard or required, meaning that the efficacy of a given model for subpopulations may be unclear, perpetuating inequality by advantaging patients who are already advantaged.

Two additional key informational elements emerged from our interviews with experts. Questions about the 

 were central to evaluation among experts. A model applied to anticipate high cost and limit access to care for patients, for example, would have fundamentally different implications than a model facilitating access to necessary treatment (see 

] for example quotes). Clarity about the issue to be addressed by a model was fundamentally important for experts considering a model’s value.

A model’s interaction with or reflection of 

 was also critically important. For example, multiple experts separately described biased algorithms related to kidney disease and cardiovascular function that exacerbated existing racial disparities. Because Black patients face structural barriers to health care, for example, diagnosis of kidney disease is often delayed. Black patients historically have been sicker by the time they receive a diagnosis, and a “race correction” reflecting this dynamic was built into an algorithm for future treatment. Knowledge of these social and historical dynamics and information about how a model engages with or addresses them was of primary concern. For the experts interviewed, clear communication about this aspect of a given model was necessary both for consideration in model development and for providers to understand the risks to their patients.

In this study, we identified information necessary for informed decision-making related to predictive models and explored equity implications with experts. Our findings focus on 2 domains for health systems to consider in the development and use of predictive models: critical informational elements required for users and foundational equity concerns to address.

In phase 1, administrators and clinicians described some of the informational elements required to evaluate and potentially use predictive models. In addition to demonstrating an understanding of foundational methodological concerns relevant to clinical care such as population validation, they also noted the challenge of evaluating every detail of the specific statistical methods. For some, it was necessary to rely on information about the use of models by trusted institutions, experts, or organizations that adopted a model. This was especially true when combined with prioritized methodological information affecting patient care. Our interviews also revealed the consistent desire on the part of clinicians to know if and how a model is validated on their patient population, or a very similar one, to ensure the appropriateness of the model. This informational element is important for health equity because homogenous training data sets lead to less accurate predictions for minority populations and marginalized groups.

Our study results underscore ways in which informed decision-making and equity matter in the use of predictive models. The confidence and trust of clinicians are required for utilization of predictive models, but they must also be earned through validated, trustworthy models and communication of key informational elements, some of which we identify here.

 For some, leveraging trust in scientific processes such as peer review and trust in organizations that are using specific models will inform whether a user will adopt a model, suggesting the importance of those organizations themselves being trusted and trustworthy agents.

Our findings also highlight the importance of communicating evidence that models are trustworthy and responsive to equity concerns. Health systems would be well served by establishing transparent policies related to population validation and communicating this critical informational element to clinicians and other users. Communication about predictive models also needs to include information and evidence on their relationships to structural inequity. Clarifying the purpose of a model in practice will facilitate critical engagement with potential negative consequences for patients. Individuals and systems considering use of these models should engage in discussions from early stages to identify any potential limitations of the models and could even work with health systems and designers to ensure equity of the model design and its subsequent use. These critical reflections, at a system level, could be important for improving the validity of models.

Alternative Approach to Development and Use of Predictive Models in Health Care

These study findings revealed some of the negative consequences of excluding expertise in equity from model development. Based on this outcome, we propose that perspectives of equity experts are incorporated throughout the development and implementation of predictive models and in the evaluation of and communication about predictive models in order to anticipate negative consequences of a given predictive tool. In the traditional approach, models are often developed and used in silos of expertise. After implementation, analysis of negative consequences may be conducted and bias can be identified. However, this means that patients have already been harmed. To better anticipate these consequences, an alternative approach (

) elicits and integrates the perspectives of a more diverse group of experts who are equipped to identify potential risks for marginalized patients. Future studies should expand on existing literature to examine specific outcomes and metrics that could be used to assess the equity implications of predictive models in health care. Patient perspectives should also be sought to understand perceived risks and benefits associated with predictive models.

This is an exploratory study based in a single academic institution.

 Our findings may not generalize, especially to smaller, nonacademic institutions that do not have similar model development capacity or familiarity with predictive models. Because this varies significantly and could be associated with specific EHR characteristics, future work should analyze the perspectives of various health systems.

Further validation, evaluation, and expansion in other settings will be important in future work. However, information labels, such as the one prototyped in this project, are increasingly in demand in consumer and health care domains, and our work provides an initial application that should be further explored.

As the health care system continuously works toward evidence-based medicine, the evidence on bias and inequity must be included and integrated into predictive analytics. When this expertise or evidence is treated as secondary and unrelated to model development, the negative consequences of predictive models will remain “unintended” but preventable. Designing policy around model transparency, communication of key informational elements, and analysis of equity implications are important steps that health systems can take to facilitate informed decision-making and to prevent harm to patients. If models are used caveat emptor, this leaves patients and clinicians vulnerable. More extensive evaluation and communication about model functions will be critical. Formal oversight mechanisms within and across organizations that incorporate expertise on equity in the key informational elements about predictive models will guide users in their assessments of model value and facilitate more broadly informed model utilization.

The design team for phase 1 of this study included Jade Crump, Katherine Jones, Sarang Modi, and Sam Bertin. They designed the product information label prototypes and elicited responses from interviewees. The authors also want to acknowledge Tevah Platt and Drs Marie Grace Trinidad, Daniel Thiel, and Sharon Kardia for their input and contributions to the research project.


 Department of Health Management and Policy (PN) and Department of Learning Health Sciences (JP), University of Michigan, Ann Arbor, MI; Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign (MR), Champaign, IL.

 Knowledge to Treatment Optimization Program through the Michigan Department of Health and Human Services.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (PN, JP); acquisition of data (PN, MR, JP); analysis and interpretation of data (PN, MR); drafting of the manuscript (PN); critical revision of the manuscript for important intellectual content (PN, MR, JP); obtaining funding (JP); administrative, technical, or logistic support (PN); and supervision (JP).

 Paige Nong, BA, Department of Health Management and Policy, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109. Email: ptassie@umich.edu.

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Articles

As predictive models proliferate, providers and decision makers require accessible information to guide their use. Preventing and combating bias must also be priorities in model development and in communication with providers and decision makers.

Integrating Predictive Models Into Care: Facilitating Informed Decision-Making and Communicating Equity Issues

Patients may have new needs, concerns, and expectations around technology-mediated health care within the context of coronavirus disease 2019 (COVID-19).

Some patients may develop a preference for care via telehealth whereas others may have concerns about privacy or about the quality of care they are receiving virtually.

Clinicians and health care organizations should proactively address patient concerns (eg, about privacy) given that telehealth is likely to remain an option beyond COVID-19.

It is important to understand how to evaluate the quality of care delivered via telehealth, including its implications for patient-centeredness.

Over the past decade, there has been a push to help patients become activated and engaged, positioning them in the “driver’s seat” of their health care.

 This framework requires greater attention, consideration, and responsiveness to patients’ expectations and their needs, preferences, and values.

 Considering these factors helps health care organizations achieve the goals of the Quadruple Aim, with incentives introduced to further emphasize the need to pursue patient-centered care.

However, being a patient in a pandemic is different. As the nature of health care delivery evolves—with perhaps one of the most salient changes induced by coronavirus disease 2019 (COVID-19) being a rapid increase in virtual care delivery—the experience of receiving care has also changed. With the move to telehealth modalities, patients have needed to acclimate to new ways of receiving care and communicating with, or “seeing,” their doctors. Elements of this unprecedented shift in care delivery are likely to remain as we move forward; however, the emergence from quarantine into what has been described as the “next normal” will be complex.

 Health care organizations and clinicians must develop sustainable approaches to align with shifting patient expectations and concerns. Policy makers must consider the implications of virtual care delivery, including incentives for encouraging high-quality, patient-centered care. Here, we discuss 3 areas that reflect changing patient expectations: (1) preferences for continuing to use telehealth, (2) concerns about the privacy of health information, and (3) uncertain expectations about the quality of virtual care. We then offer patient-centered recommendations for how clinicians, organizations, and policy makers can respond to these challenges and suggest the need to continue to examine the concept of patient-centered care in the telehealth context.

Telehealth Is More Convenient…But Is It Meeting My Needs?

The hallmarks of health care delivery during COVID-19 have included rapid adoption of telehealth modalities, increased use of remote monitoring, expanded access to digital therapies, and more home-based care.

 Although a “digital divide” persists as some patients struggle to access care due to limited computer and/or broadband access, others who received virtual care via these modalities have reacted favorably to telehealth. Looking ahead, some patients will likely question the value of and need for brick-and-mortar office visits.

 For instance, virtual care options can be convenient for patients because they can mitigate the risks associated with COVID-19 and reduce the costs associated with in-person visits, such as childcare, transportation, or time off from work.

Despite the added convenience that telehealth offers, it will require that clinicians emphasize building and maintaining rapport with patients and that they work toward shared decision-making virtually.

 For instance, in this context, one decision to be made is whether patient needs can be adequately addressed virtually or whether an in-person visit would be needed. Optimal shared decision-making in this scenario may require that clinicians encourage patients to express their preferences and/or concerns about modes of care delivery; they may potentially need to engage with family caregivers to understand patients’ social circumstances. Clinicians will also need to discuss safety protocols with patients, particularly when in-person visits are strongly preferred or required. In contrast, when virtual care is both appropriate and preferred, clinicians and their organizations need ways to assess patients’ capacity to use telehealth in light of the digital divide.

 In this assessment, organizations could ask whether patients have access to devices that enable telehealth visits and whether they have adequate digital literacy prior to scheduling an appointment that involves telehealth.

 Shared decision-making could also include a discussion with patients about referral procedures and policies, as well as encouraging patients to ask their insurance provider about the need for prior approvals and/or referrals for virtual visits.

When patients communicate with clinicians via phone, email, telehealth platforms, or asynchronously via secure messaging, they may worry about how their personal information is collected, used, stored, and shared with clinicians and across networks.

 Given recent breaches involving personal information, patients may be more aware of and concerned about the protection and confidentiality of their information and may expect information about their clinician’s and organization’s data sharing policies and procedures before engaging in telehealth encounters.

Patients and clinicians have also begun leveraging applications (eg, FaceTime) to use patients’ personal devices (eg, smartphones) for conducting virtual visits.

 Patients may now share information from the comfort, convenience, and safety of their homes, or they may be challenged by the need to participate in visits from public establishments such as work settings or cafés.

 Clinicians now have the ability to learn about their patients’ social and environmental contexts. This new information could improve care quality if clinicians are better able to assess health and safety risks, such as fall risks, or challenges with other social determinants of health (SDOH).

 However, patients may have concerns about involuntarily exposing aspects of their lives (eg, housing situation) to their clinicians or disclosing sensitive information about their clinical conditions or stigmatized diagnoses in front of other household members.

Clinicians and health care organizations should help prepare patients for upcoming telehealth visits by explaining how their personal health information will be shared with other providers who are comanaging their care and by ensuring that patient consent is obtained before releasing records. Clinicians, who are gatekeepers of personal health information, could explain to patients how an understanding of their environment may support health care decision-making, and they can suggest that patients move to a comfortable area of their home that meets their privacy preferences. Policies will need to be developed to (1) facilitate family caregiver involvement in telehealth visits and (2) govern the use of SDOH data that may be gathered during telehealth visits.

How Can I Trust That I Am Still Getting the Best Care Virtually?

, Richard Goggin reflected on his experience of going to a cancer center for a part of his treatment during COVID-19 and being told that he should not be at a hospital; he asked, “Do you think my cancer knows that?”

 Similarly, patients may be concerned that their health needs are being compared against COVID-19 and subsequently overlooked by clinicians. Building relationships, expressing empathy for the changes that everyone is working through, and increasing trust between patients and clinicians will be critical to the transition to the next normal. For example, in virtual environments, mistrust may be exacerbated when there are limited opportunities for building trust and rapport between clinicians and patients and, further, if patients do not trust the technology used in the interaction.

 In the context of virtual care delivery, patients will need to trust their clinicians to (1) give sufficient attention to non–COVID-19–related concerns, (2) be honest about the most appropriate mode of care delivery given patient circumstances and needs, and (3) protect their personal health information, including their social and environmental circumstances.

Indeed, clinicians may face organizational restrictions affecting their ability to see patients without COVID-19 in person, or they may be concerned about putting patients without COVID-19 at risk of infection transmission by allowing them to come to in-person visits. Patients may also compare in-person experiences with their clinician against virtual visits to determine whether and how much they trust the quality of care delivered via virtual means.

 It will be critical to examine what quality means in the telehealth context and how it differs from our extant definitions of quality in traditional clinical encounters (eg, structure, process, outcomes; the 6 dimensions of health care quality—safety, equity, efficiency, effectiveness, timeliness, and patient-centeredness), as well as to evaluate the implications of these differences from the perspectives of both patients and clinicians. For instance, differences in how we measure and evaluate quality could have implications for telehealth payment parity because traditional quality frameworks may not be directly applicable to virtual care delivery.

Clinicians and health care organizations may benefit from performing outreach to invite patients to obtain the care they may need and discuss challenges, concerns, and alternatives with patients who are skeptical of virtual care delivery; this can contribute to building trust with patients. For example, clinicians supporting patients with limited English proficiency may consider reaching out to ensure that they have access to an interpreter and are not forgoing appointments, are not feeling reluctant to disclose their needs due to concerns about communication, and are not relying on family members who are not licensed professionals for interpretation.

 In such cases, health care organizations should provide patients with access to licensed interpreters who follow regulations, for example, by including the interpreter on the video display,

 and consider opportunities to connect patients with clinicians who speak other languages. Researchers and organizations should examine how the rapid growth of telehealth has affected access to remote interpretation services, the challenges associated with implementing these services during COVID-19, and whether the quality of in-person interpretation services is different from that of virtual interpretation.

As we look ahead to the next normal, it will be important for clinicians and organizations to ensure that the shift to telehealth does not overlook patients’ needs, values, and preferences. Policy makers should work with health care organizations to advocate for adequate reimbursement rates for virtual visits to further enable clinicians to use telehealth modalities to monitor, manage, and address patients’ health needs. Patients’ expectations are likely to evolve as they acclimate to virtual care delivery options, and how clinicians respond and adapt to these expectations can shape how health care is delivered beyond the current pandemic.

 Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign (MR), Champaign, IL; CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University (MJD, ASM), Columbus, OH; David D. Reh School of Business, Clarkson University (ALS), Potsdam, NY; Center for Primary Care, Department of Global Health and Social Medicine, Harvard Medical School (ES), Boston, MA; Department of Public Health Policy and Management, New York University School of Global Public Health (AYL), New York, NY; Department of Management and Organizations, New York University Stern School of Business (AYL), New York, NY; Department of Work and Social Psychology, Faculty of Psychology and Neuroscience, Maastricht University (BF), Maastricht, Netherlands; Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto (AS), Toronto, Ontario; Department of Family and Community Medicine, College of Medicine, The Ohio State University (ASM), Columbus, OH; Division of Health Services Management and Policy, College of Public Health, The Ohio State University (ASM), Columbus, OH; Department of Primary Care and Population Health, Stanford University School of Medicine (SCT), Stanford, CA; Intermountain Healthcare Delivery Institute, Intermountain Healthcare (SCT), Murray, UT.

 Concept and design (MR, MJD, ALS, ES, AYL, BF, AS, ASM, SCT); acquisition of data (AS); analysis and interpretation of data (AS); drafting of the manuscript (MR, MJD, ALS, ES, AYL, BF, AS, ASM, SCT); critical revision of the manuscript for important intellectual content (MR, MJD, ALS, ES, AYL, BF, AS, ASM, SCT); administrative, technical, or logistic support (MJD, AYL, BF, ASM); and supervision (ASM).

 Minakshi Raj, PhD, MPH, Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign, 1206 S Fourth St, Champaign, IL 61820. Email: mraj@illinois.edu.

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With a rapid shift to telehealth during the coronavirus disease 2019 (COVID-19) pandemic, clinicians, health care organizations, and policy makers must consider and address patients’ evolving needs, concerns, and expectations.

Minakshi Raj, PhD, MPH

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