Adam S. Wilk, PhD

Teams of healthcare providers use health information technology (HIT) to facilitate communication and collaboration. Effective team-based care requires trust, yet we know relatively little about how physicians build and maintain trust with their fellow providers and, further, how HIT affects trust among provider team members. We describe priority areas for advancing our understanding of trust within healthcare teams, which may inform policies and HIT design in efforts to improve clinical decision making, provider satisfaction, quality of care, and patient health outcomes.

In the context of team-based care, providers often communicate via forms of health information technology (HIT). Studies of trust within this context can inform provider decision making and collaborative practices, with implications for provider and patient well-being.

In the context of team-based care delivery, communication and collaboration increasingly take place using HIT systems (eg, electronic health record notes, interprofessional consults, artificial intelligence); this may affect how providers come to trust one another.

Studies of provider team trust that account for trust’s multiple dimensions, bidirectionality, and context can present opportunities for improving the quality of providers’ team-based care and, ultimately, patient health outcomes.

It is important to understand how to measure trust in care teams, violations of trust, and rebuilding of trust, as well as how HIT-mediated communication affects these dynamics.

Physicians are expected to coordinate patient care effectively with other providers in care teams—which might include a mix of other physicians, nurses, trainees, technicians, or physician’s assistants&mdash;with different combinations of these other providers when comanaging the care of patients with different needs. Physicians are also often incentivized to use health information technology (HIT) to facilitate team-based care. For example, the patient-centered medical home is a team-based care delivery model that is expected to measure and improve quality, track and coordinate care, and improve patient experience and provider satisfaction while also reducing costs

; many of these objectives are supported with HIT in the context of face-to-face team-based care.

 Team-based care and interprofessional consults also increasingly occur in the patient’s absence via phone or HIT systems such as the electronic health record (EHR). Importantly, effective team-based care delivery, which promotes collaboration and high-quality care outcomes, requires trust among its participants.

 For example, trust is an important construct in explaining relationships and learning between clinicians and staff in practices transitioning to patient-centered medical homes.

 Although physicians typically serve as leaders of provider teams, there remains much to learn about how physicians and their fellow providers trust one another. How can we measure trust in care teams? What are the causes and implications of violations of trust, and how can we best rebuild trust when it has been breached? How will trust among provider team members be affected as provider teams increasingly communicate via EHRs and other HIT systems?

Although academic researchers have begun examining the roles of artificial intelligence, telemedicine, and EHR systems on trust between physicians and patients, our review found that trust within provider teams, in addition to how HIT-mediated communication can affect provider team members’ trust in one another, has received considerably less attention.

 This represents an important gap in our collective understanding of both team-based care models and HIT. In settings where HIT-mediated communication affects trust among provider team members, significant opportunities may exist to improve HIT interfaces or health system policies with the immediate goals of improving providers’ job satisfaction, reducing burnout, and enhancing productivity or efficiency (eg, by cutting out work-arounds or repeated activities).

 Downstream, the potential benefits of well-managed HIT-mediated communication and the subsequent improved trust in provider teams may be significant in improving clinical decision making and care quality, as well as patient safety and health outcomes—something long promised by HIT advocates.

In this commentary, we summarize priority areas for enhancing our understanding of trust among care team providers and the mediating role of HIT, guided by established conceptual models of trust and communication and the findings of our relevant systematic review.

 We offer examples of each priority area, drawing from our ongoing study of the dynamics of physician trust within care teams.

Trust is defined as a willingness to be vulnerable to another person performing a given task, based on expectations about another’s intentions of behavior.

 Yet no single measure or characterization fully represents trust in a given relationship; trust is multidimensional and can be described using multiple aspects of the construct (eg, competence, fidelity, integrity). Further examination of how the dimensions that may be most relevant in forming trust among providers may be modified as the mode of communication changes (eg, in person, over the phone, through HIT systems) is greatly needed.

 For example, physicians may have fewer opportunities to observe another provider’s thought process—a measure of trustworthiness by virtue of competence&mdash;when they communicate via HIT versus when meeting in person.

It is important to examine trust as a bidirectional phenomenon to avoid reinforcing asymmetry between team members. A trustor, such as a physician, may trust another individual—a trustee&mdash;such as a nurse. However, the nurse&mdash;in the converse role as the trustor&mdash;may not trust the physician likewise. This could also be in part a result of the hierarchical nature of the relationship. Future studies of trust dynamics in healthcare settings should assess trust from each perspective&mdash;the physician as well as the trainee, nurse, or technician. These assessments should also weigh how each experiences trust, or a lack thereof, in the other and how each experiences or perceives being trusted by the other. This can help us to understand the signals and observations that providers use to build and maintain trust that can be used in team formation and to encourage team cohesion.

A research area related to the bidirectionality of trust is asynchronous communication through HIT systems (eg, telemedicine). For instance, when nurses use the HIT tool, they may (1) trust that fellow providers will interpret their notes accurately and act accordingly and (2) assume that other providers will trust them to input information faithfully into the system.

Defining and measuring trust requires recognizing that it is situational (eg, a physician may trust a trainee in some contexts or to perform some tasks but not others). In different contexts or situations, different dimensions of trust may be more relevant; thus, we need to better understand the role of context in trust to construct meaningful measures. Additionally, when empirical researchers use measures of trust to examine its driving factors or its impact on care processes or patient outcomes, they must account for important features of the study’s context to ensure that inferences are appropriate.

How HIT modifies trust dynamics in provider teams can also be situational. For example, in an emergency department with little time for interpersonal interactions, EHRs and patient monitoring tools could be helpful for accessing patient information quickly and may not meaningfully affect providers’ trust relationships. In contexts like oncology care, in which teams provide care over an extended period of time, communication via HIT may inhibit trust building by limiting social interactions that promote familiarity and comfort among team members.

We also know little about the principal causes and implications of violations of trust and how the nature of these violations may affect downstream provider interactions (including approaches to rebuilding trust). Disrespectful challenging by fellow providers, bullying, and dishonesty are some examples of behaviors that may lead to mistrust or distrust within care teams.

 In our developing work, physicians described violations of trust stemming from misuse of the EHR (eg, copying and pasting template data over several days without updates) leading to complications or delays in patient care due to inaccuracy.

 Further, patterns of delayed or absent responses to EHR communications could have both direct effects (eg, clinical decision making based on inaccurate patient information, negative patient outcomes) and indirect effects through mechanisms of bidirectionally violated trust. For instance, not receiving a response through the EHR may be perceived differently from not receiving a response by phone, perhaps because of the expectations that the EHR should expedite communication and improve coordination overall.

A trustor may be willing to rebuild trust in some instances of violation but not others; for example, violated trust resulting in a tragic patient outcome may be handled differently from a violation resulting only in care task redundancies (eg, due to confusion about EHR documentation). Empirical studies of violations and rebuilding of trust should be approached via both cross-sectional and longitudinal studies so that we can gain an understanding of the dynamic nature of trust over time.

 We need to learn more about (1) when and why one decides to rebuild trust, (2) alternative approaches to rebuilding trust once it is violated and how HIT may enable or disable these approaches, (3) implications of violations of trust in the longer term, and (4) costs of rebuilding trust to prevent these outcomes.

There is a need to examine the downstream impacts of trust (and mistrust) on patient care, patient outcomes, and provider well-being measures such as burnout, job satisfaction, and turnover.

We also need to learn more about the longer-term implications of trust when HIT is used to manage relationships or coordinate care. For example, current medical trainees practice medicine exclusively in a context that includes HIT, whereas providers who also practiced medicine prior to the advent of HIT may be more inclined to supplement HIT use with in-person interactions. Consequently, older and younger physicians may build trust and collaborate in team-based care settings differently. Moreover, when trust is violated in these circumstances, preferred approaches for rebuilding trust may vary across care team members, slowing its rebuilding. On the other hand, the advent of HIT may have improved collective trust because of its transparency or its capacity to hasten the production of knowledge and support the use of evidence-based treatment to improve clinical outcomes.

As long as HIT continues to evolve rapidly, providers’ use of HIT will evolve likewise. It is critical that we continue to study these developments to optimize providers’ communication and collaboration in the context of team-based care. Future research can draw on qualitative methods to identify how HIT influences trust dynamics between physicians and other providers and the rebuilding of trust following violations. These studies can also inform the development of quantitative surveys and new, multidimensional, contextually relevant measures of trust among care team providers. Such measures could be used to examine both organizational and provider-level factors that foster trust and the relationship between trust and patient outcomes.

 Department of Health Management and Policy, University of Michigan School of Public Health (MR), Ann Arbor, MI; Department of Learning Health Sciences, University of Michigan Medical School (MR, JEP), Ann Arbor, MI; Department of Health Policy and Management, Emory University (ASW), Atlanta, GA.

 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 (MR, JEP, ASW); acquisition of data (MR); analysis and interpretation of data (MR); drafting of the manuscript (MR, JEP, ASW); and critical revision of the manuscript for important intellectual content (MR, JEP, ASW).

 Minakshi Raj, MPH, Department of Health Management and Policy, University of Michigan School of Public Health, 1420 Washington Heights, Ann Arbor, MI 48109. Email: miraj@umich.edu.

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Articles

Rapidly advancing health information technologies are changing the nature of team-based care; there is a critical need to examine how trust functions in contemporary team-based care.

Trust in Provider Care Teams and Health Information Technology–Mediated Communication

Medicare’s Merit-based Incentive Payment System and other value-based payment systems hold providers accountable for population-level measures that are driven by many factors, including those beyond the providers’ direct control (eg, psychosocial stressors, environmental hazards). In this commentary, we identify and illustrate this misalignment of accountability and capability at the individual clinician level and at the provider organization level, which can hinder population health improvement. We also envision the roles of administrators and frontline clinicians amid a new culture of shared accountability and rigorous evaluation, with the potential to accelerate advances in population health care delivery.

The American Journal of Accountable Care. 2019;7(1):28-30

The healthcare system’s ongoing transition from fragmented care and volume-based payment toward coordinated population healthcare and value-based payment has been slow. This transition has been hampered by the misalignment of measurement and accountability systems with population health care delivery goals. The Institute of Medicine report 

For the Public’s Health: The Role of Measurement in Action and Accountability

 articulated that a well-functioning accountability system for improving health must identify bodies “with a clear charge to accomplish particular steps toward health goals.”

 Current efforts to improve population health have been stymied significantly because it is not clear which entities—within the healthcare system or across the broader public health system&mdash;have such a charge and in what contexts.

When identifying where accountability for achieving population health goals should lie, a natural first choice is the individual clinician, given the importance of physicians, nurse practitioners, and other frontline clinicians in determining patients’ care pathways. Consequently, current value-based payment systems often integrate performance measures that hold the individual clinician accountable for patient outcomes. This is problematic, however, as these outcomes are often affected by numerous factors, many of which are beyond the clinician’s direct control.

To illustrate, Medicare’s Merit-based Incentive Payment System (MIPS) holds clinicians accountable for care practices such as major depressive disorder prevention and antidepressant medication management. Clinicians may reasonably be expected to screen patients for depression and administer Patient Health Questionnaire-9 (PHQ-9) evaluations to help reduce the percentage of patients remaining on antidepressant medications after a designated time period. However, the population-level impact of these efforts is likely to be small, whereas the potential impact could be much greater with better-coordinated, collective efforts—those integrating both medical and nonmedical actors&mdash;to manage the underlying psychosocial stressors and complex, multifactorial processes that drive depressive symptoms.

(PHM) embodies the vision of achieving population health improvement by these means. Coordinated, multifaceted PHM interventions may include established models of chronic disease prevention and care management, as well as new collaboration with social services, improving the physical environment and safety, engaging in community education and outreach, and addressing food insecurity and other sequelae of poverty. Significant economies of scale are needed to efficiently perform most PHM interventions, such as geographic analysis of community-level health hazards and other sophisticated measurement and reporting functions,

 which require substantial investments in information technology and big data analytics. Moreover, it is beyond the training of most clinicians to prioritize, coordinate, and execute such multidimensional initiatives effectively. For both reasons, such outcomes-based MIPS measures place too much accountability on the individual clinician and thus may be ineffective in improving population health.

This misalignment is increasingly recognized among thought leaders, payers, and policy makers, as reflected in proposed redesigns of MIPS

 and the growing interest in new value-based payment models such as Accountable Health Communities, which emphasize managing attributed populations collectively.

 Provider organizations have begun to demonstrate how they may leverage their greater economies of scale to advance PHM goals, including through hospital leadership and shared services agreements among groups of smaller organizations.

Still, skeptics worry that provider organizations are just as unprepared as individual clinicians for the challenges of implementing effective PHM interventions. Doing so may require undertaking tasks far removed from traditional modes of medical care, such as forging and maintaining relationships with social services agencies and other public health institutions across communities. Indeed, few healthcare provider organizations—let alone individual providers&mdash;have demonstrated the ability to undertake them effectively.

Provider organizations have also experienced challenges effectively coordinating PHM interventions internally. The system-level measures used to determine payments within value-based contracts (eg, use of certified electronic health record technology, diabetes screening rate) hinge on the actions of the system’s individual clinicians. For example, if an organization leverages its big data capabilities to microtarget screening interventions to a high-risk patient subgroup, the individual clinician must still identify the patient in his or her exam room as high risk and perform the screening. Yet, rarely have provider organizations’ leadership effectively articulated how their clinician employees benefit when the organization’s PHM initiatives are executed faithfully or made substantial efforts to cultivate buy-in and engender a culture of collaboration among clinicians and staff in support of PHM. Perhaps more importantly, provider organizations’ leaders typically have not passed along to or shared with clinician employees the financial incentives the organization faces under value-based contracts, nor have they provided organizational or financial guardrails to ease clinician employees’ transitions into population health—oriented delivery and payment models.

 Although provider organizations are indeed starting to build in PHM-oriented performance bonuses for their clinicians, the predominant compensation structures remain salary based or productivity driven, unlinked to population health improvement. Thus, although payment systems mislay accountability when focused on the individual clinician, it is unclear whether population health will improve when accountability for PHM rests with the provider organization instead.

Individual clinicians and provider organizations each have important roles to play in improving population health care delivery: principally, in executing the organization’s PHM initiatives and in formulating and resourcing these initiatives, respectively. It is incumbent on administrators and frontline clinicians alike to understand these roles and coordinate the organization’s care delivery priorities together.

 Internally, such coordination must include, for employed clinicians, negotiated internal compensation models that align with the terms of the organization’s various payer contracts. Externally, provider organizations must coordinate with payers to negotiate payment contract incentives that can be easily passed along to or shared with their employed clinicians. Respecifying MIPS quality measures so that they focus on individual clinicians’ activities (eg, administering PHQ-9 evaluations) rather than downstream outcomes (eg, antidepressant medication management) represents one federal opportunity to foster providers’ internal coordination in this way. Provider organizations may also work with local payers to facilitate partnership agreements between network providers and entities (eg, social services) that can enhance their PHM initiatives’ capabilities and scale. Importantly, these arrangements must evolve over time with the community’s PHM efforts and as all involved parties identify opportunities to improve their effectiveness.

Robust scientific evaluations and evidence-building efforts are critical for overcoming barriers to improving PHM efforts, especially considering that it remains unknown what the most effective models of delivering population health are and how to apportion accountability for them. Given the multifaceted nature of the psychosocial determinants of population health, in most communities it may be critical that payers and other organizations traditionally ancillary to the healthcare industry (eg, employers, municipal health departments) participate in community-level deliberations to define the community’s population health goals, formulate plans and economic incentives for achieving them, and evaluate efforts. MIPS could foster this by modifying its Improvement Activities list to include, among other potential options, substantial (eg, 25%) representation from these community entities on the provider organization’s board or documentation of referrals and coordinated handoffs of underserved patients to local social services programs for nutrition or housing support. Moreover, each community must conduct its own evaluations, as coordinated PHM is a local endeavor. For instance, the set of organizations needed at the table to support achieving a particular population health goal may vary with the community’s sociodemographic and environmental characteristics and with the evolution of community organizations as they take on new roles and build up the “capacity to undertake the required activities”

Throughout the course of a community’s innovation, intervention, evaluation, and evolution, effective PHM will accelerate when the coordinated networks of the community’s partner organizations expand and each partner is held accountable more precisely for achieving what it is capable of achieving in the interest of improving the population’s health. MIPS and other value-based payment systems will continue to significantly inform how shared accountability is defined, incentivized, and implemented in many communities. It is important that they do so deliberately, for until shared accountability is realized, our efforts to realize meaningful gains in population health will continue to lag.

 Rollins School of Public Health, Emory University (ASW), Atlanta, GA; The Health Management Academy (SJ), Washington, DC.

 Dr Wilk is employed by Emory University, which engages in population health management efforts through Emory Healthcare, and owns stock as a minority shareholder in United Health Group. Dr Jain is employed by The Health Management Academy, which is a network of delivery systems.

 Concept and design (ASW, SJ); analysis and interpretation of data (SJ); drafting of the manuscript (ASW, SJ); critical revision of the manuscript for important intellectual content (ASW, SJ); and administrative, technical, or logistic support (ASW, SJ).

 Adam S. Wilk, PhD, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322. Email: adam.s.wilk@emory.edu.

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Current population health efforts have been hampered by the misalignment of accountability. Thus, population health management will improve when shared accountability among stakeholders is achieved.

Effective Population Health Care Delivery Under Medicare’s Merit-based Incentive Payment System: Realigning Accountability With Capability

While most payers have been slow to embrace models that would incentivize value-based care delivery, providers have a unique opportunity to take the lead in this endeavor.&ensp;We examine best practices to develop a Population Health Care Delivery Model (PHCDM) to guide delivery systems as they design and pilot population health programs across the continuum of patient care and to facilitate their coordination with individual clinicians.

 A systematic review of observational studies and health system case studies was conducted.&ensp;We examined the effectiveness of population health—oriented programs and specific quality improvement initiatives in improving the health outcomes of patient populations across the continuum of care. Our assessment was primarily focused on the structural and design features of successful programs.

We find that population health improvement is a result of (1) prevention and well care and (2) disease management initiatives that are both patient-centered and population-oriented in structure. We identified differences in care delivery objectives as the severity of disease increases across the patient care continuum. The corresponding PHCDM presents a framework for providers to systematically pilot and evaluate population healthcare programs.

The delivery system—clinician partnership is essential to coordinating evidence-based practices across the care continuum and, as a result, strengthening relationships with payers to further incentivize population healthcare delivery.

There is growing recognition that paying for the delivery of high-quality, efficient healthcare is necessary to improve population health. Such value-based payment systems place a growing amount of revenue at risk for clinicians, although most continue to practice in care models developed under fee-for-service incentives. This asymmetry has raised concerns among clinicians about these systems’ implications for patient care, reporting requirements, and payment levels.

 The concurrent shift toward increased employment of physicians in large delivery systems

 may reflect, in part, clinicians looking to their delivery systems to help navigate the complexity of value-based payment systems. 

At the core of these emerging payment models is the expectation that care providers must be accountable for the health outcomes of entire patient populations across the continuum of care. Delivery systems must be reoriented to achieve these aims. At times, this work will be inexact, challenging, and even conflicted when other health system stakeholders’ responses to the system’s new population health objectives are asynchronous or askew with the providers’ efforts. Alternately, the pace of advancement toward population health management will accelerate if the delivery system’s efforts are supported by robust payment systems that are developed in collaboration with payers and reward incremental progress toward value-based care objectives. Although most payers have been slow to fully embrace alternative payment models (APMs) that would strongly incentivize value-based care delivery and promote population health, providers have a unique opportunity to take the lead in this endeavor.

Delivery systems are uniquely positioned both to help clinicians adapt to new payment regimes and to lead the way in developing and evaluating new models of population healthcare delivery. Delivery systems can play key roles in innovation, infrastructure investment, measurement, and contracting, as well as provide strategic direction and leadership in this enterprise. However, a delivery system’s investments and reorientation toward system-level functions

 could lead to diminished emphasis on patient-centeredness in care at the individual level. To ensure that patient care is not compromised, the delivery system must engage and collaborate with all stakeholders, particularly clinicians, and reflect their priorities in their new population healthcare delivery models.

 This system—clinician partnership is essential to coordinating evidence-based practices across all silos of the care continuum and strengthening relationships with payers to incentivize population healthcare delivery.

Balancing Population-Centered Care and Patient-Centered Care

It is a long-established ethic of the individual clinician to focus intently and autonomously on the individual patient in the exam room and deliver optimal patient-centered care in accordance with the patient’s preferences and needs.

 Likewise, each delivery system’s approach to delivering high-value population health must be unique to the needs of the population it serves. Where these approaches are successful, it will be in no small part because individual clinicians are effective in their new roles as executors of the delivery system’s population health initiatives and resources. However, individual clinicians may view these tasks as in conflict with their traditional patient-centered care delivery approaches, occupying time and resources that should be devoted to the patient’s immediate needs in the exam room. Hence, clinicians depend on the delivery system to recognize how they are being pulled in these 2 different directions and set reasonable expectations as they grow accustomed to their dual roles. Balancing these expectations requires the system to be diligent in monitoring clinician deviations from evidence-based recommended care practices and distinguish systematically between appropriate variation (eg, due to high-acuity complex patient needs) and avoidable variation in practice.

Ideally, clinicians’ care delivery models and the delivery system’s strategy are well-coordinated to ensure that the patient population receives efficient population-centered care while individual patients continue to receive effective patient-centered care. In this paper, we present a new framework, the Population Health Care Delivery Model (PHCDM) (

), to guide delivery systems as they design, pilot, and implement population health programs across the continuum of patient care and to facilitate their coordination with individual clinicians. The care continuum, as patients experience it, is useful for framing the objectives of a population health—oriented system in a way that recognizes the individual clinician’s autonomy and patient-centered perspective. In the following sections, we explain the aspirational goals of the PHCDM, rather than specific recommended tasks, recognizing that the effective delivery of population health requires nuanced approaches tailored to the needs of different patient populations. We also delineate new terms of collaboration between the delivery system and the front-line clinician within this framework.

As the delivery system embarks upon population health management, it must first obtain complete and timely information about the population’s health needs and preferences. To do this, the system must work with clinicians, public health agencies, and community organizations to acquire and review disparate data sources. These sources may include risk assessments, surveys, electronic health records (EHRs), and claims data, which can be used to conduct integrated data analyses to classify (1) populations with elevated risks of adverse outcomes and (2) populations for whom population health programs have the greatest potential for improving outcomes. Performing this analytic exercise affords the delivery system the capacity to provide clinicians with key information for realigning and implementing targeted evidence-based initiatives. This data platform is crucial for measuring progress and conducting self-evaluations of the system’s population health initiatives, which will be invaluable in gaining buy-in and financial support from local community agencies and health plans.

The Population Health Care Delivery Model

If the goals of population health are to be realized, the focus must extend beyond the traditional clinical setting to reduce the incidence and prevalence of disease. Accordingly, prevention and wellness efforts must address both clinical risk factors and social determinants (eg, physical environment) that cause or exacerbate illnesses.

 Traditionally, clinicians have provided limited preventive care, focusing instead on diagnostics and postdiagnosis patient care.

 The delivery system needs to invest more in primary and secondary prevention, facilitating clinicians’ prediagnosis delivery of care.

Additionally, the system must establish and coordinate care teams. This is particularly necessary because physicians often have limited bandwidth to prescribe, let alone provide, recommended preventive services.

 Systems may consider emulating Maine’s Primary Care Medical Home Pilot or Vermont’s Blueprint for Health in their use of the Community Health Team (CHT)

 structure, which includes care coordinators, nutritionists, behavioral and mental health specialists, nurses and nurse practitioners, and social, public health, and community health workers. Relative to established care delivery models, it is more efficient and cost-saving for the delivery system to identify prevention priorities and for physicians to manage CHT teams to carry out screenings and other well-care tasks (eg, breast self-examination education, creatinine screening for chronic kidney disease).

 Although the composition of care teams will vary with the needs of specific patient populations, careful consideration should be given to the competencies and capacity of the team for delivering targeted prevention strategies.

The delivery system should evaluate and monitor its patient population’s environment and identify potential risks for triggering certain conditions well before diagnosis. Systems can do so following the model tested by the CDC, which uses claims data and geospatial analytics to identify local hot spots for high-risk conditions, such as diabetes.

 Others use DNA sequencing to pinpoint individuals with genetic predispositions that warrant early intervention. Evidence suggests genetic screenings are comparable in costs to traditional screening activities and may yield more specific clinical insights, particularly for diagnosis of common cancers (eg, cervical, colorectal, breast).

 Beyond clinical information, the system should also integrate social determinant factors. Such initiatives may include obtaining data from local police departments and school districts to account for behavioral risk factors.

 Risk stratification efforts can indicate priority areas and opportunities for collaboration with community and public health organizations, potentially including delivery system partnerships with organizations such as the Occupational Safety and Health Administration or local housing coalitions to mitigate risks in the work and home environments (eg, to reduce mold and lead exposure).

 Because primary prevention activities diverge the most from traditional disease management, delivery systems should consider leveraging federal APMs (eg, Accountable Health Communities) and local or state funding

 to pilot strategies for aligning community and clinical services to address the health and social needs of patient populations.

Secondary prevention programs rooted in the Chronic Care Model are generally effective in detecting disease in early states and preventing further disease progression.

 To slow the growth of many conditions, the delivery system should increase screening rates for individuals with high-risk clinical indicators, such as obesity, hypertension, and high blood glucose levels. Lifestyle modification programs like the Diabetes Prevention Program (DPP) can substantially reduce the risk of many chronic conditions.

 The DPP model alone has been shown to reduce the risk of developing diabetes by more than 50% among adults with prediabetes. As the DPP expands as a covered benefit for Medicare beneficiaries,

 with a likely ripple effect on private health plans, delivery systems should strongly consider leveraging similar evidence-based programs and other social services

 as cost-saving strategies for improving health outcomes. Determinations to implement such interventions may be most easily made in communities where population turnover is relatively low, as any savings would be more likely to accrue to the investing delivery system. In higher-turnover communities, rigorous evaluations accounting for local conditions may be needed to demonstrate cost-effectiveness.

Upon diagnosis, the delivery system’s objective should be to coordinate health services to maximize patient function in a manner that avoids further health deterioration and onset of comorbid conditions. The system can provide care teams with information technology tools to assign patients individual risk scores using pharmacy data, for example, to help clinicians track the likelihood of future hospitalizations and prioritize preventive services accordingly.

 These preventive actions may be as simple as administering routine flu shots and other immunizations to prevent further complications. Moreover, following the lead of the Vermont Chronic Care Initiative,

 the delivery system must facilitate ways to complement the care team’s prescribed regimens with medication therapy management, motivational coaching, health literacy, and self-management skill-building to increase patient adherence and engagement. Clinicians will likely need to draw upon primary prevention resources and other community resources to provide patients with additional self-care support, such as smoking cessation and healthy cooking workshops.

Although the delivery system may define its population health priorities, the individual clinician will ultimately execute the corresponding initiatives. For instance, the delivery system may suggest cost-effective, conservative therapy options, as through decision support tools, and the clinician is expected to select from among them. However, the clinician may have important insights that lead them to recommend a different, less conservative therapy for a given patient. It is appropriate for the delivery system to enable and support such deviations when justified, even when they run counter to the delivery system’s established protocol. Emory Healthcare’s sepsis protocol is one such model of an effective collaborative intervention. Emory’s EHR system includes a “trigger” algorithm that uses patients’ vital signs to alert clinicians when a patient may be septic. The trigger provides the care team with several diagnostic options (eg, urine tests) to scan for infection. If the care team determines that the patient is septic, then the EHR will prompt the care team leader—often the physician&mdash;with a recommended evidence-based order set. In this model, the delivery system attempts to make delivering population healthcare less of an encumbrance by equipping clinicians with treatment suggestions based on real-time data analysis. Ultimately, however, the system allows the clinician to decide what orders are best for the individual patient.

The delivery system should further support clinicians by taking steps to maximize access to prescribed therapies. This may involve addressing barriers in patient transportation, finances, and noncompliance. Likewise, shared decision-making tools provided by the delivery system, such as patient dashboards and other technology-enabled devices for evaluating patient symptoms and care plan adherence, can assist clinicians in maintaining strong communication with patients and tracking their health needs.

After setting their initial treatment plans, clinicians should focus on minimizing exacerbation of established conditions and recalibrating treatment plans as needed. Quality evaluations (eg, Six Sigma) should prompt care teams and the delivery system to jointly devise both patient- and population-level interventions to address any identified gaps in treatment.

 Clinicians can evaluate treatment appropriateness and effectiveness using quality measures in conjunction with physical examinations and patient-reported feedback. These patient-level measures should be integrated into delivery system performance measurement systems and analyzed to ensure that all interventions and initiatives are, in fact, improving population health outcomes.

As conditions become more advanced and complications arise, patients will require regular follow-up care. Both the delivery system and care team should look to identify potential “breakdown” points in maintenance therapies for ongoing conditions and minimize risks that could trigger acute care episodes. The intent is to manage the population with low-intensity care management and fewer clinician interactions while preventing escalations in care. Banner Health has undertaken this approach through a unique partnership with Philips, using telehealth software to remotely monitor patient vitals using biometric sensors and identifying patients at risk for acute flare-ups.

 For patients facing severe conditions that will likely necessitate hospice or other long-term care services, and for those who do not respond to efforts to mitigate risks of more frequent exacerbations, the care team should consider initiating end-of-life education and planning conversations to re-establish the patient’s preferences.

Concerted efforts must also be made to institute systemwide processes to provide seamless transitional care across settings, including the patient’s home. Delivery systems should leverage dedicated personnel to manage patient flow (eg, case managers) and technological tools to track transitions between and within care facilities. For instance, Christiana Care Health System created its own CareLink system to improve its traditional discharge process by streamlining tasks such as medication reconciliation between pre- and post hospitalization.

 For most patients, routine clinical follow-ups or telehealth consults will be sufficient post discharge. For more complex patients, systems may consider extending care to the patient’s home using community paramedicine services in addition to contracting with home health agencies. Community paramedics can help reduce nonemergent hospital readmissions by following up with recently discharged patients, assessing the home environment, helping patients comply with discharge instructions, and transporting patients to more appropriate care settings (eg, urgent care centers).

 As the patient’s condition progresses to more advanced stages, the focus should shift to providing more comfort and palliative care, rather than preventive or active treatment. The delivery system should consider reconstituting the patient’s care team to include more coaching, therapeutic guidance, and pain management providers to reduce patient isolation, stress, and discomfort. To this extent, it is important for the system and its clinicians to continue addressing the psychosocial and home needs of these complex patients. The University of Wisconsin’s commitment to delivering high-quality palliative care is reflected in its provision of theater-style workshops for clinicians facilitating end-of-life conversations with patients.

 Similar communication and skills training programs should be offered by all delivery systems, distinguishing between primary care and specialist approaches as appropriate.

 Incorporating additional social aspects of palliative care—a focus on emotional well-being, caregiver burdens, and bereavement&mdash;can also be a valuable complement to the broader goals of population health care delivery.

Transition toward a population health orientation will be smoother in areas where payers have adopted contracts and incentives that are well aligned with the delivery system’s new goals and priorities. The challenge, however, is that the uncertainty of investment return (as in the case of populations with high rates of turnover) deters payers from taking on significant risk.

 Given this dynamic, the delivery system must consider modifying its internal compensation structure to align clinician payment with value-based care delivery, as well as leveraging outcome data from phase 1 and phase 2 pilots to demonstrate feasibility to health plans. The delivery system must provide proof of concept that it can in fact bend the cost curve while delivering high-quality care. Recognizing this is an ambitious endeavor, some delivery systems will be more effective in obtaining buy-in from payers and their clinicians if they demonstrate quality and financial gains after implementing more feasible medical care—focused population health interventions first. For systems in less mature markets, this suggests that care teams may have to focus more on phase 2 of the PHCDM to create immediate efficiency gains through better management of chronic conditions, transitions in care gaps, and reducing high-cost unexplained variations before investing in phase 1 strategies with a longer time horizon for return on investment. As delivery systems gradually build up their organizational capacity for value-based care and navigate both phases of the PHCDM, payers, both public and private, will increasingly provide financial support in tandem.

Population health management is a fundamental transformation of how healthcare is delivered. This shift requires the delivery system to embrace the principles of a learning system

 to improve the health of the population while allowing clinicians the flexibility to address the unique needs of patients. The delivery system depends on a dedicated team of healthcare professionals across the care continuum to execute population health initiatives precisely. Moreover, these clinicians must also deliver care insightfully by communicating with leadership about situations in which interventions systematically impede effective patient care and, in parallel, offering solutions that allow the system to further its population health improvement goals. Thus, delivery of high-value care requires delivery systems to coordinate both population-centered care and patient-centered care.

Although there is no standardized approach for how population health practices should be integrated into clinical operations, the PHCDM offers a framework for delivery systems to better understand and execute the objectives of delivering population health—oriented and patient-centered care jointly throughout the course of a patient’s care experience.

The Health Management Academy (SJ), Alexandria, VA; Department of Health Policy and Management, Rollins School of Public Health, Emory University (ASW, KET), Atlanta, GA; Emory Healthcare (SPH), Atlanta, GA.

 Dr Jain contributed to this work as part of doctoral training at Emory University prior to employment with The Health Management Academy. Dr Wilk reports employment with the Rollins School of Public Health, which is affiliated with Emory Healthcare, which is engaged in population health management activities. Dr Thorpe reports board membership for LHC Group and chairman responsibilities for the Partnership to Fight Disease. The remaining author reports 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 (SJ, ASW, KET, SPH); acquisition of data (SJ); analysis and interpretation of data (SJ, ASW); drafting of the manuscript (SJ, ASW, KET, SPH); critical revision of the manuscript for important intellectual content (SJ, ASW, KET, SPH); administrative, technical, or logistic support (SJ); and supervision (ASW, KET).

Sanjula Jain, PhD, The Health Management Academy, 515 Wythe St, Alexandria, VA 22314. Email: sanjula@hmacademy.com.

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The Population Health Care Delivery Model presents delivery systems with a framework for developing, piloting, and implementing population health programs across the continuum of care.

Adam S. Wilk, PhD

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