Exploration of reported findings from healthcare transformation research and use cases surfaced several apparent gaps in prior research and in translation to practice. The significance of these gaps is discussed in this section and the implications for designing and implementing projects are discussed in the subsequent section on Results.

One significant impediment central to progress is a lack of clarity around what patient centric truly means and how that translates into practice for healthcare delivery. The term “patient centric” is widely used in the literature and in practice with a broad range of definitions, descriptions, and associated practices. Today's healthcare system coordinates care around providers NOT patients (2, 17, 37, 38). Digital transformation proponents would argue that until we can make that transition, we will not truly have patient-centric (or patient-centered) care (2, 37–40, 97). Thus, the issue becomes what impedes progress in moving in that direction, which has been advocated for over 35 years now, and how can digital information technologies support that transition.

The concept of patient-centric care is generally associated with the issue of quality of care. The Picker Institute pioneered a revolutionary methodological approach beginning in 1988 by directly asking patients, families, and recently discharged patients what mattered most during their healthcare experiences. This bottom-up methodology contrasted sharply with top-down provider or administrator definitions of quality care.

Through extensive empirical research documented in Through the Patients' Eyes (39), the Picker Institute identified eight dimensions that patients themselves considered essential to their care experience: 1) Respect for patients' values, preferences, and expressed needs. 2) Coordination and integration of care. 3) Information, communication, and education. 4) Physical comfort. 5) Emotional support and alleviation of fear and anxiety. 6) Involvement of family and friends. 7) Continuity and transition. 8) Access to care. This patient-derived framework represented a significant departure from healthcare quality metrics traditionally defined by clinical outcomes, provider performance, or institutional efficiency measures.

The Institute of Medicine incorporated this patient-centric framework into its influential 2001 report Crossing the Quality Chasm: A New Health System for the 21st Century. The IOM established patient-centeredness as one of six core aims for healthcare improvement, alongside safety, effectiveness, timeliness, efficiency, and equity. The report defined patient-centric care as “providing care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions.” (18) The IOM built upon the Picker Institute's empirical work, citing Gerteis et al. (1993) and noting that “The Picker Institute in Boston, Massachusetts, has been tracking patients' experiences in hospitals, clinics, and other settings since 1988” (18). In discussing dimensions of patient-centric care, the report referenced the Picker framework, though presenting a condensed version in some sections, while the full eight-dimension framework appeared in the source Gerteis work.

Critically, the IOM recognized that achieving patient-centric care required technological infrastructure: “The automation of clinical, financial, and administrative information and the electronic sharing of such information among clinicians, patients, and appropriate others within a secure environment are critical if the 21st-century health care system envisioned by the committee is to be realized” (18).

Healthcare thought leader Dr. Donald M. Berwick, former administrator of the Centers for Medicare and Medicaid (CMS), President Emeritus of the Institute for Healthcare Improvement, and Harvard Professor, has articulated patient-centeredness as fundamentally more radical than most healthcare rhetoric suggests. Rather than viewing it as improved customer service or bedside manner, Berwick frames patient-centric care as “a radical transfer of power from clinicians and the system to the patient” (38). In his view, genuine patient-centeredness means:

Control resides with patients: The proper source of control is the patient. A patient-centric system would put the patient in control of their care.

The problem extends beyond simple misperception: healthcare professionals bring their own experiential “lens” shaped by clinical training, personal experiences, and professional detachment that systematically colors their interpretation of patient needs (40). As one participant reflected, staff wanted to provide companionship because “I hate to see or think of people being on their own and having no-one,” yet the very structure of care left patients feeling abandoned despite surrounding activity (40). This suggests that achieving truly patient-centric care requires more than policy mandates; it demands deliberate mechanisms to disrupt entrenched provider perspectives and reveal care as patients actually experience it. This is a transformational change of mindset (or paradigm shift). As one manager summarized the effect, shadowing gave staff “a thirst for quality improvements to improve patient experience,” and “as soon as you've been there, it makes it personal.” (40) The same issue arises when change is based on perceptions of academic researchers or IT analysts instead of the realities of care at the frontlines.

More recently, the concept of Integrated Care Systems (ICS) has gained attention as a strategic approach for making care more patient-centric (24). ICS is defined as “the coordination of services across the continuum of care, multiple service providers, settings, and levels of care to achieve improved patient outcomes, enhanced patient experience, and optimized resource utilization” (42, 43, 73, 74, 98). A recent study by Ebo et al. (24) identifies several core principles including patient-centeredness, where care is centered around the individual's needs and preferences; population health focus, addressing the health needs of entire communities; collaborative governance involving multiple stakeholders in decision making; and valued-based care that emphasizes outcomes rather than volume of services. According to Ebo et al. (24). ICS typically encompass primary care, secondary care, mental health services, social care, and public health functions, creating a unified framework for service delivery.

Beyond provider use of health IT, recent research shows that patients are now becoming significant users of health IT (44). This phenomenon is fundamentally changing patient expectations and the relationship between patients and providers. The outcome of this transition has been significant. Several studies on use of patient portals have shown promise in facilitating self-management across a number of critical health conditions by allowing patients' input and use of self-monitoring devices, facilitating communication, and delivering educational materials (44–49).

A frequently cited critical success factor for transformation initiatives is committed executive leadership (2, 21, 22, 24, 26, 28, 50–52, 100). However, a closer examination shows significant variance in how that leadership role is fulfilled. Evidence shows that the level of commitment, intensity, and hands-on involvement of CEOs and Boards of Directors in the most successful initiatives has been extraordinary. One such example is Virginia Mason Medical Center's transformation initiative chronicled in a book by Charles Kenney (2). Their approach was spearheaded by Dr. Gary S. Kaplan, Virginia Mason CEO with strong support of their Board of Directors. It is an extraordinary example of leadership, vision, and dedication in transforming from a deeply provider-centric to a patient-centric healthcare system, a more than 10-year journey. Central to the approach was the focus on the complex, systemic nature of today's healthcare delivery, and the necessity of working at the frontlines of patient care from both provider and patient perspectives. Recognizing the absence of any management framework for addressing systemic transformation in healthcare, leaders looked outside the healthcare system to the extraordinary quality results achieved by the Toyota Production System process (TPS), first studying it in depth and then adapting it for application in healthcare. Among the many concepts and methods transferable to healthcare were elimination of waste and inefficiencies, quality focus with zero error tolerance, focus at the frontlines of operation, and standardization of processes. Among lessons learned was the hard, hard work, perseverance, and long-term perspective required. Other critical challenges involved provider buy-in at all levels and understanding healthcare delivery from the perspective of the patient experience, which differed drastically from provider perceptions. The efforts and strategies for changing mental mind sets and helping providers envision possibilities for transforming the care delivery system, understanding the care delivery process as patients experience it, and implementing standards and procedures for eliminating errors and making the right thing the easy thing to do were extraordinary and cannot be understated. Moreover, the active role of the CEO with support of the Board of Directors were critical especially in addressing strong pushbacks from providers. Addressing problems actively at the frontlines was central to designing and implementing solutions.

Similarly, Kaiser Permanente's $4billion, 10-year journey in healthcare transformation chronicled by Dr. Louise Liang demonstrates the extraordinary leadership of CEO George Halvorson (21). Under the leadership of Dr. Halvorson and Dr. Liang, Kaiser Permanente's nationwide HealthConnect network has transformed care delivery with significant impact on patient care outcomes, efficiency, safety, and patient engagement and satisfaction. Research on success vs. failure (27–30, 54, 57, 100) also found that committed CEO leadership was one of the most dominant characteristics in effective implementation of the EHR Meaningful Use program.

The predominant approach to implementing changes in clinical practice has been translational science—designing a potential solution, testing it in a clinical trial environment, and then translating it to the frontlines of care. However, the reported success of this approach for uptake and sustainability of changes has proven to be low (8, 55). In contrast, healthcare systems reporting significantly superior results generally report taking problem-driven approaches bringing solution development closer to the frontlines of care and engaging users earlier in the process in both defining improvement targets and in shaping the solutions (21–23). Critical success factors reported in successful projects include clearly defined problems, executive leadership, user buy-in and involvement, physician leadership, and well-defined patient outcomes (Not just process or institutional outcomes) (14). It is all too easy to assume everyone understands the problem since they are living with it every day. However, given the complexity of our healthcare delivery system, shortcutting the process of getting to the heart of the problem and understanding how it impacts patient care at the frontlines, can be highly consequential because in many ways, how the problem is framed, predetermines the set of potential solutions. Moreover, when solutions are developed remotely by technical experts, managers, or providers, they are often insensitive to patient perspectives and how patients experience the care pathway. A distinguishing point among healthcare success stories is the amount of time and effort invested at the outset in engaging cross-cutting stakeholders at the frontlines in studying the problems, developing explicit targets for improvement, and bringing a new mindset to developing solutions. It turns out that designing a solution for zero error tolerance, for example, does not elicit the same thinking as designing for 98 percent tolerance. In other words, a solution designed to allow a 2 percent error tolerance is very likely to produce precisely that result (2, 21–23, 56–58). Research by Andrews, Pritchett, and Woodcock at Harvard Kennedy School (56) provides insight into how a problem focus in project design and implementation can ensure that the focus is solving specified problems as the goal (rather than introducing a pre-designed solution) as well as allowing continuous changes in the design to ensure the problem is effectively addressed. Moreover, according to Andrews, a problem-driven approach promotes a search for alternatives that offer a solution (rather than just providing new ways of doing things). They suggest that many projects claiming to be problem-driven, are not problem-driven at all, and thus “their strategy has no real means to draw attention to the need for change, provide a rallying point for coalition building, or offer a ‘true north’ destination of ‘problem solved’ to guide, motivate and inspire action” (56).

It is widely recognized that information technology is not the solution, but rather an enabler, or a catalyst, for designing new approaches for solving long-standing problems. Nonetheless, when planning digital transformation projects, healthcare institutions all too often allocate 90% of the resources to implementing the technology with little time, effort, and resources devoted to establishing clinical objectives or transforming care delivery practices. The data shows that less than 0.1 percent of the total $3 billion plus spend on healthcare annually is currently devoted to research designed to improve how we deliver healthcare (58, 59). Multiple factors contribute to this failure including the previous discussion about lack of clarity around framing the problem and designing solutions. Moreover, technology implementation is far more tangible and less complex than transforming care delivery processes (5, 24, 60). It is far too easy to assume that making technology available will automatically lead to innovation, which in fact is seldom the case—especially in healthcare where solution development almost always intersects across multiple fragmented care pathways and silos of operation. Our research shows that healthcare institutions achieving transformation goals invest at least as much, if not more, in designing and implementing transformation initiatives as they do on technology implementation (2, 14, 21–23, 27, 28, 61, 62).

The need for health information technology interoperability and data sharing is an essential component of the 2001 Institute of Medicine's Crossing the Quality Chasm recommendations: “The automation of clinical, financial, and administrative information and the electronic sharing of such information among clinicians, patients, and appropriate others within a secure environment are critical if the 21st-century health care system envisioned by the committee is to be realized.” (18) Yet here we are more than 25 years later still struggling to make these digital capabilities a reality. After multiple earlier attempts, a national TEFCA (Trusted Exchange Framework and Common Agreement) health information network finally became a reality in December 2024. (53, 63) TEFCA is designed as a network of networks to provide the technical standards, capabilities, and governance structures needed for digital health information exchange nationwide. Concurrently, newly developed U.S. Core Data for Interoperability (USCDI) standards were also released. FHIR standards for application interfaces continue to evolve as well and are now supported by TEFCA. Development and expansion of all three continue, but resistance to adoption remains high. Why the delay and why the importance?

Globally, organizations such as the World Health Organization have developed frameworks like the SMART guidelines (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) to support the uptake of digital health standard and interoperability, emphasizing stakeholder engagement and customization to local contexts as critical success factors (64). The World Health Organization also has developed Digital Adaptation Kits (DAKs) as a software-neutral mechanism for translating clinical guidelines into standardized digital formats that support interoperability and quality assurance in the transition from paper to digital systems (65).

Despite a continuing focus and incentives to promote widespread information sharing, our research suggests general low awareness of the potential benefits and the power (affordances) of information sharing as an essential foundation for making care more patient centric by improving care coordination and integration (66–68). Although information sharing as the foundation for coordination is long-established in other industries—banking, airlines, shipping, and manufacturing supply chains, for example—in healthcare delivery it runs counter to current paradigms (or mindsets) about safeguarding patient data and privacy. The potential for redesigning workflows and improving care pathways, intersects across multiple operational units and often multiple medical practices or institutions. Efforts may also be undermined by the sometimes-perverse financial incentives of the U.S. third-party payer system. It is not a coincidence that many of the healthcare systems that have been most successful with their transformation efforts have been integrated care systems, and thus they have more directly benefitted financially from improved outcomes and efficiencies (42).

Several leading healthcare institutions have demonstrated the power of information sharing for care coordination and integration and achieving more patient-centric care pathways. Nonetheless, implementation remains more of an exception than a reality for most healthcare institutions (2, 21–23, 26, 52). A 2019 study by Poku, Kagan and Yehia found that only 7% of healthcare executives, clinical leaders, and clinicians indicated that their patients' care is fully coordinated across various health settings (66). While the need for integrated care, portability of health information, and alignment of value incentives across stakeholders have been widely recognized, only a handful of organizations have been able to implement aspects of these goals within their health systems and fewer still have been able to integrate with outside organizations (66).

Addressing digital healthcare transformation from a systems perspective raises several considerations. The IOM reports (18) clearly differentiate problems with the healthcare delivery system as a “systems problem” rather than a problem of individual performance, but what are the implications for designing and implementing solutions? Addressing this question intersects with other healthcare considerations as well. A prime consideration is healthcare standards of evidence-based medicine. Implementing IT-based changes in workflows or protocols at the frontlines of care, generally requires appropriate research methodologies to validate the impact on patient outcomes. Changing who does what during the care pathway might also intersect with professional licensing practice parameters or other policy or regulatory requirements. For example, telehealth has run into multiple licensing issues related to practicing across state borders as well as payer issues.

Tools and frameworks for user-centered design, including human personas and human behavioral modelling, have been well-established in engineering disciplines (69) and offer potential methodologies for healthcare to adapt in designing more patient-centric delivery systems. However, research also shows that user-centered design principles, while widely espoused, are often not effectively employed in digital health initiatives, with common shortcomings occurring at all stages of the design process (70). Our research suggests that institutions that took a more team-based, iterative design science approach to transformation were far more likely to achieve intended performance outcome goals (1, 2, 13, 21–23, 38, 57, 61, 71–75). Design science differs from more traditional translational science approaches in multiple ways that appear to be consequential in achieving results. For example, it brings the transformation initiative to the frontlines of care from the outset of the design process rather than at the later stages of the implementation process. Building buy-in and engaging providers at the frontlines early in the process was often cited among “lessons learned” as important to reported successes. In-depth use case evidence offers insightful examples of how the dynamics of the process can lead to new thinking and provider buy-in.

Design science aligns with the IOM concept of the Learning Healthcare System (LHS) (58, 76, 77). LHS can be seen as an evolution of the earlier notion of “knowledge translation and exchange” that fuses research and healthcare delivery by embedding advanced research methods within the context of everyday care (78, 79). The concept of the learning healthcare system was first conceptualized and promoted by the Institute of Medicine in the early 1990's (18, 80). The original concept was subsequently expanded by conceptualizing that LHSs operate at three nested levels: (a) the micro-clinical level, where learning activities center on using best evidence for clinical decision-making by providers and patients; (b) the meso-organizational level that focuses on generating and using evidence on health service delivery arrangements and population health; and (c) the macro-system level where the focus is on policy making including resource allocation and regulatory requirements (81).

“A comprehensive LHS approach is particularly relevant in the complex multicomponent changes to how healthcare is organized, financed, and delivered” (79). While the concept of LHS is broadly supported in the healthcare community, guidance on the best practices for operationalizing the concept in practice remains underdeveloped (79). “Existing LHS models and frameworks lack practical detail about how different streams of evidence and research methods intersect in the learning activities, and where (and with whom) relationships between researchers and health system operators are formed and sustained” (79). More broadly, these findings suggest that there is need to revisit existing project management frameworks and methodologies to better address the complex dynamics of implementing digital transformation. This issue is explored further in a later section of the paper.

Understanding about the organizational change management process itself has been changing, especially in the domain of digital transformation. Researchers have identified several distinctive challenges beyond the dynamics of the generally accepted practices of change management theory (57, 75, 82). Most of the relevant research about digital transformation comes out of the social sciences research on organizational psychology/organizational behavior (OB) and organizational development (OD) rather than the current body of technology literature on technology acceptance. Fundamentally transformational change occurs not just at the organizational and operational levels, but even more significantly at the cultural level, changing the underlying dynamics of organizational culture.

Working at the level of transformational change, involves three critical and unique dynamics: (1) the future state cannot be fully known in advance; (2) significant change in organizational culture and in individual mindsets and behaviors are required, and (3) the change process itself cannot be tightly controlled and is difficult to manage because outcomes are uncertain, and the human dynamics are too unpredictable (75). These unique dynamics present many challenges for working within traditional scientific research methodologies and institutional management infrastructures. Addressing these challenges requires a new mindset, with a whole-system, long-term, process perspective. Transformation is not something that can be led apart from where real “work” happens (i.e., the frontlines)—it must be intertwined, embedded, and integrated into activities and infrastructure that drive action in healthcare organizations (75). Transformational change is a long-term process—7–10 years. The three unique dynamics identified by Anderson and Anderson are clearly evident in the Virginia Mason transformation initiative with multiple examples of strategies for addressing them (although there is no direct reference or correspondence between the two books).

Research around Affordance Theory is also relevant, albeit, not as directly. First introduced by J.J. Gibson, affordance theory suggests that the possibilities for action inherent in an object or environment are directly perceived by an individual, depending on both the object's physical properties and the user's own capabilities or objectives. The concept suggests that the design of an object should align with a user's needs and abilities to create intuitive and accessible experiences. While initially focused on physical environments, the concept was extended for digital environments by Donald Norman to application in Human-Computer Interaction (HCI) for user interface design. In these contexts, perceived affordances become critical, as designers must ensure that users can correctly interpret the intended actions, especially in the absence of physical cues. In healthcare, affordance principles can be useful in providing insight and underscoring the importance of creating products and environments that are clear, intuitive, and easy to use. Conceptually, in the case of transformational change, it is the affordances for changing the system paradigm that are of prime interest.

Transformational change requires well-planned orchestration focused simultaneously on problems that intersect across clinical, technical, behavioral, and organizational domains. Approaches must consider both the affordances provided by new technologies as well as the changes in mindset and organizational culture needed to gain buy-in and commitment of major stakeholders (i.e., changing the paradigm of the care delivery system). A preponderance of evidence from healthcare transformation initiatives suggests that a critical success factor has been moving the implementation process to the frontlines and engaging users early on in problem analysis, solution design, and implementation. For example, there are consequential differences in engaging stakeholders in advisory roles verses engaging them directly from the outset of problem analysis, making the stakeholders the advocates for change (56, 86). People support what they help to create. Importantly, developing a deep understanding of the problem becomes a motivating factor for the next stage of designing solutions (57, 75). Even more importantly, when problem analysis at the frontlines encompasses patients and how they experience care pathways, it can be eye opening for providers, helping to focus their improvement efforts (40). As previously pointed out, research using patient shadowing often reports how surprised providers were about the wide disparity in viewpoints.

Our research findings also indicated that iterative approaches to design and implementation frequently incorporated the IOM learning healthcare system (LHS) concept with feedback loops supporting a continuous improvement process (22, 77). A comprehensive LHS approach is particularly relevant in the complex multicomponent changes to how healthcare is organized, financed, and delivered (78, 79).

Research shows that institutions realizing the greatest benefit from new technology take a far more integrated approach from a holistic, ecosystem perspective (2, 28, 74, 87–89). An ecosystem approach takes into consideration people, process, technology, and structure. This perspective makes complex contexts such as healthcare more understandable by applying system-level thinking (9, 61, 90, 91). Systems theory frameworks, such as the Neuman Systems Model, provide theoretical approaches to addressing the “wicked problems” inherent in integrated care design—problems characterized by unclear, unpredictable, and incomplete data (92). Ecosystems are dynamic, constantly changing on multiple system levels (micro, meso, and macro). While these levels are different, they are interdependent within the whole system. Thus, changes in one unit or level have ripple effects (sometimes unanticipated) across other levels. The implication is that making changes in one unit or level requires corresponding change on other levels to achieve the anticipated benefits. This is a major gap in the prevalent model of siloed disciplinary innovation, which often results in failure to attain sustainability (14, 99). From a patient perspective, care pathways often cross multiple silos spanning multiple, sometimes competing, healthcare institutions.

Digital transformation starts with cultivating new thinking about old problems. Thus, a problem-driven approach is recommended because it provokes reflection, mobilizes attention, and promotes targeted and context-sensitive engagement (56, 93). A problem driven approach also can be empowering for users. It is important to recognize, however, that leaders cannot change the thinking of others—a reality that can be difficult to acknowledge in organizational settings where leaders are accustomed to directing the goals and activities of their employees (57). Leaders can only facilitate changes in thinking by creating an environment and experiences that prompt individuals to challenge their thinking and consider new options (57). As the age-old adage goes: You can lead a horse to water, but you cannot make him drink.

Surfacing the underlying thinking about current processes or problems was a critical starting point discussed in almost all the in-depth cases we studied. The specific approaches varied, but the objectives were similar: Probing to gain insight into the root cause and seeking to identify system performance deficiencies that cannot be ignored and that matter to key stakeholders. One of the most powerful ways to identify opportunities employed by transformational leaders was to listen to the patient's perspective (2). Some healthcare systems, for example, assigned healthcare workers to silently shadow a patient through their entire care pathway for diagnosing and treating a healthcare problem (2). Talking with patients and listening closely for the challenges they face can be enlightening and develop a deeper understanding of the patient's experience. It is important to keep in mind also that new solutions don't always mean throwing out past practice. Sometimes it is a matter of modifying or expanding on current practice—an approach that Ayars & Clifford (57) refer to as “And Thinking.”.

Starting with the end in mind was commonly identified as a critical success factor among healthcare transformation leaders (94). Another critical factor that set most successful transformation initiatives apart was the clarity with which they articulated patient outcome goals along with their metrics for tracking progress (2, 21–23). In other words, what will, or should, the problem look like when it is solved, and what will be the benefits for patients and providers?

The team-based healthcare delivery model has been gaining support as an important strategy for improving care coordination and transitioning to patient-centric care (13, 95). Once again, moving to team-based care represents a major paradigm shift from long-established norms. Team-based care is designed to enhance efficiency and access, improve quality of care, increase satisfaction for all involved in medical care, patients, providers, and staff.

 (95) Teams are generally led by one or more physicians and comprised of multiple clinical staff such as registered nurses, physician assistants, licensed practical nurses, medical assistants, population health navigator, and sometimes clinical pharmacists, or social workers.

Although often promoted as a strategy for improving care coordination, the literature and examples suggest that it is far from patient-centric. For example, guidelines for designing team-based care published by the American Academy of Family Physicians (AAFP) (95) are predominantly organized around optimizing performance of care providers. Thus, although the strategy offers significant potential for improving care delivery, it perpetuates the current paradigm of provider-centric care and it could be misleading to assume that it was advancing goals to make care more patient-centric. However, that does not mean necessarily that a care team approach could not be designed to be optimized around patient needs. The AAFP does advocate for the importance of practice transformation and workflow innovation and redesign and incorporation of technologies such as telehealth, which affirms that potential for evolving into more patient-centric models. Once again, however, it cannot be assumed that team-based models automatically lead to patient-centric care in the absence of intentional goals, strategies, and resources for achieving that aim. A key component for team-based models is a comprehensive digital information system to support care coordination, provide clinical decision support, and prevent mistakes. Ensuring that all team members always have access to the most current patient information and that handoffs from one team member to another are seamless is critical.

The concept of bringing care to the patient may seem radical at first blush. However, in essence this model is already in many ways the practice for acute hospital care where providers come to the bedside. With technological advances many specialty services can be brought to the patient bedside, such as with the use of portable x-ray equipment or other imaging technologies and EKG tests. However, an often-missing piece is a lack of communication across the different providers involved in the patient's care. Bringing care to the patient is far from common practice for out-patient care (i.e., ambulatory care), however, where patients may travel far and wide to different specialists and specialty care facilities. Nevertheless, if we think historically back to simpler times where a family doctor made house calls carrying his toolkit with him, we might view it as “back to the future.” Patient medical history was one patient-one record, with family history encompassing multiple generations. As medical science has advanced, healthcare has become increasingly complex and specialized, but also increasingly fragmented, error prone, and costly. Increased quality issues and errors are often attributed to the multiple handoffs across fragmented care specialties and services. With the growing population of aging patients living with multiple chronic conditions, it raises the question: Does it realistically make sense to require the sickest of patients to run all over the place for care? Especially given that many may be driving partially impaired or reliant on finding others to drive them. Can we do better?

In Figure 1, we provide a conceptual framework for thinking about managing digital healthcare transformation from a systemic perspective with a focus on making it more patient centric. How are we defining patient-centric? What are the impediments to making the transformation from today's fragmented provider-centric ecosystem? How do our redesign strategies and research need to change? And what are our outcome goals?