Healthcare IT will play a critical role in helping providers capture data and transfer knowledge from every patient interaction—the basis for higher-quality care at reduced cost.


At a Glance

IT advances that will support healthcare providers’ transition toward becoming “learning organizations” include the following:

  • The increase in “big data” (patient data captured in EHRs, coupled with data from imaging, molecular medicine, patient-provided data, and insurance claims)
  • Real-time analytics and novel decision aids
  • Ease-of-use advancements and effective data capture methods
  • Efforts to increase facile interoperability
  • Extended reach of EHRs in gathering data from other processes and sources

Although numerous efforts are under way to improve the quality, cost, accessibility, and coordination of care across the nation, the challenges in achieving greater value continue to vex providers. 

Last September, the Institute of Medicine (IOM) delivered a challenge to the U.S. healthcare system via a nearly 400-page report, Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. In the report, the IOM describes a healthcare system that squanders an estimated $750 billion a year—roughly 30 cents of every dollar—through unnecessary care, excessive administrative costs, missed prevention opportunities, and other waste, all while failing to deliver reliable care and top-notch outcomes.

Based on its findings, the IOM outlined a series of recommendations to improve the nation’s healthcare system, such as by increasing transparency related to health system performance, rewarding providers for continuous learning and quality, and recognizing that patients should be considered partners in their health decisions. Effective use of healthcare IT is at the center of three of the IOM’s 10 recommendations (see the sidebar on page 58) and critical to the others. 

Furthermore, the IOM proposes that Americans “should be served by a healthcare system that consistently delivers reliable performance and constantly improves, systematically and seamlessly, with each experience and transition.” It also provides a vision and road map for building a healthcare delivery system that learns from and evolves with every patient interaction. 

Becoming a learning organization has never been more critical for providers—and the effective use of healthcare IT will guide and support hospitals and health systems in making the transition.

Getting There from Here

In his seminal work The Fifth Discipline: The Art and Practice of the Learning Organization, author Peter Senge describes a learning organization as a place where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning. 

The foundation for a learning organization in healthcare is continuous knowledge development—the formation of a closed “learning loop,” in which information generated by clinical research is methodically captured and translated into evidence that can provide the basis for improving patient care.

Seems simple enough. However, we know that insights from science are at times poorly managed, evidence-based practices may not be broadly adopted for years, and care interventions can be skipped or inadequately documented. Thus, missed opportunities, waste, and potential harm plague the current system.

The IOM’s vision suggests that if we wrap the appropriate incentives, culture, and leadership around this closed learning loop, generating evidence that is used to improve care, we can make the transformation into a continuously learning healthcare system. (See the exhibit below.)

Exhibit

Glaser_Exhibit

“Single-loop learning” is adjusting an action to solve or avoid a mistake based on observations of the outcomes of the action. “Double-loop learning” goes a step further: It corrects the underlying causes behind a problematic action or accentuates actions that contribute to superior performance. 

In the case of poor performance, underlying causes may be an organization’s norms and policies, individuals’ motives and assumptions, and informal and ingrained practices that block inquiry on these causes. 

Collective learning in the healthcare industry typically comprises the following elements.

Decision guidance. The focus for such guidance is on designing guidelines or standards of care to support the decision-making processes in patient care by separating out high-value practices from those that have little or no value. 

Practices/processes. The aim here is to establish a defined series of patient interventions and decisions resulting in outcomes (e.g., use and timing of thrombolytic agents in acute myocardial infarction).

Organizational arrangements. Such arrangements involve logical structures that provide the framework for managing knowledge, evaluating trends, recommending actions, and more (e.g., patient safety committees, quality improvement teams).

Evaluation of outcomes and impact. Analytic tools enable the organization to evaluate whether the implemented practices and processes are having the desired impact, identify new opportunities for improvement, and elicit evidence regarding new approaches.

Just as the information revolution has transformed many other industries, healthcare IT—in the form of growing stores of healthcare data, advances in connectivity, and the transition to electronic health records (EHRs)—holds the same promise for making effective and sustainable improvements in health care by enabling providers to make significant progress toward becoming learning organizations. 

To the extent that we can develop the infrastructure to generate and transfer knowledge from every patient interaction, we will begin to broadly and consistently deliver reliable performance as the IOM envisions.

Leveraging IT to Support Learning

Although advances in IT and data management techniques will enable providers to truly push the envelope in becoming learning organizations, providers must start by ensuring that they have a comprehensive foundation of healthcare IT in place. 

The industry as a whole has made some gains in automating core healthcare transactions, leveraging clinical decision support, and implementing analytics to assess provider performance—essentially providing the means to deliver guidance, manage processes, and capture and report outcomes. The adoption of EHRs is at the core of these gains. This transactional base of IT also can help to cement organizational arrangements and begin to connect interdisciplinary teams of care providers. 

However, much work remains in ensuring that best practices are routinely followed and new knowledge is defined. 

For example, we know that the prevalence of chronic conditions has increased over time. Research also shows that almost half of those over the age of 65 receive treatment for at least one chronic condition, and more than 20 percent receive care for multiple chronic diseases. Although clinical practice guidelines for a single disease may be effective, such guidelines coupled with multimedication use may lead to conflicting recommendations for a patient with multiple chronic conditions. This example illustrates the complexity of clinical decision making.

As a result of advances in medicine and changes in payment structures, healthcare providers now have more to do, more to manage, and more knowledge to acquire than ever before. Thus, providers will require intelligence-based EHRs that rely upon evolving evidence to help guide diagnostic and therapeutic decisions, inform providers of evidence-based care guidelines, monitor the execution of core clinical processes, and capture, report, and integrate information into quality and performance improvement.

Additionally, sophisticated business intelligence and analytics are needed to facilitate proactive management of key performance metrics. For example, there will be a greater need to assess care quality and costs, examine variations in practice, and compare outcomes. As such, business intelligence will become the platform upon which an organization not only monitors performance, but also makes critical decisions to uncover new revenue opportunities, reduce costs, reallocate resources, and improve care quality and operational efficiency. 

Supporting the Transition to a Learning Organization

The IT applications and infrastructure necessary to support providers as they work toward becoming learning organizations begin with data derived from the EHR. At their core, EHRs support the transactional aspects of care delivery, such as writing a prescription, retrieving a result, and documenting findings and care delivery.

However, the IOM recommendations have two major effects on providers’ traditional approach to EHRs and their implementation.

The intelligent EHR. Supporting care transactions will always be a critical capability of EHRs. However, the need for decision guidance and practice/process support will require that the EHR become more intelligent. Intelligent EHRs will be characterized by sophisticated and flexible rules engines, process monitoring engines,intelligent displays of important patient data, access to knowledge resources, means to collect data from multiple care settings through a health information exchange, and tools that enable provider collaboration.

Knowledge management. To manage knowledge effectively, providers should be using a range of strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences. While laying the IT foundation for a learning organization environment, providers should carefully consider the tools and technologies available to support knowledge management. For example, what organizational processes will be used to manage the processes of updating and retiring decision guidance?

The EHR’s reach should be extended to more effectively engage patients in managing their health, with the personal health record being an important IT capability. In addition, business intelligence capabilities are essential in monitoring an organization’s performance. These capabilities should become more embedded in EHRs, supporting real-time assessment of care performance.

IT Advances to Support the Transition

Both across the IT industry and within healthcare, advances are occurring that will enable providers to significantly extend the capabilities of current applications (such as the “typical” EHR) to support the type of provider learning organizations envisioned by the IOM. These advances will progressively unfold, and healthcare providers and IT vendors will begin to absorb these advances into their core portfolios.

The increase in “big data.” A tectonic shift is occurring in healthcare analytics and data warehousing, one that will challenge and transform many of our presuppositions about the solutions traditionally used to facilitate decision-making in the provider setting. Tomorrow’s EHRs will be shaped by concepts such as big data, machine learning, and predictive analytics.

The “big” part of big data will be the result of the growth in patient data captured in EHRs, coupled with data from imaging, molecular medicine, patient-provided data, and insurance claims. Big-data use sophisticated analytics to comb through mounds of data from these disparate sources with the aim of uncovering patterns that might solve a particular healthcare problem or provide insight into developing personalized treatment options. Big-data approaches also will be used to support postmarket surveillance, comparative effectiveness, and clinical trial hypothesis framing. 

Real-time analytics and novel decision aids. In an era of accountable care, providers will need to leverage a new generation of real-time analytics that will measure quality and process performance and assess guideline adherence, financial operations, and variations in treatments and outcomes. 

For example, predictive models will be able to alert providers if a particular patient is at greater risk of needing care (often elderly patients with multiple chronic diseases). Such patients should be identified as early as possible and surrounded with the care and resources necessary to ensure they maintain their health and avoid readmissions. Likewise, providers will need to be able to assess the costs and quality of alternative care settings and model the implications of moving patient care to settings other than the hospital or physician’s office. 

Industry mechanisms for managing knowledge in the IT base. Whether through the use of content management tools, communities of practice, or e-learning initiatives, knowledge transfer and management has become a strategic imperative in healthcare. Yet the industry still struggles with issues related to management of a particular base of knowledge. For example, is knowledge management the responsibility of the provider or vendor? How does one manage tens of thousands of items of medical knowledge (e.g., order sets, medication decision support, chronic disease pathways) that can be embedded in an EHR?

Web-based technology is a tremendous enabler of knowledge management, in that it simplifies the collaborative process, makes knowledge instantly available globally, and provides the structure for publishing content in a searchable form for knowledge capture and reuse. If we are to truly change the culture of health care and evolve into a system that continuously learns, we must make a sustainable commitment to investing in new approaches to become more collaborative.

Ease-of-use advancements and effective data capture methods. With its intuitive user interface, touch-screen technology, and embedded voice recognition software, there’s a reason why Apple’s iPhone continues to attract millions of users of all ages. And just as the iPad is a hit with consumers, its ranks of physician users also are growing—a trend likely to prove beneficial for the adoption of EHRs. Indeed, if we are to better ensure IT support of core clinical processes, we must exploit innovations in the usability, mobility, and design features of the technology we are putting in clinicians’ hands, often by capitalizing on innovations in consumer-directed mobile devices. 

Efforts to increase facile interoperability. Having information easily available when and where it is needed is critical to improving IT’s ability to support providers as they evolve into learning organizations. However, ensuring that information flows seamlessly between and among disparate systems and providers is no easy task, as anyone working on healthcare IT standards can attest. Nonetheless, interoperability and broad health information exchange deployment is a necessary foundational element for better individual and community health. 

Multiple industry efforts are under way to accelerate the definition of data exchange standards and address related issues, such as ensuring appropriate privacy and security mechanisms.

Extended reach of the EHR in gathering data from other processes and sources. As we add intelligence capabilities to tomorrow’s EHR, the scope of clinical data gathered about a patient, including data from other providers, data provided by patients via personal health records and home-based monitoring, and genomic and proteomic data, will be expanded. The additional data will enable providers to develop a more comprehensive view of patient populations to proactively identify those who require specific interventions. 

The broad extension of the scope of data gathered by the EHRs of tomorrow poses significant challenges for healthcare organizations related to storing data, setting data vocabularies, tracking data correction (such as an amended note) as the data moves across care settings, addressing data inconsistencies and quality problems, and ensuring that clinicians are not overwhelmed with data.

An Era of Learning in Health Care

Although the concept of hospitals and health systems as learning organizations may best be viewed as an ideal, the era that is upon us in health care is like no other. Many organizations are already engaged in constantly revamping and retooling themselves, perhaps unknowingly reaching for that ideal goal of becoming a learning organization. In fact, in this modern age of IT and swift change, learning, like an IT implementation, never stops.

As so well illustrated in the IOM’s report, there is no denying the paradox that is health care in America today: an explosion of medical knowledge, tremendous innovation in therapies and procedures, and vast computing capabilities, yet disappointing results in key performance indicators such as quality, costs, and outcomes. As the IOM contends, if the current shortfalls in the performance of the U.S. healthcare system are left unaddressed, the ramifications on both the cost and quality dimensions will have an impact on generations of patients to come. 

Overcoming these challenges will require broad and sophisticated IT applications and infrastructure and related management processes. These applications and processes are different from those that we have traditionally brought to bear as we implement electronic health records. Moreover, advances in information technologies and the management of these technologies will continue to change the ways that IT can enable a learning organization.

Realizing the potential of a continuously learning healthcare system will require a collective recognition that we need to create a new culture of care—a culture hallmarked by aligned incentives, an ongoing commitment to improvement, greater transparency, and strong leadership within and across organizations.

Finally, the need for collaboration among stakeholders to raise our health system’s level of performance and achieve sustainable change cannot be underestimated. By working together to build a delivery system that truly learns and evolves, we, too, can continuously learn and evolve.


John Glaser, PhD, is CEO, health services business unit, Siemens Healthcare, Malvern, Pa. (john.glaser@siemens.com). 

J. Marc Overhage, MD, PhD, is chief medical informatics officer, health services business unit, Siemens Healthcare, Malvern, Pa. (marc.overhage@siemens.com).


sidebar

Becoming a Learning Organization:The Institute of Medicine’s Recommendations

 

Digital infrastructure. Improve the capacity to capture clinical, care delivery process, and financial data for better care, improved performance, and the generation of new knowledge.

Data utility. Streamline and revise research regulations to improve care, promote the capture of clinical data, and generate knowledge.

Clinical decision support. Accelerate integration of the best clinical knowledge into care decisions.

Patient-centered care. Involve patients and families in decisions regarding health and health care, tailored to fit their preferences.

Community links. Promote community-clinical partnerships and services aimed at managing and improving health at the community level.

Care continuity. Improve coordination and communication within and across organizations.

Optimized operations. Continuously improve healthcare operations to reduce waste, streamline care delivery, and focus on activities that improve patient health.

Financial incentives. Structure payment to reward continuous learning and improvement in the provision of higher-quality care at lower cost.

Performance transparency. Increase transparency on healthcare system performance.

Broad leadership. Expand commitment to the goals of a continuously learning healthcare system.

Source: Best of Care at Lower Cost: The Path to Continuously Learning Health Care in America, Institute of Medicine, September 2012 

Publication Date: Friday, February 01, 2013