The Learning Healthcare System Workshop Summary (2007) / Chapter Skim
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4 New Approaches—Learning Systems in Progress
4 New Approaches—Learning Systems in Progress
Pages 185-216
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From page 185... ...
The clinical data within electronic health records (EHRs) are structured such that data can be aggregated from within VA or with other systems such as Medicare to provide a rich source of longitudinal data for health services research (VA Diabetes Epidemiology Cohort [DEpiC]
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From page 186... ...
Citing a critical need for rapid advance in the evidence base for clinical care, Lynn Etheredge makes the case for the potential to create a rapidly learning healthcare system if we build wisely on existing resources and infrastructure. In particular he focused on the potential for creating virtual research networks and the improved use of EHR data.
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From page 187... ...
. These advances are in part related to VA's leadership in the development and use of the electronic health record, which has fostered veteran-centered care, continued improvement, and research.
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From page 188... ...
. VistA: VA's Electronic Health Record Because VA was both a payer and a provider of care, its information system was developed to support patient care and its quality with clinical information, rather than merely capture charges and facilitate billing.
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. VistA-CPRS allows clinicians to access and generate clinical information about their individual patients, but additional steps are needed to yield insights into population health.
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From page 190... ...
. The influence of gender and race or ethnicity can also be more fully explored using EHR data (Safford et al.
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From page 191... ...
. VA's EHR provides a unique opportunity to construct less "gameable" quality measures that assess how well care is managed for the same individual over time for diseases such as diabetes where metrics of process quality, intermediate outcomes, and complications (vision loss, amputation, renal disease)
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From page 192... ...
. The richness of EHR data allows VA to refine its performance measures.
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From page 193... ...
EHR data show that for most measures in the VA, only a small fraction (≤ 2 percent) of the variance is attributable to indiidual primary care providers (PCPs)
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From page 194... ...
Perhaps most importantly, the VA's EHR allows for effective care coordination across providers in order to communicate patients' needs, goals, and clinical status as well as to avoid duplication of services. Care Coordination and Telehealth for Diabetes In-home monitoring devices now can collect and transmit vital data for high-risk patients from the home to a care coordinator who can make early interventions that might prevent the need for institutional intervention (Huddleston and Cobb 2004)
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From page 195... ...
Through the MHV web portal, veterans can securely view and manage their personal health records online, as well as access health information and electronic services. Veterans can request copies of key portions of their VA health records and store them in a personal "eVAult," along with self-entered health information and assessments, and can share this information with their healthcare providers and others inside and outside VA.
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From page 196... ...
Enhanced decision support capabilities will help clinicians provide care according to guidelines and understand situations where it is appropriate to deviate from guidelines. The reengineered EHR will also link orders and interventions to problems, greatly enhancing VA's clinical data-mining capabilities.
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From page 197... ...
For patient care management, VA's EHR has developed an infrastructure and system for collecting and organizing information from which a diabetes database (DEpiC) evolved to provide valuable information related to disease prevalence, comorbidities, and costs that are necessary for quality improvement, system-wide planning, and research.
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From page 198... ...
Because of the vastly larger scale of the healthcare enterprise and the changing needs of veterans, VA's focus now has models in place to shift to issues involving clinical decision support, content standardization, and enhanced interaction between patients, VA providers, and other systems. These capabilities are made possible by VA's EHR, and the VA experience may provide a model for how federal health policies can help the United States create a learning healthcare system.
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From page 199... ...
More recently, PBRNs have begun to blur the lines between research and quality improvement, forming learning communities that "use both traditional and nontraditional methods to identify, disseminate, and integrate new knowledge to improve primary care processes and patient outcomes" (Mold and Peterson 2005)
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From page 200... ...
Even if the practices in a learning healthcare system are not organized into formal PBRNs, they will need to share some of the same characteristics and have some of the same resources to be successful. These include (1)
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From page 201... ...
stabilized funding that is not tied to a particular study, but rather sustains operations and communication systems across and between research projects. There is some evidence that this is beginning to happen: • Several institutes have formed or are evaluating clinical trial net works including the National Cancer Institute; the National Heart, Lung, and Blood Institute; and the National Institute of Neurologi cal Disorders and Stroke.
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From page 202... ...
Although primary care has made important inroads in some academic centers, many academic centers lack PBRNs and have too few experienced ambulatory care investigators to ensure a bidirectional exchange of information or provide enough sharing of resources to stabilize an ambulatory care research infrastructure. A learning healthcare system can learn a great deal from PBRNs, particularly for ambulatory care -- the bulk of the clinical enterprise, the location most neglected by research and quality improvement efforts, and the setting where most Americans receive medical care.
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From page 203... ...
HealthPartners If consistent improvement in health and care is to be achieved across the entire country, individual learning healthcare organizations will need to be knit together by a national infrastructure in a learning system for the nation. If this is not done, individual examples of progress such as the Veterans Administration, Mayo Clinic, Kaiser Permanente, and HealthPartners will remain exceptions in a disconnected fragmented healthcare system.
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Excellence Norms Purchasing Scorecard Safety* Health Plans*
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From page 205... ...
A National quality Improvement Process and Architecture To knit together a learning system for the country and create an NQIPA, a national strategy and infrastructure is needed that enables individual healthcare providers and their organizations to know the quality of care they deliver, to have the incentives and tools necessary to improve care, and to provide information critical to individual patients and the public about the quality of care they receive. The work may be described as a seven-step process model for quality improvement (Isham and Amundson October 2002)
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From page 206... ...
was designed by a work group that used the sevenstep model in developing its recommendations. Institutions in place in Minnesota that enable this model include the Institute for Clinical Systems Improvement, which is a collaborative of Physician Medical Groups and Health Plans that develops and implements evidence-based clinical practice guidelines and provides technical assistance to improve clinical care (Institute for Clinical Systems Improvement 2006)
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From page 207... ...
The AqA Alliance The AQA Alliance (www.aqaalliance.org) is a broad-based national collaborative of physicians, consumers, purchasers, health insurance plans, and others that has been founded to improve healthcare quality and patient safety through a collaborative process.
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From page 208... ...
has produced principles for data sharing and aggregation; provided a recommendation for a National Health Data Stewardship Entity (NHDSE) to set standards, rules, and policies for data sharing and aggregation, described desirable characteristics of an NHDSE; developed guidelines and key questions for physician data aggregation projects, and established principles to guide the use of information technology systems that support performance measurement and reporting so as to ensure that electronic health record systems can report these data as part of routine
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From page 209... ...
They include the California Cooperative Healthcare Reporting Initiative, Indiana Health Information Exchange, Massachusetts Health Quality Partners, Minnesota Community Measurement, Phoenix Regional Healthcare Value Measurement Imitative, and Wisconsin Collaborative for Healthcare Quality. These pilots are to serve as learning labs to link public and private datasets and assess clinical quality, cost of care, and patient experience.
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From page 210... ...
With advances in the understanding of the human genome and a doubling of the NIH research budget to more than $28 billion, there may be an even faster stream of new treatment options. Neither government regulation, healthcare markets, consumers, physicians, nor health plans are going to be able to deal with these technology issues, short of rationing, unless there are more rapid advances in the evidence base for clinical care.
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From page 211... ...
Large research projects that need to access paper health records from multiple sites are now administratively complicated, time-consuming, expensive, and done infrequently. In contrast, studies with computerized EHR databases and new research software will be done from a computer terminal in hours, days, or a few weeks.
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From page 212... ...
This paper identifies many opportunities for the public and private sectors to collaborate in building a learning healthcare system.
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From page 213... ...
2006. Effect of the implementation of an enterprise-wide electronic health record on productivity in the Veterans Health Administration.
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:1, S12-S20. National Cancer Institute.
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Poster Session, VHA Health Services Research and Development Conference, February 26, 2006. Raths, D
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