Evidence-based medicine-Engineering the Learning Healthcare System

Institute of Medicine, USA.
Studies in health technology and informatics 01/2010; 153:145-57. DOI: 10.3233/978-1-60750-533-4-145
Source: PubMed


Whether for the generation or application of evidence to guide healthcare decisions, the success of evidence-based medicine is grounded in principles common to engineering. In the Learning Healthcare System envisioned by the Institute of Medicine's (IOM) Roundtable on Evidence-Based Medicine, evidence emerges as a natural by-product of care delivery, which is thoroughly documented, pooled for continuous monitoring and analysis, integrated with insights from related studies, and fed back seamlessly to improve the consistency and appropriateness of care decisions by clinicians and their patients. Drawing from lessons shared at the IOM/NAE symposium, Engineering a Learning Healthcare System, this paper provides an overview of the state-of-play in health care today, some of its key challenges, the vision and features of a learning healthcare system, applicable commonalties and principles from engineering, and potential collaborative opportunities moving forward to the benefit of both fields.

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    • "A learning health system (LHS) integrates clinical studies done in routine care settings, leverages structured data capture at every encounter, and incorporates quality improvement methods to implement advances in new knowledge and care delivery, with active and meaningful patient participation.4–8 While disease-specific examples of pediatric LHSs have shown tremendous improvement in clinical outcomes,9–11 a national digital architecture to support the rapid implementation of LHSs across multiple pediatric conditions does not exist.12 13 "
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    ABSTRACT: A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.
    Journal of the American Medical Informatics Association 05/2014; 21(4). DOI:10.1136/amiajnl-2014-002743 · 3.50 Impact Factor
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    ABSTRACT: This article describes the development of a modeling hierarchy for complex enterprise networks. Drawing on the extant modeling literature, these network models are elaborated in terms of 4 salient problem characteristics: conversions, flows, controls, and social/organizational relationships. The authors relate these 4 characteristics to phenomena, representations, micromodels, macromodels, and modeling tools. The roles of information and incentives in complex enterprises networks are considered. Examples of 2 domains, global manufacturing and healthcare delivery, are woven through these discussions of alternative representations and models. The authors conclude by providing a structured comparison of these 2 domains, discussing theoretical and practical implications, and presenting opportunities for future enterprise transformation research.
    07/2011; 1(3-3):208-230. DOI:10.1080/19488289.2011.601397
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    ABSTRACT: The learning health care system refers to the cycle of turning health care data into knowledge, translating that knowledge into practice, and creating new data by means of advanced information technology. The electronic Primary Care Research Network (ePCRN) was a project, funded by the U.S. National Institutes of Health, with the aim to facilitate clinical research using primary care electronic health records (EHRs). We identified the requirements necessary to deliver clinical studies via a distributed electronic network linked to EHRs. After we explored a variety of informatics solutions, we constructed a functional prototype of the software. We then explored the barriers to adoption of the prototype software within U.S. practice-based research networks. We developed a system to assist in the identification of eligible cohorts from EHR data. To preserve privacy, counts and flagging were performed remotely, and no data were transferred out of the EHR. A lack of batch export facilities from EHR systems and ambiguities in the coding of clinical data, such as blood pressure, have so far prevented a full-scale deployment. We created an international consortium and a model for sharing further ePCRN development across a variety of ongoing projects in the United States and Europe. A means of accessing health care data for research is not sufficient in itself to deliver a learning health care system. EHR systems need to use sophisticated tools to capture and preserve rich clinical context in coded data, and business models need to be developed that incentivize all stakeholders from clinicians to vendors to participate in the system.
    The Annals of Family Medicine 05/2012; 10(1):54-9. DOI:10.1370/afm.1313 · 5.43 Impact Factor
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