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Today we are pleased to publish Health IT Enabled Quality Improvement: A Vision to Achieve Better Health and Health Care. This paper describes ONC’s vision for advancing the use of health IT to support transformational improvement in health care quality and value.  It invites health IT stakeholders – clinicians, consumers, hospitals, public health, technology developers, payers, researchers, policymakers and many others – to join ONC in shaping the future with a renewed focus on health and care quality as the “why” that aligns with the “what” of interoperable health information systems.

What are we talking about?

Quality measurement has a long tradition in health care.  The National Committee for Quality Assurance (NCQA), National Quality Forum (NQF), The Joint Commission, the Centers for Medicare & Medicaid Services (CMS) and others have long track records of creating and maintaining quality programs that have enabled care delivery organizations to identify gaps in care quality, and to define and motivate organizations toward best practice.  But measurement is only one part of an improvement program.  We don’t improve drivers’ alignment with the speed limit (and therefore the safety of drivers and pedestrians) by giving tickets, or by mailing notices days, weeks or months later.  Rather, real-time feedback loops work to improve the quality of the driving as the driving is being done!  New technology being applied to driving include notifications or even corrective action when the driver drifts out of unanticipated object lane, comes to close to another car, or encounters an unanticipated object while driving in reverse.

A “quality report” delivered to a hospital, practice or care provider about how they performed in the past will likewise have only limited (if any) effect on quality improvement, just as giving students mediocre grades doesn’t help students do better in the future.

As , a feedback loop requires four components:

First comes the data: A behavior must be measured, captured, and stored. This is the evidence stage. Second, the information must be relayed to the individual, not in the raw-data form in which it was captured but in a context that makes it emotionally resonant. This is the relevance stage. But even compelling information is useless if we don’t know what to make of it, so we need a third stage: consequence. The information must illuminate one or more paths ahead. And finally, the fourth stage: action.

The medical informatics community has worked for nearly thirty years on decision support functionality in health information technology.  Historically, CDS (Clinical Decision Support) and CQM (Clinical Quality Measures) have been developed using different tools, by different technical teams, with different expertise, and different (but convergent) goals.  CDS developers have generally had explicit clinical goals: preventing unsafe prescriptions, alerting clinicians to missed care delivery opportunities, notifying providers of abnormal laboratory results, and many other clinical optimizations.  These two communities have been performing this parallel play  for …read more