Digital data improvement priorities for continuous learning in health and health care : workshop summary /
Claudia Grossmann, Brian Powers, and Julia Sanders, rapporteurs ; Roundtable on Value & Science-Driven Health Care, Institute of Medicine of the National Academies.
Washington, D.C. : The National Academies Press, [2013], c2013
xviii, 60 p. : ill. ; 23 cm.
030925941X, 9780309259415
More Details
Washington, D.C. : The National Academies Press, [2013], c2013
contents note
Data quality challenges and opportunities in a learning health system -- Digital health data uses : leveraging data for better health -- Issues and opportunities in the emergence of large health-related datasets -- Innovations emerging in the clinical data utility -- Strategies going forward.
catalogue key
Includes bibliographical references.
A Look Inside
Description for Bookstore
Digital health data are the lifeblood of a continuous learning health system. A steady flow of reliable data is necessary to coordinate and monitor patient care, analyze and improve systems of care, conduct research to develop new products and approaches, assess the effectiveness of medical interventions, and advance population health. The totality of available health data is a crucial resource that should be considered an invaluable public asset in the pursuit of better care, improved health, and lower health care costs. The ability to collect, share, and use digital health data is rapidly evolving. Increasing adoption of electronic health records (EHRs) is being driven by the implementation of the Health Information Technology for Economic and Clinical Health (HITECH) Act, which pays hospitals and individuals incentives if they can demonstrate that they use basic EHRs in 2011. Only a third had access to the basic features necessary to leverage this information for improvement, such as the ability to view laboratory results, maintain problem lists, or manage prescription ordering. In addition to increased data collection, more organizations are sharing digital health data. Data collected to meet federal reporting requirements or for administrative purposes are becoming more accessible. Efforts such as provide access to government datasets for the development of insights and software applications with the goal of improving health. Within the private sector, at least one pharmaceutical company is actively exploring release of some of its clinical trial data for research by others. Digital Data Improvement Priorities for Continuous Learning in Health and Health Care: Workshop Summary summarizes discussions at the March 2012 Institute of Medicine (2012) workshop to identify and characterize the current deficiencies in the reliability, availability, and usability of digital health data and consider strategies, priorities, and responsibilities to address such deficiencies.
Table of Contents
Introductionp. 1
Data Sources in the Digital Health Utilityp. 3
Moving to a Continuously Learning Health Systemp. 4
Workshop Scope and Objectivesp. 5
Organization of the Summaryp. 6
Data Quality Challenges and Opportunities in a Learning Health Systemp. 9
Introductionp. 10
Challenges for Data Collection and Aggregationp. 10
Patient-Reported Data and Maximizing Patient Value in the Learning Health Systemp. 12
Digital Health Data Uses: Leveraging Data for Better Healthp. 15
Introductionp. 17
Practice Managementp. 17
Clinical Researchp. 19
Translational Informaticsp. 21
Supporting Public Health and Surveillance at the National Levelp. 23
Supporting Public Health and Surveillance at the Local Levelp. 24
Issues and Opportunities in the Emergence of Large Health-Related Datasetsp. 27
Introductionp. 28
The Challenge of Bias in Large Health-Related Datasetsp. 28
Moving from Analytics to Insightsp. 30
Innovations Emerging in the Clinical Data Utilityp. 33
Introductionp. 34
Distributed Queriesp. 34
Data Harmonization and Normalizationp. 36
Data Linkagep. 38
Strategies Going Forwardp. 41
Current Data Sources: Better Awareness and Assessmentp. 41
Data Input: Improve Patient Orientation, Quality, and Utilityp. 42
Data Analysis: Improve Access, Tools, and Capacityp. 42
Public and Patient Engagement: Ramp Up Involvementp. 43
Building a Clinical Data Learning Utilityp. 44
Clarity on Governancep. 45
Speaker Biographiesp. 47
Workshop Agendap. 57
Table of Contents provided by Ingram. All Rights Reserved.

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