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El. knyga: Digital Data Improvement Priorities for Continuous Learning in Health and Health Care: Workshop Summary

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  • Formatas: 78 pages
  • Išleidimo metai: 26-Mar-2013
  • Leidėjas: National Academies Press
  • Kalba: eng
  • ISBN-13: 9780309259422
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Digital Data Improvement Priorities for Continuous Learning in Health and Health Care: Workshop SummaryDidesnis paveikslėlis
  • Formatas: 78 pages
  • Išleidimo metai: 26-Mar-2013
  • Leidėjas: National Academies Press
  • Kalba: eng
  • ISBN-13: 9780309259422
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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 Health.Data.gov 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



Front Matter 1 Introduction 2 Data Quality Challenges and Opportunities in a Learning Health System 3 Digital Health Data Uses: Leveraging Data for Better Health 4 Issues and Opportunities in the Emergence of Large Health-Related Datasets 5 Innovations Emerging in the Clinical Data Utility 6 Strategies Going Forward Appendix A: Speaker Biographies Appendix B: Workshop Agenda
1 Introduction
1(8)
Data Sources in the Digital Health Utility
3(1)
Moving to a Continuously Learning Health System
4(1)
Workshop Scope and Objectives
5(1)
Organization of the Summary
6(3)
2 Data Quality Challenges and Opportunities in a Learning Health System
9(6)
Introduction
10(1)
Challenges for Data Collection and Aggregation
10(2)
Patient-Reported Data and Maximizing Patient Value in the Learning Health System
12(3)
3 Digital Health Data Uses: Leveraging Data for Better Health
15(12)
Introduction
17(1)
Practice Management
17(2)
Clinical Research
19(2)
Translational Informatics
21(2)
Supporting Public Health and Surveillance at the National Level
23(1)
Supporting Public Health and Surveillance at the Local Level
24(3)
4 Issues and Opportunities in the Emergence of Large Health-Related Datasets
27(6)
Introduction
28(1)
The Challenge of Bias in Large Health-Related Datasets
28(2)
Moving from Analytics to Insights
30(3)
5 Innovations Emerging in the Clinical Data Utility
33(8)
Introduction
34(1)
Distributed Queries
34(2)
Data Harmonization and Normalization
36(2)
Data Linkage
38(3)
6 Strategies Going Forward
41(6)
Current Data Sources: Better Awareness and Assessment
41(1)
Data Input: Improve Patient Orientation, Quality, and Utility
42(1)
Data Analysis: Improve Access, Tools, and Capacity
42(1)
Public and Patient Engagement: Ramp Up Involvement
43(1)
Building a Clinical Data Learning Utility
44(1)
Clarity on Governance
45(2)
Appendixes
A Speaker Biographies
47(10)
B Workshop Agenda
57
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