Computational Technology for Effective Health Care Immediate Steps and Strategic Directions (2009) / Chapter Skim
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3 Crossing the Health Care IT Chasm
3 Crossing the Health Care IT Chasm
Pages 25-29
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From page 25... ...
Although seeing these successes was encouraging, in the committee's judgment they fall far short, even in the aggregate, of what is needed to support the Institute of Medicine's (IOM's) vision of quality health care.
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From page 26... ...
and virtually no attention being paid to helping the clinician understand how the voluminous data collected could relate to the overall health care status of any individual patient. Care providers spent a great deal of time in electronically documenting what they did for patients [C1O3]
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From page 27... ...
In the quality domain, various improvement efforts have failed to improve health care outcomes, and have sometimes even done more harm than good. Similarly, based on an examination of the multiple sources of evidence described above and viewing them from the committee's perspective, the committee believes that the nation faces the same risk with health care IT -- that current efforts aimed at the nationwide deployment of health care IT will not be sufficient to achieve the vision of 21st century health care, and may even set back the cause if these efforts continue wholly without change from their present course. Success in this regard See, for example, Institute of Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century, National Academy Press, Washington, D.C., 2001; President's Information Technology Advisory Committee, Revolutionizing Health Care Through Information Technology, National Coordination Office for Networking and Information Technology, Washington, D.C., 2004, available at http://www.nitrd.gov/pitac/reports/20040721_hit_report.pdf; Office of the National Coordinator for Health Information Technology, The ONC-Coordinated Federal Health Information Technology Strategic Plan: 2008-2012, U.S.
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From page 28... ...
Data mining converts raw data signals into clinical vari ables and models to provide a rich source for new approaches to evidence-based medicine and personalized care. Examples range from identification of a marker for breast cancer therapeutic response from microarray data, through mining the text literature for little-known drug-drug interactions, to mining multimedia electronic health records to identify a patient's condition from a text note or a change in heart size from a sequence of images, and extracting ideas or relationships from a recent publication in a leading journal and pushing the information to the physicians who are treating patients who may benefit from those findings.
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From page 29... ...
This point is the central conclusion articulated in this report. So that the nation can cross the health care IT chasm, the committee advocates re-balancing the portfolio of investments in health care IT; adhering to proven principles for success; and accelerating research in computer science, social sciences, and health/biomedical informatics (and concomitant education about each field for practitioners in the others)
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