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Massive Data Sets: Proceedings of a Workshop (1997)
Commission on Physical Sciences, Mathematics, and Applications (CPSMA)

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. "Massive Data Sets: Guidelines and Practical Experience from Health Care." Massive Data Sets: Proceedings of a Workshop. Washington, DC: The National Academies Press, 1997.

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1 Introduction

From the moment of birth to the signing of the death certificate, medical records are maintained on almost every individual in the United States (and many other countries). Increasing quantities of data are abstracted from written records, or entered directly at a workstation, and submitted by providers of healthcare to payer and regulatory organizations. Providers include physician offices, clinics, and hospitals, payers include managed care corporations and insurance companies, and regulatory organizations include state and federal government. Trends are towards making the flow of data easier, more comprehensive, and multi-faceted: through EDI (electronic data interchange), CHINs (Community Health Information Networks), and a seemingly ever more intrusive, detailed, and specific involvement by payors in the handling of care and compensation by and for providers.

These so-called clinical and financial administrative health care data are routinely massive. The Health Care Financing Administration's annual MEDPAR data base contains around 14 million discharge abstracts of every medicare-funded acute-care hospital stay. Individual state's administrative data of hospital discharges may include several million records annually. Data are collected in certain standard formats, including uniform billing (UB82, now UB92) for administrative data on hospital stays, and HEDIS (1.0, 2.0, 3.0) on patients in managed care. The more detailed data is often proprietary: for example HEDIS data is often proprietary to the specific payer organization, and includes data only on the organization's enrollees. More ambitious data collecting is underway in selected locations, through the systematic abstraction of supplementary clinical measures of patient health from medical records, through recording of additional patient characteristics, or through recording of more detailed financial information.

In principal the entire medical record is available. A written version might be available online in digital form as an image. Record linkage, for example between members of a family (mother and child), or through the use of unique patient identifiers across multiple episodes of treatment, or from administrative data to the registry of vital statistics (death certificates) and to cancer registries, provides important additional information. However, the availability of such information is, again, restricted.

A traditional uses of health data is in public health assessment and the evaluation of clinical efficacy of particular treatments and interventions.

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FRONT MATTER (R1-R10)
Opening Remarks (1-2)
PART I Participant's Expectations for the Workshop (3-12)
PART II Applications Papers (13-14)
Earth Observation Systems: What Shall We Do with the Data we Are Expecting in 1998? (15-22)
Information Retrieval: Finding Needles in Massive Haystacks (23-32)
Statistics and Massive Data Sets: one View from the Social Sciences (33-38)
The Challenge of Functional Magnetic Resonance Imaging (39-46)
Marketing (47-50)
Massive Data Sets: Guidelines and Practical Experience from Health Care (51-68)
Massive Data Sets in Semiconductor Manufacturing (69-76)
Management Issues in the Analysis of Large-Scale Crime Data Sets (77-80)
Analyzing Telephone Network Data (81-92)
Massive Data Assimilation/Fusion in Atmospheric Models and Analysis: Statistical, Physical, and Computational Challenges (93-103)
PART III Additional Invited Papers (103-104)
Massive Data Sets and Artificial Intelligence Planning (105-114)
Massive Data Sets: Problems and Possiblities, with Application to Environmental Monitoring (115-120)
Visualizing Large Datasets (121-128)
From Massive Data Sets to Science Catalogs: Applications and Challenges (129-142)
Information Retrieval and the Statistics of Large Data Sets (143-148)
Some Ideas About the Exploratory Spatial Analysis of Large Data Sets (149-156)
Massive Data Sets in Navy Problems (157-168)
Massive Data Sets Workshop: The Morning After (169-184)
PART IV Fundamental Issues and Grand Challenges (185-186)
Panel Discussion (187-202)
Items for Ongoing Consideration (203-204)
Closing Remarks (205-206)
Appendix: Workshop Participants (207-208)