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Effectiveness and Outcomes in Health Care Proceedings of an Invitational Conference (1990) / Chapter Skim
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14 Issues in the Use of Large Data Bases for Effectiveness Research
Pages 94-106

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From page 94...
... At the same time, I think there is a real risk of promising more than these approaches can deliver and compromising the future of this research. DEFINING LARGE DATA SETS I begin by explaining what a large data set is because I think the term has been too narrowly construed at times.
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From page 95...
... ORIGINAL PURPOSE Large data bases are typically collected for some purpose other than that for which researchers wish to use them, and they often lack, therefore, some features or data researchers want. They are typically strong on size, on longitudinal detail, and on linkability to other data sets, but they are particularly likely to be thin on clinical detail and on functional status.
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From page 96...
... The Office of Research at HCFA has a triple agenda in the area of effectiveness, namely, to look at the closely linked issues of the comparative effectiveness of providers, of procedures, and of payment systems. Large data sets have a variety of applications in these areas.
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From page 97...
... LIMITATIONS OF LARGE DATA BASES Large data sets have obvious and less obvious limitations. DATA QUALITY Many of the quality issues in large data sets will be familiar to any investigator who has used such secondary data.
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From page 98...
... They focus on whether large data sets can be used to assess the effectiveness of a procedure or a provider or the relative effectiveness of procedures or providers.
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From page 99...
... Our limited capability for risk adjustment, relative ignorance about how providers select procedures, and ignorance about the interactions between providers and procedures create very serious difficulties when we try to employ this fundamental theorem in the real world. RISK ADJUSTMENT Our best risk adjustment instruments account for less than 30 percent of variation in mortality among individuals with the same condition, which leaves 70 percent or more to be explained by other factors.
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From page 100...
... This selection phenomenon involves interaction between the competence of the people who perform a procedure and the effectiveness of the procedure. STATISTICAL ISSUES Without becoming highly technical, I wish to note two statistical issues that are important in using large data sets.
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From page 101...
... Large data bases introduce more problems in evaluating data, but these problems arise in evaluating data that would not otherwise be available to clinicians at all. PROSPECTS What can we clearly use large data sets for now, and how might we be expand those uses in the future?
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From page 102...
... Third, we need to do a lot of linking of various kinds of data sets. The HCFA Office of Research has been experimenting with SEER, has worked with the Social Security Administration, and has done a bit with mortality registries.
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From page 103...
... I am very ambivalent about proposing this because I fear that unmeetable promises are being made to promote some state data bases. Nevertheless, I think the data sets that are being created in Pennsylvania, Colorado, and Iowa are extremely interesting sources of information.
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From page 104...
... Major health organizations are beginning to work toward such a consensus, and developing that consensus may be an important step in working toward consensus on how to use data from other large data bases. Cutting across all these issues is the challenge of doing as much as we can without promising more than we can deliver.
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From page 105...
... Administrative data sets typically do not have sufficiently detailed clinical information for this purpose. This information can sometimes be obtained by reviewing the patients' hospital records.
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From page 106...
... and the wider research community. The data set was intended to contain far more detailed clinical data than were available heretofore in the HCFA data files.
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Key Terms

  • administrative data
  • clinical data
  • uniform clinical
  • functional status
  • relative effectiveness

  • adjustment tools
  • primary data
  • surgical procedures
  • detailed clinical
  • financing administration

  • claims data
  • random sample
  • real world
  • secondary data
  • medicare data

  • care financing
  • health standards
  • clinical information
  • quality bureau
  • evaluating data

  • nursing home
  • available heretofore
  • krakauer discusses
  • infarction using
  • state data

  • death certificate
  • previous treatment
  • clinical richness
  • security administration
  • health events


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