Adding Laboratory Data to Hospital Claims Data to Improve Risk Adjustment of Inpatient/30-Day Postdischarge Outcomes

Michael Pine, Donald E. Fry, Edward L. Hannan, James M. Naessens, Kay Whitman, Agnes Reband, Feng Qian, Joseph Schindler, Mark Sonneborn, Jaclyn Roland, Linda Hyde, Barbara A. Dennison

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Numerical laboratory data at admission have been proposed for enhancement of inpatient predictive modeling from administrative claims. In this study, predictive models for inpatient/30-day postdischarge mortality and for risk-adjusted prolonged length of stay, as a surrogate for severe inpatient complications of care, were designed with administrative data only and with administrative data plus numerical laboratory variables. A comparison of resulting inpatient models for acute myocardial infarction, congestive heart failure, coronary artery bypass grafting, and percutaneous cardiac interventions demonstrated improved discrimination and calibration with administrative data plus laboratory values compared to administrative data only for both mortality and prolonged length of stay. Improved goodness of fit was most apparent in acute myocardial infarction and percutaneous cardiac intervention. The emergence of electronic medical records should make the addition of laboratory variables to administrative data an efficient and practical method to clinically enhance predictive modeling of inpatient outcomes of care.

Original languageEnglish (US)
Pages (from-to)141-147
Number of pages7
JournalAmerican Journal of Medical Quality
Issue number2
StatePublished - Mar 1 2017


  • cardiovascular disease
  • clinically enhanced claims data
  • patient outcomes
  • patient safety (measurement)
  • quality of care
  • risk adjustment

ASJC Scopus subject areas

  • Health Policy


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