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Hospital factors associated with clinical data quality

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  • Sutherland, Jason M.
  • Steinum, Olafr

Abstract

Objectives As chronic conditions affect the evaluation, treatment, and possible clinical outcomes of patients, accurate reporting of chronic diseases into the patient record is expected. In some countries, the reported magnitude of comorbidity inaccuracy and incompleteness is compelling. Beyond incentives provided in payment systems, the role and significance of other factors that contribute to inaccurate and incomplete reporting of chronic conditions is not well understood. A complementary approach that identifies factors associated with inaccurate and incomplete data is proposed.Methods In a two-step process, the method links hospitalizations of patients who are repeatedly hospitalized over a determined period and identifies characteristics associated with accurate and complete reporting of chronic conditions. These methods leverage the high prevalence of chronic conditions amongst patients with multiple hospitalizations. The study is based on retrospective analysis of longitudinal hospital discharge data from a cohort of Ontario (Canada) patients.Results There are a multitude of factors associated with incomplete clinical data reporting. Patients discharged from community or small hospitals, discharged alive, or transferred to another acute inpatient hospital tend to have less complete comorbidity reporting. For some chronic diseases, very old age affects chronic disease reporting.Conclusions Longitudinally analyzing chronically ill patients is a novel approach to identifying incompletely reported clinical data. Using these results, coding quality initiatives can be focused in a directed manner.

Suggested Citation

  • Sutherland, Jason M. & Steinum, Olafr, 2009. "Hospital factors associated with clinical data quality," Health Policy, Elsevier, vol. 91(3), pages 321-326, August.
    • Handle: RePEc:eee:hepoli:v:91:y:2009:i:3:p:321-326
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    References listed on IDEAS

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    1. Sutherland, Jason M. & Botz, Chas. K., 2006. "The effect of misclassification errors on case mix measurement," Health Policy, Elsevier, vol. 79(2-3), pages 195-202, December.
    2. Reinhard Busse & Jonas Schreyögg & Peter Smith, 2006. "Editorial: Hospital case payment systems in Europe," Health Care Management Science, Springer, vol. 9(3), pages 211-213, August.
    3. Becker, David & Kessler, Daniel & McClellan, Mark, 2005. "Detecting Medicare abuse," Journal of Health Economics, Elsevier, vol. 24(1), pages 189-210, January.
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    1. Jason Sutherland & Jeremy Hamm & Jeff Hatcher, 2010. "Adjusting case mix payment amounts for inaccurately reported comorbidity data," Health Care Management Science, Springer, vol. 13(1), pages 65-73, March.
    2. Alys Havard & Louisa R Jorm & Sanja Lujic, 2014. "Risk Adjustment for Smoking Identified through Tobacco Use Diagnoses in Hospital Data: A Validation Study," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.

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