title: The utility of hospital administrative data for generating a screening program to predict adverse outcomes.
abstract: A system to predict which patients will suffer medical complications or poor financial outcomes during a hospitalization would be very useful to providers of medical care. To develop such a system, we applied two previously developed indices that predict in-hospital complications to all 321,558 adult patients discharged from our hospital network. The indices identified 26,377 patients (8.2%) who experienced one or more medical complications. For these patients, high-risk admitting diagnoses were identified. We tabulated 4235 admitting diagnoses and focused on 26 (0.6%) diagnoses that were high-risk and high-volume for complications. We found that 25% of patients with these admitting diagnoses experienced complications during hospitalization. Prevention of these complications could have saved 1241 hospital days, 11 lives, and $10.5 million. Administrative data available at the time of admission can be useful in identifying the small subset of patients who are likely to experience adverse clinical outcomes during a hospitalization and those who are likely to generate adverse financial outcomes for the hospital.
authors.affiliation: Department of Medicine, Loyola University Medical Center, Maywood, Ill., USA.
authors.forename: E J
authors.initials: EJ
authors.lastname: Zarling
authors.forename: F A
authors.initials: FA
authors.lastname: Piontek
authors.forename: R
authors.initials: R
authors.lastname: Kohli
mesh_terms: D000328:Adult; D000368:Aged; D019985:Benchmarking; D006739:Hospital Administration / Q000706:statistics & numerical data*; D017722:Hospital Charges; D017052:Hospital Mortality; D006801:Humans; D007049:Iatrogenic Disease / Q000453:epidemiology; D007902:Length of Stay; D006278:Medicare / Q000706:statistics & numerical data; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000191:economics / Q000379:methods*; D011183:Postoperative Complications / Q000453:epidemiology; D011379:Prognosis; D020379:Risk Adjustment; D018570:Risk Assessment / Q000191:economics / Q000379:methods*; D012720:Severity of Illness Index; D012984:Software; D014481:United States / Q000453:epidemiology