Identifying Complications of Care Using Administrative Data

TLDR

The Complications Screening Program (CSP) uses standard hospital discharge data to identify 27 potentially preventable in‑hospital complications such as pneumonia, hemorrhage, medication incidents, and wound infection. The study aims to evaluate the CSP before it can be applied beyond research. The CSP was applied to 1.9 million adult medical/surgical cases from 1988 California discharge data. Complication cases were older, had higher mortality, charges, and lengths of stay; chronic conditions increased risk; larger and teaching hospitals had higher rates; and logistic regression models predicting complications had modest discrimination (C statistics 0.64–0.70).

Abstract

The Complications Screening Program (CSP) is a method using standard hospital discharge abstract data to identify 27 potentially preventable in-hospital complications, such as post-operative pneumonia, hemorrhage, medication incidents, and wound infection. The CSP was applied to over 1.9 million adult medical/surgical cases using 1988 California discharge abstract data. Cases with complications were significantly older and more likely to die, and they had much higher average total charges and lengths of stay than other cases (P < 0.0001). For most case types, 13 chronic conditions, defined using diagnosis codes, increased the relative risks of having a complication after adjusting for patient age. Cases at larger hospitals and teaching facilities generally had higher complication rates. Logistic regression models to predict complications using demographic, administrative, clinical, and hospital characteristics variables, had modest power (C statistics = 0.64 to 0.70). The CSP requires further evaluation before using it for purposes other than research.