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Detecting Adverse Events with AHRQ Patient Safety Indicators: Assessing the Usefulness of Post-Discharge Administrative Data in the Veterans Health Administration

Mull HJ, Borzecki AM, Loveland S, Shin M, Chen Q, Rosen AK. Detecting Adverse Events with AHRQ Patient Safety Indicators: Assessing the Usefulness of Post-Discharge Administrative Data in the Veterans Health Administration. Poster session presented at: AcademyHealth Annual Research Meeting; 2012 Jun 24; Orlando, FL.


Research Objectives: The AHRQ Patient Safety Indicators (PSIs) use administrative data to flag cases with potentially preventable adverse events (AEs) attributable to inpatient care. The PSI algorithms do not use post-discharge data; however, using these data may improve AE detection by flagging events related to the inpatient stay that were not identified until after hospital discharge. Studies of inpatient AEs show that the effects of poor quality care can extend beyond the period of hospitalization and result in additional healthcare utilization including unplanned readmissions and emergency department visits. The Veterans Health Administration (VA), with its network of inpatient and outpatient care, is an ideal healthcare setting to evaluate the incidence of PSI-flagged AEs following discharge. Study Design: We ran the PSI software (version 3.1a) on FY03-07 administrative inpatient and outpatient data for selected PSIs: Decubitus Ulcer, Iatrogenic Pneumothorax, Selected Medical Infections, Postoperative Hemorrhage and Hematoma, Postoperative Metabolic Derangement, Postoperative Respiratory Failure, Postoperative Pulmonary Embolism/Deep Vein Thrombosis (PE/DVT), Postoperative Sepsis, Postoperative Wound Dehiscence, and Accidental Puncture or Laceration. To avoid flagging cases with an AE that was present on admission, the PSIs look for secondary ICD-9-CM diagnoses and procedure codes associated with specific events during an inpatient stay. We examined PSI-eligible cases that were not flagged to determine whether any PSI criteria were met post-discharge in a principal diagnosis field. We limited our analysis to 30 days post-discharge. Principal Findings: Applying PSI codes to post-discharge data detected an additional 11,077 AEs among PSI-eligible cases over the five-year time period, compared to the 40,491 PSI-flagged cases in our original dataset. The majority of AEs were PE/DVTs (n = 4,348) and Decubitus Ulcers (n = 3,201). The proportion of the PSI-eligible cases with an AE detected post-discharge ranged from less than 0.03% for Postoperative Respiratory Failure and Accidental Puncture or Laceration, to a high of 0.86% for Postoperative PE/DVT. The vast majority of Decubitus Ulcer, Postoperative Hemorrhage and Hematoma, PE/DVT, and Sepsis cases were detected in the outpatient setting, while the majority of Selected Medical Infections, Postoperative Metabolic Derangement, and Postoperative Wound Dehiscence cases were detected in subsequent admissions. Most cases, between 56% for Iatrogenic Pneumothorax and 93% for Postoperative Wound Dehiscence, were flagged within 14 days post-discharge. Conclusions: The number of cases with specific ICD-9-CM diagnosis and procedure codes reflecting individual PSIs in post-discharge data varied across PSIs. The contribution of post-discharge data to detecting PSI events suggests that AEs attributable to inpatient care may not be adequately captured if PSI algorithms are limited to data from the inpatient stay. Implications for Policy, Practice or Delivery: Modifying PSI algorithms to include post-discharge data may increase the utility of the PSIs for quality improvement and surveillance efforts. Future studies should test the specificity and sensitivity of these post-discharge events, and consider whether post-discharge events should be included in public reporting. Key Words: Patient safety, ICD-9-CM coding, criterion validation, adverse events, administrative data

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