Literature DB >> 18419045

How often are potential patient safety events present on admission?

Robert L Houchens1, Anne Elixhauser, Patrick S Romano.   

Abstract

BACKGROUND: Data fields that capture whether diagnoses are present on admission (POA)--distinguishing comorbidities from potential in-hospital complications--became part of the Uniform Bill for hospital claims in 2007. The AHRQ Patient Safety Indicators (PSIs) were initially developed as measures of potential patient safety problems that use routine administrative data without POA information. The impact of adding POA information to PSIs was examined.
METHODS: Data were used from California (CA) and New York (NY) Healthcare Cost and Utilization Project (HCUP) state inpatient databases for 2003, which include POA codes. Analysis was limited to 13 of 20 PSIs for which POA information was relevant, such as complications of anesthesia, accidental puncture, and sepsis.
RESULTS: In New York, 17% of cases revealed suspect POA coding, compared with 1%-2% in California. After suspect records were excluded, 92%-93% of secondary diagnoses in both CA and NY were POA. After incorporating POA information, most cases of decubitus ulcer (86%-89%), postoperative hip fracture (74%-79%), and postoperative pulmonary embolism/deep vein thrombosis (54%-58%) were no longer considered in-hospital patient safety events. DISCUSSION: Three of 13 PSIs appear not to be valid measures of in-hospital patient safety events, but the remaining 10 appear to be potentially useful measures even in the absence of POA codes.

Entities:  

Mesh:

Year:  2008        PMID: 18419045     DOI: 10.1016/s1553-7250(08)34018-5

Source DB:  PubMed          Journal:  Jt Comm J Qual Patient Saf        ISSN: 1553-7250


  31 in total

1.  The sensitivity of adverse event cost estimates to diagnostic coding error.

Authors:  Gavin Wardle; Walter P Wodchis; Audrey Laporte; Geoffrey M Anderson; G Ross Baker
Journal:  Health Serv Res       Date:  2011-10-27       Impact factor: 3.402

2.  The impact of the present on admission indicator on the accuracy of administrative data for carotid endarterectomy and stenting.

Authors:  Margriet Fokkema; Rob Hurks; Thomas Curran; Rodney P Bensley; Allen D Hamdan; Mark C Wyers; Frans L Moll; Marc L Schermerhorn
Journal:  J Vasc Surg       Date:  2013-08-28       Impact factor: 4.268

3.  Using multiple sources of data for surveillance of postoperative venous thromboembolism among surgical patients treated in Department of Veterans Affairs hospitals, 2005-2010.

Authors:  Richard E Nelson; Scott D Grosse; Norman J Waitzman; Junji Lin; Scott L DuVall; Olga Patterson; James Tsai; Nimia Reyes
Journal:  Thromb Res       Date:  2015-01-26       Impact factor: 3.944

4.  Capturing diagnosis-timing in ICD-coded hospital data: recommendations from the WHO ICD-11 topic advisory group on quality and safety.

Authors:  V Sundararajan; P S Romano; H Quan; B Burnand; S E Drösler; S Brien; H A Pincus; W A Ghali
Journal:  Int J Qual Health Care       Date:  2015-06-04       Impact factor: 2.038

5.  Identification of adverse drug events: the use of ICD-10 coded diagnoses in routine hospital data.

Authors:  Jürgen Stausberg; Joerg Hasford
Journal:  Dtsch Arztebl Int       Date:  2010-01-15       Impact factor: 5.594

6.  Determinants of adverse events in vascular surgery.

Authors:  Tina Hernandez-Boussard; Kathryn M McDonald; John M Morton; Ronald L Dalman; Fritz R Bech
Journal:  J Am Coll Surg       Date:  2012-03-15       Impact factor: 6.113

7.  Validity of selected AHRQ patient safety indicators based on VA National Surgical Quality Improvement Program data.

Authors:  Patrick S Romano; Hillary J Mull; Peter E Rivard; Shibei Zhao; William G Henderson; Susan Loveland; Dennis Tsilimingras; Cindy L Christiansen; Amy K Rosen
Journal:  Health Serv Res       Date:  2008-09-17       Impact factor: 3.402

8.  Hospital quality, efficiency, and input slack differentials.

Authors:  Vivian G Valdmanis; Michael D Rosko; Ryan L Mutter
Journal:  Health Serv Res       Date:  2008-09-08       Impact factor: 3.402

9.  Measuring hospital inefficiency: the effects of controlling for quality and patient burden of illness.

Authors:  Ryan L Mutter; Michael D Rosko; Herbert S Wong
Journal:  Health Serv Res       Date:  2008-09-08       Impact factor: 3.402

10.  Development of a validation algorithm for 'present on admission' flagging.

Authors:  Terri J Jackson; Jude L Michel; Rosemary Roberts; Jennie Shepheard; Diana Cheng; Julie Rust; Catherine Perry
Journal:  BMC Med Inform Decis Mak       Date:  2009-12-01       Impact factor: 2.796

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