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Observational study to determine the utility of hospital administrative data to support case finding of English patients at higher risk of severe healthcare-related harm
  1. Helen Hogan1,
  2. Nora Cooke-O’Dowd2,
  3. Kaushik Chattopadhyay1,
  4. Jan van der Meulen1,
  5. Christopher Sherlaw-Johnson2,
  6. Nick Black1
  1. 1 Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
  2. 2 Nuffield Trust, London, UK
  1. Correspondence to Dr Helen Hogan; helen.hogan{at}lshtm.ac.uk

Abstract

Objectives To identify ways of using routine hospital data to improve the efficiency of retrospective reviews of case records for identifying avoidable severe harm

Design Development and testing of thresholds and criteria for two indirect indicators of healthcare-related harm (long length of stay (LOS) and emergency readmission) to determine the yield of specified harms coded in Hospital Episode Statistics (HES).

Setting Acute National Health Service hospitals in England.

Participants HES for acute myocardial infarction (AMI), bowel cancer surgery and hip replacement admissions from 2014 to 2015.

Interventions Case-mix-adjusted linear regression models were used to determine expected LOS. Different thresholds were examined to determine the association with harm. Screening criteria for readmission included time to readmission, length of readmission and diagnoses in initial admission and readmission. The association with harm was examined for each criterion.

Results The proportions of AMI cases with a harm code increased from 14% among all cases to 47% if a threshold of three times the expected LOS was used. For hip replacement the respective increase was from 10% to 51%. However as the number of patients at these higher thresholds was small, the overall proportion of harm identified is relatively small (15%, 19%, 9% and 8% among AMI, urgent bowel surgery, elective bowel surgery and hip replacement cohorts, respectively). Selection of the time to readmission had an effect on the yield of harms but this varied with condition. At least 50% of surgical patients had a harm code if readmitted within 7 days compared with 21% of patients with AMI.

Conclusions Our approach would select a substantial number of patients for case record review. Many of these cases would contain no evidence of healthcare-related harm. In practice, Trusts may choose how many reviews it is feasible to do in advance and then select random samples of cases that satisfy the screening criteria.

  • hospital administrative data
  • case finding
  • healthcare-related harm

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors HH, NB, CSJ and JVM designed the study. CSJ, NCO and KC carried out the analyses. NB, CSJ and HH wrote the first draft of the manuscript. All authors provided input and approved the final version for submission.

  • Funding This work was supported by Department of Health Policy Research Programme (PR-R9-0114-14001).

  • Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the Department of Health. The funders of the study had no role in the study design; data collection, analysis, and interpretation; or composition of the report.

  • Competing interests None declared.

  • Ethics approval This study was approved by North West- Lancaster Research Ethics Committee (15/NW/0941). As data were pseudoanonymised individual patient consent was not required. Hospital Episode Statistics data ©2017, re-used with the permission of the Health & Social Care Information Centre. All rights reserved.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Further details on statistical models and definitions are available from the Nuffield Trust at research@nuffieldtrust.org.uk.

  • Patient consent for publication Not required.