PMID- 36622740
OWN - NLM
STAT- MEDLINE
DCOM- 20230224
LR  - 20230928
IS  - 1549-8425 (Electronic)
IS  - 1549-8417 (Linking)
VI  - 19
IP  - 2
DP  - 2023 Mar 1
TI  - Automated Detection of Patient Harm: Implementation and Prospective Evaluation of 
      a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited 
      Resources.
PG  - 128-136
LID - 10.1097/PTS.0000000000001096 [doi]
AB  - OBJECTIVES: This study aimed to prospectively validate an application that 
      automates the detection of broad categories of hospital adverse events (AEs) 
      extracted from a basic hospital information system, and to efficiently mobilize 
      resources to reduce the level of acquired patient harm. METHODS: Data were 
      collected from an internally designed software, extracting results from 14 
      triggers indicative of patient harm, querying clinical and administrative 
      databases including all inpatient admissions (n = 8760) from October 2019 to June 
      2020. Representative samples of the triggered cases were clinically validated 
      using chart review by a consensus expert panel. The positive predictive value 
      (PPV) of each trigger was evaluated, and the detection sensitivity of the 
      surveillance system was estimated relative to incidence ranges in the literature. 
      RESULTS: The system identified 394 AEs among 946 triggered cases, associated with 
      291 patients, yielding an overall PPV of 42%. Variability was observed among the 
      trigger PPVs and among the estimated detection sensitivities across the harm 
      categories, the highest being for the healthcare-associated infections. The 
      median length of stay of patients with an AE showed to be significantly higher 
      than the median for the overall patient population. CONCLUSIONS: This application 
      was able to identify AEs across a broad spectrum of harm categories, in a 
      real-time manner, while reducing the use of resources required by other harm 
      detection methods. Such a system could serve as a promising patient safety tool 
      for AE surveillance, allowing for timely, targeted, and resource-efficient 
      interventions, even for hospitals with limited resources.
CI  - Copyright (c) 2023 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Saikali, Melody
AU  - Saikali M
AD  - From the Quality and Patient Safety Department, Lebanese Hospital 
      Geitaoui-University Medical Center.
FAU - Bekarian, Garine
AU  - Bekarian G
AD  - From the Quality and Patient Safety Department, Lebanese Hospital 
      Geitaoui-University Medical Center.
FAU - Khabouth, Jose
AU  - Khabouth J
AD  - Department of Internal Medicine, Faculty of Medicine, Lebanese University, 
      Beirut, Lebanon.
FAU - Mourad, Charbel
AU  - Mourad C
AD  - Department of Medical Imaging, Faculty of Medicine, Lebanese University, Beirut, 
      Lebanon.
FAU - Saab, Antoine
AU  - Saab A
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221219
PL  - United States
TA  - J Patient Saf
JT  - Journal of patient safety
JID - 101233393
SB  - IM
MH  - Humans
MH  - *Patient Harm
MH  - Medical Errors
MH  - Retrospective Studies
MH  - Hospitals
MH  - Hospitalization
MH  - Patient Safety
COIS- The authors disclose no conflict of interest.
EDAT- 2023/01/10 06:00
MHDA- 2023/02/25 06:00
CRDT- 2023/01/09 11:53
PHST- 2023/01/10 06:00 [pubmed]
PHST- 2023/02/25 06:00 [medline]
PHST- 2023/01/09 11:53 [entrez]
AID - 01209203-202303000-00010 [pii]
AID - 10.1097/PTS.0000000000001096 [doi]
PST - ppublish
SO  - J Patient Saf. 2023 Mar 1;19(2):128-136. doi: 10.1097/PTS.0000000000001096. Epub 
      2022 Dec 19.