Surveillance of Medical Device–Related Hazards and Adverse Events in Hospitalized Patients
Abstract
CONTEXT: Although adverse drug events have been extensively evaluated by computer-based surveillance, medical device errors have no comparable surveillance techniques. OBJECTIVES: To determine whether computer-based surveillance can reliably identify medical device-related hazards (no known harm to patient) and adverse medical device events (AMDEs; patient experienced harm) and to compare alternative methods of detection of device-related problems. DESIGN, SETTING, AND PARTICIPANTS: This descriptive study was conducted from January through September 2000 at a 520-bed tertiary teaching institution in the United States with experience in using computer tools to detect and prevent adverse drug events. All 20 441 regular and short-stay patients (excluding obstetric and newborn patients) were included. MAIN OUTCOME MEASURES: Medical device events as detected by computer-based flags, telemetry problem checklists, International Classification of Diseases, Ninth Revision (ICD-9) discharge code (which could include AMDEs present at admission), clinical engineering work logs, and patient survey results were compared with each other and with routine voluntary incident reports to determine frequencies, proportions, positive predictive values, and incidence rates by each technique. RESULTS: Of the 7059 flags triggered, 552 (7.8%) indicate a device-related hazard or AMDE. The estimated 9-month incidence rates (number per 1000 admissions [95% confidence intervals]) for AMDEs were 1.6 (0.9-2.5) for incident reports, 27.7 (24.9-30.7) for computer flags, and 64.6 (60.4-69.1) for ICD-9 discharge codes. Few of these events were detected by more than 1 surveillance method, giving an overall incidence of AMDE detected by at least 1 of these methods of 83.7 per 1000 (95% confidence interval, 78.8-88.6) admissions. The positive predictive value of computer flags for detecting device-related hazards and AMDEs ranged from 0% to 38%. CONCLUSIONS: More intensive surveillance methods yielded higher rates of medical device problems than found with traditional voluntary reporting, with little overlap between methods. Several detection methods had low efficiency in detecting AMDEs. The high rate of AMDEs suggests that AMDEs are an important patient safety issue, but additional research is necessary to identify optimal AMDE detection strategies.
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Incident reports provided by healthcare professionals have been shown to be useful in examining the risks and harm caused by healthcare.
“Incident reports provided by healthcare professionals have been shown to be useful in examining the risks and harm caused by healthcare (eg, falls, medication errors, therapeutic devices, and equipment).18 19”
From An analysis of computer-related patient safety incidents to inform the development of a classification (2010) · cites this paper as [19]
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- Authors
- Samore, M. H.
- Affiliations
- University of Utah (US)
- Corresponding author
- Samore, Matthew H.
- ORCID
- Samore, Matthew H.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2004-01-21
- Volume
- 291
- Issue
- 3
- Pages
- 325
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2125794271
- Cited by
- 142
- References
- 37
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 2 · 2022: 2 · 2021: 8 · 2020: 5 · 2019: 2 · 2018: 4 · 2017: 6 · 2016: 1 · 2015: 5 · 2014: 10 · 2013: 6 · 2012: 7
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Public Health Service (223-99-6101)
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- medical device errors, hospital patient safety, incident reporting, positive predictive value, hospitalized patients
- MeSH terms
- Adult, Aged, Computing Methodologies, Equipment and Supplies, Equipment Failure, Female, Hospital Records, Hospitalization, Hospitals, Teaching, Humans, Male, Middle Aged, Pilot Projects, Product Surveillance, Postmarketing, United States, Medical Records Systems, Computerized, Medical Errors, International Classification of Diseases
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