Surveillance of Medical Device–Related Hazards and Adverse Events in Hospitalized Patients
- Matthew H. Samore, MD;
- R. Scott Evans, MS, PhD;
- April Lassen, RN;
- Patricia Gould, RN;
- James Lloyd, BS;
- Reed M. Gardner, PhD;
- Rouett Abouzelof, RN;
- Carrie Taylor, RN;
- Don A. Woodbury, MHA;
- Mary Willy, PhD;
- Roselie A. Bright, ScD
- Author Affiliations: University of Utah School of Medicine (Drs Samore, Evans, and Gardner), LDS Hospital/Intermountain Health Care (Drs Samore and Evans, Mss Lassen, Gould, Abouzelof, and Taylor, and Mssrs Lloyd and Woodbury), and Veterans Affairs Salt Lake City Health Care System, Salt Lake City; Center for Drug Evaluation and Research (Dr Willy) and Center for Devices and Radiological Health (Dr Bright), Food and Drug Administration, Rockville, Md. Ms Lassen is now with Alta View Hospital, Sandy, Utah.
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.
