Surveillance of medical device-related hazards and adverse events in hospitalized patients.
BACKGROUND
Although adverse drug events have been extensively evaluated by computer-based surveillance, medical device errors have no comparable surveillance techniques.


OBJECTIVE
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.


METHODS
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.


METHODS
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.
Samore
Matthew H
MH
University of Utah School of Medicine, Salt Lake City 84132, USA.
Evans
R Scott
RS
Lassen
April
A
Gould
Patricia
P
Lloyd
James
J
Gardner
Reed M
RM
Abouzelof
Rouett
R
Taylor
Carrie
C
Woodbury
Don A
DA
Willy
Mary
M
Bright
Roselie A
RA
D000328:Adult; D000368:Aged; D003205:Computing Methodologies; D004868:Equipment Failure / Q000706:statistics & numerical data*; D004864:Equipment and Supplies / Q000009:adverse effects*; D005260:Female; D006753:Hospital Records; D006760:Hospitalization / Q000706:statistics & numerical data*; D006784:Hospitals, Teaching / Q000706:statistics & numerical data*; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D019300:Medical Errors / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D010865:Pilot Projects; D011358:Product Surveillance, Postmarketing / Q000379:methods*; D014481:United States