The effect of a mobile clinical decision support system on the diagnosis of obesity and overweight in acute and primary care encounters.
The purpose of the study was to compare the proportion of obesity-related diagnoses in clinical encounters (N = 1874) documented by nurses using a personal digital assistant-based log with and without obesity decision support features. The experimental group encounters in the randomized controlled trial had significantly more (P = .000) obesity-related diagnoses (11.3%) than did the control group encounters (1%) and a significantly lower false negative rate (24.5% vs 66.5%, P = .000). The study findings provide evidence that integration of a decision support feature that automatically calculates an obesity-related diagnosis increases diagnoses and decreases missed diagnoses and suggest that such systems have the potential to improve the quality of obesity-related care.
Lee
Nam-Ju
NJ
College of Nursing, Seoul National University, Seoul, Republic of Korea. njlee@snu.ac.kr
Chen
Elizabeth S
ES
Currie
Leanne M
LM
Donovan
Mary
M
Hall
Elizabeth K
EK
Jia
Haomiao
H
John
Rita Marie
RM
Bakken
Suzanne
S
D000328:Adult; D015992:Body Mass Index; D016009:Chi-Square Distribution; D034201:Computers, Handheld; D003422:Critical Care; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003951:Diagnostic Errors; D004282:Documentation; D055318:Evidence-Based Nursing; D005260:Female; D019983:Guideline Adherence; D006801:Humans; D008297:Male; D008875:Middle Aged; D009518:New York; D009722:Nurse Practitioners / Q000193:education / Q000458:organization & administration*; D009730:Nursing Assessment / Q000458:organization & administration*; D015403:Nursing Evaluation Research; D009765:Obesity / Q000175:diagnosis*; D017410:Practice Guidelines as Topic; D011320:Primary Health Care