Healthcare provider perceptions of clinical prediction rules.
OBJECTIVE
To examine internal medicine and emergency medicine healthcare provider perceptions of usefulness of specific clinical prediction rules.


METHODS
The study took place in two academic medical centres. A web-based survey was distributed and completed by participants between 1 January and 31 May 2013.


METHODS
Medical doctors, doctors of osteopathy or nurse practitioners employed in the internal medicine or emergency medicine departments at either institution.


METHODS
The primary outcome was to identify the clinical prediction rules perceived as most useful by healthcare providers specialising in internal medicine and emergency medicine. Secondary outcomes included comparing usefulness scores of specific clinical prediction rules based on provider specialty, and evaluating associations between usefulness scores and perceived characteristics of these clinical prediction rules.


RESULTS
Of the 401 healthcare providers asked to participate, a total of 263 (66%), completed the survey. The CHADS2 score was chosen by most internal medicine providers (72%), and Pulmonary Embolism Rule-Out Criteria (PERC) score by most emergency medicine providers (45%), as one of the top three most useful from a list of 24 clinical prediction rules. Emergency medicine providers rated their top three significantly more positively, compared with internal medicine providers, as having a better fit into their workflow (p=0.004), helping more with decision-making (p=0.037), better fitting into their thought process when diagnosing patients (p=0.001) and overall, on a 10-point scale, more useful (p=0.009). For all providers, the perceived qualities of useful at point of care, helps with decision making, saves time diagnosing, fits into thought process, and should be the standard of clinical care correlated highly (≥0.65) with overall 10-point usefulness scores.


CONCLUSIONS
Healthcare providers describe clear preferences for certain clinical prediction rules, based on medical specialty.
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York, USA.
Safiya
S
Richardson
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York, USA.
Sundas
S
Khan
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York, USA.
Lauren
L
McCullagh
Biostatistics Division, Feinstein Institute for Medical Research, Manhasset, New York, USA.
Myriam
M
Kline
Department of Medicine, Boston University, Boston, Massachusetts, USA.
Devin
D
Mann
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York, USA.
Thomas
T
McGinn
D003661:Decision Support Techniques; D004635:Emergency Medicine; D055317:Evidence-Based Practice; D005260:Female; D006282:Health Personnel / Q000592:standards*; D006801:Humans; D007388:Internal Medicine; D008297:Male; D011237:Predictive Value of Tests; D036301:Qualitative Research; D014481:United States / Q000453:epidemiology
HEALTH SERVICES ADMINISTRATION & MANAGEMENT; INTERNAL MEDICINE; MEDICAL EDUCATION & TRAINING