Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation.
OBJECTIVE
To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs).


METHODS
The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients.


RESULTS
The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory.


CONCLUSIONS
The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients' risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature.


CONCLUSIONS
Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support.
Johns Hopkins Department of Emergency Medicine, 1830 East Monument Street, Suite 6-100, Baltimore, MD 21287, USA Johns Hopkins Health System Operations Integration, 600 N. Wolfe Street, Administration Bldg. Suite 420, Baltimore, MD 21287, USA mtoerper@jhu.edu.
Matthew F
MF
Toerper
Johns Hopkins Heart and Vascular Institute, 600 N. Wolfe Street, The Johns Hopkins Hospital, Baltimore, MD 21287, USA.
Eleni
E
Flanagan
Department of Civil Engineering, Johns Hopkins Systems Institute, Johns Hopkins University, 3400 N Charles Street, Baltimore, MD 21218, USA Department of Applied Mathematics and Statistics, Johns Hopkins Systems Institute, Johns Hopkins University, 3400 N Charles Street, Baltimore, MD 21218, USA.
Sauleh
S
Siddiqui
Johns Hopkins Health System Operations Integration, 600 N. Wolfe Street, Administration Bldg. Suite 420, Baltimore, MD 21287, USA.
Jeff
J
Appelbaum
Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Edward K
EK
Kasper
Johns Hopkins Department of Emergency Medicine, 1830 East Monument Street, Suite 6-100, Baltimore, MD 21287, USA Johns Hopkins Health System Operations Integration, 600 N. Wolfe Street, Administration Bldg. Suite 420, Baltimore, MD 21287, USA.
Scott
S
Levin
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D006328:Cardiac Catheterization; D057286:Electronic Health Records; D005260:Female; D005544:Forecasting; D006333:Heart Failure; D006739:Hospital Administration; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D010343:Patient Admission; D011446:Prospective Studies; D012372:ROC Curve; D012737:Sex Factors
cardiac care unit; cardiac catheterization; forecasting; hospital; patient admission