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. 2016 Apr;23(e1):e49-57.
doi: 10.1093/jamia/ocv124. Epub 2015 Sep 5.

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

Matthew F Toerper  1 , Eleni Flanagan  2 , Sauleh Siddiqui  3 , Jeff Appelbaum  4 , Edward K Kasper  5 , Scott Levin  6
Affiliations

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

Matthew F Toerper et al. J Am Med Inform Assoc. 2016 Apr.

Abstract

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.

Discussion: 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.

Conclusion: 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.

Keywords: cardiac care unit; cardiac catheterization; forecasting; hospital; patient admission.

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Figures

Figure 1:
Figure 1:
Cardiology inpatient admission sources for the entire 26-month study period.
Figure 2:
Figure 2:
Catheterization forecast algorithm flow diagram for the entire 26-month cohort. “Outpatient” and “inpatient” designations describe the catheterization laboratory admission sources. “Discharge” and “admit” designations describe the binary outcome predicted.
Figure 3:
Figure 3:
Residual analyses – Left panel: Percentage of each daily residual (predicted beds needed - number of beds used) in prospective cohort. Right panel: forecasted admissions (beds predicted) vs observed admissions (number of beds used) in prospective cohort.
Figure 4:
Figure 4:
Catheterization laboratory inpatient forecast tool web interface.
See this image and copyright information in PMC

References

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