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Cardiac Catheterization Laboratory Inpatient Forecast Tool: A Prospective Evaluation

Matthew F. Toerper, Eleni Flanagan, Sauleh Siddiqui, Jeff Appelbaum, Edward K. Kasper, Scott R. Levin
Published 2016 · Computer Science, Medicine
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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.
This paper references
10.1109/IEEM.2014.7058870
An efficient genetic algorithm for flexible job-shop scheduling problem
Ali Mokhtari Moghadam (2014)
10.1002/ccd.22100
Defining the length of stay following percutaneous coronary intervention: an expert consensus document from the Society for Cardiovascular Angiography and Interventions. Endorsed by the American College of Cardiology Foundation.
Charles E Chambers (2009)
Optimizing Patient Flow by Managing Its Variability
Eugene Litvak (2005)
10.1007/s12630-013-9935-5
Predictors of unanticipated admission following ambulatory surgery: a retrospective case-control study
Amanda Whippey (2013)
ACC/SCA&I EXPERT CONSENSUS DOCUMENT American College of Cardiology/Society for Cardiac Angiography and Interventions Clinical Expert Consensus Document on Cardiac Catheterization Laboratory Standards
Eric R. Bates (2001)
10.1213/00000539-199912000-00004
Factors contributing to a prolonged stay after ambulatory surgery.
Frances F Chung (1999)
10.1016/j.hrtlng.2010.09.005
Improved bed use with creation of a short-stay unit in a cardiac catheterization recovery room.
P Obrien (2011)
10.1111/j.1553-2712.2011.01125.x
Predicting hospital admissions at emergency department triage using routine administrative data.
Yan Sun (2011)
10.1111/j.1553-2712.2012.01435.x
Predicting emergency department inpatient admissions to improve same-day patient flow.
Jordan Peck (2012)
10.1016/S0952-8180(97)00098-6
Prolonged surgery increases the likelihood of admission of scheduled ambulatory surgery patients.
M. Mingus (1997)
[Patient dissatisfaction following prolonged stay in the post-anesthesia care unit due to unavailable ward bed in a tertiary hospital].
Oleg Dolkart (2013)
10.1136/bmjqs.2008.030007
Evaluating the effects of increasing surgical volume on emergency department patient access
Scott R. Levin (2011)
10.1016/j.jacc.2012.02.010
2012 American College of Cardiology Foundation/Society for Cardiovascular Angiography and Interventions expert consensus document on cardiac catheterization laboratory standards update: A report of the American College of Cardiology Foundation Task Force on Expert Consensus documents developed in co
Thomas M. Bashore (2012)
10.1186/1751-0473-3-17
Purposeful selection of variables in logistic regression
Z. Bursac (2008)
10.1002/clc.4960161113
Unplanned admissions after outpatient cardiac catheterization.
Vivian L. Clark (1993)
10.1161/01.cir.0000441139.02102.80
Heart disease and stroke statistics--2014 update: a report from the American Heart Association.
A. Go (2014)
10.1097/CCM.0b013e31825bc399
Real-time forecasting of pediatric intensive care unit length of stay using computerized provider orders
S. Levin (2012)
update : a report from the American Heart Association
TM Bashore
Using Tracking Tools to Improve Patient Flow in Hospitals Executive Summary
(2011)
10.1109/IEEM.2007.4419497
Stranded on emergency isle: Modeling competition for cardiac services using survival analysis
S. Levin (2007)
10.1007/BF03012088
Unanticipated admission after ambulatory surgery — a prospective study
J L Fortier (1998)
Heart Disease and Stroke Statistics—2013Update
A. Go (2012)
10.1007/s10729-006-9000-9
Short term hospital occupancy prediction
Steven J. Littig (2007)
10.1016/j.jacc.2012.08.966
A contemporary view of diagnostic cardiac catheterization and percutaneous coronary intervention in the United States: a report from the CathPCI Registry of the National Cardiovascular Data Registry, 2010 through June 2011.
Gregory J. Dehmer (2012)
10.1097/00132586-199004000-00030
Unanticipated admission to the hospital following ambulatory surgery.
B. Gold (1989)
10.1016/j.jamcollsurg.2012.12.046
Re-engineering the operating room using variability methodology to improve health care value.
C. Daniel Smith (2013)
10.2307/2532419
Applied Logistic Regression
David W. Hosmer (1989)
10.2307/2669422
Applied Survival Analysis: Regression Modeling of Time to Event Data
P. V. Rao (2000)
10.1016/J.ANNEMERGMED.2007.03.033
Prolonged emergency department stays of non-ST-segment-elevation myocardial infarction patients are associated with worse adherence to the American College of Cardiology/American Heart Association guidelines for management and increased adverse events.
Deborah B. Diercks (2007)
10.1111/acem.12244
Generalizability of a simple approach for predicting hospital admission from an emergency department.
J. Peck (2013)
10.1016/j.ahj.2008.07.007
Optimizing cardiology capacity to reduce emergency department boarding: a systems engineering approach.
S. Levin (2008)
Cost and quality under managed care: irreconcilable differences?
E. Litvak (2000)
10.1001/archsurg.142.3.263
A novel index of elevated risk of inpatient hospital admission immediately following outpatient surgery.
L. Fleisher (2007)
10.1016/S0735-1097(01)01346-8
American College of Cardiology/Society for Cardiac Angiography and Interventions Clinical Expert Consensus Document on cardiac catheterization laboratory standards. A report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents.
T. Bashore (2001)



This paper is referenced by
10.2139/ssrn.3301665
Cardiology Admissions from Catheterization Laboratory: Time Series Forecasting
Avishek Choudhury (2018)
10.18745/TH.21813
Demand and Capacity Modelling of Acute Services Using Simulation and Optimization Techniques
Muhammed Ordu (2019)
10.1002/hpm.2771
A comprehensive modelling framework to forecast the demand for all hospital services.
Muhammed Ordu (2019)
10.15265/IY-2017-027
Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.
Aurélie Névéol (2017)
10.3390/ijerph16050769
Merging Data Diversity of Clinical Medical Records to Improve Effectiveness
Berit Irene Helgheim (2019)
10.7912/C2PP9Z
An Interoperable Electronic Medical Record-Based Platform for Personalized Predictive Analytics.
Hamed Abedtash (2017)
Forecasting Cardiology Admissions from Catheterization Laboratory
Avishek Choudhury (2018)
10.1109/HealthCom.2017.8210801
Short-term forecasting of hospital discharge volume based on time series analysis
Li Luo (2017)
10.1080/24725579.2017.1346729
Evaluating nurse staffing levels in perianesthesia care units using discrete event simulation
Sauleh Siddiqui (2017)
10.1080/01605682.2019.1630328
A review of the literature on big data analytics in healthcare
Panagiota Galetsi (2019)
10.1007/s10916-018-0988-4
An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model
Diego A. Martinez (2018)
10.1016/J.IJINFOMGT.2019.05.003
Big data analytics in health sector: Theoretical framework, techniques and prospects
Panagiota Galetsi (2020)
10.1016/j.jbi.2018.04.009
Identifying and characterizing highly similar notes in big clinical note datasets
R. Gabriel (2018)
10.1109/HealthCom.2017.8210774
A survey of data mining technology on electronic medical records
Wencheng Sun (2017)
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