Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

Hospital bed management remains a critical challenge in healthcare systems. While prior research has shown that patient demographics and procedure types influence admission likelihood, no prior work had resolved how to integrate these factors into a real-time forecasting system. Existing approaches often rely on historical averages or manual estimates, which lack precision. The need for a data-driven tool that can predict daily bed needs from catheterization procedures has not been fully addressed. Prior studies have demonstrated that clinical indicators can predict outcomes, but none have translated this into a web-based application linked to electronic medical records. This gap motivated the development of a model that could use routinely available clinical data to forecast inpatient admissions. The literature suggests that age and procedure type are relevant, but no study had tested these variables in a prospective setting. The absence of a validated tool for this specific use case highlights the need for further innovation in hospital operations.

This study aimed to create a predictive model for forecasting inpatient admissions following cardiac catheterization procedures. The primary objective was to develop a web-based tool that could use electronic medical record data to estimate daily bed needs. The specific problem addressed was the lack of accurate, real-time forecasting in hospital bed management. The motivation stemmed from the inefficiencies of current admission prediction methods. By integrating clinical data from EMRs, the study sought to improve hospital resource planning. The approach focused on using a multivariable logistic regression model trained on historical patient data. The goal was to provide cardiology providers with a practical tool for operational decision-making. The study's contribution lies in its prospective evaluation of the model's accuracy in a real-world setting.

The research team used a 13-month retrospective dataset of 6384 patients who underwent cardiac catheterization procedures. Predictor variables included patient demographics, scheduled procedures, and clinical indicators extracted from free-text notes in the electronic medical records. These data were input into a multivariable logistic regression model to estimate the probability of inpatient admission. The model was then embedded into a web-based application connected to the local EMR system. The tool was tested prospectively on a separate 13-month cohort of 7029 patients to evaluate its accuracy. The model's performance was measured using the area under the receiver operating characteristic curve. Daily aggregate forecasts were compared against actual admission numbers to assess precision. The study also examined which clinical factors were most strongly associated with the likelihood of admission.

The forecast model achieved an area under the receiver operating characteristic curve of 0.722, indicating moderate predictive accuracy. Daily forecasts were within one bed of actual admissions on 70.3% of days and within three beds on 97.5% of days. The model identified older age, male gender, invasive procedures, and a history of congestive heart failure as risk factors for admission. Diagnostic procedures and less acute clinical indicators were associated with lower admission risk. The web-based application was actively used by cardiology providers to estimate daily admissions. The model's predictions were integrated into hospital operations to support bed management decisions. The study confirmed that data-driven analytics can provide actionable insights for hospital planning. These results suggest the model's potential for improving resource allocation in cardiac catheterization units.

The study demonstrated that a data-driven model can predict inpatient admissions from cardiac catheterization procedures with reasonable accuracy. The authors propose that integrating such models into hospital operations can enhance bed management efficiency. The findings suggest that clinical indicators extracted from EMRs can serve as reliable predictors of admission risk. The model's performance was supported by existing literature on patient risk factors. The authors suggest that this approach could be adapted to other hospital settings with similar data sources. The study's results align with prior research on the role of age and procedure type in admission decisions. The authors emphasize that the model's accuracy is sufficient for operational use despite its site-specific limitations. These conclusions highlight the potential of predictive analytics in clinical decision-making.