Dataset Information

Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

ABSTRACT: Effective capacity management of operation rooms is key to avoid surgery cancellations and prevent long waiting lists that negatively affect clinical and financial outcomes as well as patient and staff satisfaction. This requires optimal surgery scheduling, leveraging essential parameters like surgery duration, post-operative bed type and hospital length-of-stay. Common clinical practice is to use the surgeon's average procedure time of the last N patients as a planned surgery duration for the next patient. A discrepancy between the actual and planned surgery duration may lead to suboptimal surgery schedule. We used deidentified data from 2294 cardio-thoracic surgeries to first calculate the discrepancy of the current model and second to develop new predictive models based on linear regression, random forest, and extreme gradient boosting. The new ensamble models reduced the RMSE for elective and acute surgeries by 19% (0.99 vs 0.80, p = 0.002) and 52% (1.87 vs 0.89, p < 0.001), respectively. Also, the elective and acute surgeries "behind schedule" were reduced by 28% (60% vs. 32%, p < 0.001) and 9% (37% vs. 28%, p = 0.003), respectively. These improvements were fueled by the patient and surgery features added to the models. Surgery planners can benefit from these predictive models as a patient flow AI decision support tool to optimize OR utilization.

SUBMITTER: Nikolova-Simons M

PROVIDER: S-EPMC10630382 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

Nikolova-Simons Mariana M Keldermann Rikkert R Peters Yvon Y Compagner Wilma W Montenij Leon L de Jong Ymke Y Bouwman R Arthur RA

NPJ digital medicine 20231107 1

Effective capacity management of operation rooms is key to avoid surgery cancellations and prevent long waiting lists that negatively affect clinical and financial outcomes as well as patient and staff satisfaction. This requires optimal surgery scheduling, leveraging essential parameters like surgery duration, post-operative bed type and hospital length-of-stay. Common clinical practice is to use the surgeon's average procedure time of the last N patients as a planned surgery duration for the n ...[more]

PMID: 37935901

Similar Datasets

Project description:IntroductionPersonalized disease management informed by quantitative risk prediction has the potential to improve patient care and outcomes. The integration of risk prediction into clinical workflow should be informed by the experiences and preferences of stakeholders, and the impact of such integration should be evaluated in prospective comparative studies. The objectives of the IMplementing Predictive Analytics towards efficient chronic obstructive pulmonary disease (COPD) treatments (IMPACT) study are to integrate an exacerbation risk prediction tool into routine care and to determine its impact on prescription appropriateness (primary outcome), medication adherence, quality of life, exacerbation rates, and sex and gender disparities in COPD care (secondary outcomes).MethodsIMPACT will be conducted in two phases. Phase 1 will include the systematic and user-centered development of two decision support tools: (1) a decision tool for pulmonologists called the ACCEPT decision intervention (ADI), which combines risk prediction from the previously developed Acute COPD Exacerbation Prediction Tool with treatment algorithms recommended by the Canadian Thoracic Society's COPD pharmacotherapy guidelines, and (2) an information pamphlet for COPD patients (patient tool), tailored to their prescribed medication, clinical needs, and lung function. In phase 2, we will conduct a stepped-wedge cluster randomized controlled trial in two outpatient respiratory clinics to evaluate the impact of the decision support tools on quality of care and patient outcomes. Clusters will be practicing pulmonologists (n ≥ 24), who will progressively switch to the intervention over 18 months. At the end of the study, a qualitative process evaluation will be carried out to determine the barriers and enablers of uptake of the tools.DiscussionThe IMPACT study coincides with a planned harmonization of electronic health record systems across tertiary care centers in British Columbia, Canada. The harmonization of these systems combined with IMPACT's implementation-oriented design and partnership with stakeholders will facilitate integration of the tools into routine care, if the results of the proposed study reveal positive association with improvement in the process and outcomes of clinical care. The process evaluation at the end of the trial will inform subsequent design iterations before largescale implementation.Trial registrationNCT05309356.

Dataset Information

Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

Publications

Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets