Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach.

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.

Development and Application of a Comprehensive Measure of Access to Health Services to Examine COVID-19 Health Disparities.

Research on access to health services during the COVID-19 pandemic is limited, and the conceptualization of access has not typically included access to community resources. We developed and tested an access-to-health-services measure and examined disparities in access among individuals in the U.S. during the pandemic. Data are from a U.S. sample of 1491 respondents who completed an online survey in August 2021. Linear regression models assessed the relationships between the access-to-health-services-measure components, including impact on access to medicine and medical equipment, impact on access to healthcare visits, and confidence in accessing community resources, and predictor variables, including sociodemographic- and health-related factors. Disparities in access to healthcare during the pandemic were associated with sociodemographic characteristics (i.e., race, gender, and age) and health-related characteristics (i.e., chronic illness, mental health condition, and disability). Factors such as race, gender, income, and age were associated with individuals' degree of confidence in accessing community services. Our study presents a new access-to-health-services measure, sheds light on which populations may be most vulnerable to experiencing reduced access to health services, and informs the development of programmatic interventions to address the salient needs of these populations.

Regression and Random Forest Machine Learning Have Limited Performance in Predicting Bowel Preparation in Veteran Population.

BACKGROUND: Inadequate bowel preparation undermines the quality of colonoscopy, but patients likely to be affected are difficult to identify beforehand. AIMS: This study aimed to develop, validate, and compare prediction models for bowel preparation inadequacy using conventional logistic regression (LR) and random forest machine learning (RFML). METHODS: We created a retrospective cohort of patients who underwent outpatient colonoscopy at a single VA medical center between January 2012 and October 2015. Candidate predictor variables were chosen after a literature review. We extracted all available predictor variables from the electronic medical record, and bowel preparation from the endoscopy database. The data were split into 70% training and 30% validation sets. Multivariable LR and RFML were used to predict preparation inadequacy as a dichotomous outcome. RESULTS: The cohort included 6,885 Veterans, of whom 964 (14%) had inadequate preparation. Using LR, the area under the receiver operating characteristic curve (AUC) for the validation cohort was 0.66 (95% CI 0.62, 0.69) and the Brier score, in which a lower score indicates better performance, was 0.11. Using RFML, the AUC for the validation cohort was 0.61 (95% CI 0.58, 0.65) and the Brier score was 0.12. CONCLUSIONS: LR and RFML had similar performance in predicting bowel preparation, which was modest and likely insufficient for use in practice. Future research is needed to identify additional predictor variables and to test other machine learning algorithms. At present, endoscopy units should focus on universal strategies to enhance preparation adequacy.