This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and non-readmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis.

Manufacturing systems have attracted substantial research attentions during the last 50 years. In recent years, there has been growing interest in health care systems research to improve efficiency, safety and care quality. The similarities identified between manufacturing systems and health care delivery systems heighten the importance of transferring the experience and knowledge in manufacturing to health care. In this paper, based on the lessons we learned and the experience we obtained during our journey from production systems research to health care delivery systems study, we discuss the similarities between production systems and health care delivery systems in system modelling, design, performance evaluation and continuous improvements and investigate the differences and difficulties that stem from variability, constraints, dynamics and human behaviour. Building upon these, the opportunities encompassing care operations, planning and scheduling, patient transitions, and safety and teamwork in health care delivery systems are discussed. Finally, the challenges and future directions are proposed. We expect this work to serve as a catalyst to stimulate more in-depth and comprehensive studies.

Background: Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI.Methods: We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis).Results: Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR≥90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered.Conclusions: This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.

Responsible for 25% of all US cancer deaths, lung cancer presents complex care-delivery challenges. Adoption of the highly recommended multidisciplinary care model suffers from a dearth of good quality evidence. Leading up to a prospective comparative-effectiveness study of multidisciplinary vs. serial care, we studied the implementation of a rigorously benchmarked multidisciplinary lung cancer clinic. We used a mixed-methods approach to conduct a patient-centered, combined implementation and effectiveness study of a multidisciplinary model of lung cancer care. We established a co-located multidisciplinary clinic to study the implementation of this care-delivery model. We identified and engaged key stakeholders from the onset, used their input to develop the program structure, processes, performance benchmarks, and study endpoints (outcome-related process measures, patient- and caregiver-reported outcomes, survival). In this report, we describe the study design, process of implementation, comparative populations, and how they contrast with patients within the local and regional healthcare system. Trial Registration: ClinicalTrials.gov Identifier: NCT02123797. Implementation: the multidisciplinary clinic obtained an overall treatment concordance rate of 90% (target >85%). Satisfaction scores were high, with >95% of patients and caregivers rating themselves as being "very satisfied" with all aspects of care from the multidisciplinary team (patient/caregiver response rate >90%). The Reach of the multidisciplinary clinic included a higher proportion of minority patients, more women, and younger patients than the regional population. Comparative effectiveness: The comparative effectiveness trial conducted in the last phase of the study met the planned enrollment per statistical design, with 178 patients in the multidisciplinary arm and 348 in the serial care arm. The multidisciplinary cohort had older age and a higher percentage of racial minorities, with a higher proportion of stage IV patients in the serial care arm. This study demonstrates a comprehensive implementation of a multidisciplinary model of lung cancer care, which will advance the science behind implementing this much-advocated clinical care model.

This study presents a comprehensive framework for analyzing Parkinson’s disease (PD) progression and optimizing medication recommendations based on multimodal data integration and reinforcement learning (RL) techniques. The research framework integrates diverse biomarkers from structured clinical assessments, imaging data, and biospecimen information, creating various single, bimodal, and trimodal datasets. Deep learning-based embedding networks are employed to transform high-dimensional data into lower-dimensional representations, capturing essential patterns related to PD progression. The learned representations are evaluated using visualization methods and predictive modeling to differentiate clinically relevant outcomes. Subsequently, medication recommendation models are developed based on the learned representations, assisting clinicians in making informed decisions to manage PD progression effectively. Optimal medication policies are derived considering different modality settings, revealing insights into medication prescribing tendencies across modalities. The findings highlight the significance of integrating diverse data modalities in understanding and managing PD progression, showcasing the potential of RL-based medication recommendation systems in clinical decision-making. This research contributes to the advancement of personalized medicine strategies for PD patients, emphasizing the importance of tailored medication regimes based on multimodal data insights.