In male fetal non-invasive prenatal testing (NIPT), the success of result interpretation depends strongly on the gestational age at blood draw, which makes “when to test” both a clinical and an appointment-management issue. We propose a rolling-horizon scheduling framework that integrates patient-specific success-probability curves with capacity, no-show, retest, and execution dynamics. Using a public cohort of 5,543 singleton male fetal samples, a polynomial-feature logistic regression was fitted to 3,880 records and evaluated in 1,663 held-out patients. The held-out area under the receiver operating characteristic curve was 0.799 (95 -0.020 and slope 1.018. The operational layer was evaluated as a prospective operational-feasibility simulation on a constructed 105-day timeline. Across ten common random seeds, the Memetic Algorithm (MA) achieved eligible-patient completion of 99.09%± 0.23 percentage points, mean success probability among completed tests of 98.44%± 0.03 percentage points, and eligible-patient risk coverage of 98.86%± 0.78 percentage points. Under prespecified weights, its composite fitness and custom load-distribution score with a front-loading penalty exceeded those of six executable rules, which retained advantages in waiting time, completion, mean success probability, or risk coverage. On four reduced instances, the mean gap from exact mixed-integer linear programming was 3.37%± 1.94 percentage points under the common linearized surrogate objective. The framework offers an integrated and interpretable approach to coordinating individualized testing timing with dynamic appointment capacity in NIPT services.
Business and engineering scholarship's relevance largely relies on its practical applicability. As health care delivery grows to represent a major portion of the global economy, its distinct culture and unique challenges necessitate stronger relationships between health care practice and academia. This paper explores academic collaborations with the health care delivery industry, emphasizing the compelling benefits for health care organizations (and their patients), researchers, and students in the context of care delivery operations. Through four case studies, and by drawing upon our experiences as a diverse set of operations scholars, we highlight successful models of collaboration between operations academia and health care institutions. These models enable many of the benefits realized, such as reductions in patient wait times and visit lengths, more equitable physician scheduling, faster ambulance dispatch, and reduced surgical appointment cancellations, among others, while also improving relevance for students and practical application for theory. While the potential benefits are clear, challenges persist, often emanating from cultural differences, funding uncertainties, and the nuances of industrial vs. academic objectives. However, when navigated effectively, these collaborations can advance both sectors, bridging the gap between academic theory and real-world application. Drawing from existing literature and supplemented by rich case study evidence, this article offers guidance for care institutions and individual scholars seeking to establish and foster similar partnerships. By delineating the parameters of successful collaborations and drawing from a diverse portfolio of experiences, this paper hopes to encourage and enable more synergistic efforts, potentially transforming health care operations and leading to improved outcomes for patients, institutions, and academia. • The article presents four unique case studies and multiple other examples from the literature to explore successful collaborations between academic institutions and health care delivery organizations. These case studies and examples provide insights into the collaboration mechanisms, challenges faced, and outcomes achieved. • The collaborations detailed in the article have led directly to improvements in health care delivery operations, such as more efficient patient scheduling, better resource allocation, and enhanced care delivery performance. This framework is valuable for health care managers, clinicians, and care organizations looking to improve their operations, systems, and patient outcomes. • The paper identifies several characteristics of successful collaboration, such as clear communication and alignment of goals between academic and industry partners. We emphasize the importance of stakeholder engagement, which is crucial for managers, academics, and policy-makers aspiring to foster effective partnerships. • Several of the collaborative models described here offer significant educational opportunities for students, providing them with practical experience in real-world health care operational settings. This benefits academic researchers and educators by preparing students for careers, both applied and scholarly, in healthcare operations and management. • By engaging in these collaborations, both academia and industry benefit from bridging the gap between scholarly theory and practical application. This is particularly valuable for healthcare delivery organizations aiming to implement evidence-based practices and innovations in their operations.
The rapidly evolving landscape of lung allocation policy, including the recent adoption of the Continuous Distribution framework, demands simulation tools that are both flexible and computationally efficient. Existing lung allocation simulators are often difficult to adapt to new policy designs and can be computationally burdensome when large numbers of replications are needed for uncertainty quantification and optimization. This paper introduces the Modular Lung Allocation Policy Simulation Model (MoLAPS), a high-fidelity, discrete-event simulation framework designed to address these limitations through a modular policy architecture and parallel execution. MoLAPS integrates detailed patient and donor data, dynamic medical status updates, and a predictive organ-offer acceptance model. It is implemented with parallel computing support, substantially reducing runtimes for multi-replication and longer-horizon experiments and enabling the exploration of large-scale policy design spaces. The model features a modular architecture that isolates policy components, including allocation, acceptance, and prioritization rules, allowing policymakers to easily evaluate existing and proposed allocation policies, or hypothetical alternatives. A surrogate-based calibration procedure aligns simulation outputs with national organ transplant data, improving empirical validity. MoLAPS is rigorously validated using prediction intervals, quantile-quantile plots, and Chi-squared tests. Simulation results closely align with real-world data across key performance indicators, including transplant rates, waitlist mortality, and organ nonuse. MoLAPS provides clinicians, policymakers, and researchers with a timely, open, and extensible platform for policy evaluation. By enabling realistic, data-driven assessments of lung allocation strategies, it supports more equitable, efficient, and evidence-based transplant policy decisions.
In most health care systems, a primary care physician (PCP) is both the first instance consulted by patients with medical concerns and the instance coordinating patients’ continued access to medical care. Due to the PCP’s pivotal role, we address challenges of a high-quality primary care service by interday appointment scheduling on a tactical decision level. In addition to patients who seek medical care by appointments, we also consider patients who complicate the PCP’s schedule planning by seeking immediate care as walk-ins without prior notice or failing to show up to their appointments. We study appointment scheduling systems based on so-called masks and focus on the balanced workload of the PCP in form of the mask design problem. To account for different uncertainties in demand for treatment, we extend the mask design problem to the robust mask design and the robust multimask design problem. For all three problems, we provide a combinatorial interpretation by a network flow and design model. We develop a solution approach that combines binary search with compact formulations (of extensions) of minimum cost flow problems. Finally, we conduct an extensive case study by agent-based simulation. We evaluate the mask-based appointment scheduling systems and compare them with five appointment scheduling systems from the literature on the basis of key performance indicators such as the PCP’s overtime and idle time as well as the number of unsuccessful treatment requests or rejected walk-ins.
This study estimates the causal impact of surgical team familiarity on surgery duration across various procedures in a hospital, under an assumed Directed Acyclic Graph (DAG) structure. While existing literature indicates that greater familiarity among surgical team members, measured by their shared work experience, is linked to reduced surgery times, these studies typically focus on a specific type of surgical procedure and only suggest a correlational relationship. Our study is the first to examine the hospital-wide relationship between surgical team familiarity and procedure duration using a formal causal inference framework. To explore this relationship, team familiarity is measured based on interaction frequency, and data across procedures are standardized to facilitate analysis via structural causal modeling and regression analysis. An analysis of 33,374 procedures performed in a high-volume academic medical center in the US from January 2017 to November 2023 reveals a negative causal relationship between surgeon-scrub familiarity and surgery duration under our DAG assumptions. Specifically, increasing familiarity by one standard deviation leads to a reduction of 1.9 to 3.7 minutes in surgery duration. Similar, albeit smaller, effects are observed for surgeon-circulator familiarity, whereas no significant impact is found for scrub-circulator familiarity. To our knowledge, this is the first study to estimate the causal effect of surgical team familiarity across an entire hospital’s surgical portfolio under the assumptions of a DAG, moving beyond prior correlational analyses limited to specific specialties or methods. Ultimately, the study reveals that improving team familiarity, particularly between surgeons and scrub nurses, could hold significant implications for improving surgical team efficiency. This is especially relevant in high-volume hospitals, where even minor time savings can accumulate significantly, underscoring the value of leveraging team familiarity to optimize performance in hospital operating rooms.
