Amidst critical levels of nurse shortages, we partnered with Indiana University Health (IUH) to pioneer a novel suite of advanced data and decision analytics to support a new model of nurse staffing. This statewide program leverages a flexible pool of resource nurses who can move between the 16 IUH hospitals located in five diverse regions and serving more than 1.4 million residents. This program breaks the mold of traditional travel and resource nurses by adding flexibility to move nurses between hospitals to dynamically respond to short-term patient census fluctuations. This paradigm shift necessitated the development of analytics to execute these interhospital transfers. Specifically, we develop analytics to create a two-week advance on-call list for travel and a 24- to 48-hour call-in decision. Our Delta Coverage Analytics Suite was launched in October 2021 as a Microsoft PowerBI application and provides an integrated solution that has supported and continues to support this new staffing approach at a statewide scale. The suite contrasts with existing nurse scheduling tools that primarily cater to single hospitals or units. It incorporates (1) a novel patient census forecast based on a deep generative model capturing complex spatialtemporal correlations and avoiding error accumulation occurring in traditional time-series models and (2) a stochastic optimization that prescribes optimal on-call and deployment decisions. The pilot, conducted from May to June 2023, produced a remarkable reduction in understaffing, with estimated annual savings of $2.5 million to IUH and over $1.5 billion on a national scale compared with the conventional solution of hiring travel nurses. As the first program of its kind, our methods establish new benchmarks for evidence-based and data-driven nurse workforce management with the potential to transform how healthcare institutions approach the national nursing shortage crisis.

The opioid crisis has ravaged the United States, taking 69,000 lives in 2020, with prescription opioids accounting for 98% of opioid abuse. Although this epidemic is often considered a White public health crisis nationally, overdose deaths among people of color doubled from 2017 to 2019. As research has shown that the crisis was fueled by excessive supply from the pharmaceutical industry, several individual firms have received significant public criticism. However, we find evidence that the scope of the blame transcends individual actors to indict the very structure of complex supply chains, which may have exacerbated the crisis by dispensing significantly more opioids. In specific, we posit that supply chain complexity allowed mass quantities of opioids to escape detection by the Drug Enforcement Administration (DEA). Further, we find new evidence showing the greater impact of complexity on opioid dispensing in non-White communities, which underscores their exclusion from the public discourse and governmental response surrounding the crisis and suggests possible racial bias in the DEA’s regulatory policies. Our analysis was made possible by the 2019 release of the DEA’s Automation of Reports and Consolidated Orders System database, which logged every shipment in the US opioid supply chain from 2006 to 2014. Using a fixed effects model, we find that a one-unit increase across three dimensions of supply chain complexity is associated with a 16% increase in opioid dispensing. This effect is intensified in non-White communities, where a 10% increase in the non-White population is associated with a 3.39% (1.33%) increase in opioid dispensing by pharmacies that have supply chains with high (average) complexity. To verify that high-complexity pharmacies’ excess dispensing supplied non-medical/recreational demand, we exploit the reformulation of OxyContin (designed to prevent recreational use) as an exogenous shock to the market. In a novel approach, we leverage the fact that different pharmacies received their first shipment of reformulated OxyContin at different times and use a difference-in-differences model to estimate the heterogeneous effect of the shock on dispensing. As the reformulated OxyContin stifled demand, high-complexity pharmacies experienced a 15.31% greater reduction in dispensing compared to lower-complexity pharmacies, suggesting that their excess dispensing was indeed satisfying non-medical/recreational demand.

Problem definition: Coordinated care network (CCN) is a burgeoning paradigm where patients’ diagnosis and treatment plans are developed based on collaboration between multiple, colocated medical specialties to holistically address patients’ health needs. A primary performance metric for CCNs is how quickly patients can complete their itinerary of appointments at multiple medical services in the network. Rapid completion is critical to care delivery but also presents a major operational challenge. Because information about a patient’s condition and treatment options evolves over the course of the itinerary, care paths are not known a priori. Thus, appointments (except for the first one) cannot be reserved in advance, which may result in significant delays if capacity is not allocated properly. Methodology/results: We study capacity allocation for the patient’s first (root) appointment as the primary operational lever to achieve rapid itinerary completion in CCNs. We develop a novel queueing-based analytical framework to optimize this root appointment allocation, maximizing the proportion of patients completing care by prespecified deadlines. Our framework accounts for the complex interactions among all patients in the network through the blocking process, which contrasts with conventional siloed planning. We provide an exact characterization of the itinerary time and develop a mean-field approximation with convergence guarantees that permits tractable solutions for large-scale network problems. In a simulation case study of Mayo Clinic, our solution improves on-time completion from 60% under the current plan to more than 93%. Managerial implications: We demonstrate that root appointment allocation is a multifaceted problem and that ignoring any of those facets can lead to poor performance. Simultaneously accounting for all of these complexities makes manual template design or traditional optimization methods inadequate, highlighting the significance of our integrated approach. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0649 .

Hospitalists are medical doctors that specialize in the care of hospitalized patients, a role that until recently belonged to primary care physicians. We develop an operational model of hospitalist-patient interactions with rounding and responding service modes, optimizing hospitalist caseload and case-mix to achieve the maximal reduction in patient length of stay (LOS). We show that hospitalists are effective at reducing LOS for patients with complex conditions, corroborating intuitive reasoning. However, the optimal hospitalist case-mix also includes “simple” patients with few interventions and short LOS, as they can effectively reduce discharge delays. This actionable insight is particularly salient for small community hospitals with simple, short-stay patients, where hospitalists may be undervalued due to the prevailing belief that they are primarily effective for complex patients. We conduct a comparative case study of a small community hospital and a large academic hospital, drawing a stark contrast between the two in terms of ideal caseload and patient coverage. Despite the fact that the academic hospital treats higher complexity patients, hospitalists at the community hospital should actually have a lower caseload than hospitalists at the academic hospital due to shorter stays in the community hospital. We find that both hospitals are understaffed but for different reasons: the academic hospital needs to staff more hospitalists to reduce the current caseload of its hospitalists, whereas the community hospital needs to staff more hospitalists to expand its hospitalist coverage to more patients. We estimate that these hospitals can save on average $1.5 million annually by implementing the optimal staffing policies. This paper was accepted by Stefan Scholtes, healthcare management. Funding: This work was supported by a PSC-CUNY Award, jointly funded by The Professional Staff Congress and The City University of New York. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4342 .

At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.

The gap between medical research on diagnostic testing and clinical workflow can lead to rejection of valuable medical research in a busy clinical environment due to increased workloads, or rejection of medical research in the laboratory that may be valuable in practice due to a misunderstanding of the system‐level benefits of the new test. This has implications for research organizations, diagnostic test manufacturers, and hospital managers among others. To bridge this gap, we develop a Markov decision process (MDP) from which we create “adoption regions” that specify the combination of test characteristics medical research must achieve for the test to be feasible for adoption in practice. To address the curse of dimensionality from patient risk stratification, we develop a decomposition algorithm along with structural properties that shed light on which patients and when a new diagnostic test should be used. In a case study of a partner Emergency Department, we show that the conventional myopic medical criterion can lead to poor decision making in both research development and clinical practice. In particular, we find that specificity—long a secondary consideration and often overlooked in the research process—is, in fact, the key to effective implementation of new tests into clinical environments. This myopic approach can lead to overvaluing or undervaluing new medical research. This mismatch is accentuated when a simple (current) policy is used to integrate research into the clinical environment compared with our MDP’s policy—poor implementation of a new test can also lead to unnecessary rejection. Our framework provides easily interpretable guidelines for medical research development and clinical adoption decisions that can guide medical research as to which test characteristics to focus on to improve the chances of adoption.

We develop a data-driven simulation model in partnership with Tippecanoe County Community Corrections to evaluate assignment policies of reintegration programs. These programs are intended to help clients with their transition back to society after release, with the goal of ending the “revolving door of recidivism.” Leveraging client-level and system-level data, we develop a queueing-based network model to capture the movement of clients in the system. We integrate a personalized recidivism prediction to capture heterogeneous risks, along with estimated effects of reintegration programs from literature. Using simulation, we find that the largest benefit is achieved by implementing any kind of re-integration program, regardless of assignment policy, as the savings in the societal and re-incarceration costs (from recidivism) outweigh program costs. Assignment policy based on predictive analytics achieves a 1.5-time larger reduction in recidivism compared to current practice. In expanding capacity, greater consideration should be given to investing in analytic-driven program assignments.

Background After release of the Comprehensive Care for Joint Replacement bundle, there has been increased emphasis on reducing readmission rates for total knee arthroplasty (TKA). The potential for a separate, clinically-relevant metric, TKA revision rates within a year following surgery, has not been fully explored. Based on this, we compared rates and payments for TKA readmission and revision procedures as metrics for improving quality and cost. Methods We utilized the 2013 Nationwide Readmission Database (NRD) to examine national readmission and revision rates, the reasons for revision procedures, and associated costs for elective TKA procedures. As data are not linked across years, we examined revision rates for TKA completed in the month of January by capturing revision procedures in the subsequent following 11-month period to approximate a 1-year revision rate. Diagnosis and procedure codes for revision procedures were collected. Average readmission and revision procedure costs were then calculated, and the cost distributed across the entire TKA population. Results We identified 20,851 patients having TKA surgery. The mean unadjusted 30- and 90-day TKA readmission rates were 3.4% and 5.8%, respectively. In contrast, the mean unadjusted 3-month and approximate 1-year reoperation rates were 1.0% and 1.6%, respectively. The most common cause for revision was periprosthetic joint infection, which accounting for 62% of all reported revision procedures. The mean payment for 90-day readmission was roughly half ($10,589±$11,084) of the mean inpatient payment for single reoperation procedure at 90 days ($20,222±$17,799). Importantly, nearly half (46%) of all 90-day readmissions were associated with a reoperation event within the first year. Conclusions Readmission following TKA is associated with a 1-year reoperation in approximately half of patients. These reoperations represent a significant patient burden and have a higher per episode cost. Early reoperation may represent a more clinically relevant target for quality improvement and cost containment.

Problem definition: Major hospitals frequently lack adequate space to accommodate emergency patients. Managers can take actions to create surge capacity, an immediate additional supply of medical services to accommodate increased demand. We study operational strategies that improve surge capacity, and we identify how they can be most effectively deployed based on the characteristics of individual hospitals. Academic/practical relevance: Recent government regulations in the United States have increased pressure on hospitals to improve emergency preparedness. Specifically, hospitals must be able to show that they have taken adequate measures to manage surge capacity. Methodology: We formulate an optimization model of early disposition actions that can be used to create surge capacity in a hospital. We analyze the model to understand its structural properties and compare two strategies to improve surge capacity: coordinated early discharge, which occurs during the response, and inpatient workload smoothing, which can help mitigate the need for response actions. Results: We show analytically that without coordination, hospitals always act too conservatively in discharging patients to accommodate surge arrivals and that smoothing the elective inpatient workload reduces the expected cost of surge response. In the numerical study, we find a utilization sweet spot in which smoothing is best at increasing surge capacity, and we show coordination increases the number of surges and number of early discharges, while smoothing mitigates these effects, making surges less frequent and less costly. Managerial implications: Coordination is effective at increasing surge capacity for all types of hospitals, but when considering the holistic impact to the hospital, coordination and workload smoothing are often complementary strategies for improving surge response. Moreover, hospitals with sufficiently many electives and moderately high utilization should prioritize mitigation efforts when planning for emergencies.

Three steps that hospitals can take today to prepare for COVID-19 or any similar pandemic are (i) limit predictable variability by cancelling or smoothing elective surgeries in advance of an epidemic’s impact rather than waiting until inpatient units are overwhelmed, (ii) centralize staffing, resource-planning, and allocation decisions, while employing healthcare coalitions to quantify and locate scarce resources like ICU beds and ventilators, and (iii) prepare a plan for triage guided by the principle “do the greatest good for the greatest number”.

Importance:The Hospital Readmissions Reduction Program (HRRP) is a Centers for Medicare and Medicaid Services policy that levies hospital reimbursement penalties based on excess readmissions of patients with 4 medical conditions and 3 surgical procedures. A greater understanding of factors associated with the 3 surgical reimbursement penalties is needed for clinicians in surgical practice. Objective:To investigate the first year of HRRP readmission penalties applied to 2 surgical procedures-elective total hip arthroplasty (THA) and total knee arthroplasty (TKA)-in the context of hospital and patient characteristics. Design, Setting, and Participants:Fiscal year 2015 HRRP penalization data from Hospital Compare were linked with the American Hospital Association Annual Survey and with the Healthcare Cost and Utilization Project State Inpatient Database for hospitals in the state of Florida. By using a case-control framework, those hospitals were separated based on HRRP penalty severity, as measured with the HRRP THA and TKA excess readmission ratio, and compared according to orthopedic volume as well as hospital-level and patient-level characteristics. The first year of HRRP readmission penalties applied to surgery in Florida Medicare subsection (d) hospitals was examined, identifying 60 663 Medicare patients who underwent elective THA or TKA in 143 Florida hospitals. The data analysis was conducted from February 2016 to January 2017. Exposures:Annual hospital THA and TKA volume, other hospital-level characteristics, and patient factors used in HRRP risk adjustment. Main Outcomes and Measures:The HRRP penalties with HRRP excess readmission ratios were measured, and their association with annual THA and TKA volume, a common measure of surgical quality, was evaluated. The HRRP penalties for surgical care according to hospital and readmitted patient characteristics were then examined. Results:Among 143 Florida hospitals, 2991 of 60 663 Medicare patients (4.9%) who underwent THA or TKA were readmitted within 30 days. Annual hospital arthroplasty volume seemed to follow an inverse association with both unadjusted readmission rates (r = -0.16, P = .06) and HRRP risk-adjusted readmission penalties (r = -0.12, P = .14), but these associations were not statistically significant. Other hospital characteristics and readmitted patient characteristics were similar across HRRP orthopedic penalty severity. Conclusions and Relevance:This study's findings suggest that higher-volume hospitals had less severe, but not significantly different, rates of readmission and HRRP penalties, without systematic differences across readmitted patients.

You have accessJournal of UrologyBladder Cancer: Epidemiology & Evaluation II (PD66)1 Apr 2019PD66-11 EXPLORING PATIENT AND CAREGIVER PERSPECTIVES REGARDING READMISSION FOLLOWING RADICAL CYSTECTOMY: A QUALITATIVE APPROACH TO IDENTIFYING NOVEL TARGETS FOR READMISSION REDUCTION Tudor Borza*, Daniela A Wittmann, Kevin Zhu, Benjamin Li, Ken Urish, Jonathan E Helm, Mariel Lavieri, Bruce L Jacobs, and Ted A Skolarus Tudor Borza*Tudor Borza* More articles by this author , Daniela A WittmannDaniela A Wittmann More articles by this author , Kevin ZhuKevin Zhu More articles by this author , Benjamin LiBenjamin Li More articles by this author , Ken UrishKen Urish More articles by this author , Jonathan E HelmJonathan E Helm More articles by this author , Mariel LavieriMariel Lavieri More articles by this author , Bruce L JacobsBruce L Jacobs More articles by this author , and Ted A SkolarusTed A Skolarus More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557472.47033.baAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Readmission following radical cystectomy is common with rates remaining around 30% for over 15 years. Efforts relying on clinical and administrative data have failed to identify predictors or targetable factors to reduce readmissions. For these reasons, we conducted a qualitative study to explore patient and caregiver perspectives regarding readmission with the aim of identifying novel targets for readmission reduction efforts. METHODS: We identified patients readmitted within 30 days of discharge following radical cystectomy. We performed semi-structured interviews with patients and, when possible, caregivers intended to elicit their perspectives on factors contributing to readmission. Interviews were recorded and transcribed verbatim. Using a Grounded Theory approach, transcripts were coded to identify overarching themes by 2 independent reviewers. Coding was performed iteratively and interviews updated. Study accrual was stopped once thematic saturation was achieved. RESULTS: We performed 13 interviews: 6 with patient-caregiver pairs and 7 patients only. Five themes that highlight the patient experience regarding readmission emerged: (i) patients uniformly felt ready for discharge; (ii) patients rely on caregivers to perform routine activities as they face complex physical and emotional challenges in the immediate postoperative period; short (<24h) delays in care arise from (iii) patient (confusion regarding postoperative expectations, minimizing and efforts to self-manage complications) and (iv) systems issues (difficulty navigating medical system); (v) caregivers frequently regard complications and seeking care more urgently than patients. CONCLUSIONS: This is the first study to explore patient and caregiver perspective on readmission following radical cystectomy and provides possible targets for intervention in the immediate postoperative period. These readmissions do not appear preventable or secondary to early discharge or other system access factors. Uncertainty in differentiating between expected postoperative course and complications leads to delays in presentation and may be alleviated with improved education in the pre- and perioperative setting. Patients without close caregivers are at risk for delays in presentation and may benefit from increased clinical contact (visiting nurse assistance, clinic phone calls, etc). Source of Funding: University of Michigan Rogel Cancer Center Endowment for Discovery Fund Grant G020079 (TB, DAW, TAS) Madison , WI; Ann Arbor, MI; Pittsuburg, PA; Bloomington, IN; Ann Arbor, MI; Pittsuburg, PA; Ann Arbor, MI© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1197-e1197 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tudor Borza* More articles by this author Daniela A Wittmann More articles by this author Kevin Zhu More articles by this author Benjamin Li More articles by this author Ken Urish More articles by this author Jonathan E Helm More articles by this author Mariel Lavieri More articles by this author Bruce L Jacobs More articles by this author Ted A Skolarus More articles by this author Expand All Advertisement PDF downloadLoading ...

When patients leave the hospital for lower levels of care, they experience a risk of adverse events on a daily basis. The advent of value-based purchasing among other major initiatives has led to an increasing emphasis on reducing the occurrences of these post-discharge adverse events. This has spurred the development of new prediction technologies to identify which patients are at risk for an adverse event as well as actions to mitigate those risks. Those actions include pre-discharge and post-discharge interventions to reduce risk. However, traditional prediction models have been developed to support only post-discharge actions; predicting risk of adverse events at the time of discharge only. In this paper we develop an integrated framework of risk prediction and discharge optimization that supports both types of interventions: discharge timing and post-discharge monitoring. Our method combines a kernel approach for capturing the non-linear relationship between length of stay and risk of an adverse event, with a Principle Component Analysis method that makes the resulting estimation tractable. We then demonstrate how this prediction model could be used to support both types of interventions by developing a simple and easily implementable discharge timing optimization.

Hospital readmissions affect hundreds of thousands of patients every year, negatively impacting patients and placing a tremendous burden on the national healthcare system. Post‐discharge checkup policies can reduce readmissions through early detection of health conditions, however, the methods behind designing effective checkup policies are poorly understood. Under current practice, up to 67% of readmitted patients return to the hospital before their first scheduled office visit. This work aims to develop effective checkup plans to monitor patients following hospital discharge, using a variety of checkup methods, including phone calls and office visits. We develop and analyze a new delay‐time analysis model to identify the optimal type and timing of checkups to implement post‐discharge monitoring plans. By analyzing the structure of optimal policies, we develop checkup schedules that can detect up to 43.7% more readmission‐causing conditions experienced by readmission‐bound patients. Further, we uncover simple rules of thumb that can help doctors design and improve monitoring plans even in the absence of advanced computer software or complex computations.

BACKGROUND:Payment models, including the Hospital Readmissions Reduction Program and bundled payments, place pressures on hospitals to limit readmissions. Against this backdrop, we sought to investigate the association of post-acute care after major surgery and readmission rates. METHODS:We identified patients undergoing high-risk surgery (abdominal aortic aneurysm repair, coronary bypass grafting, aortic valve replacement, carotid endarterectomy, esophagectomy, pancreatectomy, lung resection, and cystectomy) from 2005 to 2010 using the Healthcare Cost and Utilization Project's State Inpatient Database. The primary outcome was readmission rates after major surgery. Secondary outcome was readmission length of stay. RESULTS:We identified 135,523 patients of whom 56,720 (42%) received post-acute care. Patients receiving post-acute care had higher readmission rates than those who were discharged home (16% versus 10%, respectively; P < 0.001). The risk-adjusted readmission length of stay was greatest for patients who received care from a skilled nursing facility, followed by those who received home care, and lowest for those who did not receive post-acute care (7.1 versus 5.4 versus 4.8 d, respectively; P < 0.001). CONCLUSIONS:The use of post-acute care was associated with higher readmission rates and higher readmission lengths of stay. Improving the support of patients in post-acute care settings may help reduce readmissions and readmission intensity.

Li, Benjamin Y. BS; Zhu, Kevin Y. BS; Urish, Kenneth L. MD, PhD; Jacobs, Bruce L. MD, MPH; Qin, Yongmei MD, MS; Borza, Tudor MD, MS; Hollenbeck, Brent K. MD, MS; Helm, Jonathan E. PhD; Lavieri, Mariel S. PhD; Skolarus, Ted A. MD, MPH, FACS Author Information

Background: Radical cystectomy has one of the highest 30-d hospital readmission rates but circumstances leading to readmission remain poorly understood. Objective: To examine the postdischarge period and better understand hospital readmission after radical cystectomy. Design, setting, and participants: We conducted a retrospective cohort study of patients treated with radical cystectomy for bladder cancer from 2005 to 2012 using our institutional database. Outcome measurements and statistical analysis: We assessed patient communication with any healthcare system after hospital discharge based on timing, methods, and concern types. Logistic regression and Cox proportional-hazards analyses were used to compare postdischarge concerns among readmitted and nonreadmitted patients. We internally validated the logistic model using a bootstrap resampling technique. Results and limitations: One-hundred patients (23%) were readmitted within 30 d of index discharge. Readmitted patients were more likely to use the emergency department with initial concerns compared with nonreadmitted patients (27% vs 1.0%, p < 0.001). Patients who took longer to first communicate their concerns and who were able to tolerate their symptoms longer had lower odds of readmission. Patients who reported infection (adjusted hazard ratio: 2.8, 95% confidence interval: 1.4-5.8) and failure to thrive concerns (adjusted hazard ratio: 4.4, 95% confidence interval: 2.0-9.3) were more likely to be readmitted compared with those who communicated noninfectious wounds and/or urinary concerns. Conclusions: Radical cystectomy patients who contact the health system soon after discharge or communicated infectious or failure to thrive symptoms (fever, poor oral intake, or vomiting) are more likely to experience readmission as opposed to those that endorse pain, constipation, or ostomy issues. Better understanding of this pre-readmission interval can optimize postdischarge practices. Patient summary: We looked at bladder cancer patients who had surgery and the reasons why they were readmitted to hospital. We found patients who had a fever or difficulty with eating and maintaining their weight had the highest chance of being readmitted. (C) 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Malaria is a major health concern for many developing countries. Designing strategies for efficient distribution of malaria medications, such as Artemesinin Combination Therapies, is a key challenge in resource constrained countries. This paper develops a solution methodology that integrates strategic-level and tactical-level models to better manage pharmaceutical distribution through a three-tier centralized health system, which is common to sub-Saharan African countries. At the strategic level, we develop a two-stage stochastic programming approach to address the problem of demand uncertainty. In the first stage, an initial round of shipments is sent before the malaria season to each local clinic from district hospitals, which receive medications from regional warehouses. After the malaria season begins, a recourse action is triggered to avoid shortages in the form of (i) lateral transshipment or (ii) delayed shipment. The optimal solutions developed by the strategic model identify small clinic clusters possessing exclusive transshipment policies. Therefore, we decompose the problem at the tactical level, solving each clinic cluster independently using a Markov decision process approach to determine optimal periodic transshipment policies. A case study of our proposed distribution system is performed for 290 facilities controlled by the Malawi Ministry of Health. Numerical analysis of Malawi's distribution system indicates that our proposed cluster-based decomposition method could near optimally reduce shortage incidents. Moreover, such an approach is robust to challenges of developing countries such as slow paper-based inventory review, uncertain transportation infrastructure, the need for equitable distribution, and seasonal and correlated demand associated with malaria transmission dynamics.

To manage chronic disease patients effectively, clinicians must know (i) how to monitor each patient (i.e., when to schedule the next visit and which tests to take), and (ii) how to control the disease (i.e., what levels of controllable risk factors will sufficiently slow progression). Our research addresses these questions simultaneously and provides the optimal solution to a novel linear quadratic Gaussian state space model. For the objective of minimizing the relative change in state over time (i.e., disease progression), which is necessary for managing irreversible chronic diseases while also considering the cost of tests and treatment, we show that the classical two‐way separation of estimation and control holds. This makes a previously intractable problem solvable by decomposition into two separate, tractable problems while maintaining optimality. The resulting optimization is applied to the management of glaucoma. Based on data from two large randomized clinical trials, we validate our model and demonstrate how our decision support tool can provide actionable insights to the clinician caring for a patient with glaucoma. This methodology can be applied to a broad range of irreversible chronic diseases to devise patient‐specific monitoring and treatment plans optimally.

The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and overtime.