Healthcare Operations Management Research Articles

An empirical analysis of process improvement from best practice adoption: A study of stroke care best practices

AbstractThis study empirically examines how induced learning through adopting a set of best practices and learning‐by‐doing improved a hospital's care of ischemic stroke patients using ad hoc teams. While previous studies in healthcare operations management conducted in ad hoc team environments predominantly focused on volume‐based learning (learning by doing, team familiarity via interactions among team members), our study focuses on induced learning in ad hoc teams through best practice adoptions. The analysis uses secondary data (Data period: January 2009–March 2017) about stroke patients from a comprehensive stroke center (CSC) in a U.S. tertiary teaching hospital as it adopted the U.S. American Heart Association/American Stroke Association (AHA/ASA) Target:Stroke best practices. The ad hoc stroke teams provide the initial care and their performance is measured using “Door‐to‐Needle (DTN)” time and its sub‐time segments. The DTN time is measured as the time elapsed between the stroke patient's arrival at the hospital's emergency department (ED) and the appropriate infusion of “Tissue Plasminogen Activator (TPA)” (i.e., a thrombolytic medication informally referred to as a “clot buster”). We found that adopting these best practices improved ischemic stroke care beyond improvement due to repetition. We also found that the neurologist's recent experience providing stroke care for the prior patient is positively associated with meeting the time performance goal for the current patient. This study provides insights into the use of management mechanisms to adopt and sustain best practices in healthcare that are generalizable to other organizations with ad hoc team environments.

Government Subsidies and Policies for Mask Production Under COVID-19: Is It Wise to Control Less?

Under the COVID-19 pandemic, governments worldwide have subsidized manufacturers or consumers on the production or purchase of masks. However, the impacts of these subsidies on the mask supply chain (MSC) operations are unclear. Motivated by our interview with a mask manufacturer as well as the observed real-world practices, we establish consumer utility-based stylized models to analytically examine government subsidies and policies in the MSC. We utilize the infection transmission model to capture the social health risk during the COVID-19 outbreak. The government aims to maximize social welfare, which includes the manufacturer's profit, consumer surplus, social health risk, and government subsidy expenditure. Results indicate that when the price is not controlled (i.e., the manufacturer decides it), the manufacturer and consumer subsidy programs are equally efficient in enhancing consumer surplus as well as reducing harm to social health risk under COVID-19. Thus, the government can conduct a subsidy scheme that is easier to implement in practice. However, we surprisingly find that the government's excessive intervention will cause disequilibrium in the MSC. When the price or the manufacturer's dishonest behavior is fully controlled by the government, subsidizing the MSC is not always advisable. Besides, our findings are consistent with the public interest theory; that is, the proper implementation of dishonesty prevention and pricing control policies can improve social welfare but sacrifice consumer surplus. Our results contribute to healthcare operations management and generate managerial insights for MSC management during COVID-19 with industrial validation.

Teaching business leadership skills to professionals in healthcare cybersecurity, biodefense and biotechnology through experiential learning methods

The COVID-19 pandemic has not only reshaped the global economic landscape but also underscored the critical need for strategic leadership and management skills in organizations spanning diverse sectors. This paper delves into the compelling imperative of nurturing strategic leadership and management competencies among technical experts, with a particular focus on pivotal fields such as biodefense, healthcare cybersecurity, biotechnology, and healthcare operations management. It asserts that the post-COVID-19 era necessitates a profound shift in organizational priorities, urging a renewed commitment to fostering comprehensive leadership capabilities alongside technical proficiency within graduate and degree completion programs. To meet this imperative, the paper advocates for the adoption of innovative and student-centric pedagogical approaches. In today’s dynamic landscape, traditional methods fall short in preparing technical managers for the multifaceted challenges they face. Instead, the paper champions the utilization of experiential learning approaches, such as activity-based learning, problem-based learning, design thinking, and Learn-Teach-Do. These approaches go beyond theory, immersing students in collaborative, reflective, and integrative experiences that simulate real-world scenarios, thus equipping them with the leadership acumen needed to excel in technical healthcare roles.

Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis.

The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities.

Critical Success Factors for Successful Implementation of Healthcare 4.0: A Literature Review and Future Research Agenda.

The digitization of healthcare services is a major shift in the manner in which healthcare services are offered and managed in the modern era. The COVID-19 pandemic has speeded up the use of digital technologies in the healthcare sector. Healthcare 4.0 (H4.0) is much more than the adoption of digital tools, however; going beyond that, it is the digital transformation of healthcare. The successful implementation of H 4.0 presents a challenge as social and technical factors must be considered. This study, through a systematic literature review, expounds ten critical success factors for the successful implementation of H 4.0. Bibliometric analysis of existing articles is also carried out to understand the development of knowledge in this domain. H 4.0 is rapidly gaining prominence, and a comprehensive review of critical success factors in this area has yet to be conducted. Conducting such a review makes a valuable contribution to the body of knowledge in healthcare operations management. Furthermore, this study will also help healthcare practitioners and policymakers to develop strategies to manage the ten critical success factors while implementing H 4.0.

A Dataset of Service Time and Related Patient Characteristics from an Outpatient Clinic

Outpatient clinics’ productivity largely depends on their appointment scheduling systems. It is crucial for appointment scheduling to understand the intrinsic heterogeneity in patient and service types and act accordingly. This article describes an outpatient clinic dataset of consultation service time with heterogeneous characteristics. The dataset contains 6637 consultation records collected from 381 half-day sessions between 2018 and 2019. Each record includes encrypted session and patient IDs, consultation start and (approximated) end times, the month and day of the week, whether it was on a holiday, the patient’s visit count for a specific medical condition, gender, whether the consultation was cancer-related, and the distance from the patient’s mailing address to the clinic. These features can be used to classify patients into heterogeneous groups in studies of appointment scheduling. Therefore, this dataset with rich, heterogeneous patient characteristics provides a valuable opportunity for healthcare operations management researchers to develop, test, and benchmark the performance of their models and methods. It can also be used for studying appointment scheduling in other service industries. More generally, it provides pedagogical value in areas related to management science and operations research, applied statistics, and machine learning.

Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions

The new age economy is primarily driven by Industry 4.0 and Industry 5.0, which facilitate smartification of organizations by helping them integrate and automate decision making. Recent advances in information and communication technologies, such as the cloud, big data, Internet of things, and artificial intelligence and nanotechnology, have accelerated the adoption of Industry 4.0 and Industry 5.0. Because of these advancements, organizations are now facing new challenges in the form of cybersecurity risks that are partly caused by these technologies. In recent years, there has been a spike in the number of cyberattacks, and organizations are taking steps to minimize the impacts of these attacks. To address this critical issue, in this article, we discuss possible future research directions that production and operations management (POM) researchers can undertake to help organizations, supply chains, and governments develop robust strategies for reducing the number of attacks and their repercussions. In particular, we identify several avenues for future research in the following domains of POM: (1) global operations strategy, (2) healthcare operations management, (3) public policy, (4) management of technology, (5) supply chain management, and (6) disruptive technologies. Research on the topic of cybersecurity is not only an opportunity for operations management researchers but also critical for industry and society to overcome the challenges of cybersecurity risks.

Examining the Role of Single versus Dual Decision-Making Approach for Patient Care

Decisions related to patient treatment during their hospital stay are done by two key decision-makers: The attending physician (AP) and the operating physician (OP). Under the single decision-making approach (S-DMA), the AP and OP are the same, whereas, under the dual decision-making approach (D-DMA), the AP and OP are different. In recent years, hospitals have seen an increasing trend towards D-DMA over S-DMA approach to accommodate physician schedules. While research outside healthcare operations management has argued for benefits from both S-DMA and D-DMA approaches, very little knowledge exists on what is effective during healthcare delivery. In this study, we address this gap by investigating the DMA within a hospital through physicians effectively improves patient care outcomes. Using detailed data from the state of Florida on cardiology patients over three years, we find that a S-DMA is associated with reduced patient length of stay, treatment cost, and mortality. This runs counter to some of the trends observed in practice. Further, we find that the effect is more significant for patients with greater disease complexity. A follow-up post-hoc analyses also reveal interesting insights: (1) S-DMA is more beneficial for community hospitals than teaching and academic hospitals (2) S-DMA is more critical than physician specialization. Our results are robust to alternate explanations and demonstrate the role that physicians can play in effective care treatment during a hospital stay.

Ancillary Cost Implications of Physicians Multisiting and Inter‐Organizational Collaboration During Healthcare Delivery

This study investigates diagnostic testing and pharmacy expenditures, commonly referred to as ancillary costs in the United States acute‐care hospitals. These ancillary costs are charges associated with services provided to support patient treatment, including laboratory, radiology, and pharmacy charges. Despite of investment in inter‐organizational and technological collaboration practices, these costs continue to rise. Our research examines the role of individual healthcare providers, namely multisiting physicians, who practice at more than one hospital, in reducing ancillary cost. We also look at how inter‐organizational collaboration measured by the hospital's affiliation in an accountable care organization (ACO) model affect this relationship. To do this, we assembled a unique data set of 163,617 patients treated by 4411 physicians at 182 hospitals in Florida from 2014 to 2016. Using an econometric estimation approach accounting for endogeneity, we find that patients treated by multisiting physicians experienced, on an average, per‐hospitalization reduction in laboratory and radiology charges of 58.57% and 30.11%, respectively. These savings increase when multisiting physicians have less practice experience. We also find that hospitals participating in ACO model complement the savings achieved through multisiting physicians in the form of even lower pharmacy costs. In post‐hoc analyses, we find such cost reduction comes from reducing the total number of procedures or tests ordered. We also show that the lower treatment charges do not compromise the clinical quality of patients treated by multisiting physicians. Collectively, the findings offer important insights to the healthcare operations management literature on the interaction between individual‐organizational collaboration efforts and patient‐level outcomes.

Collaboration Structures in Integrated Healthcare Delivery Systems: An Exploratory Study of Accountable Care Organizations

Problem definition: This study explores the performance implications of collaboration structures in an integrated healthcare delivery system, namely, an accountable care organization (ACO). ACOs consist of providers from various stages of the care continuum (preacute, acute, and postacute) that voluntarily assume collective responsibility for the quality and cost of care for a defined patient population. Academic/practical relevance: ACOs’ unsatisfied outcomes are largely due to a lack of provider collaboration. There is a dearth of empirical studies on how to develop collaboration structures. Studies in the healthcare operations management primarily have focused on collaboration within a single organization, shedding little light on this problem. We address this issue by exploring two distinct dimensions of collaboration: partnership scope and scale. Partnership scope measures the presence of providers from the preacute, acute, and/or postacute care-continuum stages, whereas partnership scale measures the presence of providers within a single care-continuum stage. Methodology: We assemble a unique data set of provider types, collaboration structures, and system-level performance for 528 Medicare Shared Savings Program (MSSP) ACOs from 2013–2016. To investigate the impact of partnership scope and scale on ACO performance, we then use econometric estimation approaches that account for endogeneity in collaboration structure decisions. ACO performance is measured by experiential quality and 30-day readmission rates. As additional tests, we also investigate our research questions by assembling data sets at both the hospital level (20,975 hospital-year panel data spanning 2009 to 2015) and patient level (859,145 Medicare patients admitted to 39 California hospitals over a four-year period from 2012 to 2015). Results: We find that synergies exist between partnership scope and scale with respect to ACO performance. Specifically, an average-sized ACO can realize 3.2% more improvement in experiential quality and a 6.6% greater reduction in 30-day readmission rates through partnership scope and scale synergies in the preacute care stage. We also show that the benefits of increasing partnership scope are consistent across providers and patient-level analysis. Further, we find that these benefits come at some cost, suggesting an initial cost-quality trade-off when developing collaboration structures. Managerial implications: Our results offer important insights into the healthcare operations management literature on designing effective healthcare delivery systems extending beyond a single organization.

Data-Driven Learning and Resource Allocation in Healthcare Operations Management

Data-Driven Learning and Resource Allocation in Healthcare Operations Management

Surgery Sequencing Coordination with Recovery Resource Constraints

Surgical practice administrators need to determine the sequence of surgeries and reserved operating room (OR) time for each surgery in the surgery scheduling process. Both decisions require coordination among multiple ORs and the recovery resource in the postanesthesia care unit (PACU) in a surgical suite. Although existing studies have addressed OR time reservation, surgery sequencing coordination is an open challenge in the stochastic surgical environment. In this paper, we propose an algorithmic solution to this problem based on stochastic optimization. The proposed methodology involves the development of a surrogate objective function that is highly correlated with the original one. The resulting surrogate model has network-structured subproblems after Lagrangian relaxation and decomposition, which makes it easier to solve than the impractically difficult original problem. We show that our proposed approach finds near-optimal solutions in small instances and outperforms benchmark methods by 13%–51% or equivalently an estimated saving of $760–$7,420 per day in surgical suites with 4–10 ORs. Our results illustrate a mechanism to alleviate congestion in the PACU. We also recommend that practice administrators prioritize sequencing coordination over the optimization of OR time reservation in an effort for performance improvement. Furthermore, we demonstrate how administrators should consider the impact of sequencing decisions when making strategic capacity adjustments for the PACU. Summary of Contribution: Our work provides an algorithmic solution to an open question in the field of healthcare operations management. This solution approach involves formulating a surrogate optimization model and exploiting its decomposability and network-structure. In computational experiments, we quantitatively benchmark its performance and assess its benefits. Our numerical results provide unique managerial insights for healthcare leadership.

Effects of Online–Offline Service Integration on e‐Healthcare Providers: A Quasi‐Natural Experiment

E‐healthcare platforms start to integrate medical services across online and offline channels, where providers can perform online consultations, schedule patients’ offline visits, synchronize relevant medical records, and finally, answer online inquiries regarding follow‐up and recovery anytime and anywhere within one operations management function (i.e., online–offline service integration). In this study, we seek to quantify the effects of online–offline service integration on the e‐healthcare providers’ demand and reputational outcomes, noting that it is not altogether clear how the service integration function will affect the providers who adopt such a function in e‐healthcare platforms. Leveraging a quasi‐natural experiment on an e‐healthcare platform, we conducted difference‐in‐differences analyses in tandem with a variety of matching strategies, including propensity score matching and look‐ahead propensity score matching. Furthermore, we explored the moderating roles of provider‐specific characteristics. Our results reveal a set of robust and interesting findings: (i) e‐healthcare providers, on average, experience increases in online demand and decreases in offline demand post online–offline service integration; (ii) the service integration function also improves the professional reputation of participating providers; (iii) the impact of channel integration on the outcomes is weaker for providers with lower (vs. higher) professional titles; and (iv) the providers who specialize in treating chronic (vs. acute) diseases experience greater increases in online demand and reputational outcomes, yet insignificant changes in offline demand. This work contributes to related prior literatures on healthcare operations management, e‐healthcare, and online–offline channel integration, offering design implications for the service operations of e‐healthcare platforms.

Ancillary Cost Implications of Multisiting and Organizational Boundary Spanning during Healthcare

This study investigates the management of diagnostic testing and pharmacy expenditures in U.S. acute-care hospitals. These so-called ancillary costs are charges associated with services provided to support patient treatment, including laboratory, radiology, and pharmacy charges. Previous studies suggest that investing in structural and technological boundary-spanning practices may help reduce the increasing ancillary costs. In this research, we examine the role of individual boundary spanners, namely multisiting physicians who practice at more than one hospital, in reducing ancillary cost. We also look at how organizational boundary spanning abilities measured by the hospital’s affiliation in an accountable care organization (ACO) model affect this relationship. To do this, we assembled a unique data set of 163,617 patients treated by 4,411 physicians at 182 hospitals in Florida from 2014 to 2016. Using an econometric estimation approach accounting for endogeneity, we find that patients treated by multisiting physicians experienced per-hospitalization reductions in laboratory and radiology charges of 49.16% and 32.04%, respectively. These savings increase, moreover, when multisiting physicians have less practice experience. We also find that hospitals participating in ACO model complements the savings achieved through multisiting physicians in the form of even lower pharmacy costs. In our post-hoc analysis, we find the cost reduction that multisiting physicians achieve come through reducing the total number of procedures or tests ordered. We also find that lower treatment charges do not compromise the clinical quality of patients treated by multisiting physicians. Collectively, the findings offer important insights to the healthcare operations management literature on the interaction between individual-organizational boundary-spanning efforts and patient-level outcomes.

Reducing search times and entropy in hospital emergency departments with real-time location systems

Although the consequences of hospital ED crowding have been studied extensively, the causes of crowding are still not well understood. Throughput factors in ED crowding models are difficult to study in a controlled fashion in a dynamic environment where healthcare demand changes rapidly, and physical and human resources suddenly become limited. Opportunities for automated, simultaneous, and low-cost observation of the location and movement of multiple units, patients and staff have recently arisen with the introduction of small, non-intrusive real-time location systems (RTLS). One such RTLS deployment reported here has initiated renewed consideration of quality and industrial statistics as applied to healthcare operations management. Novel metrics for essential constructs of throughput factors in ED crowding such as efficiency and effectiveness are proposed. In particular, causality is explained in terms of understanding of each construct, modeled in terms of entropy, information, and order. Experimental demonstration is given of how labor reduction and the probability of patients, personnel and equipment meeting in terms of less uncertainty can be explained. These novel metrics are expected to facilitate monitoring of how an ED reacts to different levels of crowding, provide insight into crowding dynamics, help evaluate interventions to decrease crowding, and ultimately improve care.

Optimal Testing under Limited Capacity

Motivated by the persistent lack of testing capacity in the COVID-19 pandemic, we study the question “who should be tested?” when capacity is limited, tests have errors, and patients differ in their prior probability of being infected. While health officials initially prioritized testing patients who were highly likely to be infected, we find that it may be better to focus on patients who are less likely to be infected, particularly when the false-negative rate is substantial. We also consider pooled testing, a method of grouping samples to conserve tests. When pooled testing is an option, it may be optimal to test two groups of individuals: those who are very likely to be infected and those who are very unlikely to be infected. Moreover, even though pooling is an option, it can still be optimal to reserve some individual tests for samples from patients for whom the outcome is most uncertain. The results apply to any situation where testing capacity is inadequate to test the number of potentially infected individuals, and they can inform both public health policy and healthcare operations management.

Effects of Online-Offline Service Integration on e-Healthcare Providers: A Quasi-Natural Experiment

E-healthcare platforms start to integrate medical services across online and offline channels, where providers can perform online consultations, schedule patients’ offline visits, synchronize relevant medical records, and finally, answer online inquiries regarding follow-up and recovery anytime and anywhere within one operations management function (i.e., online-offline service integration). In this study, we seek to quantify the effects of online-offline service integration on the e-healthcare providers’ demand and reputational outcomes, noting that it is not altogether clear how the service integration function will affect the providers who adopt such a function in e-healthcare platforms. Leveraging a quasi-natural experiment on an e-healthcare platform, we conducted difference-in-differences analyses in tandem with a variety of matching strategies, including propensity score matching and look-ahead propensity score matching. Further, we explored the moderating roles of provider-specific characteristics. Our results reveal a set of robust and interesting findings: i) e-healthcare providers, on average, experience increases in online demand and decreases in offline demand post online-offline service integration; ii) the service integration function also improves the professional reputation of participating providers; iii) the impact of channel integration on the outcomes are weaker for providers with lower (vs. higher) professional titles; and iv) the providers who specialize in treating chronic (vs. acute) diseases experience greater increases in online demand and reputational outcomes, yet insignificant changes in offline demand. This work contributes to related prior literatures on healthcare operations management, e-healthcare, and online-offline channel integration, offering design implications for the service operations of e-healthcare platforms.

When to Account for the Patient? The Effect of Clinical Treatment of Social Determinants of Health on Patient Health Outcomes

Due to the ever-increasing availability of personalized patient information, it is vital for us in healthcare operations management to understand how physicians utilize this information to make their treatment decisions. One set of information that medical educators have encouraged clinicians to account for are a patient’s social determinants of health (SDOH), as we know from the personalized medicine literature that acknowledging patient-specific uniqueness is associated with optimal health outcomes. However, in circumstances where physicians receive no formal training in patient sensitivity, we posit that physicians may still intuitively adjust treatment programs from standard practices due to the influence of a patient’s socioeconomic status. It is crucial to understand how SDOH driven treatment adjustments are taking place and how they affect a patient’s short-term and long-term health outcomes. We explore using a two-phase model whether SDOH factors affect the quality of care a patient receives during initial treatment visits and whether these variations in quality significantly impact readmission likelihood. Our data show physicians do account for SDOH when making treatment decisions, but these adjustments do not impact long-term health outcomes. However, SDOH factors still impact readmission likelihood, indicating that physician treatment adjustments are having an indirect effect on patient engagement post-care.

Data-Driven Management of Post-transplant Medications: An Ambiguous Partially Observable Markov Decision Process Approach

Problem definition: Organ-transplanted patients typically receive high amounts of immunosuppressive drugs (e.g., tacrolimus) as a mechanism to reduce their risk of organ rejection. However, because of the diabetogenic effect of these drugs, this practice exposes them to a greater risk of new-onset diabetes after transplantation (NODAT), and hence, becoming insulin dependent. We study and develop effective medication management strategies to address the common conundrum of balancing the risk of organ rejection versus that of NODAT. Academic/practical relevance: Our research contributes to the healthcare operations management literature by developing a robust stochastic decision-making framework that allows for incorporating (1) false-positive and false-negative errors of medical tests, (2) inevitable estimation errors when data sets are used, (3) variability among physician’ attitudes toward ambiguous outcomes, and (4) dynamic and patient risk-profile-dependent progression of health conditions. Methodology: We apply an ambiguous partially observable Markov decision process (APOMDP) approach where dynamic optimization with respect to a “cloud” of possible models allows us to make decisions that are robust to potential misspecifications of risks. Results: We first provide various structural results that facilitate characterizing the optimal medication policies. Utilizing a clinical data set, we then compare the performance of the optimal medication policies obtained from our APOMDP model with the policies currently used in the medical practice. We observe that, in one year after transplant, our proposed policies can improve the life expectancy of each patient up to 4.58%, while reducing the medical expenditures up to 11.57%. Managerial implications: Balancing the risks of organ rejection and diabetes complications and considering factors such as physicians’ attitudes toward ambiguous outcomes, partial observability of medical tests, and patient-specific risk factors are shown to result in more cost-effective strategies for management of post-transplant medications compared with the current medical practice. Finally, simultaneous management of medications can facilitate the care coordination process between transplantation/nephrology and endocrinology departments of a hospital that are typically in charge of administering such medications.

Revisiting the unholy alliance of health-care operations: payor–provider integration of occupational health services

PurposeTo investigate the effects of payor–provider integration on the operational performance of health service provision. The research explores whether integration governs agency problems and tilts the incentives of diverse actors toward more systematic outcomes.Design/methodology/approachA two stage multimethod case study of occupational health services. A qualitative stage aimed to understand the reasons, mechanisms, and outcomes of payor–provider integration. A quantitative stage evaluated the performance of the integrated hospital against fee-for-service partner hospitals with a sample of 2,726 patients.FindingsPayor–provider integration mitigates agency problems on multiple levels of the service system by complementing formal governance mechanisms with informal mechanisms. Compared to partner hospitals, the integrated hospital yielded 9% lower the total costs of occupational injuries achieved primarily by emphasizing conservative care and faster recovery.Research limitations/implicationsFocuses on occupational health services in Finland. Provides initial evidence of the effects of payor–provider integration on the operational performance.Practical implicationsVertical integration may provide systematic outcomes but requires mindful implementation of multiple mechanisms. Rigorous change management initiative is advised.Social implicationsFor patients, the research shows payor–provider integration of health services can be implemented in a manner that it reduces care costs while not compromising care quality and customer satisfaction.Originality/valueThis study provides a rare longitudinal analysis of payor–provider integration in health-care operations management. The study adds to the knowledge of operational performance improvement of health services.