Optimal Health Care Delivery Research Articles

Optimizing Quality of Hospital Services and Inpatient Satisfaction through Lean Principles

Aim: The aim of this study is to evaluate the impact of Lean principles on improving hospital service quality and inpatient satisfaction tertiary care hospital in Pune, India. It focuses on reducing key inefficiencies to enhance patient experiences and operational efficiency. Information & Methods: This quantitative study was conducted at tertiary care hospital in Pune, India, with 110 inpatients who had been admitted for at least three days. Data were collected through a closed-ended questionnaire based on Lean’s seven waste categories. Methods: A cross-sectional design was used, and data were analyzed using SPSS version 20. Descriptive statistics summarized demographics and survey responses, while chi-square tests and multivariate logistic regression assessed the relationships between Lean variables and inpatient satisfaction. Findings: The findings reveal that Lean principles significantly improved hospital service quality and inpatient satisfaction. Reductions in "waiting" and "motion" wastes were strongly correlated with higher patient satisfaction, highlighting the importance of streamlined processes and reduced wait times. Efficient inventory management also emerged as a key factor in enhancing satisfaction, while "excess processing" and "overproduction" showed less influence on patient satisfaction, indicating areas for further improvement. These results underscore the effectiveness of Lean in optimizing healthcare delivery and improving patient experiences. Conclusion: The study concludes that Lean principles effectively improve hospital service quality and inpatient satisfaction by reducing key wastes such as waiting, motion, and inventory inefficiencies. Continuous implementation of Lean practices can lead to more efficient and patient-centered healthcare delivery.

Antecedents For the Adoption of Telemedicine in India: Scale development and validation

Background: Telemedicine is increasingly recognized as a promising solution to healthcare challenges in India, particularly in remote areas. However, the country's vast population and geographic diversity present significant obstacles to providing accessible and high-quality healthcare services to all citizens. By leveraging technology, telemedicine has the potential to bridge this gap and enable remote delivery of medical services. Objective: This study aims to develop and validate a scale that assesses the factors influencing the adoption of telemedicine in the Indian context. Understanding these factors is crucial for identifying key drivers and barriers to telemedicine adoption in India. Design: Quantitative methods were employed for scale development. The instrument development process involved several stages: factor identification, item generation, pre-testing, pilot testing, and scale validation. A structured questionnaire was administered to healthcare professionals, industry experts, and patients who have used or intend to use telemedicine. Setting: Confirmatory factor analysis and subsequent tests, such as reliability and validity tests were conducted to establish the internal consistency of the scales. These statistical analyses aimed to identify underlying factors and ensure accurate measurement of latent variables that affect the adoption of telemedicine. Results: The study established a robust scale to assess the five key factors, which are the vital explanatory variables in telemedicine adoption in India. These meticulously validated scales, encompassing technology, government policy, user attitudes, societal demand, and healthcare professional perspectives, demonstrate high reliability and validity in understanding the adoption dynamics of telemedicine in the context of India. Conclusion: This research offers a validated scale for assessing telemedicine adoption in India, crucial for healthcare service providers, policy makers, and researchers in this field. It enables informed decisions in implementing telemedicine, addressing unique challenges and opportunities, and significantly contributing to optimizing healthcare delivery across India's diverse socio-economic and geographical landscape.

An overview of plantar pressure distribution measurements and its applications in health and medicine.

Measuring plantar pressure distribution is critical for understanding foot-ground interactions, providing valuable insights for diagnosing and managing various health conditions. Since its initial studies in 1984, this field has garnered increasing attention within healthcare and medicine due to its broad applications across clinical settings. How does measuring plantar pressure distribution affect healthcare outcomes across different age groups and health conditions? This review thoroughly explores the literature concerning plantar pressure distribution, focusing on studies conducted from 1984 onwards. It investigates the methodologies and clinical applications associated with plantar pressure measurement, tracing the evolution of research and practice in this field. The review identifies and classifies key methods used to measure plantar pressure distribution, emphasizing their application in clinical and research contexts. It highlights the considerable influence these measurements have on the diagnosis and management of various health conditions across all age groups and demonstrates the practical benefits of plantar pressure analysis in improving diagnostic accuracy and informing more tailored treatment plans. Understanding plantar pressure distribution is crucial for advancing healthcare practices, particularly in diagnostic and therapeutic interventions. This review underscores the clinical importance of plantar pressure measurement, illustrating its value in improving patient outcomes and contributing to broader medical research. The findings aim to assist healthcare professionals, researchers, and clinicians in effectively leveraging plantar pressure data to optimize healthcare delivery and patient care.

Impact of family doctor contracted services on the health of migrants: a cross-sectional study in China

This study investigates the impact of the family doctor contracted service system on the health of migrants in China, utilizing data from the 2018 China Migrants Dynamic Survey (CMDS). The study employs a double machine learning model to estimate the effect of family doctor contracted services (FDCS) on migrants’ self-rated health (MSRH). The sample consists of 137,851 migrants, with family doctor service contract status, health education, and health records as key variables. To address potential endogeneity issues, an instrumental variable approach using the regional family doctor contracting rate was implemented. Mediation analysis was conducted to examine the roles of health education and health records in this relationship. The findings indicate that FDCS significantly improve MSRH. This positive effect is robust across various machine learning models, including Lassocv, Random Forest, and Gradient Boost. The instrumental variable approach confirms the validity of these results, mitigating concerns about endogeneity. Mediation analysis reveals that the positive impact of FDCS on MSRH is fully mediated by health education and health records, highlighting their critical roles in enhancing health outcomes. The FDCS play a crucial role in improving the health of migrants by providing continuous and comprehensive care. Enhanced health education and effective health records management are significant pathways through which these services exert their positive effects. Policy recommendations include expanding access to family doctor services, enhancing health education programs, and improving health records management to optimize healthcare delivery for migrants. Future research should consider longitudinal studies to further validate these findings and explore their applicability to specific subgroups or regions.

Transforming Healthcare Through Effective Health Administration Practices: A Systematic Review

The role of effective health administration practices is crucial in transforming healthcare organizations by enhancing efficiency, improving patient outcomes, and promoting operational sustainability. This systematic review synthesizes the current body of knowledge on health administration practices that have demonstrated transformative impacts within healthcare settings. A comprehensive literature search across major databases, including PubMed, Scopus, and Google Scholar, was conducted, focusing on studies published in recent years. Key themes emerging from the review include workforce management, patient-centered care, digital health solutions, quality improvement methods, and financial management strategies. Findings suggest that effective health administration practices contribute significantly to optimizing healthcare delivery, reducing costs, and increasing patient satisfaction. However, challenges such as staff resistance and high implementation costs remain barriers to adopting these practices widely. This review highlights the need for continued research into long-term impacts, cost-effectiveness, and adaptability of these practices in diverse healthcare environments. The findings provide valuable insights for healthcare administrators and policymakers aiming to implement evidence-based strategies to foster continuous improvement and adaptability within healthcare systems.

The Impact and Potential of Quick Response (QR) Codes in Healthcare: A Comprehensive Review

In today’s rapidly evolving healthcare landscape, the integration of innovative technologies is becoming increasingly essential to enhance patient care, medication and overall healthcare outcomes. Quick Response (QR) codes have emerged as a versatile tool with significant potential to revolutionize such aspects of the healthcare industry. Through a review of 30 studies, we uncover the significant impact of integrating QR codes into healthcare systems. Across various domains, QR codes prove instrumental in enhancing patient care, streamlining workflows, and improving communication between healthcare professionals and patients. Notable findings include the role of QR in ensuring medication safety and advancing education as well as training within healthcare settings. Despite some challenges, user satisfaction remains high, underscoring QR codes’ effectiveness in optimizing healthcare delivery and outcomes from a human-centred perspective. This includes considering usability, cognitive load, and ergonomic design, all of which are crucial for maximizing the benefits of QR code technology in healthcare.

Integrating predictive modeling and causal inference for advancing medical science

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Utilization of big data analytics to identify population health trends and optimize healthcare delivery system efficiency

Big data analytics has emerged as an incredibly valuable tool in understanding population health trends and improving the effectiveness of healthcare delivery systems. By leveraging big data sources from various domains, including the patients’ electronic health records, claims data, wearables, and social media accounts, healthcare organizations can obtain novel and rich insights regarding population health characteristics, predisposing factors, and disease progression. The arrival of big data has transformed healthcare organizations by providing a scientific population health management and health system enhancement tool. Using superior analytical tools, healthcare entrepreneurs and policymakers can discover relations, rates, and patterns that are concealed in large data sets. Such knowledge can be used for early detection of possible interventions, management of resources, and prevention measures, thus leading to better health and less spending on health issues. Moreover, big data analytics helps in early diagnostics and the development of management strategies for high-risk groups, which in turn improves the functioning and effectiveness of healthcare systems. It is also important to note that big data solutions in healthcare are not limited to population health management, but also include functional aspects of healthcare organizations. Additionally, using data about patient movements, resource consumption, and clinical activity, it is possible to determine inefficiencies and opportunities to improve processes in healthcare organizations. This approach of collecting and analyzing data helps in decision making thus reducing time and improving patient flow and experience. However, incorporating real-time data into the clinical decision support systems can improve diagnostic capabilities, treatments offered, and patient tracking resulting in improved quality of services delivered.

Managing no-shows in healthcare: insights from an urban Health District in Northern Italy

Abstract Background Non-attendance at healthcare appointments, known as no-show, poses significant challenges to healthcare systems, disrupting patient care continuity, exacerbating waiting times, and straining resources. Addressing this issue is crucial for optimizing health care delivery and containing costs. This study explores the phenomenon of patient no-shows and aims to identify factors associated in a densely populated healthcare district in Turin, Northern Italy. Methods Data were extracted from the healthcare management system covering outpatient care appointments in the health district in 2022 and 2023. The analysis included various patient and service-related variables. Missing information on key variables were managed with listwise deletion. Descriptive statistics provided an overview of missed appointment rates, while multivariate analysis explored various patient and service-related variables. Results A 5.1% (n = 6198) no-show rate was observed out of 120,405 records. The total cost incurred due to missed appointment fees amounted to €127,244. Several factors were associated with a significantly increased likelihood of no-show (p < 0.05), including initial visit (OR = 1.11), deferred priority (OR = 1.22), non-exempt status from healthcare co-payments (OR = 1.36), exemption based on income (OR = 1.82), age under 18 (OR = 2.46) and under 65 (OR = 2.32), male gender (OR = 1.17), residency outside the Piedmont Region (OR = 1.31) and outside Italy (OR = 2.46), and longer waiting time (OR = 1.01). Conclusions This study sheds light on the widespread issue of patient no-shows and its economic implications. It provides clear direction for implementing tailored interventions in healthcare settings. As a response to these findings, a telephone recall project was launched. Its efficacy will be evaluated in subsequent assessments to determine if it can effectively alleviate the burden of no-shows, thus enhancing healthcare efficiency and ensuring continuity of care for patients. Key messages • The no-show phenomenon has important implications both organizationally and economically. • The magnitude and major determinants of patient no-shows must be recognized to guide the deployment of tailored interventions in a real-world setting.

The power of artificial intelligence for managing pandemics: A primer for public health professionals.

Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence-powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI-driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision-making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI-generated health communications shown to be more effective than human-generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for 'false confirmations', where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.

Managing medicine shortages.

Medicine shortages are increasingly common and disruptive to the optimal delivery of health care. They are caused by a variety of factors, including manufacturing and supply-chain issues, regulatory and trade issues, and fluctuations in demand. Prescribers and pharmacists in Australia can manage a shortage by switching to another brand, strength or dosage form of the same medicine, switching to a different registered medicine, or accessing an unregistered medicine that has been made available via section 19A of the Therapeutic Goods Act 1989 or through the Special Access Scheme. There are a range of resources and tools that can assist clinicians with identifying and managing medicine shortages in Australia. Shortages are managed most effectively when prescribers, pharmacists and nurses work together, in collaboration with patients, to develop, implement and monitor strategies to manage the shortage.

Gender and Sexuality Awareness in Medical Education and Practice: Mixed Methods Study.

The integration of gender and sexuality awareness in health care is increasingly recognized as vital for patient outcomes. Despite this, there is a notable lack of comprehensive data on the current state of physicians' training and perceptions in these areas, leading to a gap in targeted educational interventions and optimal health care delivery. The study's aim was to explore the experiences and perceptions of attending and resident physicians regarding the inclusion of gender and sexuality content in medical school curricula and professional practice in Israel. This cross-sectional survey targeted a diverse group of physicians across various specializations and experience levels. Distributed through Israeli Medical Associations and professional networks, it included sections on experiences with gender and sexuality content, perceptions of knowledge, the impact of medical school curricula on professional capabilities, and views on integrating gender medicine in medical education. Descriptive and correlational analyses, along with gender-based and medical status-based comparisons, were used, complemented, and enhanced by qualitative analysis of participants' replies. The survey, encompassing 189 respondents, revealed low-to-moderate exposure to gender and sexuality content in medical school curricula, with a similar perception of preparedness. A need for more comprehensive training was widely recognized. The majority valued training in these areas for enhancing professional capabilities, identifying 10 essential gender-related knowledge areas. The preference for integrating gender medicine throughout medical education was significant. Gender-based analysis indicated variations in exposure and perceptions. The study highlights a crucial need for the inclusion of gender and sexuality awareness in medical education and practice. It suggests the necessity for curriculum development, targeted training programs, policy advocacy, mentorship initiatives, and research to evaluate the effectiveness of these interventions. The findings serve as a foundation for future directions in medical education, aiming for a more inclusive, aware, and prepared medical workforce.

Development of a novel cancer diagnostic center to facilitate care coordination for patients with new or suspected diagnoses of cancer.

81 Background: Navigating a cancer diagnosis is a multifaceted challenge, characterized by intricate care coordination that creates potential for workup and treatment delays, lapses in care, and potentially negatively affecting clinical outcomes. We have developed a novel physician and advanced practitioner-led cancer diagnostic program (CDP) to help navigate patients with a suspected cancer through the diagnostic and staging workup, discussion of results, and linkage to oncologic care. Patients are referred to the CDP, an administrative intake coordinator creates a medical chart, verifies insurance, gathers records, and schedules appointments. An intake nurse reaches out within 24 hours to obtain clinical information and review of systems. Patients are offered an in-person or video visit with the advanced practitioner for history and physical, labs, and further coordination of testing. Here, we aim to describe the demographic and clinical patterns of referral and clinical outcomes of patients who were referred to a CDP over one calendar year. Methods: We retrospectively analyzed 154 patients with a new cancer diagnosis to have at least 1 encounter at the Johns Hopkins CDP between January 1, 2022, and December 31, 2022. Data were retrospectively collected from medical records and stored within Johns Hopkins REDCap. Descriptive analyses were used to describe the data. Results: Among the 154 total patients included in this analysis, 65.6% were White, 24.7% were Black, 3.2% were Asian, and 1.9% were of Hispanic, Latino, or Spanish origin. Insurance coverage was most frequently (46.8%), Medicare (40.3%), and Medicaid (8.4%). Referral distribution shows 50 inpatient and 104 outpatient referrals. Primary cancer diagnoses were most often gastrointestinal (39.6%), upper airway and lung (26.6%), and genitourinary (9.1%). Conclusions: The Johns Hopkins CDP exemplifies the potential for dedicated diagnostic and treatment initiation programs to improve oncologic care, particularly for vulnerable populations. The center facilitates expedited diagnostic resolution, to increased ease of workup for patients and providers, and potentially improve clinical outcomes such as linkage to care and time to treatment. Accepting referrals from inpatient and outpatient settings captures a broad patient pool, including those who may otherwise experience lapses in care owing to complex care navigation needs. In conclusion, we describe a novel care delivery model to potentially optimize healthcare delivery in oncologic care. Inpatient Referral Outpatient Referral Percent minority populations 34% 23.1% Stage 4 at diagnosis 74% 52.9% Patients initiating treatment with curative intent 42.3% 30% Median length of hospitalization 7 days N/A Median time from pathology results to discharge -2.5 days N/A Median time from pathology results to treatment initiation 20 days 16.5 days

Multilevel integrated healthcare: The evaluation of Project ECHO® networks to integrate children’s healthcare in Australia

The present empirical study aims to explore medical knowledge sharing in the Australian healthcare context, aiming to broadly evaluate the potential impact of Project ECHO®, an online mentoring and networking health program. We focus on health-related knowledge sharing practices among the network of professionals through formal and informal channels, and across different health and non-health sectors and organisational systems. Studying knowledge transmission among professional networks is essential for optimizing healthcare delivery, promoting innovation, and providing insights on improvement of patient experiences within the healthcare system. We utilize a multilevel approach to shape our data collection strategy. Employing network measures and Multilevel Exponential Random Graph Models, we aim to explore how advice and knowledge sharing behaviours among healthcare professionals and their institutions are interdependently connected. Then, we incorporate network generated results within an evaluation framework for establishing some aspects of the efficiency of the ECHO program along four pillars: Acceptability, Capability, Reachability, and Integration. Our investigation found that among ECHO members, hierarchy is less pronounced compared to across levels and organizations, with certain individuals emerging as central in advice-sharing. The multilevel network perspective showed complex, informal patterns of knowledge and information sharing, including inter-organizational hierarchy, role and sector homophily, brokerage roles with popularity across health organizations, and connectivity through knowledge-sharing in cross-level small group clusters.

A Review- Optimizing Healthcare Delivery: Navigating the Challenges and Benefits of the Satellite Medical Model in Modern Hospital Management

In the rapidly evolving landscape of healthcare management, the Satellite Medical Model has emerged as a strategic solution to enhance service delivery by decentralizing care through a network of satellite facilities connected to a central hub hospital. This model not only improves accessibility and optimizes resource utilization but also addresses workforce shortages, particularly in underserved regions. Rooted in the hub-and-spoke design from the transportation sector, the Satellite Medical Model facilitates efficient healthcare services, allowing primary care units to manage routine cases while more complex conditions are referred to specialized facilities. Despite its advantages, the implementation of this model faces significant challenges, including technical issues related to data security and interoperability, financial constraints, and operational complexities. This paper explores the potential of the Satellite Medical Model in modern hospital management, examining both its benefits and challenges. By identifying strategic approaches to mitigate these barriers, this study aims to provide healthcare leaders with actionable insights to effectively adopt this model, ultimately leading to improved patient outcomes and greater operational efficiency in healthcare delivery.

The Use of AI in Enhancing Patient Safety

The integration of Artificial Intelligence (AI) into patient monitoring systems has marked a transformative shift in healthcare, offering enhanced capabilities for tracking and analyzing patient health data. This paper investigates the application of AI in patient monitoring systems, emphasizing its role in overcoming challenges related to physiological signal capturing and health status estimation. It provides an overview of how AI methodologies, including deep learning and machine learning, are leveraged to improve real-time monitoring, predictive accuracy, and response time in both hospital and home settings. The paper also addresses the current limitations of AI technologies and the potential for future advancements. Also, by examining various applications and discussing the benefits and constraints of AI in this field, the paper aims to highlight the significant impact AI has on improving patient outcomes and optimizing healthcare delivery. Keywords: Artificial Intelligence, Patient Monitoring Systems, Biosignal Analysis, Deep Learning, Machine Learning.

Enhancing Diagnostic Accuracy in Radiology: The Role of Efficient Radiology Staff at Eskag Sanjeevani Hospital, Kolkata, India

Diagnostic imaging is a cornerstone of modern medical diagnosis and treatment, yet missed diagnoses remain a significant issue within radiology, often resulting in delayed treatment and adverse patient outcomes. This article examines the pivotal role of radiology technicians in mitigating missed diagnoses and enhancing diagnostic accuracy. Through a detailed analysis of practices at Eskag Sanjeevani Hospital in Kolkata, India, the article highlights how the radiology team's comprehensive approach to imaging encompassing rigorous adherence to protocols, pattern recognition, and thorough communication contributes to superior diagnostic precision. The discussion includes case studies illustrating the team’s proactive identification of incidental findings, such as abnormalities in paranasal sinuses during a brain CT and posterior abnormalities detected during an abdominal CT, which led to additional diagnostic procedures and timely intervention. These practices demonstrate the significant impact of efficient radiology staff on reducing diagnostic errors, improving patient outcomes, and optimizing healthcare delivery.

Probable Causative Agents and Demographic Patterns of Encephalitis, Meningitis, and Meningoencephalitis in a Single Tertiary Care Center.

Introduction Encephalitis, meningitis, and meningoencephalitis present significant challenges in clinical management owing to their diverse etiologies and potential complications. A high suspicion index is critical for guiding treatment strategies and improving patient outcomes. Understanding the demographic characteristics and frequency of causes of these conditions is essential to deliver optimized care. Objective This study aimed to investigate epidemiological causes and relative outcomes, including mortality, based on cultures, laboratory investigations, and demographic factors among patients with encephalitis, meningitis, and meningoencephalitis in a Saudi Arabian tertiary care center. Methods A retrospective cross-sectional study was conducted at King Abdulaziz Medical City (KAMC) in Jeddah, Saudi Arabia. Data were collected from patients admitted between April 2016 and December 2022 who met the specified inclusion criteria. Results Among 233 patients, meningitis was the most prevalent diagnosis (65.77%), with bacterial agents being the predominant causative agents (79.74%). Higher mortality was significant with pediatrics <5 years and adults >60 years. Conclusion This study provides valuable insights into the epidemiology and clinical outcomes of central neurological infections based on a Saudi Arabian cohort. These findings underscore the importance of an accurate diagnosis and tailored management strategies. Further studies are warranted to enhance our understanding and to inform more predictable characteristics targeted in optimizing healthcare delivery for patients with such conditions.

Charting the Path: Nursing Leadership in Artificial Intelligence Integration into Healthcare

Artificial intelligence, including generative AI, rapidly transforms healthcare by enhancing diagnostic accuracy, patient monitoring, and personalized care. Nurse leaders are pivotal in overseeing AI's ethical implementation and utilization to help ensure patient safety and maintain human-centered care. As AI integration increases, nurse leaders must thoughtfully consider usage training and effective governance. Exploring AI's multifaceted, powerful nature, this article delves into the potential to optimize healthcare delivery and streamline administrative processes while tangling the importance of data privacy, security, and fairness. All hold vital significance for nurse leaders, enabling them to steer the responsible integration of AI in healthcare.

Trends in the integration of medical corporation hospitals in Japan: a national wide longitudinal study between 2017 and 2021

BackgroundMedical corporations, which are non-profit organizations that aim to operate hospitals, clinics, or long-term care facilities, account for more than half of all hospital beds and play a central role in the health care system in Japan. However, limited analysis of hospital integration has been performed. Examining the hospital integration trends of medical corporations can provide valuable insights for optimizing health care delivery and resource allocation in Japan. This study conducted a comprehensive analysis of trends in the hospital integration of medical corporations and market concentration in Japan using nationwide longitudinal data.MethodsThis longitudinal study evaluated the hospital integration of medical corporations in Japan between 2017 and 2021 using medical corporation financial data provided by the Neostage Company. The target population was medical corporations that own hospitals in Japan. The primary outcomes were the horizontal and vertical integration of medical corporation hospitals. Horizontal integration was defined as the integration of two or more hospitals within the same corporation, while vertical integration was defined as the integration of a hospital with different types of health care facilities, such as clinics or long-term care facilities, within a single medical corporation. The Herfindahl–Hirschman Index (HHI) was calculated to measure hospital market concentration using hospital bed data for medical corporations in all prefectures.ResultsThe number of hospitals and hospital beds within medical corporations decreased from 5,670 to 848,174 in 2017 to 5,486 and 814,462 in 2021, respectively. Both horizontal and vertical integration among medical corporation facilities showed a gradual upward trend, increasing from 24.92% to 55.75% in 2017 to 26.92% and 59.42% in 2021, respectively. The mean (standard deviation [SD]) HHI increased slightly from 262.7 (178.6) in 2017 to 275.2 (187.9) in 2021, with rural areas being more concentrated than urban areas, although both remained unconcentrated.ConclusionsThis longitudinal study revealed a gradual downward trend in the number of hospitals and hospital beds owned by medical corporations, while horizontal and vertical integration gradually progressed without any particular medical corporation dominating the market. These findings are expected to contribute to policymaking efforts aimed at providing optimal health care services in Japan.