The journal "Health Care Management Science" publishes research papers related to health care delivery, management, and policy. Papers should focus on decision-making and employ quantitative methods, including management science, operations research, analytics, machine learning, and other emerging fields. Articles must clearly articulate the relevance of the work and its realized or potential impact. Applied research will be considered and of particular interest if there is evidence that it has been implemented or has informed the decision-making process. Submissions that merely describe the routine application of known methods are discouraged. Authors are encouraged to disclose all data and analyses and, where appropriate, provide computational code. The editorial statements for various departments are as follows: Health Care Analytics Department Editors: MARGRÉT BJARNADÓTTIR (University of Maryland) and KONG (Purdue University). With the explosive growth of computational power and available data, we are witnessing rapid changes in the application of analytics in health care. The Health Care Analytics Department welcomes papers that apply a wide range of analytical methods, including those rooted in machine learning, survival analysis, and complex event analysis, which enable health care professionals to improve health system management, patient engagement, spending, and diagnosis. We particularly encourage papers that combine predictive and prescriptive analytics to enhance decision-making and health care outcomes. Contributions can span multiple dimensions, including new methods, new modeling techniques, and health care through real-world cohort studies. Methodologically focused papers must demonstrate practical relevance as well. Similarly, application-centered papers should clearly show improvements over the status quo and available methods through rigorous analysis. Health Care Operations Management Department Editors: NILAY TANIK ARGON (University of North Carolina at Chapel Hill) and BOB BATT (University of Wisconsin). This department invites high-quality papers on the design, control, and analysis of health care system operations. We seek research on traditional operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional issues driven by evolving health care practices. Empirical, experimental, and model-based analytical approaches are all welcome. Papers should draw on interdisciplinary theories and provide insights into operational improvements from the perspectives of patients, service providers, organizations (municipal/government/industry), and/or society. Health Care Management Science Practice Department Editor: VIKRAM TIWARI (Vanderbilt University Medical Center). This department seeks research from academics and practitioners focusing on management science-based solutions directly related to health care practice. Relevance is assessed by the impact on practice and the degree of engagement of researchers with practitioners in understanding the problem context and developing solutions. Effectiveness, or the extent to which the presented results are applicable or will be applicable in practice, is a key evaluation criterion. In addition to meeting the journal's originality standards and making substantial contributions to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable lessons to address the reasons for the project's lack of success. Health Care Productivity Analysis Department Editor: JONAS SCHREYÖGG (Hamburg University). This department invites papers with rigorous methodologies that have significant implications for policy and practice. Papers typically apply theories and techniques to measure the productivity of health care organizations and systems. The journal welcomes cutting-edge parametric and non-parametric techniques, such as data envelopment analysis, stochastic frontier analysis, or partial frontier analysis. Contributions can be multifaceted, including new methods, new combinations of existing methods, or applications of existing methods in new environments. Empirical papers should yield results that are generalizable beyond a selected group of health care organizations. All papers should include a section on the managerial or policy implications for improving productivity. Public Health Policy and Medical Decision-Making Department Editors: EBRU BISH (University of Alabama) and JULIE L. HIGLE (University of Southern California). This department invites high-quality papers using data-driven methods to address significant issues in public health policy and medical decision-making. We welcome submissions that develop and apply mathematical and computational models to support data-driven and model-based analyses addressing these issues. The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that involve high-impact issues related to health policy, treatment planning and design, and clinical applications; develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines; and use model-based analyses as decision-making tools to identify optimal solutions, insights, and recommendations. Articles must clearly articulate the relevance of the work to decision-makers and/or policymakers and its potential impact on patients and/or society. Papers will include explicit contributions within the methodological domain, which may include modeling, analysis, or computational methods. Emerging Topics Department Editor: ALEX MORTON (University of Strathclyde). The Emerging Topics Department will address papers that use innovative quantitative methods to elucidate health care management and frontier policy issues. Such papers may address analytical challenges arising from new health technologies or new organizational forms. Papers in this department may also involve the analysis of new forms of data that are increasingly captured as health systems become more digitalized.