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Open research questions in Healthcare Operations and Scheduling Optimization

42 unresolved questions extracted from the limitations and future-work sections of 414 Healthcare Operations and Scheduling Optimization papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Background Diagnosis-related group (DRG)-based prospective payment is intended to improve efficiency and control healthcare costs, but its impact on high-complexity surgical services remains uncertain.

    DRG-based payment and governance in high-complexity thoracic surgery: implications for efficiency and care delivery · 2026 · DOI

  • While this systematic review provides valuable insights, limitations should be acknowledged. The majority of the studies included are methodologically weak (75.4%), consisting of exploratory or pilot descriptive studies. Given the methodological weaknesses of existing stud- ies and the limited number of large-scale BPMN imple- mentation projects, the validity of the results presented remains questionable. Indeed, most studies focused on prototypes [42,46,49,51,70,73,81,88], small-scale descrip- tive projects [43,45,50,53,57,62,63,77,80,82-85,91-98], or theoretical explorations [41,44,55-57,59,66,68,69], underscor- ing the need for further real-world research. Furthermore, the concentration of studies included in European countries (68.9%) is a key factor limiting the generalizability of our results.

    The Implementation of a Business Process Model and Notation for Modeling Patient Health Care Trajectories: Systematic Review. · 2026 · DOI

  • in modeling complex decision scenarios create demand for alternatives such as CMMN, which enables dynamic, condition-based task activation. CFIR Domain III: Inner Setting Domain The CFIR domain III corresponds to the structural characteristics of the setting, including the existing IT infrastructure. J Med Internet Res 2026 | vol. 28 | e78506 | p.

    The Implementation of a Business Process Model and Notation for Modeling Patient Health Care Trajectories: Systematic Review. · 2026 · DOI

  • 7.1 Deepening intelligent construction In response to the national policies of medical digital transforma- tion and “Internet Plus Healthcare,” the hospital’s current multiple systems (appointment, medical record, finance, etc.) have not achieved interconnection, which affects work efficiency. On the basis of inte- grating 10 independent systems into 2 platforms, the hospital will build a unified patient service data model relying on the data middle platform to realize full-process data linkage analysis. In the future, it is necessary to deepen the integration of artificial intelligence and digi- tal technologies, such as using natural language processing (NLP) to analyze patient complaints and feedback and identify pain points in real time; introducing machine learning to predict outpatient peaks and dynamically adjust the number of windows; developing intelligent RPA to handle cross-system data entry and release human resources; adopting AI guidance and intelligent medical insurance Q&A to divert consultation pressure, bridge the “digital divide” for older adults patients, and create a “seamless queuing” model. Replacing “patient running around” with “information flow” will further improve service convenience. 7.2 Expanding service connotation boundaries Closely following the medical reform orientation of full-cycle health management, value-added services such as health consultation, chronic disease follow-up, and medication guidance will be added for groups such as older adults and chronic disease patients, extending services from medical treatment processes to full-cycle health man- agement. Psychological counseling specialists will be introduced in response to patients’ emotional pain points to enhance the human- centered nature of services. 7.3 Improving data-driven management system To meet the requirements of refined medical management, a sophisticated management system will be constructed based on the structured data accumulated by the service center. Performance appraisal will be optimized by incorporating “one- time resolution rate” and “patient emotional improvement degree” into assessment indicators linked to compensation, so as to stimu- late staff motivation. A closed-loop governance mechanism of “data monitoring–problem positioning–scheme optimization– effect evaluation” will be established to optimize processes through the PDCA cycle. The scope of data collection will be expanded to build prediction models, realizing the transformation of management from “passive response” to “active prevention”.

    Construction and practice of a four-dimensional integrated one-stop outpatient service model based on patient journey mapping · 2026 · DOI

  • This study provides new insights, but several limitations should be considered. First, the study was conducted in a single tertiary hospital, which may limit the generalizability of the findings. Second, although we adjusted for a range of demographic, socioeconomic, and clinical variables, residual confounding and bias may still remain because of the retrospective design and the absence of some potentially relevant factors, such as psychological FIGURE 2 Subgroup analyses of the association between preoperative waiting time and no-show risk in day surgery. Odds ratios (ORs) with 95% confidence intervals (CIs) are presented for quartiles of preoperative waiting time: Q1 (0–<3 days), Q2 (3–<6 days), Q3 (6–<11 days), and Q4 (≥11 days). Estimates were derived from multivariable logistic regression models. be protective. This provides empirical support for viewing preoperative waiting time may represent a potentially modifiable operational factor. Unlike previous research focusing on overall association (3, 10), this study reveals risk heterogeneity through subgroup analyses. The elevated risk observed among patients aged 45 to 65 years in the longest waiting group may reflect conflicts between professional responsibilities, family obligations, and scheduled surgery.

    Preoperative waiting time and no-show risk in day surgery: a large-scale cohort study · 2026 · DOI

  • Leadership involvement in AI adoption varies widely, with only 44% of respondents rating leadership support at levels 4-5, while 26% rated it at levels 1-2, suggesting inconsistent institutional commitment to AI transformation.

    Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals · 2026 · DOI

  • Staff resistance to digital change exists due to lack of AI tooling knowledge, fear of changing working habits, and concerns about job loss, indicating a need for purposeful training efforts and organizational commitment to digital change.

    Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals · 2026 · DOI

  • Budget constraints are a major barrier, with only 16% of hospitals having high or very high budgets and 28% having no budget at all, significantly hindering AI tool adoption and technical infrastructure upgrades.

    Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals · 2026 · DOI

  • Technological infrastructure restrictions are a significant issue, with 74% of participants rating severity at level 3 or above, including problems with outdated servers, slow networks, and insufficient hardware that prevent hospitals from fully supporting cutting-edge AI systems.

    Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals · 2026 · DOI

  • Lack of data is reported as a major barrier by 24% of respondents, with 40% rating data quality as moderate and 36% rating it as low or very low, causing data inconsistencies, incomplete documentation, and decreased performance and reliability of AI-based prediction models.

    Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals · 2026 · DOI

  • The mean costs of delayed discharge also varied widely (between 142 and 31,935 USD PPP adjusted), reflecting the variability in mean days of delay per patient.

    Delayed Hospital Discharges of Older Patients: A Systematic Review on Prevalence and Costs · 2017 · DOI

  • CONCLUSION In conclusion, overestimation and underestimation of scheduled operation times represent a widespread concern that warrants further attention.

    Predicting the unpredictable: A retrospective cohort study to determine the accuracy of estimated operative duration in orthopedic surgery · 2026 · DOI

  • When supply is scarce, system performance varies little across notification thresholds, with later notification offering practical protection of priority access.

    Optimizing Jumper Notification Timing to Minimize Vaccine Waste and Requester Waiting Time · 2026 · DOI

  • Models that predict operative time in spine surgery to promote efficient use of OR resources are lacking.

    Predicting Operative Time of Single-level Lumbar Laminectomy Based on Patient Factors · 2026 · DOI

  • There is insufficient specific data on the financial costs of missed pre-surgery and pre-procedure appointments within the Veterans Administration, but general healthcare knowledge indicates significant potential costs related to resource underutilization and care delays.

    What are the costs of missed appointments for pre-surgery and pre-procedures in the Veterans Administration? · 2026 · DOI

  • KEYWORDS modular surgical supply kit, operating room efficiency, preoperative preparation, randomized controlled trial, thyroid surgery, workflow…

    Application of modular surgical supply kits to preoperative preparation for thyroid surgery: a randomized controlled study · 2026 · DOI

  • The study was geographically limited to hospitals in Dhaka and Chattogram, suggesting findings may not be generalizable to other regions of Bangladesh or different healthcare contexts.

    Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals · 2026 · DOI

  • There are also opportunities for further research on a range of nurse staff planning aspects: skill mix, nursing work other than direct patient care, quantifying risks and benefits of staffing below or above a target level, and validating staffing methods in a range of hospitals.

    How many nurses do we need? A review and discussion of operational research techniques applied to nurse staffing · 2019 · DOI

  • Despite a long history of health services research that indicates that having sufficient nursing staff on hospital wards is critical for patient safety, and sustained interest in nurse staffing methods, there is a lack of agreement on how to determine safe staffing levels.

    How many nurses do we need? A review and discussion of operational research techniques applied to nurse staffing · 2019 · DOI

  • The case fills a need for material that covers issues in healthcare delivery, which the basic tools of process analysis and queuing theory are insufficient to fully address.

    Case Article—Miller Pain Treatment Center—Eastern Hospital Outpatient Center · 2017 · DOI

  • However, so little is known about the proper distribution of surgeons, their contribution to rural health care, and the safety of rural surgery that policy cannot be shaped with confidence.

    Rural Hospital Inpatient Surgical Volume: Cutting‐edge Service or Operating on the Margin? · 1994 · DOI

  • This rule-identification procedure is shown to be easily adaptable for circumstances with limited knowledge about the environmental factors; it also reveals that the simple Bailey-Welch individual-appointment rules are surprisingly robust.

    Minimizing Total Cost in Scheduling Outpatient Appointments · 1992 · DOI

  • This paper views medical specialization as the outcome of a choice process which continues over an extended time period spanning, but not necessarily limited to the years of undergraduate medical training.

    A Markov chains model of medical specialty choice† · 1974 · DOI

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42 open questions have been extracted from the limitations and future-work passages of 414 Healthcare Operations and Scheduling Optimization papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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