Factors influencing hospital bed utilization: a cross-sectional study in a tertiary care center in Saudi Arabia

Introduction

Background

Hospital bed utilization is a widely used indicator of healthcare system performance and resource allocation (1). Proper bed management ensures timely access to inpatient care, reduces healthcare costs, and improves overall quality of service delivery (1,2). Inappropriate bed use—either overutilization due to prolonged stays or underutilization from delayed admissions—can strain hospital infrastructure and hinder efficiency (3). Globally, efforts to optimize bed occupancy are driven by rising demand, aging populations, and healthcare reforms aimed at improving throughput and reducing length of stay (1,2).

In Saudi Arabia, population growth, regional disparities, and seasonal surges, such as those during the Hajj season, place additional pressure on hospital capacity (4,5). The Ministry of Health (MOH) has identified efficient bed allocation as a strategic objective under Vision 2030, emphasizing data-driven approaches to healthcare transformation (6-8).

Saudi Arabia’s healthcare system is predominantly funded by the government, with the MOH delivering nearly 60% of services (9). The remainder is provided by other public entities and an expanding private sector. In contrast to many Western healthcare systems, the role of general practitioners (GPs) in Saudi Arabia is relatively limited, as outpatient and referral services are primarily overseen by specialists (10). According to World Health Organization (WHO) data, the country’s GP to population ratio remains significantly below the Organization for Economic Co-operation and Development (OECD) average (10). Additionally, the long-term care infrastructure, including nursing homes and geriatric facilities, remains underdeveloped, placing greater pressure on hospitals to accommodate patients requiring extended inpatient care. These systemic factors contribute to longer hospital stays, particularly among older adults who require post-acute support.

Rationale and knowledge gap

While international literature has documented several contributing factors to hospital bed inefficiencies, including patient readiness, physician decision-making, and administrative processes, research in the Gulf region remains limited and fragmented (11). Few studies in Saudi Arabia have simultaneously examined the combined influence of patient-related, physician-related, and administrative-related factors on bed utilization, especially from the perspective of healthcare providers.

Furthermore, most existing research isolates individual domains, such as discharge planning or hospital information systems, without integrating the provider experience into systemic assessments. Understanding frontline perspectives is essential because healthcare providers directly observe operational bottlenecks and clinical barriers that impact patient flow and bed turnover (12). This study aims to bridge that gap by capturing provider-based insights from a tertiary care context in Makkah, providing a holistic understanding of the factors influencing hospital bed utilization.

Recent international analyses continue to underscore the ‘nearness-to-death’ (NTD) effect, wherein end-of-life periods account for a disproportionately high share of hospital bed usage. For example, Jones [2023] synthesizes findings showing that approximately 55% of an individual’s lifetime hospital bed occupancy occurs in the final year of life, regardless of age at death (13). This insight plays a pivotal role in refining hospital bed demand models. However, comparable data are currently lacking for Saudi Arabia and the broader region, highlighting a valuable opportunity for future research and more informed resource planning.

Objective

This study aims to identify patient, physician, and administrative-related factors affecting hospital bed utilization at King Abdullah Medical City (KAMC) in Makkah, Saudi Arabia, based on the perceptions of healthcare providers. We present this article in accordance with the STROBE reporting checklist (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-42/rc).

Methods

This study employed a cross-sectional, descriptive-analytical design to investigate factors influencing hospital bed utilization from the perspective of healthcare providers. The study was conducted at KAMC, a major tertiary care hospital in Makkah, Saudi Arabia. The target population consisted of all healthcare providers employed at KAMC, estimated to be approximately 2,000 staff members. Based on calculations using Raosoft’s online sample size calculator, set at a 95% confidence level and a 5% margin of error, the required sample size was determined to be 377 participants. Ultimately, 351 valid responses were received, yielding a response rate of 93.1% relative to the target, which is considered acceptable for descriptive and exploratory analyses. Due to the use of a convenience sampling method distributed via WhatsApp Messenger, it was not possible to track individual non-respondents. All collected questionnaires were complete, and no missing data were identified.

Participants included physicians, nurses, technicians, administrators, and other healthcare staff from various departments. Data were collected using a structured, self-administered questionnaire (Appendix 1). The instrument was adapted from a study by Kanwar et al. (14), who developed and validated a tool to explore clinician perceptions of hospital bed utilization. The original instrument covered three key domains: patient-related, physician-related, and administrative factors (Table 1). For contextual relevance, the items were reviewed and tailored to reflect the operational environment of a Saudi tertiary hospital. The questionnaire consisted of 18 items (6 per domain), each rated on a 5-point Likert scale, and was designed to assess perceived factors affecting hospital bed utilization. A summary of the questionnaire items is presented in Table 2. Cronbach’s alpha was used to evaluate internal consistency, yielding an overall alpha of 0.858, indicating excellent reliability. Domain-specific scores were as follows: patient-related (α =0.675), physician-related (α =0.677), and administrative-related (α =0.842). Although the alpha values for the patient- and physician-related domains fell slightly below the 0.70 threshold, they still reflect moderate internal consistency and are considered acceptable for exploratory research purposes.

The questionnaire was distributed electronically through the hospital’s internal system. Participation was voluntary and anonymous, and informed consent was implied through completion of the survey. To ensure data integrity, responses were collected directly by the researcher and securely stored for analysis.

Statistical analysis

Data were analyzed using IBM SPSS Statistics Version 26. Descriptive statistics, including mean (M) and standard deviation (SD), were used to assess participant demographics and responses. Pearson correlation was used to explore relationships among the three-factor domains. Statistical significance was set at a two-tailed P value ≤0.05 for all analyses. The normality of domain scores was assessed visually through histograms and Q-Q plots, and further supported by the similarity between mean, median, and mode values. These findings indicate an approximately normal distribution, justifying the use of Pearson correlation analysis.

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Review Board (IRB) at KAMC (approval number 23-1046, dated March 27, 2023), and informed consent was obtained from all individual participants.

Results

Participant characteristics

A total of 351 healthcare providers participated in the study (Table 1). Of these, 53.8% were male and 46.2% were female. Most participants were between 36 and 45 years of age (47.3%), followed by those aged 26–35 years (31.1%), and 46–55 years (16.0%). Regarding experience, 30.8% had worked for 11–15 years, and 28.5% had 6–10 years of experience. Regarding professional roles, physicians constituted the largest group (48.4%), followed by technicians (16.8%), nurses (14.8%), and administrative staff (12.8%).

Descriptive analysis of factor domains

The survey included 18 items across three domains: patient-related, physician-related, and administrative-related factors. Each item was scored on a 5-point Likert scale (Table 2).

The highest average score was observed in patient-related factors (mean =3.92, SD =0.86), indicating a strong perception of their impact on hospital bed utilization. Physician-related and administrative-related factors followed with means of 3.54 (SD =1.03) and 3.44 (SD =1.06), respectively (Figure 1).

Figure 1 Mean scores by domain.

Within each domain, specific factors were scored. For example, under patient-related factors, the item “Patient’s uncooperative attitude and refusal of discharge” had the highest mean score (M =4.11), while “Large number of hospital beds” was lowest (M =3.75). For physician-related factors, “Expertise of doctors” (M =3.99) was the highest-rated item, and “Long duty hours” (M =3.22) was the lowest. The most highly rated administrative factor was “Lengthy admission and discharge procedure” (M =3.77), while “non-proficient hospital information system” had the lowest score (M =3.37).

Inferential analysis

Pearson correlation analysis was conducted to examine associations between each factor domain and perceptions of hospital bed utilization. Table 3 summarizes the correlation coefficients and associated P values.

The results indicate statistically significant positive correlations between all three domains and hospital bed utilization, with patient-related factors showing the strongest correlation (r=0.68, P<0.001). Physician-related and administrative-related factors also demonstrated moderate positive correlations (r=0.59 and r=0.45, respectively).

Discussion

Key findings

This study revealed that healthcare providers perceive patient-related factors as the most significant contributors to hospital bed utilization, followed by physician-related and administrative-related factors. Among all items, uncooperative patient behavior and refusal to discharge were rated the most influential. Inferential analysis confirmed statistically significant correlations between all three domains and perceived bed utilization, with patient-related factors showing the strongest association.

Strengths and limitations

A significant strength of this study is that it uses a validated tool adapted for the Saudi context, which enhances relevance and reliability. However, the cross-sectional design limits causal inferences, and the study was restricted to one tertiary care center in Makkah. As such, the findings may not be generalizable to other hospitals in Saudi Arabia or different care settings. Multi-center studies are recommended to validate these results.

The use of convenience sampling may introduce selection bias, as respondents could differ systematically from those who chose not to participate. To help mitigate this limitation, the survey was distributed across multiple departments and job categories to capture a broad spectrum of staff perspectives. Although the final sample size (n=351) fell slightly short of the calculated requirement, it represents a high response rate and includes a broad cross-section of staff perspectives. Nonetheless, this limitation should be taken into account when interpreting the findings. Additionally, the sample included a disproportionately higher number of physicians relative to the hospital’s overall workforce composition. This imbalance may have influenced the aggregated perception results and potentially limited the generalizability of the findings across different staff groups. Moreover, the use of self-reported perceptions introduces the potential for perception bias, which may not accurately reflect the actual operational dynamics (15). Non-response bias is also a consideration, as those who opted not to participate may have held differing perspectives. In light of these limitations, the findings should be viewed as exploratory and intended to inform hypothesis generation rather than draw definitive conclusions. Also, this study relied on perception-based data, which may not fully capture the operational realities of hospital throughput. Key influencing factors, such as patient acuity, clinical complexity, and institutional scheduling capacity, were not directly measured, which may have limited the precision of the findings (16). Another limitation of this study is the absence of operational hospital data, such as real-time bed occupancy, patient flow metrics, and discharge delay statistics. Comparing perceived barriers with actual patient flow data would provide a more comprehensive understanding of throughput challenges and is recommended for future research. Lastly, Future research could incorporate benchmarking methods, such as plotting hospital beds per number of deaths against deaths per 1,000 population, to provide a more nuanced assessment of bed adequacy with end-of-life care needs.

Comparison with similar research

The high influence of patient behavior on bed utilization aligns with previous findings highlighting the impact of patient discharge readiness and expectations on length of stay (17,18). Patient refusal to discharge may stem from fear, a lack of post-discharge support, or a misunderstanding of their recovery status, all of which can extend hospital stays unnecessarily (19). Similar challenges have been noted in both high-income and middle-income countries, where socio-demographic and psychosocial barriers hinder timely discharge (20). However, contextual differences such as healthcare financing and social support systems may limit the direct applicability of these findings to Saudi Arabia.

Physician-related factors such as clinical expertise and defensive medicine also strongly influenced bed utilization. This reflects the findings of Kanwar et al. (14), who reported that residents’ cautious decision-making, driven by legal concerns and unclear discharge authority, can prolong patient stays. Defensive medicine, often intended to avoid litigation, contributes to the inefficient use of inpatient resources (21).

Although ranked third, administrative factors remained significantly associated with bed utilization. Lengthy admission and discharge procedures, as well as the lack of standardized operating protocols, were viewed as major obstacles. These findings are consistent with global studies that emphasize the impact of fragmented workflows, inefficient hospital information systems, and poor discharge planning on reducing hospital throughput (14,22). Hospitals with integrated information systems and streamlined processes show higher efficiency and lower average length of stay (23,24).

Explanations of findings

The prominence of patient-related factors in perceived bed utilization can be explained by the direct impact of patient cooperation on discharge processes (25). When patients are not mentally or emotionally prepared to leave, or when families are unable to provide adequate post-discharge care, delays in bed turnover are inevitable (26). In the Saudi context, cultural expectations around family caregiving, reluctance to question medical authority, and limited access to transitional care may further prolong hospital stays (27).

Physician-related factors, particularly clinical expertise and legal defensiveness, play a critical role due to physicians’ control over admission and discharge decisions (25). Experienced clinicians are more confident in managing discharge timing and navigating complex cases efficiently (14). Conversely, less experienced providers may delay discharge to avoid potential risks or complications, especially in healthcare systems where discharge policies are vague or inconsistently applied (28-30). Defensive medicine, often practiced to prevent litigation, leads to prolonged monitoring and unnecessary inpatient stays, thereby contributing to inefficient resource utilization (31).

Administrative inefficiencies, such as lengthy discharge processes, a lack of standardized procedures, and delays in interdepartmental coordination, are well-documented contributors to poor bed turnover (32).

These systemic issues are often worsened by inadequate hospital information systems or a lack of integration across departments (33). In facilities lacking real-time digital tools for bed tracking and discharge planning, even well-intentioned policies may be inconsistently implemented, thereby limiting their impact (34).

Hospital structural characteristics, particularly overall occupancy rates and the segmentation of beds by specialty, have been shown to influence patient flow and the risk of ‘turn-away’ events, where no bed is immediately available for an incoming admission. For instance, Jones [2024] found that hospitals operating near full capacity, especially those with highly segmented specialty bed pools, face greater operational strain and reduced flexibility, which in turn heightens the likelihood of turn-away incidents (35). Also, many hospitals lack real-time systems to monitor bed availability or track patient progress across phases of care, leading to missed opportunities for improving patient flow. Integrating clinical scheduling platforms with discharge readiness tools may enhance bed turnover rates and overall hospital efficiency (36). As noted by Humphreys et al. [2022], optimizing hospital capacity necessitates system-wide strategies, including dynamic scheduling, simulation modeling, and predictive analytics. These tools improve operational efficiency by aligning patient demand with available resources, offering a valuable complement to perception-based insights such as those presented in this study (37).

Internationally, efforts to reduce discharge delays have included the use of dedicated discharge coordinators embedded within multidisciplinary teams to streamline workflows and reduce the length of stay, often by up to one day (38). Physician-related factors, such as delays linked to defensive medicine, have been addressed through clearer clinical protocols and policy reforms aimed at curbing defensive practices (39). Administrative inefficiencies have been addressed through the adoption of integrated hospital information systems and electronic health records (EHRs), which enhance communication, minimize bottlenecks, and support standardized admission and discharge processes (40). A scoping review also identified that team-based, multidisciplinary discharge planning initiatives, particularly when supported by structured quality improvement frameworks, have been effective in reducing delays and improving patient flow (30). While some Saudi hospitals have piloted similar reforms, nationwide implementation remains inconsistent, highlighting a key opportunity for broader, system-level adoption.

At the national level, and in alignment with Saudi Vision 2030, the MOH launched the Health Sector Transformation Program in 2021 (41). This initiative aims to strengthen hospital capacity planning through demand forecasting, real-time bed management, and geographically targeted needs assessments. In Makkah, hospitals implement seasonal surge capacity strategies during the Hajj period, including temporary bed expansions, staff redeployment, and expedited triage and discharge protocols to manage the influx of pilgrims (5). These efforts are coordinated under the MOH’s emergency response framework for mass gatherings.

Taken together, these findings indicate that delays in hospital bed utilization are not solely the result of clinical decisions or patient behavior but arise from the intersection of cultural norms, legal pressures, and system-level administrative barriers (42,43).

Implications and actions needed

These findings highlight potential areas for future research and quality improvement efforts, where multi-level strategies—targeting patients, providers, and system-level processes—could be further explored and empirically tested. Interventions should include structured discharge planning, patient education to support transition readiness, and enhancement of physician decision-making authority and training (44). Administratively, hospitals should adopt digital tracking tools and refine their workflows to reduce admission and discharge delays (45,46). Integrating quality assurance mechanisms and revising discharge policies can also help mitigate bed bottlenecks and increase capacity for incoming cases (29).

Gathering and analyzing provider perspectives offers operational value by revealing inefficiencies and supporting more informed strategic decisions. This process aligns with the principles of the Value of Information (VOI), where systematically collected insights play a critical role in building strong business cases for quality improvement initiatives and long-term capacity planning (47). Moreover, recommended training initiatives could include structured discharge planning protocols, guidance on legal and ethical decision-making in discharge contexts, and enhanced communication skills to effectively engage patients and families in assessing discharge readiness. Lastly, future research could integrate simulation modeling, case-mix analysis, and capacity optimization techniques to quantify the underlying determinants of hospital bed utilization more precisely.

Conclusions

This study found that hospital bed utilization is significantly influenced by patient-related, physician-related, and administrative-related factors, with patient-related factors showing the most substantial impact. Uncooperative patient behavior and discharge refusal were identified as the most influential factors, followed by physician expertise and procedural inefficiencies in hospital operations.

These findings reinforce the potential value of a systems-based approach to bed management that considers clinical decision-making, patient engagement, and administrative efficiency. While no interventions were tested in this study, the results suggest that improving discharge planning, enhancing communication, and implementing standardized protocols may help optimize resource use and reduce unnecessary bed occupancy.

By capturing the perceptions of healthcare providers at a major tertiary care hospital in Saudi Arabia, this study provides valuable, context-specific insights that may inform future policy development, training programs, and workflow reforms aimed at enhancing hospital efficiency. Further empirical research is needed to validate these recommendations using objective outcome measures.

Acknowledgments

The authors would like to thank the executive board and the institutional review board of KAMC in Makkah for their approval and support in conducting this study. Sincere appreciation is extended to all healthcare professionals who participated in the survey.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-42/rc

Data Sharing Statement: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-42/dss

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-42/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-42/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Review Board (IRB) at King Abdullah Medical City (KAMC) (approval number 23-1046, dated March 27, 2023), and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Alharbi S, Alasmari A, Hanafy E, et al. Reduction of hospital bed cost for inpatient overstay through optimisation of patient flow. BMJ Open Qual 2023;12:e002142. [Crossref] [PubMed]
2. Al Harbi S, Aljohani B, Elmasry L, et al. Streamlining patient flow and enhancing operational efficiency through case management implementation. BMJ Open Qual 2024;13:e002484. [Crossref] [PubMed]
3. Savioli G, Ceresa IF, Gri N, et al. Emergency Department Overcrowding: Understanding the Factors to Find Corresponding Solutions. J Pers Med 2022;12:279. [Crossref] [PubMed]
4. Kattan W, Alshareef N. 2022 insights on hospital bed distribution in Saudi Arabia: evaluating needs to achieve global standards. BMC Health Serv Res 2024;24:911. [Crossref] [PubMed]
5. Shujaa A, Alhamid S. Health response to Hajj mass gathering from emergency perspective, narrative review. Turk J Emerg Med 2015;15:172-6. [Crossref] [PubMed]
6. Mani ZA, Goniewicz K. Transforming Healthcare in Saudi Arabia: A Comprehensive Evaluation of Vision 2030’s Impact. Sustainability 2024;16:3277.
7. Saudi Vision 2030. Health Sector Transformation Program - Vision 2030. 2022. Available online: https://www.vision2030.gov.sa/en/explore/programs/health-sector-transformation-program
8. MOH. Health Sector Transformation Plan. 2020. Available online: https://moh.gov.sa/
9. MOH. MOH Statistical Yearbook - 2023. 2023. Available online: https://www.moh.gov.sa/en/Ministry/Statistics/book/Pages/default.aspx
10. Alghaith T, Liu J, Alluhidan M, et al. editors. A Labor Market Assessment of Nurses and Physicians in Saudi Arabia: Projecting Imbalances between Need, Supply, and Demand. World Bank; 2021.
11. Alatawi A, Ahmed S, Niessen L, et al. Systematic review and meta-analysis of public hospital efficiency studies in Gulf region and selected countries in similar settings. Cost Eff Resour Alloc 2019;17:17. [Crossref] [PubMed]
12. Mihalj M, Corona A, Andereggen L, et al. Managing bottlenecks in the perioperative setting: Optimizing patient care and reducing costs. Best Pract Res Clin Anaesthesiol 2022;36:299-310. [Crossref] [PubMed]
13. Jones RP. Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System. Int J Environ Res Public Health 2023;20:7171. [Crossref] [PubMed]
14. Kanwar V, Gupta AK, Goel S, et al. Hospital Bed Utilization: Perceptions of Healthcare Practitioners from Northern India. International Journal of Hospital Research 2015;4:113-8.
15. Latkin CA, Edwards C, Davey-Rothwell MA, et al. The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland. Addict Behav 2017;73:133-6. [Crossref] [PubMed]
16. Burdett RL, Corry P, Yarlagadda P, et al. Multicriteria optimization techniques for understanding the case mix landscape of a hospital. European Journal of Operational Research 2024;319:263-91.
17. Weiss M, Yakusheva O, Bobay K. Nurse and patient perceptions of discharge readiness in relation to postdischarge utilization. Med Care 2010;48:482-6. [Crossref] [PubMed]
18. Wang M, Wang Y, Meng N, et al. The factors of patient-reported readiness for hospital discharge in patients with depression: A cross-sectional study. J Psychiatr Ment Health Nurs 2021;28:409-21. [Crossref] [PubMed]
19. Østen K, Menichetti J, Nordfalk JM, et al. Recall and Understanding of Discharge Information in Observation Ward Patients: An Explorative Observational Study. Front Commun 2021;6:736095.
20. Liljas AEM, Jensen NK, Pulkki J, et al. The influence of sociodemographic factors and close relatives at hospital discharge and post hospital care of older people with complex care needs: nurses' perceptions on health inequity in three Nordic cities. Eur J Ageing 2022;19:189-200. [Crossref] [PubMed]
21. Pierce AT, Sheaffer WW, Lu PG, et al. Impact of "Defensive Medicine" on the Costs of Diabetes and Associated Conditions. Ann Vasc Surg 2022;87:231-6. [Crossref] [PubMed]
22. Ortiga B, Salazar A, Jovell A, et al. Standardizing admission and discharge processes to improve patient flow: a cross sectional study. BMC Health Serv Res 2012;12:180. [Crossref] [PubMed]
23. McKibbon KA, Lokker C, Handler SM, et al. The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials. J Am Med Inform Assoc 2012;19:22-30. [Crossref] [PubMed]
24. Alolayyan MN, Alyahya MS, Alalawin AH, et al. Health information technology and hospital performance the role of health information quality in teaching hospitals. Heliyon 2020;6:e05040. [Crossref] [PubMed]
25. Gonsalves E. Re-engineering the hospital discharge process: an upsurge in service efficiency and patient serving capacity. Health Care Current Reviews 2017;5:50.
26. van den Ende E, Schouten B, Pladet L, et al. Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands. Int J Qual Health Care 2023;35:mzad022. [Crossref] [PubMed]
27. Alfahmi MZ, Elmorsey SA, Aburuzaizah RM, et al. Opportunities and Barriers in the Planning of Hospital Bed Capacity in a Tertiary Care Hospital in Makkah, Saudi Arabia. Saudi Journal of Health Systems Research 2023;3:75-84.
28. Everall AC, Guilcher SJT, Cadel L, et al. Patient and caregiver experience with delayed discharge from a hospital setting: A scoping review. Health Expect 2019;22:863-73. [Crossref] [PubMed]
29. Micallef A, Buttigieg SC, Tomaselli G, et al. Defining Delayed Discharges of Inpatients and Their Impact in Acute Hospital Care: A Scoping Review. Int J Health Policy Manag 2022;11:103-11. [Crossref] [PubMed]
30. Cadel L, Guilcher SJT, Kokorelias KM, et al. Initiatives for improving delayed discharge from a hospital setting: a scoping review. BMJ Open 2021;11:e044291. [Crossref] [PubMed]
31. Ries NM, Jansen J. Physicians' views and experiences of defensive medicine: An international review of empirical research. Health Policy 2021;125:634-42. [Crossref] [PubMed]
32. Siyoto S, Tule AR. Analysis of Bed Occupancy Rate (BOR) in Terms of Internal Factors (Procedures, Doctor, Nurse, Facilities and Infrastructure). Global Journal of Health Science 2019;11:77.
33. Hunter M, Peters S, Khumalo N, et al. Analysis of patient flow and barriers to timely discharge from general medical wards at a tertiary academic hospital in Cape Town, South Africa. BMC Health Serv Res 2024;24:287. [Crossref] [PubMed]
34. Keniston A, McBeth L, Pell J, et al. The Effectiveness of a Multidisciplinary Electronic Discharge Readiness Tool: Prospective, Single-Center, Pre-Post Study. JMIR Hum Factors 2021;8:e27568. [Crossref] [PubMed]
35. Jones RP. A New Approach for Understanding International Hospital Bed Numbers and Application to Local Area Bed Demand and Capacity Planning. Int J Environ Res Public Health 2024;21:1035. [Crossref] [PubMed]
36. Burdett RL, Kozan E. An integrated approach for scheduling health care activities in a hospital. European Journal of Operational Research 2018;264:756-73.
37. Humphreys P, Spratt B, Tariverdi M, et al. An Overview of Hospital Capacity Planning and Optimisation. Healthcare (Basel) 2022;10:826. [Crossref] [PubMed]
38. Bechir G, Bechir A. Reducing Delays, Improving Flow: The Importance of a Dedicated Discharge Coordinator in Hospital Discharge Planning. Cureus 2025;17:e85879. [Crossref] [PubMed]
39. Zarei E, Yousefi I, Shiranirad S, et al. Policy Options for Reducing Defensive Medicine Behaviors: A Multi-Method Study. Med J Islam Repub Iran 2024;38:86. [Crossref] [PubMed]
40. Yolanda I, Kosasih AT, Paramarta V, et al. The Influence of Management Information Systems, Process Innovation and Operational Efficiency in Hospitals. Morfai Journal 2025;5:14-21.
41. Saudi MOH. Health Sector Transformation Program Delivery Plan. Delivery plan 2020-2021 HSTP-SASH-0005.2, Ministry of Health. 2021. Available online: https://www.vision2030.gov.sa/media/u5xapka3/2021-2025-health-sector-transformation-program-delivery-plan-en.pdf
42. Åhlin P, Almström P, Wänström C. Solutions for improved hospital-wide patient flows - a qualitative interview study of leading healthcare providers. BMC Health Serv Res 2023;23:17. [Crossref] [PubMed]
43. Siciliani L, Moran V, Borowitz M. Measuring and comparing health care waiting times in OECD countries. Health Policy 2014;118:292-303. [Crossref] [PubMed]
44. Fatani A, Alzebaidi S, Alghaythee HK, et al. The Role of the Discharge Planning Team on the Length of Hospital Stay and Readmission in Patients with Neurological Conditions: A Single-Center Retrospective Study. Healthcare (Basel) 2025;13:143. [Crossref] [PubMed]
45. Ward-Stockham K, Omonaiye O, Darzins P, et al. Understanding the influences on hospital discharge decision-making from patient, carer and staff perspectives. BMC Health Serv Res 2024;24:1097. [Crossref] [PubMed]
46. Maloney CG, Wolfe D, Gesteland PH, et al. A tool for improving patient discharge process and hospital communication practices: the "Patient Tracker". AMIA Annu Symp Proc 2007;2007:493-7.
47. Tuffaha HW, Gordon LG, Scuffham PA. Value of information analysis in healthcare: a review of principles and applications. J Med Econ 2014;17:377-83. [Crossref] [PubMed]