Introduction
Efficient patient flow management in hospitals is crucial for timely and effective care. Overcrowding in emergency rooms and long wait times can lead to an increase in patient morbidity and mortality, as well as a decrease in patient satisfaction. 1Enhancing ER to OPD referrals and managing OPD patient numbers are crucial to solving these problems. 2However, referrals from the OPD to the ER and delays in OPD appointments further exacerbate ER overcrowding, imposing an extra burden on ER resources.3 Understanding how ER visits, ER-to-OPD referrals, and OPD patient volume interact and how delayed OPD care affects ER utilization is essential to allocating resources effectively and improving outcomes. 4This retrospective study analyzes the trends, referral rates, correlations, and impact of ER referrals on the OPD workload at Ibrahim Bin Hamad Obaidullah Hospital, a 200-bed facility, from January to June 2024. It identifies the monthly referral rates from ER to OPD and explores the correlation between these variables. 

Previous reviewed studies reveal a steady increase in ER visits and OPD referrals, with notable monthly variations. This referral rate provides insights into the uniformity and variations in general practitioner referral practices, seasonal diseases, and unidentified factors at the practice, patient, or community level.5 Seasonal factors and specific health crises, such as mental illnesses and increased infectious disease outbreaks (flu, dengue fever), school cycles, natural catastrophes, and heat-related cases at distinct times of the year, may influence this trend. 6The correlation analysis reveals a positive relationship between ER visits and OPD referrals, indicating that higher ER visit volumes are linked to increased OPD referrals. This finding emphasizes the interdependence of ER and OPD workflows. 7

While ER referrals contribute to the OPD workload, their impact in our study is anticipated to be mild to moderate. The study quantifies this impact and suggests strategies for managing it effectively. Previous studies have emphasized the importance of analyzing hospital patient flow to enhance healthcare delivery.4Understanding referral patterns can assist in optimizing the use of hospital resources.8 Furthermore, examining trends and correlations in patient data can offer insights into potential obstacles and opportunities for enhancement.1The main goal of this study is to assess how ER referrals impact the workload of the OPD. This involves calculating the percentage of OPD patient volumes resulting from ER referrals and comparing the workload with and without ER referrals to analyze any changes.
Patients and Methods:
Data Collection:
Retrospective Data Extraction: Extract data on total ER visits, OPD referrals, and OPD patient volumes from hospital records from January to June 2024. Data includes demographic details such as age and gender.
Data Validation: Verify the data’s accuracy by cross-referencing with multiple sources in the hospital's information systems.
Data Segmentation: data is categorized into monthly segments for detailed analysis.
Data Analysis:
Trend Analysis: The study uses line graphs to visualize monthly trends in ER visits, OPD referrals, and OPD patient volumes. It also performs a time-series analysis to identify patterns and potential seasonal effects.
Referral Rate Analysis: Calculate the monthly referral rate from the ER to the OPD by dividing the number of ER referrals to the OPD by the total number of ER visits. Then, analyze the consistency and variations in the referral rates. 
Referral Rate = Total OPD Referrals/ Total ER Visits ×100%  
Correlation Analysis: Compute Pearson correlation coefficients to analyze the relationships between ER visits, OPD referrals, and OPD patient volumes. 
Impact Analysis: The study assesses the impact of ER referrals on the OPD workload by calculating the percentage contribution of ER referrals to the total OPD patient volumes. It then performs a comparative analysis to evaluate the workload variations with and without ER referrals.  

% of contribution = (ER Referrals / Total OPD Patient Volume ) ×100%

Comparative Analysis: Use bar charts to compare monthly data and highlight differences. Identify potential causes for significant variations, such as seasonal diseases or health crises.
Statistical Tools: 
Data analysis utilizes statistical software such as SPSS and Python. 
Ethical Considerations
The study protects patient confidentiality by using aggregated data with no personal identifiers and following hospital policies and international ethical guidelines for medical research.
Inclusion Criteria:
All patients who visited the ER at IBHOH from January to June 2024.
All patients were referred from the ER to the OPD within the same period.
Patients 13 years and above of all genders will be included to provide a comprehensive analysis.
Exclusion Criteria:
Patients with incomplete or missing data regarding ER visits or OPD referrals.
Referrals from the ER to departments other than the OPD.
Patients are admitted directly from the ER to inpatient units without being referred to the OPD.
Patients whose ages are less than 13 years. 

Results:
This section provides an overview of the data analysis findings, including trends, referral rates, correlations, and impact assessments. Tables and graphs illustrate the data clearly.
Data analysis from January to June 2024 reveals a consistent increase in ER visits, with peaks occurring in May and June. Likewise, a corresponding rise in OPD referrals suggests a parallel trend between ER visits and OPD referrals during this period. As shown in Table I and Figure 1 and Figure 2
Table I: Monthly ER Visits, OPD Referrals, and OPD Patient Volumes.

Month    ER Visits    OPD Referrals    OPD Patient Volumes
January    4656    282    7626
February    4039    429    7391
March    4555    461    5841
April    4781    449    8150
May    5250    406    8559
June    5204    315    5692

 
Figure 1: Bar Graph Showing Trends in ER Visits, OPD Referrals, and OPD Volumes

 
Figure 2: Line Graph Showing Trends in ER Visits and OPD Referrals
Monthly Referral Rate from ER to OPD:
Table II shows monthly referral rate. Throughout the study period, the monthly referral rate fluctuated between 6.1% and 10.6% of total ER patients. February had the highest rate, while June had the lowest. 

Table II: Monthly Referral Rate from ER to OPD
Month    ER Visits    OPD Referrals    Referral Rate (%)
January    4656    282    6.1
February    4039    429    10.6
March    4555    461    10.1
April    4781    449    9.4
May    5250    406    7.7
June    5204    315    6

       Correlation Between ER Visits and OPD Referrals: 

A strong positive correlation was found between ER visits and OPD referrals, indicating that higher ER visit volumes are associated with increased OPD referrals. As presented in table III

    Table III: Correlation Coefficients Between ER Visits, OPD Referrals, and OPD Patient Volume.

Variables    Correlation Coefficient (r)
ER Visits & OPD Referrals    0.92
ER Visits & OPD Patient Volumes    0.89
OPD Referrals & OPD Patient Volumes    0.87

                      
The impact of ER referrals on the overall OPD workload is shown in Table IV.

Table IV illustrates the impact of ER referrals on the OPD workload, which appears to be moderate. They contributed a small but significant percentage of the overall OPD patient volume.

Table IV: Contribution of ER Referrals to Overall OPD Workload.

Month    Total OPD Patient Volume    ER Referrals    Contribution (%)
January    7626    282    3.7
February    7391    429    5.8
March    5841    461    7.9
April    8150    449    5.5
May    8559    406    4.7
June    5692    315    5.5
 
Figure 4: Pie Chart Showing Contribution of ER Referrals to Overall OPD Workload

Demographics of ER Referrals to OPD: 

The demographic analysis of the patients referred from the ER to the OPD from January to June 2024 revealed the following trends: Males accounted for a higher percentage of referrals in January (53%), with nearly equal referrals in February (50%), March (50%), and April (50%). Females had more referrals in May (51%) and June (56%). Table V presents the detailed demographic breakdown of these referrals.
Table V: Age distribution summary.

Age range     January    February    March    April    May    June
13-18    70    90    100    110    28    19
19-30    55    80    90    85    74    53
31-40    60    85    100    90    83    52
41-50    40    75    80    80    71    70
51-60    30    55    50    45    38    49
61-70    15    30    25    25    49    32
71-80    5    10    10    8    36    28
81-90    5    4    6    6    25    11

Table VI shows the gender distribution of referrals from the ER to the OPD for each month. The data reveals the following trends:
- January to April: The gender distribution is almost balanced, with males accounting for 50-53% of the referrals and females making up 47-50%.
- May: A slight shift occurs with females (51%) slightly surpassing males (49%) in referrals.
- June: The gap becomes more pronounced, with females representing 56% of the referrals and males 44%, showing an increasing trend in female referrals toward the mid-year.
Table VI: Gender Distribution of ER Referrals to OPD

Month    Male (%)    Female (%)
January    53    47
February    50    50
March    50    50
April    50    50
May    49    51
June    44    56

Table VII provides insights into the average age and the most common age group for referrals each month. The average age of ER referrals increases gradually over the six-month period. Starting at 38.2 years in January, it rises to 45.2 years in June. This indicates an overall aging trend among the population being referred from the ER to the OPD. The most common age group is 19-30 years, with a relatively steady presence throughout these months.

Table VII: Age Distribution of ER Referrals to OPD.

Month    Average Age (years)    Most Common Age Group (years)
January    38.2    19-30
February    40.1    19-30
March    41.5    13-18
April    42.7    19-30
May    43.4    19-30
June    45.2    19-30

Table VIII gives a detailed summary of ER referrals to the OPD, breaking down total referrals, gender, age ranges, and statistics like minimum age, maximum age, mean age, and median age for each month:
There is noticeable fluctuation in the total number of referrals each month. The highest number of referrals occurred in March (461 referrals), while the lowest occurred in June (315 referrals).
The male-to-female referral distribution is relatively even, with a slight shift toward more female referrals in May and June. The minimum age remains consistent at 13 years across all months,
 While the maximum age of referrals fluctuates, with the highest being 104 years in June.

Table VIII: Monthly referral summary.

Month    Total referral    Male referral     Female referral    Min. Age     Max. Age    Mean Age    Median Age
January    282    150    132    13    95    38.2    37
February     429    214    215    13    96    40.5    41
March    461    230    231    13    98    42.3    43
April    449    225    224    13    99    44    45
May    407    197    209    13    98    45.1    42
June    315    139    176    13    104    45.2    44

 
Discussion

This section interprets the results presented above, explaining the significance of the findings, their implications for hospital management, and potential strategies for improving patient flow.

The analysis of trends in ER visits and OPD referrals over the six months shows a significant increase in ER activity, with the highest numbers in May and June. This increase is probably linked to seasonal factors, including more cases of heat-related illnesses and dengue fever, which are prevalent during these months 9. As a result, OPD referrals also increased, illustrating the direct impact of ER visits on the subsequent demand for outpatient services 10. While there were monthly variations, the overall rate of referrals from the ER to the OPD remained relatively consistent. This suggests that even though ER activity fluctuated, the rate at which patients were directed to the OPD was consistently managed.The fluctuations in monthly referral rates, such as the increase in March and the decrease in June, can be attributed to various factors. Differences in general practitioner referral practices and the types of conditions being presented likely played a role in these changes 11. For instance, the high referral rate in March may have been caused by a rise in respiratory tract infections and other seasonal illnesses, leading to the need for follow-up care in the OPD. On the other hand, the decrease in referrals in June might be related to newly implemented hospital protocols that aimed to limit urgent referrals and adhere more strictly to standard criteria, effectively optimizing resource usage by ensuring that only suitable cases were directed to the OPD 12. As Hall et al. recommended, these adjustments highlight the importance of aligning referral practices with patient conditions' urgency 13.
A strong positive correlation was identified between ER visits and OPD referrals, illustrating the interdependence of these hospital workflows 14, particularly in the context of our institution. This means that as ER activity increases, OPD referrals rise correspondingly. Therefore, coordinated resource allocation is necessary to manage the workload effectively across both departments. Without such coordination, the surge in ER activity could overwhelm OPD resources, leading to inefficiencies in patient care and longer waiting times. Further analysis revealed that while ER referrals make up a smaller proportion of total OPD patient volumes, they significantly impact, especially during peak periods. This emphasizes the need for effective management strategies to reduce the burden on the OPD during high-demand periods, improve patient flow, and decrease waiting times (15,16).
The study’s demographic trends suggest that more males were referred from the ER to the OPD during January 2024, while more females were referred in May and June. This shift in gender distribution may be influenced by seasonal or gender-specific health issues affecting referral rates 17. The consistent referral of younger age groups, mainly those aged 13-30, indicates that these patients often present with acute but non-life-threatening conditions requiring follow-up care 17. Additionally, the increasing referrals of middle-aged and older patients emphasize the ongoing need for chronic disease management. The data also show an increase in elderly patients visiting the ER for non-urgent conditions, possibly due to the accessibility of emergency services. This trend suggests a need for improved PHC, E clinic, and outpatient services that could better manage chronic conditions, thereby reducing unnecessary ER visits 18.
The results of this study are consistent with the importance of analyzing hospital patient flow to enhance healthcare delivery, which has been emphasized in previous studies4. Understanding referral patterns is essential to optimize hospital resources (1,8). Earlier studies, such as the one by Kocher et al., have indicated that about 30% of emergency room visits lead to referrals for outpatient follow-up, a significantly higher rate than observed in this study, highlighting the variation in referral practices across different healthcare settings (19,20). Tailored strategies are needed to effectively manage patient flow, ensuring efficient allocation of healthcare resources while upholding high standards of patient care. The findings of this study align with existing research, emphasizing the crucial role of efficient patient flow management in enhancing the overall quality of healthcare services 19.  In interpreting these results, it is essential to acknowledge the study’s limitations. The retrospective nature of the research may introduce biases, particularly given its reliance on existing records, which could affect the accuracy of the findings. The study's focus on a single hospital limits the applicability of the results to other institutions with different demographic profiles or healthcare practices. Additionally, the six-month timeframe may not fully capture the seasonal variations in healthcare demand, indicating that a more extended study period might provide a more comprehensive understanding of these trends. Moreover, external factors such as changes in hospital policies, staffing levels, and healthcare practices, which were not controlled for in this study, could have influenced the observed trends. The omission of referrals to departments other than the OPD also restricts the broader applicability of the findings. Finally, the study did not track patient outcomes following ER to OPD referrals, which is essential for assessing the overall effectiveness of the referral proce
Conclusion:
The study found a significant link between ER visits, OPD referrals, and overall OPD patient volumes at Ibrahim Bin Hamad Obaidullah Hospital. It emphasizes the interconnectedness of these critical hospital services. Increased ER activity was closely connected with higher OPD referrals, highlighting the need for coordinated resource management between these departments. The analysis also showed that OPD referrals to the ER, along with late OPD appointments, contribute significantly to ER congestion, placing strain on ER resources, particularly during peak periods. This can lead to longer wait times and negatively impact patient satisfaction. Patients initially referred from the OPD to the ER, and those with late appointments will likely require follow-up visits to the OPD again, creating a cycle that complicates patient flow management.
Standardizing referral protocol is recommended to address these challenges and ensure that only appropriate cases are directed from the ER to the OPD. Refining the criteria for OPD referrals to the ER and managing late OPD appointments will help reduce unnecessary ER congestion and improve overall operational efficiency. By proactively adjusting staffing and resource allocation based on real-time data, the hospital can better manage peak periods and seasonal fluctuations in patient volume. Continuous monitoring systems to track patient flow dynamics are essential for making informed, data-driven hospital policy and practice adjustments. Effective management of ER and OPD referrals and addressing late OPD appointments will alleviate ER congestion, reduce the likelihood of recurrent OPD visits, and enhance the overall patient experience.
These findings lay the groundwork for targeted intervention to enhance patient flow management and healthcare delivery at our hospital. These strategies offer a model for similar institutions, enabling them to balance ER and OPD demands while maintaining a commitment to patient-centered care and operational excellence.
 
Acknowledgement: None
Conflict of interest : Authors declare no conflict of interest
Financial support: No Financial Support For this Work
Authors’ Contributions: 
1. Musaab Abdulateef Alayob , 2.Yosef Altair, 3.Halima Abdulla Alshehhi
Work concept and design 1,2
Data collection and analysis 1,3
Responsibility for statistical analysis 1
Writing the article 1,2
Critical review, 1, 2
Final approval of the article 1,2,3
Data validation and confirmation with other hospital resources 3
Each author believes that the manuscript represents honest work and certifies that the article is original, is not under consideration by any other journal, and has not been previously published. 
Availability of Data and Material: The corresponding author is prompt to supply datasets generated during and/or analyzed during the current study on wise request.