Introduction

Long wait times before patients see a doctor have become endemic in most healthcare settings. Patients generally spend a significant amount of time in hospitals waiting to receive care from healthcare providers. The extent to which patients are satisfied is widely thought to correlate with hospital waiting times [1]. Information regarding waiting times, the determining factors, and the impact of prolonged waiting times on the overall healthcare system, particularly in sub-Saharan Africa, including Kenya, remains sparse.

Healthcare organisations are constantly striving to improve patient satisfaction and experience. Patient satisfaction is a commonly used indicator for measuring the quality of healthcare services that most hospitals provide [2]. It also provides valuable information on how well providers meet patients’ expectations [3]. The perceived quality of healthcare is enhanced when services are delivered in a timely and efficient manner, with a focus on patient-centeredness [4]. Patient satisfaction is usually influenced by patient-provider interactions, including time spent with the physician, the physician’s willingness to listen to the patient, patient-specific characteristics such as expectations of care, and perceived waiting times [5]. Patient satisfaction, in turn, has been linked to positive outcomes such as improved compliance, reduced litigation, and better prognosis[6].

Waiting time is the time between a patient’s arrival at the hospital and when a healthcare practitioner sees them. This includes the time spent in the registration area, waiting room, and examination room before the arrival of the healthcare provider. Waiting time is an essential quality metric in patient experience surveys and a significant predictor of patient satisfaction [7]. Prolonged waiting times in hospitals have been negatively correlated with patient satisfaction and, ultimately, patient care [8]. Long waiting times have been the focus of increasing public attention and scrutiny, particularly in outpatient facilities. Prolonged waiting times lead to reduced access to care, disruption of workflows, and lower overall patient satisfaction [9]. While measuring quality metrics such as waiting times in low and middle-income countries (LMICs) may be challenging, studies have shown that well-designed surveys and questionnaires can provide reliable and objective data to measure quality and improve overall hospital performance [10,11].

The Institute of Medicine (IOM) recommends that healthcare providers see at least 90% of their patients within 30 minutes of their visit [12]. However, achieving this has been challenging, particularly in LMIC where resources are limited and healthcare systems remain fragile. Waiting times are a good indicator of quality of care and patient satisfaction. A study conducted in the Southern region of Ethiopia revealed that waiting times were longer than the recommended average, with more than half of the participants reporting dissatisfaction [7]. Similarly, a study in Nigeria showed that waiting times varied between 11 and 354 minutes, with the average waiting time being 146 minutes, influencing patient satisfaction and care [13].

Kenya is a country in East Africa with a population of around 55 million [14]. The Aga Khan University Hospital, Nairobi (AKUHN) is a 300-bed tertiary care facility in the capital city of Nairobi that offers advanced medical care to Kenyans and patients from neighbouring countries. Most patients admitted to the hospital are received through the 24-hour emergency room which is staffed by both consultant physicians and junior doctors in training.

This study was conducted to better understand the waiting time at the ER, its impact on patients’ experience, and whether there was a correlation between waiting time and patient’s satisfaction with the care they received.

Materials and methods

Results

Demographic characteristics

A total of 1300 surveys were handed out and 72% (941) completed surveys were collected. Of the 941 participants 52.0% (n = 489) were females. 53.6% (n = 504) of the participants were between 20 and 40 years old, and 29.3% (276) were in the 40–65 age group. Of the participants who completed the survey, 52.4% (n = 493) were married, 75.9% (n = 714) had a university education, and 70.6% (n = 664) were self-employed or employed. Most of the participants, 91.1%, were of African origin, with 77.0% (n = 725) living in Nairobi County and 14.8% (n = 139) from neighbouring Kiambu County. Table 1 summarises the overall socio-demographic characteristics of the participants.

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Table 1. Socio-demographic characteristics of the participants.

https://doi.org/10.1371/journal.pone.0322015.t001

Day and time of visits

The majority of the participants sought care in the ER on Monday (17.6%, n = 166), followed by Wednesday (15.9%, n = 150) and Friday (15.6%, n = 147). Tuesday had the least number of patients, accounting for only 10.4% (n = 98). Almost half of the patients (49.4%, n = 452) came to the ER between 10 am and 4 pm. The highest number of patients (47.2%) visited the ER in the early and late morning (8 am – 12 noon) on Mondays. Similarly, of those who visited the ER in the afternoon (Noon- 4 p.m.), Wednesday had the highest number of patients (31.3%). For a more detailed summary of the days and times of visit, please refer to Fig 1.

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Fig 1. Participants day and time of visit.

https://doi.org/10.1371/journal.pone.0322015.g001

Visit experience

76.5% (n = 720) of the study participants were new patients while 23.5% (n = 221) were patients who had previously visited the ER. 51.1% (n = 481) of the participants reported waiting 31–60 minutes to see a doctor, and 25.4% (n = 239) waited more than 60 minutes. Most participants (75.3%, n = 709) were happy or somewhat satisfied with the wait time. 72.9% (n = 686) of participants reported spending less than 15 minutes in their initial consultation with the doctor. Many participants (89.1%, n = 838) were happy or somewhat satisfied with the consultation time. The majority (91.7%, n = 863) of the participants reported that they would always or occasionally return to the hospital for care, and 91.9% (n = 865) stated that they would always or occasionally recommend the hospital to their relatives and friends. Table 2 summarises the overall experiences of the participants.

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Table 2. Visit and Satisfaction characteristics of the participants.

https://doi.org/10.1371/journal.pone.0322015.t002

Association with prolonged waiting time

Based on the CTAS criteria for level 4 patients, the prolonged waiting time was defined as waiting for more that 60 minutes from registration to time seen by a healthcare provider [15, 16]. Of the participants who experienced long waiting times (> 60 minutes), 46.1% were over 40 years of age, 61.9% were married, and 74.1% were self-employed or employed. Mondays and Wednesdays had the longest waiting times; afternoons (12–4 pm) and evenings (4 pm-10 pm) were similarly associated with longer waiting times.

There was a statistically significant association between waiting times and overall satisfaction. Patients who experienced longer waiting times were more likely to be dissatisfied with their experience. On the other hand, patients who reported shorter waiting times (< 60 minutes) also reported a higher likelihood of returning for care and recommending the hospital to relatives and friends. Table 3 summarises the overall associations between various factors and prolonged waiting times.

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Table 3. Associations between prolonged waiting times.

https://doi.org/10.1371/journal.pone.0322015.t003

Furthermore, we conducted multivariate logistic regression to identify associations of overall satisfaction with prolonged waiting time after adjusting for age, marital status and employment. Participants expressing not satisfied with waiting times had higher odds of having to wait longer as compared to those that expressed neutral satisfaction (OR: 3.70, 95% CI: 1.27–11.02; p = 0.017) whereas those participants expressing satisfied with waiting times had lower odds of having to wait longer as compared to those that expressed neutral satisfaction (OR: 0.005, 95% CI: 0.002–0.010; p < 0.001). Participants expressing satisfied with service times had lower odds of having to wait longer as compared to those that expressed neutral/ not satisfied (OR: 0.014, 95% CI: 0.006–0.032; p < 0.001).

Discussion

In this study, patients reported waiting times ranging from less than 30 minutes to over 120 minutes. Most patients waited between 31–60 minutes to be seen by a healthcare provider, with nearly a quarter waiting between 61–120 minutes. Only 23.5% of patients were seen within the recommended 30 minutes of arrival. Many patients in our healthcare setting rely on the emergency room as their main entry point into the healthcare system because they don’t have a primary care physician. Our data supports this, as almost a quarter of the visits are follow-up visits. For this study, we have also defined prolonged waiting times as those exceeding 60 minutes [12,15,16].

Long waiting times are a common problem worldwide, affecting both developed and developing countries. Studies conducted in low- and middle-income countries (LMICs) have shown that patients often wait 2–4 hours before seeing a healthcare provider [13,20]. In West Africa, studies conducted in Benin and Nigeria reported average waiting times of over two hour [13,18]. However, studies conducted in South Africa [20] and Ethiopia [8] reported slightly better average waiting times at 69 and 93 minutes, respectively.

In contrast, looking at high-resource countries, a study conducted by Horowitz et al. in the United States found that fewer than one-fifth of ERs could see 90% of their patients within one hour [17]. The waiting times also varied greatly from state to state [17]. Another review by McIntyre et al., which examined turnaround times in Emergency departments as an indicator of Health Services Under Strain, reported that 29% of Canadians reported waiting over 4 hours in emergency departments [21]. Similarly, a study in California ERs showed that patients waited an average of 56 minutes to see a doctor [22]. According to the Australian Institute of Health and Welfare (2017–2018), the median waiting time ranged from 15 to 46 minutes. However, the 90th percentile waiting times varied from 78 to 159 minutes [23]. A study conducted in the United Kingdom showed that patients waited on average for 70 minutes, and longer waiting times in ERs were associated with adverse outcomes [24]. The large variations in waiting times can be attributed to the availability of healthcare resources and the geographic location of the healthcare facility. While waiting times may vary between countries and regions, our study found that waiting times at our institution were similar to those in developed countrie [17,21–23], which could be attributed to better resources available at a private institution compared to a public healthcare facility including better staffing, patient flows and queueing systems.

Most study participants (65.6%) reported that their ER consultation with the healthcare provider lasted about 5–15 minutes. Only 23.7% of patients reported spending 15–25 minutes with the healthcare provider. A study by Ming Tae-Seale et al. in an outpatient ER found that doctors spent at least 13–24 minutes with elderly patients [24]. In our study, among those who were satisfied with the consultation times, the majority waited for 31–60 minutes to see a healthcare provider with a consultation time of 5–15 minutes. However, for those not satisfied with the waiting times, the majority had waited for at least one hour. Similar to other studies, our study also showed that prolonged waiting times negatively affected patient satisfaction and influenced the general perception of the service received by patients [25]. Furthermore, a study by Bleustein and colleagues found that prolonged waiting times impacted how patients perceived information and instructions and the overall treatment provided by the care team. The authors noted that the patient experience was heavily influenced by time spent waiting to receive care, and every aspect of the care experience, including confidence in the care provider and perceived quality of care, were negatively affected by prolonged waiting times in hospitals.

According to our study, patients visiting the emergency room on Mondays and Wednesdays had to wait longer compared to the other days of the week. This could be attributed to patients visiting the ER more often after the weekend, leading to more visits. With a fixed staffing model, higher patient volumes are expected to lead to prolonged waiting times. Our findings are similar to a study conducted in the Jimna zone of Southwest Ethiopia [8], which found that patients who visited on Mondays were more likely to wait longer than the rest of the week. Moreover, the study also noted that patients who visited in the morning were more likely to wait longer than those who visited in the afternoon.

We also found that the chances of experiencing prolonged wait times in the ER were highest between 4 pm and 10 pm, compared to those who visited during morning hours (8.00 am-noon). Patients who visited the ER between 4 pm and 10 pm were more likely to wait longer to see a healthcare provider than those who visited in the morning. Our findings suggest that patients visit the ER after working hours, which corresponds to when the ER is minimally staffed.

We also found that prolonged waiting times were associated with specific demographic characteristics such as age, marital status, and employment status. Almost half of the participants who experienced a lengthy waiting time were above 40 years of age (46.1%). This is consistent with a study conducted in Riyadh, Saudi Arabia, where waiting times for individuals aged 65 years and above were significantly longer than for younger age groups [26]. This could be attributed to the higher scrutiny and triage given to the older population due to their medical complexity and the increased likelihood of having multiple medical conditions.

The patient’s employment status also seemed to be related to waiting times, where patients with a higher socioeconomic status tended to experience longer waiting times and overall dissatisfaction with the service provided at the ER. Of interest, a study conducted in Nigeria found that patients with a higher socioeconomic status were more likely to demand shorter waiting times and report negative patient experiences [27]. Additionally, our findings are similar to those of Maharlouei et al. and Arpey et al., who found that patients with better socioeconomic status were more dissatisfied with the services provided by the healthcare system [28,29]. These factors can lead to unconscious bias by physicians, which can affect their clinical judgments and eventually result in disparities in the quality of care, as shown in multiple studies [30–32]. This could be explained by the fact that employed persons are more likely to visit the ER after working hours when the ER is not adequately staffed. It is also possible that patients from a higher socioeconomic status had unrealistic expectations of receiving better and quicker service in the emergency room.

In our study, most participants said they would return to the hospital for care and recommend the ER services to their family and friends. However, 8.3% and 8.1% of the participants were unwilling to return for care or recommend the hospital to others due to long waiting times. This is consistent with other studies which showed that customers and patients who experience long waiting times tend to be dissatisfied and are less likely to return for care. This can lead to a long-term loss of profits for the organisation, as dissatisfied customers are more likely to convey their dissatisfaction to others [33–36]. These studies recommended using updated technology, adequate staffing, and patient-centred strategies to help reduce waiting times.

Our study highlights certain key factors that can be implemented in LMIC countries such as Kenya. Firstly, to focus on building primary care services that can alleviate the pressures and waiting times in the ER and allow patients to obtain longitudinal care in an appropriate clinical setting. Secondly, to explore the concept of Urgent Care Centers (analogous to those found in higher income countries) within heavily populated areas that can provide care to non-emergent patients in order to minimize waiting times and improve satisfaction. Thirdly, in resource limited settings, it is important to staff the ER based on their patient volumes and patient characteristics. In our case this would entail adequately staffing the ER on Mondays (after the weekend) and between 4pm to 10 pm (after working hours). By targeting staffing availability to patient needs one could hope to improve the patient experience, decrease wait times and improve overall satisfaction. Lastly studies assessing wait times and patient satisfaction should be conducted routinely in order to assess whether interventions such as those suggested above are impactful and sustainable.

Like other studies, our study also has several limitations. Firstly, we should have considered overall turnaround times as patients were not asked about their time in other ancillary services such as the laboratory, pharmacy and radiology, which could have affected their overall waiting time and services. Secondly, we only focused on patients seeking care in the ER and did not include patients treated at other outpatient speciality clinics. We used a convenience sample for recruitment with its known limitations. We also did not question patients on why they were either satisfied or dissatisfied with the service and wait times in the ER. Thirdly, waiting times were an approximation from the study participants and could not be fully verified via a queue system. Furthermore, even though the surveys were anonymous, patients could have felt that answering the questions might have affected their overall care in the ER. Fourthly, we included people accompanying patients in the survey however we did not state the number of patients versus the accompanying adults and their experience and satisfaction may have differed. In addition, we did not assess the staffing levels during data collection or the relationship between the number of healthcare providers and waiting times. Lasty, while machine learning methods for analyzing wait times, time series and prediction of satisfaction have shown promise in various studies [37–39], this study used a manual method to collect data and a traditional method of data analyses, and we acknowledge this as a limitation. Machine learning techniques have been shown to improve predictive accuracy and uncover patterns in wait time data that may not be easily captured through traditional statistical methods. Machine models usually require large sets of data to provide a more accurate prediction model, which we lacked during the study period. Future research could explore integrating these advanced techniques to refine predictive models and provide deeper insights into ER wait time fluctuations, ultimately supporting more efficient resource allocation and patient flow management.

Conclusion

Patient waiting time and ER experiences can influence the patient’s perception of care. In this study, prolonged waiting times were linked to patient dissatisfaction and had a negative impact on the quality of care. Healthcare institutions in LMICs must devote more time and resources to address prolonged waiting times in key clinical areas such as the ER. Identifying bottlenecks and gridlocks within healthcare facilities is essential to develop an effective plan to reduce waiting times and improve care delivery. This will ultimately translate into improved patient satisfaction and outcomes. Some mitigating strategies include queueing systems, comprehensive health information systems, staffing according to demands and needs, and improving patient flows.

Supporting information

Acknowledgments

Not Applicable