Outpatient appointment systems: A new heuristic with patient classification

The ability to model and forecast waiting and service time to increase patients' satisfaction, reduce waiting time, avoid casualties, and increase efficiency in service delivery is crucial. It encourages the identification of future pressure by using the relevant key performance indicators. In this paper, the ARIMA model is used to study the waiting and service time of patients at the {\it Federal University Gusau} Health Services Clinic. The system was a single, time-independent arrival with many service points. Based on the results found in the waiting and service processes, the service time has a lower mean and variance when compared to the waiting time. The waiting time has a lower skewness and kurtosis when compared to the service time. The Ljung-Box (Q) Statistic test shows that the correlation in the time series has been adequately captured for the waiting and service time processes, though the waiting and service time processes have 4 and 10 outliers respectively. The ARIMA (0,1,2) and ARIMA (2,1,1) are selected for modelling the waiting and service time respectively based on the evaluation metrics.

150 Background: In 2013, a patient reported satisfaction survey indicated 19% of patients waited 20-40 minutes, 8% 40-60 minutes and 4% over 1 hour. We initiated a project to objectively quantify the components of wait times to investigate opportunities for improvement. Methods: Utilizing existing technology in the practice management system, clinic staff use the Day List feature to capture time stamps as patients move through the clinic. We focused on provider appointments but these visits could also include business office, labs, infusion and diagnostics. It was important to define where the wait(s) occurred. The Time Stamp durations measured are as follows: Arrival to Depart – duration of each appointment; Arrival to site to Exam Start – duration of activity until ready to be seen by the provider, includes rooming, labs and business office activity. Used to compare to the patient satisfaction survey responses; Exam Start to Depart – the provider portion of the office visit, includes patient wait plus exam time. Three reports are generated: Time Stamp Error Report indicating the completeness of data collection; Average Wait Times Report with appointment counts by physician by site and average durations; Provider Wait Times Report with office visit counts, Wait Time Category counts ( < 10 min, 10-20, 20-40, 40-60, and > 1 hour ) and average durations. Results: There was a correlation calculation to the patient satisfaction survey of .779, with long wait times more likely to be underreported by patients. Site and physician data were available for review at site Quality Committees. The data can be used by the site to improve processes, such as lab and infusion room scheduling. Time stamps are used to communicate patient readiness for next steps in the office visit. The time stamps provide objective data to discuss patient complaints with staff. Conclusions: Patient wait times are a valued measure of patient satisfaction and quality. Full utilization of the Day List and supporting technology allows us to objectively monitor and improve this aspect of patient care. Table 1: Sample Provider Report [Table: see text]

BackgroundPrimary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing longer waiting times. No-show appointments are significant contributors to inefficiency in PHC operations, which can lead to an estimated 3%-14% revenue loss, disrupt resource allocation, and negatively impact health care quality. Emirates Health Services (EHS) PHC centers handle over 140,000 visits monthly. Baseline data indicate a 21% no-show rate and an average patient wait time exceeding 16 minutes, necessitating an advanced scheduling and resource management system to enhance patient experiences and operational efficiency.ObjectiveThe objective of this study was to evaluate the impact of an artificial intelligence (AI)-driven solution that was integrated with an interactive real-time data dashboard on reducing no-show appointments and improving patient waiting times at the EHS PHCs.MethodsThis study introduced an innovative AI-based data application to enhance PHC efficiency. Leveraging our electronic health record system, we deployed an AI model with an 86% accuracy rate to predict no-shows by analyzing historical data and categorizing appointments based on no-show risk. The model was integrated with a real-time dashboard to monitor patient journeys and wait times. Clinic coordinators used the dashboard to proactively manage high-risk appointments and optimize resource allocation. The intervention was assessed through a before-and-after comparison of PHC appointment dynamics and wait times, analyzing data from 135,393 appointments (67,429 before implementation and 67,964 after implementation).ResultsImplementation of the AI-powered no-show prediction model resulted in a significant 50.7% reduction in no-show rates (P<.001). The odds ratio for no-shows after implementation was 0.43 (95% CI 0.42-0.45; P<.001), indicating a 57% reduction in the likelihood of no-shows. Additionally, patient wait times decreased by an average of 5.7 minutes overall (P<.001), with some PHCs achieving up to a 50% reduction in wait times.ConclusionsThis project demonstrates that integrating AI with a data analytics platform and an electronic health record systems can significantly improve operational efficiency and patient satisfaction in PHC settings. The AI model enabled daily assessments of wait times and allowed for real-time adjustments, such as reallocating patients to different clinicians, thus reducing wait times and optimizing resource use. These findings illustrate the transformative potential of AI and real-time data analytics in health care delivery.

We study the effects of rescheduling on no-show behavior in an outpatient appointment system for both new and follow-up patients. Previous literature has primarily focused on new patients and investigated the role of waiting time on no-show probability. We offer a more nuanced understanding of this costly phenomenon. Using comprehensive clinical data, we demonstrate that for follow-up patients, their no-show probability decreases by 10.9 percentage points if their appointments were rescheduled at their own request, but increases by 6.2 percentage points if they were rescheduled by the clinic. New patients, in contrast, are less sensitive to who initiates rescheduling. Their no-show probability decreases by 2.3 percentage points if their appointments were rescheduled at their own request, and increases by 3.2 percentage points—but is statistically insignificant at the 10% level—if they were rescheduled by the clinic. New patients are more concerned about waiting time compared with follow-up patients. For patients whose appointments were not rescheduled, new patients’ no-show probability decreases by 1.3 percentage points if their waiting time is reduced by one week, but the waiting time has a small and statistically insignificant effect on follow-up patients’ no-show probability. Using data-driven simulation, we conduct counterfactual investigation of the impact of allowing active rescheduling on the performance of appointment systems. In particular, allowing the flexibility of patient rescheduling can reduce the overall no-show rate and increase system utilization, but at a cost of increased wait time for new patients. If patients are able to reschedule at least one week in advance, new patients’ wait time is largely reduced, whereas the no-show rate remains the same; this is equivalent to the effect of a 5% increase in the clinic’s capacity.

Background: Quality of service, as perceived by patients in any healthcare facility is to a great extent, dependent on the waiting time. Reducing patients waiting time increases patients satisfaction and improves system efficiency.&#x0D; Purpose: To measure and analyze the waiting time of patients at the service points in the ultrasound unit of a Nigerian tertiary hospital and to determine the mean examination time for the different ultrasound investigations carried out.&#x0D; Methods: This prospective cross-sectional study was carried out in the ultrasound unit of the Radiology department at the University of Nigeria Teaching Hospital (UNTH) Ituku/Ozalla, Enugu. The waiting and examination times of patients were measured directly through observation of system operations. The waiting time at the various service points identified as costing, update, payment and examination were recorded. Mean, range and standard deviation of waiting and service times formed the descriptive statistics for the. For inferential statistics, ANOVA test was carried out to test for significance in the different service point waiting times, and the different examination times for the different investigations.&#x0D; Results: Mean waiting time was 3 hours 31 seconds and average exam time was 26 minutes 31 seconds. Analysis of variance on the service point where patients wait the most showed that the point after making the payment was the most significant. There was no significant difference found in the amount of time spent on different examinations (P&lt; 0.05).&#x0D; Conclusion: Timely delivery of services is of optimum importance, considering the need for patient-centred service. With the information provided on the waiting time at the different service points in a typical teaching hospital ultrasound unit, departmental managers will be guided in the planning of the departmental operations, to enhance patient satisfaction and system efficiency.

Background: Quality of service, as perceived by patients in any healthcare facility is to a great extent, dependent on the waiting time. Reducing patients' waiting time increases patients' satisfaction and improves system efficiency. Purpose: To measure and analyze the waiting time of patients at the service points in the ultrasound unit of a Nigerian tertiary hospital and to determine the mean examination time for the different ultrasound investigations carried out. Methods: This prospective cross-sectional study was carried out in the ultrasound unit of the Radiology department at the University of Nigeria Teaching Hospital (UNTH) Ituku/Ozalla, Enugu. The waiting and examination times of patients were measured directly through observation of system operations. The waiting time at the various service points identified as costing, update, payment and examination were recorded. Mean, range and standard deviation of waiting and service times formed the descriptive statistics for the. For inferential statistics, ANOVA test was carried out to test for significance in the different service point waiting times, and the different examination times for the different investigations. Results: Mean waiting time was 3 hours 31 seconds and average exam time was 26 minutes 31 seconds. Analysis of variance on the service point where patients wait the most showed that the point after making the payment was the most significant. There was no significant difference found in the amount of time spent on different examinations (P &lt; 0.05). Conclusion: Timely delivery of services is of optimum importance, considering the need for patient-centred service. With the information provided on the waiting time at the different service points in a typical teaching hospital ultrasound unit, departmental managers will be guided in the planning of the departmental operations, to enhance patient satisfaction and system efficiency.

Objective:To demonstrate methods of adjusting data in quality improvement projects for better learning about interventions over time.Methods:A secondary analysis of data from a quality improvement project to improve patient wait times at an urban academic pediatric emergency department using electronic medical data from 2015 to 2018. The primary outcome was the wait times for low-acuity patients. Control charts were used to determine if the interventions were effective in reducing wait times. Two different data adjustment techniques were applied to account for changes in patient volume and seasonal effects on the outcome measure.Results:We more effectively demonstrated improved patient wait times after adjusting for patient volume or seasonality. Patient wait times decreased from 75.2 to 72.9 minutes after the intervention; a 3% decrease sustained over 18 months. A strong correlation between patient volume and wait times was noted. Process stability was achieved on the control charts after data adjustment, with one centerline shift after data adjustment in contrast to 5 centerline shifts required before data adjustment.Conclusion:Adjusting for seasonality or patient volume created process stability and improved learning from control charts. After adjustment, we sustained decreased patient wait times more than a year out from the original intervention Adjusting by patient volume seems to be a preferred method of adjustment. Our findings support the importance of adjusting for baseline variability affected by seasonality or patient volumes, especially in flow projects, as a high yield method for process improvement.

This study describes a field observation study and use of simulation to quantify the effect of patient arrival rate and physician practices on physician idle time and patient wait time. The observation study measured actual service (diagnosis, therapy, and charting) times for 126 patients. Subsequently, a FORTRAN simulation model examined effects of physician practices and patient arrival rate on physician utilization and patient wait time. Observations were taken in the emergency department of an urban, university-affiliated pediatric teaching hospital. Although times for initial diagnostic evaluation (diagnosis), therapy, and charting averaged 13.3, 13.8, and 11.6 minutes, respectively, maximum patient visits approached six hours. The simulation model showed that, during times of frequent patient arrivals, maximum patient wait times increased greatly. Additionally, the model predicted that physician idle time persists even during periods of frequent patient arrivals and long maximum visit times. Emergency department (ED) senior staff (fellows and attendings) often begin treating new patients when current patients leave for tests external to the ED. This practice increases physician utilization and makes more physician capacity available, but it may lead to small additional patient waits if the physician is treating another patient when the first patient returns from the external test before their original physician becomes free. On the other hand, during periods of high patient arrivals, increased physician utilization and reduced idle time result in reduced average and expected maximum patient visit times. Unfortunately the variability in visit times increased. A very small percentage of patients wait longer, owing to additional waits incurred upon returning from external testing. Overall, most patients benefit from shorter visits. Finally, the study suggested maximum rather than average wait time be considered as a measure of emergency department capacity and quality of service provided. Although average wait times seemed reasonable, maximum wait times were at times quite long and could impact both physician's and patient's perceptions of service quality.

442 PAID-A Randomized Controlled Trial: Distribution of an Informational Brochure to Patients in the ED Waiting Room Does Not Improve Overall Patient Satisfaction

Research ObjectiveThe objectives of this is study were to evaluate the influence of Department of Veterans Affairs’ (VA) Full Practice Authority (FPA) policy change on Veterans’ access to primary health care (PHC).Study DesignThis was a nonexperimental quantitative study using secondary data from 139 VAMCs, from 2017 to 2019. Dependent variables were wait times in days for (a) new patient appointments; and (b) established patient appointments. Linear regression analyses with log transformations of continuous variables was used to complete the analysis. Independent variables for this study were the number of privileged PHC nurse practitioners (NPs) and time. Due to the number of VA Medical Centers (VAMCs) without privileged APRNs (50%), we created an indicator variable (0 = no privileged APRNs, 1 = all others). We conducted sensitivity analyses by using several model specifications for robustness checks and chose the models with the best fit. We controlled for possible confounding variables identified in previous research including the following: NP SOP laws, density of Veterans, APRNs, and physicians; and complexity levels and geographic locations of VAMCs.Population StudiedAs of March 2019, there were 140 VAMCs. This study included 26 months postimplementation data for 139 VAMCs (n = 3753). Puerto Rico was excluded due to lack of inclusion in the NP practice environment database.Principal FindingsNew patients waited on average for 7.79 days for a new appointment. The model with log transformations was a good predictor of new patient wait times [F (28, 3724) = 17.68, P &lt; .001] and explained 12% of the variation. An increase of 10% in privileged PHC NPs at the facility level was associated with a decrease in new patients’ wait times by about 4% (P = .006), which was equivalent to a reduction of 0.30 days. Established patients waited on average for 4.34 days for a new appointment. However, there was no statistically significant relationship between facilities with privileged PHC NPs and established patients wait times compared with those facilities with no privileged PHC NPs (P = .545).ConclusionsOur findings indicated decreases in new patient wait times were associated with improved utilization of existing PHC NP resources as PHC providers within VHA had not significantly changed. Results indicated significant strides in VA’s goal to improve utilization of existing PHC provider resources by permitting FPA. Our study did not find the number of privileged PHC NPs influenced established wait times. However, according to VHA policy, new PHC providers are afforded 12 to 15 months to build their panels which should only be 75% of physicians. VHA’s panel management policy may account for the difference found between new and established wait times.Implications for Policy or PracticeWithin VA, the largest integrated health care system in the United States, FPA has positively influenced access in new patient wait times to PHC. Opportunity exists to further improve access to PHC, in a budget neutral manner, by decreasing the gap between the number of privileged PHC NPs and available PHC NPs resources.

Patient waiting time as an important indicator of quality of services has been a long-standing concern in health care. The aim of this study was to assess patient waiting time in primary health care settings in Rwanda. This was a mixed-method study design. In quantitative phase, Patient Flow Time Log was used to track the time patients spent waiting for the service. On exit, a structured questionnaire was administered. Observations were conducted to capture information regarding the flow and processes. In qualitative part, six focus group discussions with patients were conducted. Semi-structured interviews with healthcare providers were held. Among 410 participants, the majority were females (77.1%). The overall health centre level waiting time was 211 minutes (3.5 hours). To receive a service, patients waited an average of 81.5 minutes (1.4 hours). Three conceptual themes were identified: a) reported sections to have long wait time; b) causes of long waiting time; and c) needs for activities to spend time on as patients wait. Most patients experienced prolonged waiting times during their visit to the primary health care settings, and the major factors were the huge number of patients, few healthcare providers, and lack of medical equipment. To effectively address these challenges, more resources and personnel must be allocated to primary healthcare settings to help foster a higher level of client satisfaction with minimal primary healthcare waiting time.

High demand for intensive care unit (ICU) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine ICU bed assignment policies, accounting for patient acuity, to reduce ICU admission delays. To provide a framework for data-driven modeling of ICU patient flow, identify key measurable outcomes, and present illustrative analysis demonstrating the impact of various bed allocation scenarios on outcomes. A description of key inputs for constructing a queuing model was outlined, and an illustrative simulation model was developed to reflect current triage protocol within the medical ICU and step-down unit (SDU) at a single tertiary-care hospital. Patient acuity, arrival rate, and unit length of stay, consisting of a "service time" and "time to transfer," were estimated from 12 months of retrospective data (n = 2,710 adult patients) for 36 ICU and 15 SDU staffed beds. Patient priority was based on acuity and whether the patient originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied ICU/SDU sizes, (2) reserved ICU beds as a triage strategy, (3) lower targets for time to transfer out of the ICU, and (4) ICU expansion by up to four beds. Outcomes included ICU admission wait times and unit occupancy. With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as ICU decreased overall wait times by 7.2% to 1.06 (SD, 1.39) hours and increased bed occupancy from 80 to 84%. Reserving the last available bed for acute patients reduced wait times for acute patients from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute patients' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all ICU/SDU patients to 1 hour decreased wait times for incoming ICU patients, comparable to building one to two additional ICU beds. Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in ICU bed assignment could impact unit occupancy levels and patient wait times. Trade-offs associated with dedicating resources for acute patients versus expanding capacity for all patients can be examined.

Patient queues are prevalent in healthcare and wait time is one measure of access to care. We illustrate Queueing Theory-an analytical tool that has provided many insights to service providers when designing new service systems and managing existing ones. This established theory helps us to quantify the appropriate service capacity to meet the patient demand, balancing system utilization and the patient's wait time. It considers four key factors that affect the patient's wait time: average patient demand, average service rate and the variation in both. We illustrate four basic insights that will be useful for managers and doctors who manage healthcare delivery systems, at hospital or department level. Two examples from local hospitals are shown where we have used queueing models to estimate the service capacity and analyze the impact of capacity configurations, while considering the inherent variation in healthcare.

When patients visit primary care clinics, they can be subject to long wait times due to operational inefficiencies and bottlenecks, decreasing patient satisfaction and sometimes leading to worse health outcomes. The existing literature models primary care clinics primarily as agent-based models, which are excellent at tracking individual patients and their movements in a model of a clinic. While agent-based models can detect bottlenecks, a network flow model better detects bottlenecks in the model by correlating changes in patient flow and wait times in the healthcare network. In this paper, a network flow model is constructed, where patients flow along the capacitated edges of a network while receiving treatment at the nodes. This configuration easily identifies bottlenecks by analyzing the flow in and flow out of nodes through metrics such as efficiency and patient wait times. The capacities of the edges for this model are taken from an agent-based model of a case study of a primary care clinic and sampled as random variables. Ensemble runs of the network flow model are created to account for uncertainty in the synthetic data. By changing the topology of the network flow model, bottlenecks are removed, increasing the model efficiency and decreasing patient wait times. Finally, the model is subjected to a sensitivity analysis. The focus in this work is on the method rather than the results per se.

Self-organized regularities in terms of patient arrivals and wait times have been discovered in real-world healthcare services. What remains to be a challenge is how to characterize those regularities by taking into account the underlying patients’ or hospitals’ behaviors with respect to various impact factors. This paper presents a case study to address such a challenge. Specifically, it models and simulates the cardiac surgery services in Ontario, Canada, based on the methodology of Autonomy-Oriented Computing (AOC). The developed AOC-based cardiac surgery service model (AOC-CSS model) pays a special attention to how individuals’ (e.g., patients and hospitals) behaviors and interactions with respect to some key factors (i.e., geographic accessibility to services, hospital resourcefulness, and wait times) affect the dynamics and relevant patterns of patient arrivals and wait times. By experimenting with the AOC-CSS model, we observe that certain regularities in patient arrivals and wait times emerge from the simulation, which are similar to those discovered from the real world. It reveals that patients’ hospital-selection behaviors, hospitals’ service-adjustment behaviors, and their interactions via wait times may potentially account for the self-organized regularities of wait times in cardiac surgery services.