Understanding self-organized regularities in healthcare services based on autonomy oriented modeling

When regional healthcare service managers review the operations of healthcare services in a past period of time, they often feel confused about some of the unexpected spatio-temporal patterns in patient arrivals and wait times. How did these patterns emerge? What reasons and mechanisms account for the emerging patterns? How can patient arrivals be regulated at different hospitals and thus improve the service utilization in the region? In this chapter, we present how to use our proposed behavior-based autonomy-oriented modeling method to characterize the spatio-temporal patterns in cardiac surgery services in Ontario, Canada, with the aim of answering some of the aforementioned questions. The work shown in this chapter reveals the working mechanisms that explain how the spatio-temporal patterns in patient arrivals and wait times at a systems level emerge from individual patients’ hospital selection behavior and their interactions with hospital wait times. It also reveals that our proposed behavior-based autonomy-oriented modeling method is useful in finding the underlying reasons for emergent spatio-temporal patterns in complex healthcare systems.

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]

Category:Health Sciences ResearchIntroduction/Purpose:Wait times represent a critical component of the patient experience, and prolonged waits are correlated with decreased patient satisfaction. We hypothesized that time spent waiting for radiology is the largest contributor to total patient wait time in our orthopedic foot and ankle clinic.Methods:A prospective, observational study was conducted in the outpatient orthopaedic foot and ankle clinic at a tertiary medical center. A total of 210 new and follow-up adult patients were enrolled. Patients were tracked from arrival until checkout with multiple time points being recorded by a trained observer. The time between patient arrival and first contact with the orthopaedic surgeon was broken down into five distinct categories. Total time between patient arrival and first contact with the orthopaedic surgeon was tested for association with patient and appointment characteristics using Student’s t-test.Results:The average total time spent waiting for first contact with the orthopaedic surgeon was 57.1±30.4 minutes. The largest contributor was time spent waiting for an exam room (33.1±25.5 minutes), followed by time spent waiting for radiologic imaging (21.7±19.9 minutes), time spent waiting for resident/PA (12.2±10.9 minutes), and time spent waiting for attending surgeon after seeing resident/PA (11.7±9.3 minutes). Factors contributing to a longer overall wait included obtaining x-rays at the visit (+15.4±4.2 minutes, 95% confidence interval [CI]=+7.0 to +23.8, p<0.001) and failure to complete patient paperwork beforehand (+36.9±5.3 minutes, CI=+26.4 to +47.4, p<0.001; Table 1). In contrast, overall wait time was not associated with age≥50 years, female sex, late arrival, or outside medical records needing review.Conclusion:Time spent waiting for assignment to an exam room was the largest contributor to the time between patient arrival and first contact with the attending surgeon. In order, the other contributors were time spent waiting for radiology, time spent waiting for the resident/PA, and time spent waiting for the attending surgeon after seeing the resident/PA. Obtaining x-rays increased patient wait time and completing patient paperwork beforehand decreased patient wait time. Orthopaedic foot and ankle surgeons should work to avoid unnecessary x-rays and encourage completion of patient paperwork before arrival in order to optimize clinic flow and decrease patient wait times.

Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital’s information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study’s dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10–21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs.

Improvement in patient waiting time in dispensing pharmacies is an important element for patient and pharmacists. The One-Dose Package (ODP) of medicines was implemented in Japan to support medicine adherence among elderly patients; however, it also contributed to increase in patient waiting times. Given the projected increase in ODP patients in the near future owing to rapid population aging, development of improved strategies is a key imperative. We conducted a cross-sectional survey at a single dispensing pharmacy to clarify the impact of ODP on patient waiting time. Further, we propose an improvement strategy developed with use of a discrete event simulation (DES) model. A total of 673 patients received pharmacy services during the study period. A two-fold difference in mean waiting time was observed between ODP and non-ODP patients (22.6 and 11.2 min, respectively). The DES model was constructed with input parameters estimated from observed data. Introduction of fully automated ODP (A-ODP) system was projected to reduce the waiting time for ODP patient by 0.5 times (from 23.1 to 11.5 min). Furthermore, assuming that 40% of non-ODP patients would transfer to ODP, the waiting time was predicted to increase to 56.8 min; however, introduction of the A-ODP system decreased the waiting time to 20.4 min. Our findings indicate that ODP is one of the elements that increases the waiting time and that it might become longer in the future. Introduction of the A-ODP system may be an effective strategy to improve waiting time.

Advanced practice providers (APPs) are essential to the provision of trauma care services, particularly in the wake of residency hour restrictions. Demand for these APPs fluctuates with cyclic patient arrivals; however, most trauma teams continue to staff APPs in a linear fashion. Failure to plan for variable arrivals may contribute to excessive patient wait times and emergency department overcrowding. This study used both qualitative and quantitative approaches to evaluate the impact of APP scheduling on patient wait time and to find schedules minimizing delays in reaching the needed care at the right time. A retrospective observation of the availability of APPs and the flow of 2,249 trauma patients at a Level 1 trauma center, using both visual overlays and computer modeling, allowed us to evaluate the baseline condition, two what-if schedules, and two model-generated schedules minimizing patient time without any additional APP hours. A visual overlay of APP staffing on 2010 patient arrivals indicated substantial times of mismatch. Trauma managers considered adding an APP during weekday evenings that would have resulted in a 14.8% increase in APP hours and yielded a 27% reduction in patient wait times according to our model. An alternate schedule was developed and implemented in 2012 with a 10.5% increase in APP hours and yielding a 73% reduction in wait times. We also delineated two schedule options with 57% and 78% reductions in wait time and no increase in APP work hours. Evaluating alternate shift times and assignments using visual overlays and computer modeling can provide APP staffing solutions with up to 78% reduction in trauma patient wait time without additional APP labor. Knowing that care at the right time is crucial to arriving patients, making sure APP staffing is synchronized with arriving patients is something trauma center managers cannot ignore. Care management study, level IV.

Background: Patient waiting time is an important factor of utilizing hospital service, patient satisfaction, quality of management etc. Hence, emergency department (ED) performance can be best assessed by measuring patients’ waiting time and affecting factors at this department.&#x0D; Objective: To determine patient waiting time at ED and associated factors in a tertiary level hospital.&#x0D; Materials and methods: This cross sectional study was conducted in ED of Chattogram Medical College &amp; Hospital (CMCH), Bangladesh. A total of 175 patients or their attendants at ED were enrolled by convenient sampling technique. Data were collected by face- to- face interview with pretested semi-structured questionnaire and observational checklist. Descriptive statistics was used for analysis by MS Excel and SPSS 21st version windows software.&#x0D; Result: Around half (47.40%) was male followed by female (32%) and children (20.6%). Mean (± SD) age of the patients was 33.69 ± 21.33 years. More than half (52.6%) were from rural areas followed by urban areas (47.7%). Average monthly family income was Tk. 23891.43(±13033.76). Mean (±SD) waiting time for collecting ticket, consultation with doctor, physical examination, and nursing services were 8.06(±7.98) minutes, 5.46(±6.95) minutes, 2.30(±1.11) minutes and 8(±6.22) minutes respectively. More than two fifth (41.1%) patients had waiting time within 5 minutes. The ratio of doctor/patient, nurse/patient, ticket provider/patient and trolleys &amp; wheel chairs bearer/patient were 1:115, 1:58, 1:115 and 1:40 respectively. Majority of the patients (57.7%) were transferred to Inpatient Department (IPD). Statistically significant relationship was found between satisfaction on overall management and overall waiting time of ED (χ2 = 22.47, df =6, p=0.001).&#x0D; Conclusion: Majority (48.1%) found satisfied with waiting time at the emergency department.&#x0D; JOPSOM 2019; 38(2): 15-21

Patients often spend significant time waiting for care, which influences patient satisfaction, perceived quality, access to care, and utilization of various health care services. Waiting time and resulting implications remain understudied in low- and middle-income countries like Kenya. This study aimed to describe the impact on the perception of waiting times on patient satisfaction and care at a tertiary healthcare institution in Kenya. We conducted a prospective cross-sectional study between the first of April 2023 and the thirty-first of August 2023 at the Emergency Room (ER) at Aga Khan University Hospital, Nairobi. Data was collected through a structured questionnaire examining demographics and patient wait times. Descriptive statistics were summarized using frequencies and percentages, and univariate analyses using Fisher's exact test were conducted for group comparisons. A total of 941 patients participated in the study, with 52.0% being females and 53.6% in the 20-40-year age group. More than half of the patients were married (52.4%), had a university education (75.9%), and were employed (70.6%). Of the patients who took the survey, 51.1% reported waiting for 31-60 minutes, while 25.4% reported waiting for more than 60 minutes. Most patients presented to the ER on Monday, most frequently between 0800 and 1200 hrs. Most patients (70.8%) were likely to return for care and 71.7% were likely to recommend care at the ER to relatives and friends. Specific days and times of visits to the ER were associated with prolonged waiting times. To reduce waiting times and improve access to healthcare services, facilities should consider increasing the number of healthcare providers during these peak hours to ensure timely and quality consultations. Identifying bottlenecks and gridlocks within healthcare facilities is crucial to developing an efficient blueprint that aims to improve waiting times, leading to improved patient satisfaction and care.

N032 Cardiology + cardiac surgery= collaboration! wait list management of transcatheter aortic valve implantation (TAVI) patients

Despite widespread implementation of predicted patient wait time information systems in hospital emergency departments (EDs), the relationship between quality of announced wait time information and ED overcrowding mitigation remains unclear. This study investigates how prediction accuracy, update frequency, and patient adoption rates affect ED overcrowding level. A data-calibrated simulation model was developed using patient visit records from three metropolitan EDs in Hong Kong. We systematically varied patient adoption rates and evaluated seven wait time prediction methods across four update frequencies. Key performance metrics included the mean and standard deviation of patient wait times and percentage of patients left without being seen (LWBS rate). Accurate prediction methods combined with frequent updates significantly reduced the mean and standard deviation of patient wait times and LWBS rate as patient adoption rate increased. Conversely, inaccurate prediction methods exhibited a U-shaped performance curve. Specifically, when the patient adoption rate was sufficiently high, these methods significantly increased the mean and standard deviation of wait times and LWBS rate, compared to the case with no predicted wait time. Implementing information systems to display predicted patient wait times requires carefully balancing prediction accuracy, update frequency, and patient adoption. Accurate and timely updates can help redistribute patient load across hospital networks and improve efficiency, while poor accuracy or infrequent updates risk worsening ED congestion, especially when patient adoption rate is high. Our study calls for immediate attention from ED managers to carefully evaluate the impact of announced wait time system before wide implementation.

Why patients should arrive late: The impact of arrival time on patient satisfaction in an academic clinic

AIMS The 2021 WHO classification includes molecular characterizations to a much greater degree. Molecular markers play an important role in deciding treatment choice and predicting prognosis. Molecular markers are often performed in separate laboratories. This has led to concerns about the amount of time taken to get further molecular markers and the delay in diagnosis. Therefore our aim was to determine the waiting time for molecular markers to be returned from the lab following surgery. METHOD We retrospectively collected data for patients who had molecular analysis conducted in 2020 for gliomas at The Walton Centre, Liverpool. Descriptive statistics were conducted using SPSS V27.0. RESULTS 182 patients were included. 130 patients waited &amp;lt;4 weeks, 46 patients &amp;gt;4 weeks, and 6 patients &amp;gt;8 weeks for molecular marker results. The median waiting time was 21 days (range: 11-219, IQR: 17.00-31.00). For patients who waited &amp;gt;4 weeks and &amp;gt;8 weeks, there were 7 WHO Grade I, 7 Grade II, 2 Grade III, and 26 Grade IV tumours. The median time taken between surgery and treatment to begin was 15 working days (range: 7-86, IQR: 12.00- 25.00). M-array took the longest time to return with a median of 63.5 days (range: 48-93, IQR: 60.00-80.00). Following which was NGS results which took a median of 32 days (range: 21-219, IQR: 27.00-40.00) CONCLUSION Most patients received their results in less than 4 weeks, however nearly a third had a waiting time of greater than 4 weeks. Reasons for delays need to be explored further to allow for quicker intervention from the MDT.

Background: Perinatal mortality and morbidity remains high in Kenya over the years, and this has been attributed to the Three Delays, the third focusing on access to care once the client is in the health facility. Structured frameworks for obstetric triage have been shown to improve perinatal outcomes across the globe. In Kenya, anecdotal evidence suggests that there is no formal framework for conducting obstetric triage in public facilities. This study sought to assess the effect of a Structured Integrated Obstetric Triage Model (SIOTEL) on patient waiting time in maternity units of Kiambu Referral Hospitals. Methods: A quasi experimental design was applied in the study. The intervention facility was Kiambu County Referral Hospital and control facility was Thika Level 5 Hospital. Study population included files of patients admitted in the labour wards. The intervention (SIOTEL) was a 3-scale modified Obstetric Triage Acuity Scale, a client assessment tool that was used to conduct obstetric triage. A baseline survey was conducted followed by development, validation and implementation of the tool, then an end line survey conducted to assess effect of the model on waiting time. Quantitative data was analyzed using STATA and presented in tables and graphs. T-statistic was used to test for significance of mean waiting time in the two facilities. Results: A total of 455 files were reviewed, 231 at baseline and 224 at endline survey. The mean client waiting time at baseline was 113.19 and 124.91 minutes in the intervention and control facilities respectively. At endline, the mean waiting time was 57.46 minutes in the intervention facility and 112.92 minutes in the control facility. Findings thus revealed a significant effect of the SIOTEL on client waiting time in the intervention facility (t-3.77;p value 0.00) compared to the control facility (t-0.78; p value 0.44) Conclusions and recommendations: The study concludes that implementation of structured models for obstetric triage contributes to reduction in client waiting time, and recommendations are made to adopt such models for triage in public facilities in Kenya

e14042 Background: Nationwide, many cancer centers experience challenges with infusion center efficiency while maintaining high safety standards. Many factors contribute to long wait times for patients on the day of their infusion appointments. At University of New Mexico Comprehensive Cancer Center (UNMCCC), a contributing factor is the delays in verification or approval of medications. We conducted a project to improve order verification/approval workflow within a Plan-Do-Study-Act (PDSA) framework with the objective to decrease the infusion wait time. Methods: A multidisciplinary working group was formed consisting of the infusion floor physician lead, nurse lead, pharmacy lead, and analytics and process improvement leads. Upon exploring the infusion workflow database, disruptions in verification or approval of orders had a large impact on wait times. Order verification workflow was broken down into 3 steps: 1) physician assessment of patient and approval of orders, 2) infusion nurse assessment of patient, 3) pharmacist verification of order. Beginning Feb 2019, the following interventions were implemented in each section: 1) once patient was assessed by physician and orders approved, the patient was marked as “ready-to-treat”. 2) Pharmacist verified the order once “ready-to-treat” was communicated and initiated preparation of medications prior to arrival of patient to the infusion suit. 3) Infusion nurse assessment occurred once patient was seated on infusion chair. 4) Physicians were encouraged to pre-approve selected injections by the morning of patient appointment. Results: Prospective wait time was gathered for May 2019 using the real-time data available in the electronic medical record. Wait times were analyzed for patients receiving chemotherapy or flat dose injections. By marking appropriate patients “ready-to-treat” and moving pharmacist verification prior to infusion nurse assessment, there was an immediate decrease in wait time from 79 to 60 min. Selected injections which did not require mixing were pre-approved by the physician and stored in the medication dispensing system (Pyxis). This resulted in decrease in the injection wait time by 8.5 minutes, without wasting of drugs. Conclusions: Redesigning the medication order verification/approval workflow resulted in reduced wait times for patients receiving infusions or injections. We aim to further refine our PDSA cycles and ensure sustainability of change.

Do Sickle Cell Patients Wait Longer to See Physicians in the Emergency Department?