Waiting Time Targets Research Articles

Managing appointments with waiting time targets and random walk-ins

The random arrivals of walk-in patients significantly affect the daily operations of healthcare facilities. To improve the performance of outpatient departments, this paper attempts to make an appointment schedule by considering walk-ins and the waiting time target (WTT) for appointment patients. A stochastic programming model is proposed to solve this problem with the objective of minimizing the weighted patient waiting and makespan cost. A non-decreasing waiting cost function is used to capture the WTT fulfillment of appointment patients, whereas walk-ins incur a linear waiting cost. A finite-horizon Markov Decision Process model is formulated to establish the optimal real-time scheduling policy under a given appointment schedule. The appointment schedule is determined by a two-stage stochastic programming approximation and a local search improvement. Structural properties of the optimal appointment scheduling and real-time scheduling policies are established. In particular, it is shown that appointment overbooking is allowed only at the end of the regular session, and the optimal real-time scheduling policy is an easy-to-implement threshold policy with bounded sensitivity. Numerical experiments based on real data are performed to investigate the influence of different parameters and to compare different schedules. The optimal schedule demonstrates superior performance by allowing reasonable waiting times for appointment patients depending on their WTTs. Managerial insights are also provided to hospital managers. Finally, the basic model is extended by incorporating random service times and random arrivals of appointment patients. The latter includes the random number of patients that show up for service or call for appointments, and the random arrival time (unpunctuality). Appointment overbooking strategies are shown to have different structures under some stochastic factors.

College issues warning over waiting times target

College issues warning over waiting times target

Dynamic Insertion of Emergency Surgeries With Different Waiting Time Targets

This paper addresses the problem of emergency surgery insertion into a given elective surgery schedule of an operating theater (OT) composed of multiple operating rooms (ORs). Emergency surgeries with different emergency levels characterized by waiting time targets (WTTs) arrive according to a nonhomogeneous Poisson process and can be inserted into any OR. An event-based stochastic programming model is proposed to minimize the total cost incurred by exceeding WTTs of emergency surgeries, elective surgery delay, and surgery team overtime. A perfect information-based lower bound is proposed and the properties of the optimal policies are proved. Simple heuristic policies and a stochastic optimization (SO) approach derived from the simple policies by policy improvement are proposed. Numerical experiments show that the SO significantly outperforms the others and efficient emergency insertion significantly improves the system performance. A principal component analysis is performed to show how near-optimal policies differ from simple heuristic policies. Note to Practitioners —This paper is motivated by enhancing the efficiency of OTs by sharing the surgery capacity between the elective and emergency surgeries. More specifically, we consider the problem of inserting nonelective surgeries of different emergency levels in the execution of a given elective surgery schedule. An SO approach is proposed to dynamically prioritize emergency and elective surgeries in order to best balance meeting emergency surgery requirement, perturbation of elective schedule, and surgery team overtime. Numerical experiments based on the data collected from Saint-Joseph Hospital in Paris show the significant benefit of efficient emergency insertion over the current hospital practice. Elective surgery schedule is shown to have the most important impact on the system performance but efficient emergency insertion always adds significant improvement.

Financial performance of English NHS trusts and variation in clinical outcomes: a longitudinal observational study

ObjectivesTo examine the association between financial performance as measured by operating margin (surplus/deficit as a proportion of turnover) and clinical outcomes in English National Health Service (NHS) trusts.SettingLongitudinal, observational study...

Can we assess Cancer Waiting Time targets with cancer survival? A population-based study of individually linked data from the National Cancer Waiting Times monitoring dataset in England, 2009-2013.

BackgroundCancer Waiting Time targets have been integrated into successive cancer strategies as indicators of cancer care quality in England. These targets are reported in national statistics for all cancers combined, but there is mixed evidence of their benefits and it is unclear if meeting Cancer Waiting Time targets, as currently defined and published, is associated with improved survival for individual patients, and thus if survival is a good metric for judging the utility of the targets.Methods and findingsWe used individually-linked data from the National Cancer Waiting Times Monitoring Dataset (CWT), the cancer registry and other routinely collected datasets. The study population consisted of all adult patients diagnosed in England (2009–2013) with colorectal (164,890), lung (171,208) or ovarian (24,545) cancer, of whom 82%, 76%, and 77%, respectively, had a CWT matching record.The main outcome was one-year net survival for all matched patients by target attainment (‘met/not met’). The time to each type of treatment for the 31-day and 62-day targets was estimated using multivariable analyses, adjusting for age, sex, tumour stage and deprivation.The two-week wait (TWW) from GP referral to specialist consultation and 31-day target from decision to treat to start of treatment were met for more than 95% of patients, but the 62-day target from GP referral to start of treatment was missed more often. There was little evidence of an association between meeting the TWW target and one-year net survival, but for the 31-day and 62-day targets, survival was worse for those for whom the targets were met (e.g. colorectal cancer: survival 89.1% (95%CI 88.9–89.4) for patients with 31-day target met, 96.9% (95%CI 96.1–91.7) for patients for whom it was not met). Time-to-treatment analyses showed that treatments recorded as palliative were given earlier in time, than treatments with potentially curative intent.There are possible limitations in the accuracy of the categorisation of treatment variables which do not allow for fully distinguishing, for example, between curative and palliative intent; and it is difficult in these data to assess the appropriateness of treatment by stage. These limitations in the nature of the data do not affect the survival estimates found, but do mean that it is not possible to separate those patients for whom the times between referral, decision to treat and start of treatment could actually have an impact on the clinical outcomes. This means that the use of these survival measures to evaluate the targets would be misleading.ConclusionsBased on these individually-linked data, and for the cancers we looked at, we did not find that Cancer Waiting Time targets being met translates into improved one-year survival. Patients may benefit psychologically from limited waits which encourage timely treatment, but one-year survival is not a useful measure for evaluating Trust performance with regards to Cancer Waiting Time targets, which are not currently stratified by stage or treatment type. As such, the current composition of the data means target compliance needs further evaluation before being used for the assessment of clinical outcomes.

Validity of the South African Triage Scale in a rural district hospital.

IntroductionThe implementation of a triage system is a vital step in improving the functioning and patient flow of the emergency centre in a rural district hospital. The South African Triage Scale (SATS) is a well validated and reliable tool used widely in South Africa and other low- and middle-income countries. This study aims to assess the validity of the SATS in a rural district hospital context.MethodsThis is a cross-sectional study. All patients presenting to the Zithulele Hospital emergency centre from 1 October 2015 to 31 December 2015 were triaged using the SATS system, routinely collected data was used to determine the correlation between assigned acuity and outcome to determine rates of under- and over-triage. Patient demographics were collected and waiting times were compared to existing standards of the SATS tool.ResultsOf the 4002 patients presenting to the emergency centre during the study period, 2% were triaged as emergency patients, 15% as very urgent, 38% as urgent and 45% as routine. The assigned acuities correlate well with outcome (f = 0.37; p < 0.0001) and an acceptable rate of over-triage (49%) and under-triage (9%) was found. Waiting time targets were poorly achieved with only 49% of emergency, 23% very urgent, 46% urgent and 69% routine patients seen within ideal target times.DiscussionThe SATS is a valid tool to implement in a rural district emergency centre. Strict waiting time goals may not be achievable in this setting without structural and resource allocation changes to allow for improvements in the surge capacity of staff to manage urgent and emergency patients.

Staff optimization for time-dependent acute patient flow

The emergency department is a key element of acute patient flow, but due to high demand and an alternating rate of arriving patients, the department is often challenged by insufficient capacity. Proper allocation of resources to match demand is, therefore, a vital task for many emergency departments.Constrained by targets on patient waiting time, we consider the problem of minimizing the total amount of staff-resources allocated to an emergency department. We test a matheuristic approach to this problem, accounting for both patient flow and staff scheduling restrictions. Using a continuous-time Markov chain, patient flow is modeled as a time-dependent queueing network where inhomogeneous behavior is evaluated using the uniformization method. Based on this modeling approach, we recursively evaluate and allocate staff to the system using integer linear programming until the waiting time targets are respected in all queues of the network. By comparing to discrete-event simulations of the associated system, we show that this approach is adequate for both modeling and optimizing the patient flow. In addition, we demonstrate robustness to the service time distribution and the associated system with multiple classes of patients.

Data-driven analytics to support scheduling of multi-priority multi-class patients with wait time targets

Magnetic Resonance Image (MRI) uses powerful magnetic forces and radio frequencies to create detailed images of the organs and tissues within the body. In this paper, we first conduct descriptive analytics on MRI data of over 3.7 million patient records and determine the main factors affecting the waiting time and conduct predictive analytics to forecast the daily arrivals and the number of procedures performed at each hospital. It is the hospital’s goal to serve 90% of patients within their wait time targets. Therefore, we prescribe two simple scheduling policies based on a balance between the FIFO (First-In First-Out) and strict priority policies; namely, weight accumulation and priority promotion to improve the wait time management. Under the weight accumulation policy, patients from different priority levels start with varying initial weights, which then accumulates as a linear function of their waiting time. Under the priority promotion policy, a strict priority policy is applied to priority levels where patients are promoted to a higher priority level after waiting for a predetermined threshold of time. We evaluate the proposed policies against two performance measures: total exceeding time (the number of days by which patients exceed their wait target), and overflow proportion (the percentage of patients that exceed the wait target). To investigate the value of information, we schedule patients at different points of time from their day of arrival. The results show that hospitals can enhance their wait time management by delaying patient scheduling. We demonstrate that effective scheduling policies may result in significant reduction in patient waiting time without any costly capacity expansion.

Endoscopy in 2017: a national survey of practice in the UK

IntroductionThe Joint Advisory Group on Gastrointestinal Endoscopy (JAG), hosted by the Royal College of Physicians, London, oversees the quality assurance of endoscopy services across the UK. Additional questions focusing on...

A mathematical programming model for optimizing the staff allocation in radiotherapy under uncertain demand

As the number of people diagnosed with cancer increases, demand for radiotherapy (RT) services has been continuously growing. In RT, delays in the start of treatment have shown to increase the risk of tumor progression in various cancer types, and patients experience greater psychological distress when subject to longer waiting times. The RT process, which involves imaging and treatment planning before treatment, is subject to complexities that hamper resource planning and control. On the demand side, the amount of workload in each operation depends on the highly variable patient inflow. On the supply side, radiation therapy technologists (RTTs) have multiple skills, rotation needs and partial availability, which makes the allocation of RTTs a complex task that often leads to situations of understaffing, jeopardizing the fulfillment of the patients' waiting time standards. In this paper, we propose a stochastic mixed-integer linear programming model that optimizes the allocation of RTTs to multiple operations in RT over a set of scenarios of patient inflow. The scenarios are generated from historical patient data, and the final RTT allocation covers the workload associated with all scenarios. The goal is to maximize the (expected) number of patients completing pre-treatment within the waiting time target. Results for a case study in the RT department of the Netherlands Cancer Institute show that, on average, the number of patients able to start treatment within the maximum waiting time standards may increase from 91.3% to 97.9% for subacute patients, and from 96.3% to 100.0% for regular patients.

Which patients are not included in the English Cancer Waiting Times monitoring dataset, 2009\u20132013? Implications for use of the data in research

Background:Cancer waiting time targets are routinely monitored in England, but the Cancer Waiting Times monitoring dataset (CWT) does not include all eligible patients, introducing scope for bias.Methods:Data from adults diagnosed in England (2009–2013) with colorectal, lung, or ovarian cancer were linked from CWT to cancer registry, mortality, and Hospital Episode Statistics data. We present demographic characteristics and net survival for patients who were and were not included in CWT.Results:A CWT record was found for 82% of colorectal, 76% of lung, and 77% of ovarian cancer patients. Patients not recorded in CWT were more likely to be in the youngest or oldest age groups, have more comorbidities, have been diagnosed through emergency presentation, have late or missing stage, and have much poorer survival.Conclusions:Researchers and policy-makers should be aware of the limitations in the completeness and representativeness of CWT, and draw conclusions with appropriate caution.

COST-UTILITY ANALYSIS OF PRIVATE CONTRACTING TO REDUCE PUBLIC WAITING TIMES FOR JOINT REPLACEMENT SURGERY.

Earlier treatment of publicly funded patients may achieve health gains that justify the additional costs of reducing waiting times. This study reports on the cost-effectiveness of implementing a private contracting model to meet alternative maximum waiting time targets for publicly funded patients undergoing total knee replacement surgery in Australia. A linked decision tree and cohort Markov model was developed and populated and validated using secondary data sources to represent the pathways, costs, and quality adjusted life-years (QALYs) gained of non-urgent patients with alternative waiting times for total knee replacement surgery to a maximum age of 100 years. Assuming public waiting times are reduced through the purchase of private services, additional QALYs are gained at an incremental cost of less than $40,000. Value could be increased if lower private prices could be negotiated. Results are also sensitive to the rate of deterioration in function while waiting for surgery and the impact of functional status at the time of surgery on postsurgery outcomes. More evidence on the value of expanded capacity or new models of care may inform new funding models to support such investments and reduced prices for new technologies, leading to more efficient and sustainable publicly funded healthcare systems.

Who breaches the four-hour emergency department wait time target? A retrospective analysis of 374,000 emergency department attendances between 2008 and 2013 at a type 1 emergency department in England

BackgroundThe four-hour target is a key hospital emergency department performance indicator in England and one that drives the physical and organisational design of the ED. Some studies have identified time of presentation as a key factor affecting waiting times. Few studies have investigated other determinants of breaching the four-hour target. Therefore, our objective was to describe patterns of emergency department breaches of the four-hour wait time target and identify patients at highest risk of breaching.MethodsThis was a retrospective cohort study of a large type 1 Emergency department at an NHS teaching hospital in Oxford, England. We analysed anonymised individual level patient data for 378,873 emergency department attendances, representing all attendances between April 2008 and April 2013. We examined patient characteristics and emergency department presentation circumstances associated with the highest likelihood of breaching the four-hour wait time target.ResultsWe used 374,459 complete cases for analysis. In total, 8.3% of all patients breached the four-hour wait time target. The main determinants of patients breaching the four-hour wait time target were hour of arrival to the ED, day of the week, patient age, ED referral source, and the types of investigations patients receive (p < 0.01 for all associations). Patients most likely to breach the four-hour target were older, presented at night, presented on Monday, received multiple types of investigation in the emergency department, and were not self-referred (p < 0.01 for all associations). Patients attending from October to February had a higher odds of breaching compared to those attending from March to September (OR 1.63, 95% CI 1.59 to 1.66).ConclusionsThere are a number of independent patient and circumstantial factors associated with the probability of breaching the four-hour ED wait time target including patient age, ED referral source, the types of investigations patients receive, as well as the hour, day, and month of arrival to the ED. Efforts to reduce the number of breaches could explore late-evening/overnight staffing, access to diagnostic tests, rapid discharge facilities, and early assessment and input on diagnostic and management strategies from a senior practitioner.

Comparison of Subsidy Schemes for Reducing Waiting Times in Healthcare Systems

This study analyzes subsidy schemes that are widely used in reducing waiting times for public healthcare service. We assume that public healthcare service has no user fee but an observable delay, while private healthcare service has a fee but no delay. Patients in the public system are given a subsidy s to use private service if their waiting times exceed a pre‐determined threshold t. We call these subsidy schemes ( s, t) policies. As two extreme cases, the ( s, t) policy is called an unconditional subsidy scheme if t = 0, and a full subsidy scheme if s is equal to the private service fee. There is a fixed budget constraint so that a scheme with larger s has a larger t. We assess policies using two criteria: total patient cost and serviceability (i.e., the probability of meeting a waiting time target for public service). We prove analytically that, if patients are equally sensitive to delay, a scheme with a smaller subsidy outperforms one with a larger subsidy on both criteria. Thus, the unconditional scheme dominates all other policies. Using empirically derived parameter values from the Hong Kong Cataract Surgery Program, we then compare policies numerically when patients differ in delay sensitivity. Total patient cost is now unimodal in subsidy amount: the unconditional scheme still yields the lowest total patient cost, but the full subsidy scheme can outperform some intermediate policies. Serviceability is unimodal too, and the full subsidy scheme can outperform the unconditional scheme in serviceability when the waiting time target is long.

Waiting time targets and informal professional networks in English NHS

PurposeThis paper examines the relationship between key actors in an organisation and informal professional networks. It uses pragmatic constructivism (PC) as a research paradigm together with concepts borrowed from the theory of social networks to investigate how these actors responded to a particular set of non-financial performance targets.Design/methodology/approachThe implementation process of waiting time targets in the Accident and Emergency Department was observed as part of an in-depth study at a large English National Health Service hospital. The main sources of data were face-to-face interviews with key actors, documentary archival evidence and observation diaries.FindingsThe results indicated that with the effect of information sharing through these networks, implementation methodology switched from systems approach to actors approach. Professional connections between key actors had allowed them to generate their own understanding in responding to new performance measures. As a consequence, the perceptions of other actors in relevant networks, as well as the implementation practices were influenced.Research limitations/implicationsThis is a single-site, in-depth case study; hence, the findings are not generalizable.Practical implicationsThis study demonstrated just how influential some key actors can be in shaping the implementation of performance measures.Originality/valueThe paper contributes to PC by providing evidence on the influence of informal professional networks and structural holes in shaping organisational topos.

The development and practical application of a simulation model to inform musculoskeletal service delivery in an Australian public health service

Timely access to orthopaedic and neurosurgery services in public hospitals is difficult to achieve due to constrained resources and rising demand with the result that waiting time targets are often not met. Advanced physiotherapy-led clinics can assist in managing demand by directly assessing and managing many patients without the need for consultation with a medical specialist. The purpose of this study was to develop and apply simulation modelling to determine the scale and mix of services required to efficiently manage demand from patients with musculoskeletal conditions in one health district. DesignWe designed a simulation model to estimate service demand over five years and test different service configurations (medical specialist-led or advanced physiotherapy-led) to meet waiting time targets and reduce waiting lists in orthopaedic and neurosurgery services. ResultsWithout intervention, the combined orthopaedic and neurosurgical outpatient waiting list is predicted to grow from 11,000 to over 15,000 patients over a five year period. To achieve waiting time targets within 2 years the scale of services would need to approximately double and the most efficient combination would be to direct approximately 50% of referrals to medical specialist clinics and 50% to advanced physiotherapy-led clinics. ConclusionsIn the health system modelled, a significant gap was identified between future demand and current provision of services. Simulation modelling was able to assist service planners understand the demand and identify effective management strategies.

A qualitative study exploring the factors influencing admission to hospital from the emergency department

ObjectiveThe number of emergency admissions to hospital in England and Wales has risen sharply in recent years and is a matter of concern to clinicians, policy makers and patients alike....

Factors associated with failure of emergency wait-time targets for high acuity discharges and intensive care unit admissions.

Ontario established emergency department length-of-stay (EDLOS) targets but has difficulty achieving them. We sought to determine predictors of target time failure for discharged high acuity patients and intensive care unit (ICU) admissions. This was a retrospective, observational study of 2012 Sunnybrook Hospital emergency department data. The main outcome measure was failing to meet government EDLOS targets for high acuity discharges and ICU emergency admissions. The secondary outcome measures examined factors for low acuity discharges and all admissions, as well as a run chart for 2015 - 2016 ICU admissions. Multiple logistic regression models were created for admissions, ICU admissions, and low and high acuity discharges. Predictor variables were at the patient level from emergency department registries. For discharged high acuity patients, factors predicting EDLOS target failure were having physician initial assessment duration (PIAD)>2 hours (OR 5.63 [5.22-6.06]), consultation request (OR 10.23 [9.38-11.14]), magnetic resonance imaging (MRI) (OR 19.33 [12.94-28.87]), computed tomography (CT) (OR 4.24 [3.92-4.59]), and ultrasound (US) (OR 3.47 [3.13-3.83]). For ICU admissions, factors predicting EDLOS target failure were bed request duration (BRD)>6 hours (OR 364.27 [43.20-3071.30]) and access block (AB)>1 hour (OR 217.27 [30.62-1541.63]). For discharged low acuity patients, factors predicting failure for the 4-hour target were PIAD>2 hours (OR 15.80 [13.35-18.71]), consultation (OR 20.98 [14.10-31.22]), MRI (OR 31.68 [6.03-166.54]), CT (OR 16.48 [10.07-26.98]), and troponin I (OR 13.37 [6.30-28.37]). Sunnybrook factors predicting failure of targets for high acuity discharges and ICU admissions were hospital-controlled. Hospitals should individualize their approach to shortening EDLOS by analysing its patient population and resource demands.

OUTPATIENT FLOW OPTIMIZATION - The redesign and implementation of non face-to-face flow systems

Introduction : In a global context of increasing demand for health care and the greater demand on the quality of care, our health systems are being challenged to maintain high quality standards. In order to meet these expectations, it is necessary to apply increasingly efficient management models that safeguard and improve the user’s perception of the attention received. Limited public or private resources in health institutions urge us to seek their optimization by implementing more effective alternative modus operandi. By incorporating technological advances in the sector, particularly through new communication systems, we are given an opportunity to substantially alter doctor-patient or administration-patient relations whilst promoting co responsibility in the health process. With these premises, and a clear commitment to the patient, a project is born focused on the outpatient area of the Hospital Sant Joan de Deu de Martorell in Barcelona. The aim is to simplify the highly complex flow of patients, which increases the cost of the care process due to its numerous associated administrative procedures. This gives rise to a negative impact on the end user’s satisfaction. Using the basic principles of ‘LEAN’ methodology and process reengineering in the areas of surgical scheduling, outpatient visits, admissions and certain outpatient procedures, waiting time targets have been reduced and improved levels of accessibility to specialist consultants were established. Methods : The project is defined initially by focusing on the outpatient area, however with a view to expanding it into other hospital areas. The project has been undertaken in close collaboration with primary care teams resulting in the setting of the main following lines of action: 1. Review and redesign of patient care systems, from hospital admission to scheduling consultations and diagnostic tests by applying reduced flow time and optimization of resources. 2. Incorporation of non-face-to-face service processes by implementing mobile-phone technology as a way of bi-directional communication with the patient. 3. Creation of a preoperative care unit, with a single preoperative interaction as a specific approach in the pre-surgical procedure. 4. Application of biometric signatures, with informed consent, and the elimination of paper medical records. Results : In less than a year, after the implementation of the actions described, the results exceeded the expectations created and effected directly and indirectly all influential areas of the organization. There were substantial quantitative and qualitative improvements in the main assessment indicators. A notable 95% reduction of face events (250.000 per year) in the areas of scheduling outpatient visits, diagnostic tests and surgical procedures, through computerization of the requests generated in consultancies, along with the extension and acceptance of the non-contact care program, has been the biggest factor in allowing the users to spend only the minimum time necessary in those activities that add value to the process of health. The bi-directional communication with the patient through mobile messaging for appointments, modification and/or cancellation, has allowed a reduction in the rate of user’s presence. This has produced an 11% increase in accessibility to the agendas and has facilitated meeting current commitments to ‘Maximum Delay’ objectives for first visits in all specialties. The new preoperative care unit, which provides a single time for the patient to take all necessary surgical diagnostic tests has resulted in improvements in the level of patient risk and in the application of complementary diagnostic tests. The prior review of the need to test, avoiding duplication and unnecessary patient radiation, has meant an 8% reduction in electrocardiograms and 56% in chest x-rays. Document scanning, the implementation of the biometric signature and the incorporation of advances in communication, replacing letters and phone calls has reduced by up to 70% office supplies costs (paper, printer toner, labels, ...) and gave over 60% savings in telephone costs. Discussion : Results to date show that the implementation of mobile-phone technology is an essential tool in order to obtain a more effective and higher quality health care. Having noted the acceptance and profitability of the project in the hospital environment, we consider the feasibility of it is exportable to all primary care services, as it has been proven in our area of influence. Conclusion : From the patient's perspective, and the considerable organizational benefits, simplifying the flow of patients, reducing queues and waiting times as well as the reduction of face-to face services has facilitated the meeting of objectives giving rise to an improvement in overall user satisfaction and the perception of health care.

Coordinating clinic and surgery appointments to meet access service levels for elective surgery

Providing timely access to surgery is crucial for patients with high acuity diseases like cancer. We present a methodological framework to make efficient use of scarce resources including surgeons, operating rooms, and clinic appointment slots with a goal of coordinating clinic and surgery appointments so that patients with different acuity levels can see a surgeon in the clinic and schedule their surgery within a maximum wait time target that is clinically safe for them. We propose six heuristic scheduling policies with two underlying ideas behind them: (1) proactively book a tentative surgery day along with the clinic appointment at the time an appointment request is received, and (2) intelligently space out clinic and surgery appointments such that if the patient does not need his/her surgery appointment there is sufficient time to offer it to another patient. A 2-stage stochastic discrete-event simulation approach is employed to evaluate the six scheduling policies. In the first stage of the simulation, the heuristic policies are compared in terms of the average operating room (OR) overtime per day. The second stage involves fine-tuning the most-effective policy. A case study of the division of colorectal surgery (CRS) at the Mayo Clinic confirms that all six policies outperform the current scheduling protocol by a large margin. Numerical results demonstrate that the final policy, which we refer to as Coordinated Appointment Scheduling Policy considering Indication and Resources (CASPIR), performs 52% better than the current scheduling policy in terms of the average OR overtime per day under the same access service level. In conclusion, surgical divisions desiring stratified patient urgency classes should consider using scheduling policies that take the surgical availability of surgeons, patients’ demographics and indication of disease into consideration when scheduling a clinic consultation appointment.