Equity in waiting times for two surgical specialties: a case study at a hospital in the North West of England

BACKGROUND: Reducing inequalities in access to the National Health Service is a key government priority. This study investigates the extent to which equitable access is achieved in one routinely administered hospital waiting list system. METHODS: Using hospital episode statistics for one hospital in the North West of England, a retrospective study of waiting times to surgery was undertaken for two surgical specialties (Orthopaedics and Ophthalmology). Participants were 4306 waiting list patients (elective, first episodes) living within Health Authority boundaries, treated within the two specialties between 1 April 2000 and 31 March 2001. Multiple logistic regression analysis of the relationship between waiting times and age, gender, ethnicity and deprivation status was undertaken. Main outcome measures were length of waiting time between being referred to the waiting list and treatment, and odds ratios for being associated with longer than median waiting times by age, gender, ethnicity and deprivation. RESULTS: For ophthalmology, the median waiting time was 140 days. After adjustment, older people had an odds ratio (OR) of 1.64 (95 per cent confidence interval (CI) 1.42-1.89), patients in deprived areas an OR of 1.29 (95 per cent CI 1.08-1.55) and men an OR of 0.79 (95 per cent CI 0.68-0.92) for having longer than median waiting times. No significant association was found between waiting times and ethnicity. For orthopaedics, the median waiting time was 129 days, and there were no significant differences in waiting times according to age, gender, ethnicity or deprivation. CONCLUSION: Routine waiting list systems are not always delivered equitably. For one specialty, female, older and deprived patients were significantly more likely to experience longer than average waits. Potential explanations and implications for policy-makers are considered.     

Analysing management policies for operating room planning using simulation

In this paper we analyse the operating room planning at a department of orthopaedic surgery in Sweden. We focus on the problem of meeting the uncertainty in demand of patient arrival and surgery duration and at the same time maximizing the utilization of Operating Room (OR) time. With a discrete-event model we simulate how different management polices affect different performance metrics such as patient waiting time, cancellations and the utilization of OR time. The experiments show that the performance of the operating room department can be improved significantly by applying a different policy in reserving OR-capacity for emergency cases together with a policy to increase staff in stand-by. Moreover, the developed simulation model provides estimates for a what-if situation related to the prognosis of an increasing number of hip-joint replacements.     

A three-phase approach for operating theatre schedules

In this paper we develop a three-phase, hierarchical approach for the weekly scheduling of operating rooms. This approach has been implemented in one of the surgical departments of a public hospital located in Genova (Genoa), Italy. Our aim is to suggest an integrated way of facing surgical activity planning in order to improve overall operating theatre efficiency in terms of overtime and throughput as well as waiting list reduction, while improving department organization. In the first phase we solve a bin packing-like problem in order to select the number of sessions to be weekly scheduled for each ward; the proposed and original selection criterion is based upon an updated priority score taking into proper account both the waiting list of each ward and the reduction of residual ward demand. Then we use a blocked booking method for determining optimal time tables, denoted Master Surgical Schedule (MSS), by defining the assignment between wards and surgery rooms. Lastly, once the MSS has been determined we use the simulation software environment Witness 2004 in order to analyze different sequencings of surgical activities that arise when priority is given on the basis of a) the longest waiting time (LWT), b) the longest processing time (LPT) and c) the shortest processing time (SPT). The resulting simulation models also allow us to outline possible organizational improvements in surgical activity. The results of an extensive computational experimentation pertaining to the studied surgical department are here given and analyzed.     

Planning and scheduling of semi-urgent surgeries

This paper investigates the trade-off between cancellations of elective surgeries due to semi-urgent surgeries, and unused operating room (OR) time due to excessive reservation of OR time for semi-urgent surgeries.Semi-urgent surgeries, to be performed soon but not necessarily today, pose an uncertain demand on available hospital resources, and interfere with the planning of elective patients. For a highly utilized OR, reservation of OR time for semi-urgent surgeries avoids excessive cancellations of elective surgeries, but may also result in unused OR time, since arrivals of semi-urgent patients are unpredictable. First, using a queuing theory framework, we evaluate the OR capacity needed to accommodate every incoming semi-urgent surgery. Second, we introduce another queuing model that enables a trade-off between the cancelation rate of elective surgeries and unused OR time. Third, based on Markov decision theory, we develop a decision support tool that assists the scheduling process of elective and semi-urgent surgeries. We demonstrate our results with actual data obtained from a department of neurosurgery.     

Tactical and operational decisions for operating room planning: Efficiency and welfare implications

In this paper, we evaluate the impact on welfare implications of a 0-1 linear programming model to solve the Operating Room (OR) planning problem, taking a patient perspective. In particular, given a General Surgery Department made up of different surgical sub-specialties sharing a given number of OR block times, the model determines, during a given planning period, the allocation of those blocks to surgical sub-specialties, i.e. the so called Master Surgical Schedule Problem (MSSP), together with the subsets of elective patients to be operated on in each block time, i.e. the so called Surgical Case Assignment Problem (SCAP). The innovation of the model is two-fold. The first is that OR allocation is “optimal” if the available OR blocks are scheduled simultaneously to the proper sub-specialty, at the proper time to the proper patient. The second is defining what “proper” means and include that in the objective function. In our approach what is important is not number of patients who can be treated in a given period but how much welfare loss, due to clinical deterioration or other negative consequences related to excessive waiting, can be prevented. In other words we assume a societal perspective in that we focus on “outcome” (health improving or preventing from worsening) rather than on “output” (delivered procedures). The model can be used both to develop weekly OR planning with given resources (operational decision), and to perform “what if” scenario analysis regarding how to increase the amount of OR time available for the entire department (tactical decision). The model performance is verified by applying it to a real scenario, the elective admissions of the General Surgery Department of the San Martino University Hospital in Genova (Italy). Despite the complexity of this NP-hard combinatorial optimization problem, computational results indicate that the model can solve all test problems within 600 s and an average optimality tolerance of less than 0,01%.     

Optimization of surgery sequencing and scheduling decisions under uncertainty

Operating rooms (ORs) are simultaneously the largest cost center and greatest source of revenues for most hospitals. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. Longer than average surgery durations result in late starts not only for the next surgery in the schedule, but potentially for the rest of the surgeries in the day as well. Late starts also result in direct costs associated with overtime staffing when the last surgery of the day finishes later than the scheduled shift end time. In this article we describe a stochastic optimization model and some practical heuristics for computing OR schedules that hedge against the uncertainty in surgery durations. We focus on the simultaneous effects of sequencing surgeries and scheduling start times. We show that a simple sequencing rule based on surgery duration variance can be used to generate substantial reductions in total surgeon and OR team waiting, OR idling, and overtime costs. We illustrate this with results of a case study that uses real data to compare actual schedules at a particular hospital to those recommended by our model.     

Quick start: novel oral contraceptive initiation method

Conventional oral contraceptive (OC) starting instructions require waiting until menses to begin the OC. The conventional approach requires detailed patient education about when to begin and also may require the use of less effective or less acceptable interim contraceptive protection until menses. At our urban family planning clinic, we routinely offer patients starting the OC the option of taking the first tablet sooner. We prospectively evaluated predictors of short-term OC continuation among 250 OC requestors who were offered several approaches to OC initiation. Telephone follow-up of 91% of participants showed that women who swallowed the first OC in the clinic were more likely to continue the OC until the second package than women who planned to start the OC later (adjusted OR 2.8, 95% C.I. 1.1-7.3). Other factors associated with short-term continuation were: partner's knowledge of planned OC use, older age, and participant's agreement that she would be very unhappy about becoming pregnant in the next 6 months.     

Staff planning for operating rooms with different surgical services lines

We present a two-phase model for a staff planning problem in a surgical department. We consider the setting where staff, in particular nurse circulators and surgical scrub technicians, are assigned to one of different service lines, and while they can be ‘pooled’ and temporally assigned to other service line if needed, these re-assignments should belimited. In Phase I, we decide on the number of staff hours to budget for each service line, considering policies limiting staff pooling and overtime, and different demand scenarios. In Phase II, we determine how these budgeted staff hours should be allocated across potential work days and shifts, given estimated staff requirements and shift-related scheduling restrictions. We propose a heuristic to speed the model’s Phase II solution time. We implement the model using a hospital’s surgical data and compare the model’s results with the hospital’s current practices. Using a simulation model for the surgical operations, we find that our two-phase model reduces the delays caused by staff unavailability as well as staff pooling, without increasing the workforce size. Finally, we briefly describe a decision-support tool we developed with the objective of fine-tuning staff planning decisions.     

Why do patients bypass the nearest hospital? An empirical analysis for orthopaedic care and neurosurgery in the Netherlands

Using data for 2003, we find that both for non-emergency orthopaedic care (38%) and neurosurgery (54%) numerous Dutch patients did not visit the nearest hospital. Our estimation results show that extra travel time negatively influences the probability of hospital bypassing. Good waiting time performance by the nearest hospital also significantly decreases the likelihood of a bypass decision. Patients seem to place a lower negative value on extra travel time for orthopaedic care than for neurosurgery. The valuation of shorter waiting time also varies between these two types of hospital care. A good performance of the nearest hospital on waiting time decreases the likelihood of a bypass decision most for neurosurgery. In both samples, patients are more likely to bypass the nearest hospital when it is a university medical centre or a tertiary teaching hospital. Patient attributes, such as age and social status, are also found to significantly affect hospital bypassing. From our analysis it follows that both patient and hospital care heterogeneity should be taken into account when assessing the substitutability of hospitals.     

Managing magnetic resonance imaging machines: support tools for scheduling and planning

We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning.     

Race/ethnicity-related and payer-related disparities in the timeliness of emergency care in U.S. emergency departments

Our study objectives were to examine race/ethnicity-related and insurance-related differences in the timeliness of emergency care for a nationally representative sample of adults and to explore the role of uncertainty and location of care in explaining overall differences. We estimated a logistic regression model with hospital fixed effects to derive estimates of within-hospital group differences in the likelihood of waiting for more than 60 minutes to see a physician for several presenting conditions. We further estimated a model without hospital fixed effects to derive overall group differences. We observed race/ethnicity-related and payer-related differences in the timeliness of a medical screening exam for abdominal pain and chest pain visits but not for extremity laceration visits. Overall (within- and between-hospitals) differences in waiting time were due to patients receiving different care from the same hospital and from patients receiving care from different hospitals.     

品管圈对缩短精神科急诊患者就诊等待时间的效果探讨

目的 探讨品管圈对缩短精神科急诊患者就诊等待时间的效果.方法 选取2019年7月至2020年6月于我院精神科急诊就诊的300例患者作为研究对象,150例予以常规就诊指导的患者纳入对照组,150例予以品管圈就诊指导的患者纳入观察组.比较两组的就诊等待时间及护理满意度.结果 观察组的就诊等待时间显著短于对照组,护理满意度评...     

A Survey of Occupational Therapy Practice in Beijing,China

There is an increasing demand for rehabilitation services in China as a result of the growing number of people with physical and mental challenges, as well as the growing population of older adults. The purpose of this study was to explore the current occupational therapy (OT) resources available in Beijing, China, to serve as the first step in planning the response to increasing demand for OT services from the people of China. Specifically, using the snowball sampling survey method, we explored the work practice, including years of working experience, work setting, weekly work hours, annual income and factors related to job satisfaction among occupational therapists in Beijing, China. A total of 44 occupational therapists currently working in the Beijing area responded to our survey. The results demonstrated that most of the therapists working in Beijing area were young and inexperienced. Despite the fact that the participants had an average age of 31 years old and an average of 8 years' working experience, 61.4% of therapists were under 30 years old and more than half of therapists had less than 5 years of OT experiences. Among those included in the study sample, 50% had earned degrees in OT, and the rest of the OT personnel received OT‐related on‐the‐job training in various forms and lengths of time. A majority of the participants worked in hospital settings with adults or children with physical disabilities and used therapeutic activities and therapeutic exercises. Being an occupational therapist is not a high‐paying job. Education satisfaction, work experience and annual income are the factors related to job satisfaction for the participants. The majority of occupational therapists expressed the need to receive more support for clinical‐related trainings. We plan to expand this pilot study nationwide to gain an in‐depth and comprehensive understanding of the OT workforce in China. Copyright © 2016 John Wiley & Sons, Ltd.     

Inter-regional competition and quality in hospital care

This study analyzes the effect of episode-of-care payment and patient choice on waiting time and the comprehensive quality of hospital care. The study assumes that two hospitals are located in two cities with different population sizes and compete with each other. We find that the comprehensive quality of hospital care as well as waiting time of both hospitals improve with an increase in payment per episode of care. However, we also find that the extent of these improvements differs according to the population size of the cities where the hospitals are located. Under the realistic assumptions that hospitals involve significant labor-intensive work, we find the improvements in comprehensive quality and waiting time in a hospital located in a small city to be greater than those in a hospital located in a large city. The result implies that regional disparity in the quality of hospital care decreases with an increase in payment per episode of care.     

Waiting times and socioeconomic status: evidence from England

Waiting times for elective surgery, like hip replacement, are often referred to as an equitable rationing mechanism in publicly-funded healthcare systems because access to care is not based on socioeconomic status. Previous work has established that that this may not be the case and there is evidence of inequality in NHS waiting times favouring patients living in the least deprived neighbourhoods in England. We advance the literature by explaining variations of inequalities in waiting times in England in four different ways. First, we ask whether inequalities are driven by education rather than income. Our analysis shows that education and income deprivation have distinct effects on waiting time. Patients in the first quintile with least deprivation in education wait 9% less than patients in the second quintile and 14% less than patients in the third-to-fifth quintile. Patients in the fourth and fifth most income-deprived quintile wait about 7% longer than patients in the least deprived quintile. Second, we investigate whether inequalities arise "across" hospitals or "within" the hospital. The analysis provides evidence that most inequalities occur within hospitals rather than across hospitals. Moreover, failure to control for hospital fixed effects results in underestimation of the income gradient. Third, we explore whether inequalities arise across the entire waiting time distribution. Inequalities between better educated patients and other patients occur over large part of the waiting time distribution. Moreover we find that the education gradient becomes smaller for very long waiting. Fourth, we investigate whether the gradient may reflect the fact that patients with higher socioeconomic status have a different severity as proxied through a range of types and the number of diagnoses (in addition to age and gender) compared to those with lower socioeconomic status. We find no evidence that differences in severity explain the social gradient in waiting times.     

Common patterns in the public reporting of waiting time and waiting list information: Findings from a sample of OECD jurisdictions

We present findings from a review of published literature and administrative documentation on waiting time and waiting list reporting models for elective treatment in a sample of international jurisdictions (a subset of OECD countries, with regional reporting regimes treated as distinct jurisdictions). In this paper we identified common patterns in the measurement and reporting of waiting time and waiting list information for elective treatment. We mapped the waiting time, waiting list, and key performance indicator statistics reported by 15 English-speaking international jurisdictions. Three distinct patterns of maximum waiting time target measures for elective treatment were identified amongst our international sample following our patient pathway event time-point analysis: (i) full-pathway maximum wait time targets; (ii) separate wait time targets for “time-to-diagnosis” and “time-to-treatment”; and (iii) “Time-to-Treatment” waiting time target only. Our review also revealed common patterns in the reporting of waiting time and waiting list statistics as well as KPI measures amongst a sub-sample of English-speaking jurisdictions. These common patterns provide a starting point towards more standardised measurement and reporting of waiting time and waiting list statistics in benchmarking access to elective care internationally.     

The relationship between educational attainment and waiting time among the elderly in Norway

We investigate whether educational attainment affects waiting time of elderly patients in somatic hospitals. We consider three distinct pathways; that patients with different educational attainment have different disease patterns, that patients with different levels of education receive treatments at different hospitals, and that patient choice and supply of local health services within hospital catchment areas explain unequal waiting time of different educational groups. We find evidence of an educational gradient in waiting time for male patients, but not for female patients. Conditional on age, male patients with tertiary education wait 45% shorter than male patients with secondary or primary education. The first pathway is not quantitatively important as controlling for disease patters has little effect on relative waiting times. The second pathway is important. Relative to patients with primary education, variation in waiting time and education level across local hospitals contributes to higher waiting time for male patients with secondary education and female patients with secondary or tertiary education and lower waiting time for male patients with tertiary education. These effects are in the order of 15–20%. The third pathway is also quantitatively important. The educational gradients within catchment areas disappear when we control for travel distance and supply of private specialists.     

Optimal hospital payment rules under rationing by waiting

We derive optimal rules for paying hospitals for non-emergency care when providers choose quality and capacity, and patient demand is rationed by waiting time. Waiting for treatment is costly for patients, so that hospital payment rules should take account of their effect on waiting time as well as on quality. Since deterministic waiting time models imply that profit maximising hospitals will never choose to have both positive quality and positive waiting time, we develop a stochastic model of rationing by waiting in which both quality and expected waiting are positive in equilibrium. We use it to show that, although a prospective output price gives hospitals an incentive to attract patients by raising quality and reducing waiting times, it must be supplemented by a price attached to hospital decisions on quality or capacity or to a performance indicator which depends on those decisions (such as average waiting time, or average length of stay). A prospective output price by itself can support the optimal quality and waiting time distribution only if the welfare function respects patient preferences over quality and waiting time, if patients’ marginal rates of substitution between quality and waiting time are independent of income, and if waiting for treatment does not reduce the productivity of patients. If these conditions do not hold, supplementing the output price with a reward linked to the hospital's cost can increase welfare, though it is possible that costs should be taxed rather than subsidised.     

Waiting times for elective surgery and the decision to buy private health insurance

More than 45% of Australians buy health insurance for private treatment in hospital. This is despite having access to universal and free public hospital treatment. Anecdotal evidence suggests that avoidance of long waits for public treatment is one possible explanation for the high rate of insurance coverage. In this study, we investigate the effect of waiting on individual decisions to buy private health insurance. Individuals are assumed to form an expectation of their own waiting time as a function of their demographics and health status. We model waiting times using administrative data on the population hospitalised for elective procedures in public hospitals and use the parameter estimates to impute the expected waiting time and the probability of a long wait for a representative sample of the population. We find that expected waiting time does not increase the probability of buying insurance but a high probability of experiencing a long wait does. On average, waiting time has no significant impact on insurance. In addition, we find that favourable selection into private insurance, measured by self-assessed health, is no longer significant once waiting time variables are included. This result suggests that a source of favourable selection may be aversion to waiting among healthier people.     

Towards Decision Support for Waiting Lists: An Operations Management View

This paper considers the phenomenon of waiting lists in a healthcare setting, which is characterised by limitations on the national expenditure, to explore the potentials of an operations management perspective. A reference framework for waiting list management is described, distinguishing different levels of planning in healthcare – national, regional, hospital and process – that each contributes to the existence of waiting lists through managerial decision making. In addition, different underlying mechanisms in demand and supply are distinguished, which together explain the development of waiting lists. It is our contention that within this framework a series of situation specific models should be designed to support communication and decision making. This is illustrated by the modelling of the demand for cataract treatment in a regional setting in the south-eastern part of the Netherlands. An input–output model was developed to support decisions regarding waiting lists. The model projects the demand for treatment at a regional level and makes it possible to evaluate waiting list impacts for different scenarios to meet this demand.