Improving Physician Schedules by Leveraging Equalization: Cases from Hospitals in U.S.

In this paper, we consider physician scheduling problems originating from a medical staff scheduling service provider based in the United States. Creating a physician schedule is a complex task. An optimal schedule must balance a number of goals including adequately staffing required assignments for quality patient care, adhering to a unique set of rules that depend on hospital and medical specialties, and maintaining a work-life balance for physicians. We study various types of physician and hospital requirements with different priorities, including equalization constraints to ensure that each provider will receive approximately the same number of a specified shift over a given time period. A major challenge involves ensuring an equal distribution of workload among physicians, with the end goal of producing a schedule that will be perceived by physicians as fair while still meeting all other requirements for the group. As the number of such equalization constraints increases, the physician scheduling optimization problem becomes more complex and it requires more time to find an optimal schedule. We begin by constructing mathematical models to formulate the problem requirements, and then demonstrate the benefits of a polyhedral study on a relaxation of the physician scheduling problem that includes equalization constraints. A branch-and-cut algorithm using valid inequalities derived from the relaxation problem shows that the quality of the schedules with respect to the soft constraints is notably better. An example problem from a hospitalist department is discussed in detail, and improvements for other schedules representing different specialties are also presented

PHYSICIAN SCHEDULING IN WOMEN'S HOSPITAL

In this project, a physician scheduling problem arising from the operations of the Obstetrics and Gynecology Department at Hamad Women's Hospital in Qatar has been studied. The essence of the physician scheduling problem lies in assigning physicians with different experience levels to a set of predetermined shifts to achieve a set of clinical/non-clinical duties over a defined time horizon while considering a large set of conflicting rules and constraints including, and not limited, to hospital rules, physicians' requirements, shift coverage requirements, seniority-based workload rules, physicians' preferences, and workload balance aspects. The focus of this research project is to develop schedule for physicians (labor specialists and inpatient ward specialists) within Obstetrics and Gynecology Department at Hamad Women's Hospital for on-call shifts (evening shift and night shift) beside regular working shift (morning shift) while respecting all hard constraints, satisfying a wide range of soft constraints as far as possible, and most importantly balancing the workload among the physicians. Both labor specialists and inpatient ward specialists are the main service providers in this hospital, and therefore optimizing their work-shifts assignments would indirectly assist in providing a better service to female patients in Qatar and would result in meeting both the hospital and the physicians' satisfaction. In this work, the problem is formulated as mathematical programming model and solved by AIMMS optimization software. Optimal physicians' schedules were generated, and the proposed model was tested on real data provided from the Obstetrics and Gynecology Department in Qatar. A comparison between the resulting optimal schedules and the manual schedules used currently by the hospital was conducted. Then, a sensitivity analysis was performed in order to test the robustness of the obtained physicians' schedules of the proposed model. The proposed approach demonstrated that high quality schedules that satisfy all the constraints and mainly ensure balanced workload among the physicians can be generated with less time and effort required compared to the schedules prepared manually by the chief specialist in Women's Hospital

Solving Combinatorial Optimization Problems Using Genetic Algorithms and Ant Colony Optimization

This dissertation presents metaheuristic approaches in the areas of genetic algorithms and ant colony optimization to combinatorial optimization problems. Ant colony optimization for the split delivery vehicle routing problem An Ant Colony Optimization (ACO) based approach is presented to solve the Split Delivery Vehicle Routing Problem (SDVRP). SDVRP is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) wherein a customer can be visited by more than one vehicle. The proposed ACO based algorithm is tested on benchmark problems previously published in the literature. The results indicate that the ACO based approach is competitive in both solution quality and solution time. In some instances, the ACO method achieves the best known results to date for the benchmark problems. Hybrid genetic algorithm for the split delivery vehicle routing problem (SDVRP) The Vehicle Routing Problem (VRP) is a combinatory optimization problem in the field of transportation and logistics. There are various variants of VRP which have been developed of the years; one of which is the Split Delivery Vehicle Routing Problem (SDVRP). The SDVRP allows customers to be assigned to multiple routes. A hybrid genetic algorithm comprising a combination of ant colony optimization, genetic algorithm, and heuristics is proposed and tested on benchmark SDVRP test problems. Genetic algorithm approach to solve the hospital physician scheduling problem Emergency departments have repeating 24-hour cycles of non-stationary Poisson arrivals and high levels of service time variation. The problem is to find a shift schedule that considers queuing effects and minimizes average patient waiting time and maximizes physicians’ shift preference subject to constraints on shift start times, shift durations and total physician hours available per day. An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed. The approach is tested on real world datasets for physician schedules

Heuristic scheduling for clinical physicians.

Personnel scheduling is a problem faced by many organizations in the healthcare industry, particularly in rapidly developing outpatient centers. The task of creating a schedule that adequately covers patient demand while satisfying the preferences of employees, observing work regulations, and ensuring a fair distribution of work is highly complex. Even though this highly complex task directly affects measures such as patient waiting time and employee satisfaction, many organizations still resort to the traditional and cumbersome manual solution methods. A large segment of prior research on personnel scheduling in healthcare focuses on nurse rostering and the development of automated tools to aid in scheduling. The drawbacks to these methods include the lack of generality and the need for specialized software packages and training. The aim of this study is the development of an effective, low cost, and uncomplicated heuristic tool to aid schedulers in outpatient centers. Solution methodologies used by previous researchers in problems such as nurse rostering and aircrew rostering are adapted to the particular problem of physician scheduling in mixed specialty outpatient clinics. The developed heuristic tool obtains an initial feasible solution using a greedy algorithm and then uses the simulated annealing metaheuristic to improve the solution, which is a measure of physician satisfaction. The heuristic tool developed in this study was tested using eight randomly generated data sets to model 45 unique cases. The heuristic found the optimal solution in 19 of the 45 tested cases. The average difference from the optimal physician satisfaction rating in the other 26 cases was 0.35%

Quantitative methods of physician scheduling at hospitals

Masteroppgave i industriell økonomi og informasjonsledelse 2010 – Universitetet i Agder, GrimstadStaff scheduling at hospitals is a widely studied area within the fields of operation research and management science because of the cost pressure on hospitals. There is an interest to find procedures on how to run a hospital more economically and efficient. Many of the studies about staff scheduling at hospital have been done about nurses, which work under common labor law restrictions. The goal of nurse scheduling is to minimize the staffing cost and maximizing their preferences. While the operation rooms are the engine of the hospitals, the physicians are the fueling for the hospitals. Without the physicians the patients would not be treated well and the hospital would not earn money. This thesis studies the physician scheduling problem, which has not been studied so widely as the nurse scheduling problem. A limited number of literatures about this theme have been studied to answer the main research question: How can we categorize physician scheduling at hospitals? Studying the physician rostering problem on the search for efficiency and cost savings is an intricate process. One can read a lot about this theme develop a lot of models; and shape and test different hypotheses. However, to increase efficiency it is wise to make a plan of information to consider. The categories searched for within this literature review are the level of experience, the planning period, the field of specialty, the type of shifts, whether cyclic or acyclic schedules are used and also which models and methods are used to solve this problem. Level of experience was first divided between residents - that are still under education, and physicians - which are fully licensed. Physicians are medical trained doctors that provide medical treatment rather than surgical treatment in hospitals. After medical school, they have accomplished between three to seven years of residential internship before they obtain their license. The residents are still under education and must therefore participate in a number of assorted activities and patient treatments during their resident period to acquire their license. This situation for resident makes scheduling unique as they are in a learning period and staffing the hospital at the same time. The planning period is a category that is divided in three levels; short-term which lasts up to a month, midterm which lasts from one month up to six months and a long-term that lasts from six months up to one year. The field of specialty is divided between the specialties of the physicians. In the numerous departments at a hospital, the work is distinctive from one another. A normal workday in a department that is only open during the day can be quite different from a workday in an emergency department. Working in a hospital is unlike other type of jobs. A hospital or at least different departments in a hospital are open all day long, every day of the year. As a result, the hospital must be staffed all the time. The need for staffing varies during the day, the day of week; and during the different seasons, due to the fluctuation of the demands. An example for a solution is a variety of broad types of shifts. Scheduling these shift types can be made cyclic or acyclic. Qualitative method has been used in this master’s thesis. The research question is a typically quantitative method starting with “how”. And to answer it, this thesis builds on a definite number of case studies. These case studies are limited to concern only about physician and resident scheduling problem written in English. These cases are primarily scientific articles and conference handouts. The cases are read - and interesting information is registered in a case study database. The findings have shown different use of planning period after the level of experience. Few studies have been done with short-term planning period; physicians are mostly scheduled for a midterm planning period, whereas residents are mostly scheduled with a long-term planning period. Most studies have scheduled physicians and residents for a day, evening and night shift, often in a combination with some kind of on-call shift. The field of specialty that is most studied is within emergency medicine. As the work in an emergency department is stressful, it is a complex task to make good schedules that satisfies the physicians and residents working there. Exact approaches are the most used modeling technique used for physician scheduling

LVHN Weekly-Hazleton

https://scholarlyworks.lvhn.org/lvhn-weekly-hazleton/1020/thumbnail.jp

Problemas de programación lineal entera. Aplicación a un problema de planificación de turnos hospitalarios.

This paper aims at addressing in detail the Cutting-Plane Method and Branch-and-Bound Method - which are the two paramount Integer-Linear Programming methods- deepening the knowledge previously acquired in the module of Operation Research. Additionally, the aim of this paper is to present, formulate and solve the Emergency Room Physician Scheduling Problem.<br /

Process Improvement Initiatives in Patient Access Services

Abstract Process Improvement Initiatives in the Patient Access Services Department is a data analysis project that converts call center data into a visual representation of trends using lean principles. The outcome of this conversion was used for a visibility wall in the department to measure department metrics and growth against those of the entire network. Metrics used to measure department goals included percent service level, abandoned rate, and average answer delay. The ratio of inbound vs. outbound calls as well as the total number of calls and procedures scheduled was used to measure departmental growth. The results of this project were used to determine opportunities for improvement and the need for additional staffing in the department. Process improvement initiatives will benefit the key stakeholders by increasing productivity and patient satisfaction. Further research and data analysis will be needed into fiscal year 2015 to determine the results of further ongoing improvement initiatives in the department such as the Central Scheduling pod system implementation and the onboarding of new employees. Process Improvement Initiatives in Patient Access Services Background Improving efficiency using lean processes in the health care setting is important to give patients greater value (Aherne, 2007). This project, Process Improvement Initiatives in Patient Access Services, focuses on analyzing call volume data for the Patient Access Services Department to identify strengths and areas of improvement. The Patient Access Services Department covers the areas of Precertification, Central Scheduling (including physician scheduling), Patient Access, and the Diagnostic Care Centers. Metrics for this project are percent service level, abandoned rate, and average answer delays. Also addressed in this project is department growth, which is measured by the ratio of inbound vs. outbound calls, as well as the number of accounts created and PHS procedures scheduled. This project follows lean principles to track the department’s progress toward network goals in the form of a visibility wall. The visual process is essential to lean and tools such as these are used as communication aids and to “help drive operations and processes in real time (Parry & Turner, 2007, p.77-86).” Lean has been shown as a useful tool in the healthcare industry. The UK’s National Health Service, the NHS, even uses lean principles. By applying lean techniques, health services in the UK saw improvements in clinical record retrieval, number of patients seen, time flow of prescription dispensary, and reduction in inventory (Lipley, 2008). Additionally, “Applying lean thinking to the healthcare sector can provide significant cost and process efficiencies” (Aherene, 2007, p.13-15). Network goals that were addressed in this project include the 5 Pillars (People, Service, Quality, Cost, and Growth) as well as the Triple Aim (better health, better care, and better cost). Additional analysis completed of case study data from April 2014 shows peak hours for call volumes and where additional staffing can be used to improve efficiency during these times. Future data analysis into fiscal year 2015 will test the hypothesis of increased staffing levels having a positive impact on meeting department goals. Methodology This study’s data was collected using call center software called Avaya. The data was pulled from reports and placed into a Microsoft Excel document organized by sub-department (table below). Each department was further categorized by offered calls, answered calls, outbound calls, abandoned rate, percent service level, average answer delay, and accounts per month. Data is represented in each category by every month of FY14. A final category measures the total call volume, accounts created, and procedures scheduled for the year. To visually represent the results and trends, the data was converted into MS Excel charts corresponding to each aforementioned category. Line graphs were used to clearly depict visual trends over time. The data used for each graph was broken down into a table underneath displaying the actual numerical data for each month against monthly targets. Target goals were established and measured against the data. Goals for percent service level and abandoned rate were fixed at 80% and 6%, respectively. Other targets for accounts per month and average answer delay were calculated based on averages. Exceptions for this were the average answer delays for the Precertification and Patient Access departments, where targets were set at 30 seconds. Months that met department goals were displayed in green, and months that did not meet goals were colored in red. This color-coding process allows onlookers to quickly and easily see if targets were met without having to examine the numerical data. Finished data analyses were printed and displayed as a visibility wall in the department, which measures the department’s metrics against network metrics for FY14 of People, Service, Quality, Cost, and Growth. Service was measured based on patient wait times, while Quality was measured by the results of percent service level and abandoned rate. Additionally, Growth was measured by examining the number of accounts per month and the number of procedures scheduled. Additional case study data analysis looked at the staffing ratios during peak call hours for the Central Scheduling Department for April 2014. The data pulled showed the number of agents scheduled for each day and the times they worked. Averages were taken to assess the number of agents per day and time. This data was then broken down to calculate the average number of call agents during 30-minute intervals for the entire month. This data was then converted into a graph and measured against average monthly answer delay to determine peak hours and where additional staffing is needed to improve efficiency. Data such as this establishes the cost efficiency of hiring additional staffing, which is tied to the network goal of controlling costs. Results The visual representation of the data was able to clearly show trends and identify areas in which the department had opportunities for improvement. Sample results include data from the precertification, central scheduling, and patient access departments. Additional charts will display information on yearly department totals. In the precertification department for FY14 (graph below), a clear contrast can be seen from the first half of the fiscal year to the latter half. Percent service level dropped from 83% in December to just 64% in January. There was an additional sharp decline in percent service level in April with 43%. The data started to recover in May, showing continuous improvement in the department since that time. Additional opportunities for improvement can be seen in Central Scheduling by examining the average answer delay (graph below). The data drastically fluctuates throughout the year. Departmental targets were met until October, when answer delay spiked up to 3:16. Targets were then mostly reached until February, with the exception of December where the department missed its target by 15 seconds. Since March, wait times have been increasing. The month of June had the highest average answer delay of 4:30. A factor that could contribute to this is that June is the most popular month for colleagues to take PTO. This problem of increased wait times is likely temporary and able to be alleviated by the hiring of additional full time employees. Unlike the harsh fluctuations in Central Scheduling’s average answer delay, Patient Access’s abandoned rate shows little fluctuation (graph below). However, this department is still not meeting its goal of having the abandoned rate at 6%. New ideas and initiatives could be needed to experiment with ways to decrease the abandoned rate in this area. On the physician scheduling side of Central Scheduling, a clear trend can be seen when observing the inbound versus outbound calls (graph below). Although there are no targets for this data, a clear increase can be seen in September with the addition of Family Health Center to the Central Scheduling department. From August to September, the first month of go-live, total call volumes increased by 1,911 calls. From September to October, this increased spiked by 2,266 calls before leveling out during the rest of the fiscal year with the exception of a decline in February. Using this data is important to measure the increase in workload of the department and assess the possible need for additional staffing. Similar to the expansion of the physician scheduling workload, looking at the totals for the entire Patient Access Services department clearly shows the department’s growth in just a year’s time. The total accounts created for FY14 is 183,235 (graph below). This number includes the Patient Access and Precertification departments. This data shows that the most growth occurred during the last six months of FY14, before the addition of full time employees to take on this expanded workload. Additional total growth in the Central Scheduling Department can be seen by observing the total PHS procedures scheduled (graph below). The total number of procedures scheduled for FY14 is 392,428. This data remains relatively stable with each month scheduling around 31,000 to 38,000 procedures. The exception to this is a sharp decline in February with only 26,511 procedures scheduled. Factors that could have contributed to this decline were the harsh winter weather and the fewer number of days in this month. In the case study of the Central Scheduling staffing ratios data for April 2014, a relationship can be seen between average number of agents, number of offered calls, and average monthly delay. During peak call times of 10AM-3PM, average delay increases. Average delay also spikes up around 12PM even though there is a drop in offered calls because fewer agents are on the phones due to scheduling or are breaking for lunch. To solve this problem, more agents are needed during peak call times. This can be accomplished by hiring more employees and staggering lunch times. Conclusion The results implicate a need for improvement in specific department areas. In the beginning of June, the Central Scheduling Department implemented a new “pod” initiative, which breaks up the Central Scheduling colleagues into teams of four with the goal of increasing productivity. However, there is not enough data to determine the success or failure of this change initiative. Continued data analysis for future months will reflect the impact of this. With the addition of new full time employees starting in July 2015, the Patient Access Services Department will have more of the resources needed to respond to the high call volumes and departmental growth, which should increase the department’s productivity and service. Additionally, the onboarding of new hires as of July FY15 will alter the data for future months. The department is expecting to see improvement in all patient access areas due to this staffing increase. Additional data for FY15 is needed to measure the results of this hypothesis. Continuous use of the LEAN philosophy is necessary for improvement. An article from Nursing Management states: For the lean philosophy to meet its potential in healthcare services, leaders and managers must make a commitment to changes in organizational culture, thinking, and structure. They must therefore regard lean processes, not a short term fix, but as a part of a long term program of change (Radnor 2009). Overall, the data conversion into graphs for the department’s visibility wall was a success in that it allows for easy tracking of departmental metrics and progress toward network goals. It also allows for the easy identification of improvement opportunities that can be used to foster problem-solving and team building within the department. References Aherne, J. (2007). Think lean. (cover story). Nursing Management - UK, 13(10), 13-15. Accessed July 21, 2014. Lipley, N. (2008). Lean times ahead. Nursing Management - UK, 15(1), 7. Accessed July 21, 2014. Parry, G. and Turner, C. Application of lean visual process management tools. Production Planning & Control: The Management of Operations. February 2007. 10.1080/09537280500414991G. C. Parrya* & C. E. Turnerbpages 77-86. Accessed July 21, 2014. Radnor Z. Lean processes for lean times. Nursing Management - UK [serial online]. June 2009;16(3):8. Available from: Health Business Elite, Ipswich, MA. Accessed July 21, 2014