Access Improvement using Lean Healthcare for Radiation Treatment in a Public Hospital

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1 Access Improvement using Lean Healthcare for Radiation Treatment in a Public Hospital Carlos Frederico Pinto* #, Stela Maris Coelho* #, Robisom Damasceno Calado* *** and Messias Borges Silva* ** * School of Engineering of Guaratinguetá (FEG UNESP) Guaratinguetá, SP Brazil, ** School of Engineering of Lorena (EEL USP) Lorena, SP, Brazil ( messias@dequi.eel.usp.br) *** School of Production Engineering (EP-USF), Campinas, SP Brazil ( robisomcalado@gmail.com) # Instituto de Oncologia do Vale, São José do Campos, SP Brazil (Tel: ; carlosfpinto@iov.com.br) Abstract: Radiation treatment for cancer in Brazil faces huge access restrictions due limited resources and poor assistance design. Radiation treatment access waiting time can be as long as seven months. The purpose of this report is to show how lean thinking can be applied to healthcare by using lean methods. The case study/report presented describes how processes were improved using Lean Value Stream Mapping tools and Queue Theory tools. Major outcomes were focused on reducing waiting time to patient access to radiation treatment and to reducing daily waiting time to access to treatment slots. The performance improvement was in the order of 75% to treatment slots access, reducing from two hours to 30 minutes; and access to care waiting time reduced from more than three months to same day access to special cases; and one week waiting time or planned access to all other. The process redesign also eliminated up to three hours of overtime daily. Keywords: Lean Manufacturing, Production Control, Queues. 1. INTRODUCTION Since the first reports of lean initiatives in healthcare in US by Virginia Mason Medical Center, many others attempted to apply lean thinking in healthcare to address crowding, delays and safety (Holden 2011). One of the landmark results presented by Virginia Mason was the substantial capacity improvement with only area redesign (Womack 2005). The Brazilian government has the constitutional obligation to provide healthcare assistance to the entire Brazilian population; however, it faces serious capacity and quality restrictions while promoting it. One of the most serious restrictions related to the lack of ability to care provided by the public sector is the resource allocation that is insufficient and poorly managed. According to several national surveys, healthcare assistance is the second most cited problem needing better attention (Aranda 2012). In several countries lean thinking applied to healthcare has proven to improve access and quality of the services provided; even in private organizations or mixed systems (like Brazilian and American); lean thinking in healthcare has produced impressive results. Lean consists basically in separating waste from value; removing non-added value activities from value streams and eliminating waste in a continuous pattern. Most common wastes are transportation, activities or unnecessary processes, material or patient handling, inventory, defects or failures, delays and overproduction. Other problems addresses as waste as well are interactions between enterprises or areas; poor communication and misuse of resources. Most common benefits are associated to direct and indirect cost reduction, set free capacity and personnel. Initiatives to implement the lean thinking in medical services and healthcare are just the beginning (Womack and Jones, 2003; Spears 2005 and Silberstein, 2006). Queues is healthcare are quite frequent and are usually considered to be created by bottlenecks in the process. Radiation therapy in Brazil faces a huge challenge, where the waiting time for access are expressed in months, varying from state to state, but always superior to three months and in some regions going over seven month of

2 queue. We have circa 85 thousand patients waiting for appropriate radiation treatment in Brazil (Aranda 2012). Although the majority of reports published is positive, most are focused application lacking broad organizational reach (Mazzocato 2009); considering the public sector in other countries, lean applications in healthcare are scarce (Barraza 2009). Our study involved the use of lean thinking for waste removal and queue management in a public hospital cancer center in Taubaté, São Paulo, Brazil, which has been using lean in healthcare since The Instituto de Oncologia do Vale Radiation Oncology Unit at Hospital Regional do Vale do Paraíba (IOV-HRVP) faces huge queues for radiation treatment access (arrival to the system, input source) and for daily treatment. Our major objectives were to eliminate waste associated to two different queues - one related to the arrival of new patients to the system (queue for access), and the other related to the arrival of patients for daily radiation treatment and also, the improvement of the daily production plan (treatment slots). 2. LITERATURE REVIEW There is no common sense about the definition of lean production and there is not a single point to guide the concept definition (Howleg 2007). Lean Production is a dynamic, multifaceted and complex system with multiple interdependent connections. Academic books and articles still struggle to define clear measures for lean production (Shah 2007); one well established and popular tool, Value Stream Mapping (VSM) as proposed by Shook and Rother (1999) is used to identify and analyze, as well as suggest solutions to production problems. 2.1 Value Stream Map The value stream consists in identifying all the valueadded and non-value-added actions required to bring a specific product, service, or combination of products and services to a customer; including those in the overall supply chain as well as those in internal operations (Womack, Jones and Roos, 1990). The value stream mapping enables the organization to understand the current process, identify the three types of tasks: (1) value-added, (2) necessary but not value-added, and (3) non-added-value steps (Womack and Jones 2003); and provide a vision of gains and benefits associated with waste removal. The VSM is basically a PDCA cycle (Plan- Do-Check-Act) involving the following simplified steps: identify the value stream (current state); problem analysis; value proposition (future state); action plan (value delivery); and sustainability (action). For problem analysis we used common lean and quality tools: Pareto Analysis; Cause-Effect Diagrams; Affinity Diagrams; Five Whys; and Spaghetti Diagram (Pinto 2010). 2.2 Queue Theory Tools Queue theory is the study of queue process formation and propagation to access any kind of service and its formation is a common phenomenon is systems with unbalanced capacity and demand. Queue theory is used by lean thinking to address capacity problems and is one of the seven wastes (waiting). In the Brazilian healthcare systems, queues are usually accepted and perceived as a capacity problem: the capacity is always below demand. This assumption is frequently a misconception and expresses poor management of the queue. Queue disciplines are rules applied to queues to reduce waiting times and process (Hiller and Lieberman, 1995) 3. METHODS Case Study, problem analysis and value proposal 3.1 Scenario The case study discussed was developed at the Radiation Unit at the Hospital Regional do Vale do Paraiba, in Taubate, where 99% of treatments is performed through the National Health System (Sistema Unico de Saude SUS). This unit has one Linear Accelerator for clinical use (LINAC) and it is responsible for the treatment of 55 to 80 patients daily with 15 collaborators involved in three shifts, including doctors, nurses, technicians and office personnel. We were invited to help the unit to solve its problems of overcrowding and management, due our previous positive results using lean thinking for healthcare at the medical oncology unit. Queue for arrival to the system as a new patient varied from three to seven months, and several carve out new patient slots for urgent care. Queue for arrival to the system for daily treatment was around two hours per patient, daily. And also, several daily carve out slots for urgent care. The unit working hours is divided in three 4-hour shifts: from 6:30 AM to 10:30 AM; 10:30 AM to 2:30 PM and 2:30 PM to 6:30 PM. Overtime was a pattern for the third shift, with almost 4 hours of overtime daily; opening a fourth shift was under discussion when we started this project. The project was developed and executed between March and June of 2012, and results updated up to December Value Stream Map (VSM) The design of the VSM identified several improvement opportunities and three major queues: (1) Access to 1st medical appointment, standing from three to seven months; (2) Access to treatment planning and initiation,

3 usual 26 days on queue; and (3) Access to daily treatment, median 2 hours waiting time. These queues were related to the original process poor design. The following steps were required to general access and treatment: (A) Patients arriving to the system will join the 1st medical appointment queue. (B) After this appointment they would wait for treatment planning that involves stopping the LINAC for 15 to 20 minutes for each treatment plan, delaying daily processes and routines. (c) After the planning process, the patient would wait for a slot in the treatment schedule, usually 26 days after the 1st appointment. (d)treatment period goes from 10 days (two weeks, Monday to Friday treatment days) to 45 days (seven weeks), and slots are opened as soon as one patient ends its treatment. Treatment reviews and corrections are done weekly for every patient, and it require some extra time in the LINAC. (e) Patients requiring urgent treatment were pushed into the schedule, opening extra slots and extending even further the third shift (more overtime). When the first step of the VSM was concluded, the problem analysis spotted several issues such as: three months queues (which was attracting media interest); patient complaints usually related to delays and to the overcrowded waiting rooms; lack of available slots for treatment corrections, planning, set-up and treatment room cleaning. Besides that, the unit had three different agendas: one for appointments, one for treatment planning and one for daily treatment (daily treatment plan) which were disconnected to each other, generating innumerous conflicts; the unit expected working hours would be from 6:30 to 19:30, but it used to be extended to 23:00 or midnight (on overtime); many patients were pushed into not planned slots; working activities were unbalanced, and some personnel had free time and others overtime. From this first analysis, we decided to use queue discipline theory and process balancing tools for leveling production. 3.3 Queue Theory Besides the several improvements possible using lean thinking, the major problem for the unit was the waste associated to waiting time for queues, caused by instability or MURA (variation). This instability could be seen on weekly schedules, Daily Treatment Plan (daily production plan) and queue management. We focused on queue theory to develop stability to the entire system by applying queue discipline into several steps of the VSM and process leveling to daily and weekly schedules. The unit used the FIFO (First In-First Out) system for all of its queues and delivering poor services due long waiting time. 3.4 Value Proposal Several tools were used to organize flow: triage system to define access and make possible the use of queue discipline; first appointment access leveling based on demand; redesign of treatment slots booking system (daily production planning) and batching process; queue discipline applied to the system as three basic disciplines, earliest due date, shortest weighted service time, and FIFO. These tools use and combination resulted in major actions as listed below First Appointment Countermeasures and Triage System All new patients should be accessed before joining the queue. The triage made possible the use of queue discipline. Triage was also used to identify our incoming patients and apply leveling for 1 st appointment and queue discipline to 1 st visit as well. The countermeasure for leveling the 1 st appointment process; treatment planning and revision were leveled throughout the system, in a weekly approach. Queue Disciplines were used to address specific problems at each major queue. Countermeasures for the first visit appointment (access to the system) involved the following queue disciplines: (a) Earliest Due Date (EDD). It used to fix the latest date for treatment and move patients who could wait longer to the end of the queue, for example, some patients with prostate cancer can have their radiation treatment delayed for up to six months, with no damage to the treatment. (b) Shortest Weighted Service Time (SWST). It used to balance the access to patient requiring treatment in a short period of time in the future, but not to urgent cases. Most breast cancer patients used this criterion Daily Production Planning Countermeasures and Batching Countermeasures for the daily treatment plan at the LINAC (daily production planning) focused the redesign of treatment slots. Time frames for patient treatment were unrealistic; to cope with the increasing demand, time treatment per patient was reduced, but never met. Although it increased the number of slots per day, it also increased the waiting time and daily queue, patients usually waited for 1:30 to 2:30 to get into the LINAC (two hours and half after the booked time for radiation treatment). For instance, a patient who needs 20-day treatment would have, by the end of the treatment, waited more than 40 hours to complete it. The redesign involved process leveling to reduce queues after getting into the system. Planning treatment slots also had unrealistic time frame. The length was adjusted to the expected time consumption and demand (identified by the VSM). Daily available slots were adjusted to demand as well. Slots for urgent treatments were reviewed according to the expected demand by using three months previous demand to forecast future demand.

4 Countermeasures to reduce variation in service involved the combination of a queue discipline rule addressing the most frequent clients and batching for specific set-ups: breast cancer, prostate cancer, and head and neck cancer; as described below and shown in Appendix 1, Figure 1 and 2. Figure 1 at the Appendix 1 exhibits a sample of daily treatment plan before the intervention, where no visual aids and no daily treatment planning were used. It was basically a filling list to check treatment and for billing process. Figure 2 at Appendix 1 shows how batches for different diseases were grouped by colors and how treatment planning period was extended from 10 minutes to 30 minutes (green slots). It was used the Shortest Service Time (SST) to organize batches for breast, prostate and head and neck cancer, the most frequent treated patients. Batches were divided by similar equipment set up and fastest service served. For that purpose, breast cancer was assigned to be treated at the beginning of each shift, followed by prostate cancer (the second faster service). The use of the SST and batches allowed faster equipment setup: all breast cancer patients use the same setup, as well as prostate and head and neck cancer. We provided slots in all shifts to allow patients to have their own treatment time preference. It was also offered for these patients the Last Come First Served (LCFS) discipline when possible, so if they were on their treatment batch hour, he or she would get in the first available slot, and patients with other diseases would have to wait. The project also improved the number of patients under treatment daily, assuring more access to the system and improving global capacity. In 2012 it was expected to have 50 patients under treatment daily; but the system was able to absorb 60 patients daily, a 20% improvement. This number also impacts in the waiting time reduction to access radiation treatment (patients out of the system). 4.2 Daily Production Planning Countermeasures and Batching The waiting time for daily treatment dropped dramatically from a median of 2 hours to less than 30 minutes (75% drops), with the best result in November, with only 12 minutes on queue (median). Appendix 2 shows the unit Annual Performance Report for 2012, showing the daily median time spent by the patient in the unit, median number of patients treated daily and medical patient review on expected time. The annual goal for waiting time ( Meta number three, third yellow row in Appendix 2) was to drop the time spent by the patient in the unit from two hours to one hour. Working hours also have been reduced almost by one shift. Early in 2012, the unit working hours was from 6:30 AM up to midnight, 17 to 18 hours working hours daily. By the second half of 2012, the third shift overtime was reduced from 3 to 4 hours daily to one hour at worst; with total working hours ranging from 13 to 14; from 6:30 AM to 7:30 PM. The fourth shift opening as a proposal was abandoned. 4. RESULTS The project execution started with the triage system and planning batches early in April 2012 and all improvements were fully implemented in June. 4.1 First Appointment Countermeasures Triage System Changes in the booking and appointment system also provided leveraging the weekly agenda to open slot for new patients and slots for urgent cases. We used the previous three months demand to predict and carve out these slots, defining fixed slots. Patient access improved with the new daily production planning, but a triage process was established to develop a planned access. The system is now capable to offer new appointment slots in the same day for urgent patients and in the same week for regular patients. The waiting period dropped from 4 months to 8 days, an 80 % reduction on waiting time. And from the first appointment to the treatment itself a patient now waits 12 days (median), a 50% drop. 5. DISCUSSION Queue theory is the study of waiting lines and its behavior and predictability. According to Hiller and Lieberman (1995), Queuing theory itself does not solve this problem directly; however, it does contribute vital information required for such decisions by predicting various characteristics of the waiting lines Value Stream Mapping and Lean Thinking embraces the waiting process as one of the most frequent seven wastes. Our study addressed countermeasures to deal with a queue using lean thinking and queue theory with positive results. Leveling queue access and integrating apparently independent processes led these major results. Treating these queues with proper queue management tools allowed the unit to further improve its processes. One of the most frequent problems associated to healthcare services is long waiting lines. An organized approach addressing queue management can result in significant process improvement and resource savings. Understanding how to balance the process is crucial: patients overflow, workers free time and the ability to match the expected demand to hourly

5 capacity allowed the combination of these variables to achieve optimal flow. The unit was able to reduce 75% of waiting time per day using value stream mapping; queue management; and gathering information to improve and connect major flows in the process: the patient, the information and involved personnel. The benefit also translated into operational efficiency, with significant reduction of daily working hours by almost one shift, or three hours and 20% capacity gain. REFERENCES Aranda, F. Brasil soma 85 mil doentes sem radioterapia, Portal IG São Paulo, 26/04/2012, Barraza MFS, Smith T, Dahlgaard-Park SM (2009). Leankaizen public service: an empirical approach in Spanish local governments. The TQM Journal 21 (2): pp Hiller FS and Lieberman GJ. (1995) Introduction to Operations Research. 6 th Edition, McGraw Hill, New York,. Holden, RJ (2011). Lean Thinking in Emergency Departments: A Critical Review Annals of Emergency Medicine 57(3) Holweg M. (2007) The genealogy of lean production. Journal of Operations Management Mazzocato P, Savage C, Brommels H, et al. (2010) Lean thinking in healthcare: a realist review of the literature. Qual Saf Health Care. doi: /qshc Pinto C, (2010) Improving Wait Times at a Medical Oncology Unit in J. Aherne & J. Whelton, editors, Applying Lean in Healthcare, Productivity Press, NY. Shah R, Ward PT. (2007) Defining and developing measures of lean production. Journal of Operations Management Shook J and Rother M, (1999) Learning to See, Lean Enterprise Institute, Boston. Silberstein, A. C. L. (2006) Um estudo de casos sobre a aplicação de princípios enxutos em serviços de saúde no Brasil. Dissertação (mestrado) UFRJ/ COPPEAD/ Programa de Pós-graduação em Administração. Spears, S (2005) Fixing Health Care from the Inside, Today. Harvard Business Review Sept. Womack JP, Byrne AP, Fiume OJ (2005). Going Lean in Health Care. Cambridge, MA: Institute for Healthcare Improvement. Womack, JP, Jones, DT (2003). Lean Thinking, revised ed. Free Press, New York. Womack, JP, Jones DT, and Roos D (1990). The Machine That Changed the World. Free Press, New York. Young T and McClean S. (2008) A critical look at Lean Thinking in healthcare. Qual Saf Health Care 17:

6 Appendix 1: Daily Treatment Plan Sheets, before (Figure 1) and after (Figure 2) the intervention. Appendix 1, Figure 1: Before Intervention. Daily Treatment Plan sample before batching, leveling and SST. It was basically a patient listing for billing purpose showing name, time, diagnosis and treatment duration. White boxes are due patient privacy. Breast cancer in chemotherapy Prostate cancer Concurrent Radiochemo Planning shift Head and neck Cancer Other cancer Appendix 1, Figure 2: After the Intervention. Sample of the morning shift for the new Daily Treatment Plan Sheet. Patients were grouped by similar equipment set up (LINAC) in small batches throughout the day and treatment planning slots were assigned accordingly. Major groups were breast cancer, prostate cancer and head and neck cancer. Planning treatment slots (named Planning Shift) were assigned according with the expected length of the procedure.

7 Appendix 2: Radiation Oncology Performance Analysis Report for Appendix 2: Radiation Oncology Performance Analysis Report for Row 1: Patient with appropriate medical review. (White is expected outcome and yellow is actual outcome) Row 2: Patients under treatment daily. In 2012 the unit was able to improve the number of patients treated daily by 20%: 60 patients daily in 2012 (yellow row, Media column) versus 50 patients as expected (white row). Row 3: Median time spent in the unit daily, for each patient; goal for 2012 was one hour (white row). By the second semester of 2012, the median waiting time was 27 minutes (yellow row), more than 50% better than expected. Annual performance almost met the target (1:07), but the project was full effective only by June