Gynecologic Oncology 138 (2015) 707–711
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Gynecologic Oncology journal homepage: www.elsevier.com/locate/ygyno
Lean methodology improves efficiency in outpatient academic Gynecologic Oncology clinics☆,☆☆,★ Linda R. Duska a,⁎, Jennifer Mueller a, Heather Lothamer a, Elizabeth B. Pelkofski a, Wendy M. Novicoff b a b
Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University of Virginia, Charlottesville, VA 22908, United States Department of Public Health Sciences and Orthopaedic Surgery, University of Virginia, Charlottesville, VA 22908, United States
H I G H L I G H T S • Patient satisfaction scores are used as a measure of quality of care. • Long clinic wait times negatively affect satisfaction scores. • Lean methodology can be utilized to decrease wait times and improve efficiency.
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Article history: Received 26 May 2015 Received in revised form 1 July 2015 Accepted 1 July 2015 Available online 15 July 2015 Keywords: Lean Gynecologic Oncology Outpatient
a b s t r a c t Objectives. Patient satisfaction scores may be used as a measure of quality of care, but outpatient scores are significantly and negatively affected by long clinic wait times. Patients in academic Gynecologic Oncology clinic at UVA for chemotherapy visits experience multiple wait times during a complex multipurpose visit. The purpose of this study was to utilize Lean methodology to identify variability in patient flow in order to guide solutions for improvement. Methods. A value stream map of our clinic process was created. Patient surveys and clinical timestamps were used to identify which visit components were contributing to delays and to identify process variability. After results were analyzed, a process change was instituted, with the patient surveys then repeated. Results. In the first phase, women experienced short wait times for the first appointment, but the average wait time between appointments gradually increased, with a peak mean wait time of 65 min (range 38–108) just prior to drug infusion. The total mean wait time (including all visits) was 119 min (range 81–154). After instituting process intervention, the overall wait time decreased significantly (82 vs 119 min, p = 0.001), but was still affected by aspects of the process that were outside of the investigators' control. Conclusions. Analyzing patient flow through an academic Gynecologic Oncology clinic can elucidate inefficiencies and guide improvements. Change in process can meaningfully affect overall waiting time. Next steps include instituting a more global change in process, as well as linking results to patient satisfaction scores. © 2015 Elsevier Inc. All rights reserved.
1. Introduction In an era of rapidly rising health care costs, the United States is working toward a transition from volume-based to value-based payment for hospitals and health care providers [1]. One of the goals of the Affordable Care Act is to pay for outcomes that take into account quality of care and cost containment. By 2017, hospitals and physicians will be ☆ All authors have declared no conflicts of interest. ☆☆ The first part of this work was presented as a poster presentation at the Annual Meeting of the Society of Gynecologic Oncology meeting in Tampa in March, 2014. ★ The authors wish to acknowledge Christopher Duska MS who assisted with the data analysis ⁎ Corresponding author at: Division of Gynecologic Oncology, PO Box 800712, University of Virginia, Charlottesville, VA 22908, United States. E-mail address:
[email protected] (L.R. Duska).
http://dx.doi.org/10.1016/j.ygyno.2015.07.001 0090-8258/© 2015 Elsevier Inc. All rights reserved.
rewarded or penalized on the basis of the relative calculated value (also called the value-based modifier) of the care they provide to Medicare beneficiaries; what this means is that the quality of care provided will be taken into account when providing reimbursement [2]. While most agree that paying for value is important, the actual process of measuring value in medicine is incredibly complex. Practically speaking, it is difficult to accurately measure overall value with the tools that are currently available. Quality of care has been suggested as a surrogate for value, but the measurement of quality is also an elusive target. Currently, patient satisfaction scores are used as one aspect of measuring quality. For example, inpatient patient satisfaction scores are being considered as a measure of quality when determining hospital reimbursement for particular disease related groups (DRGs) [3]. In fact, patient-satisfaction responses make up 30% of each hospital's score
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under the federal value-based purchasing system, which could have impacted a hospital's overall Medicare payments by up to 1% (in either direction) in 2013 [2,4]. In addition, both inpatient and outpatient scores are available to the public, allowing consumers of health care to make physician decisions based on this data, for example, what hospital or which physician to utilize for care. The high value placed on patient satisfaction scores is also observed in academic medicine, where an individual provider's patient satisfaction scores may be taken into account as part of their promotion package, as well as be used as a factor in determining salaries and tenure. The competency and appropriateness of care is not always reflected in patient satisfaction scores, however [5,6]. In fact, several authors have demonstrated that patient satisfaction often does not accurately reflect quality and cost of care. In a prospective cohort study of nearly 52,000 patients and the care they received, the authors of one study concluded that higher patient satisfaction was not associated with improved quality and value but instead with greater inpatient care use, higher overall healthcare and prescription drug spending, and increased mortality [7]. Others have found that outpatient satisfaction with care is negatively influenced by the amount of time the patient spends waiting, among other factors [8–10]. Lean is a methodology utilized to eliminate waste in process and improve efficiency in work flow that has its origins in Japanese automobile manufacturing [11]. As part of a Lean analysis, the process in question must first be analyzed to identify any resources being used for a goal other than the creation of value for the customer; these resources are considered wasteful and must be eliminated. In this setting, the customer is defined as the person who is receiving services; in the case of the healthcare system, the patient is the customer. Through this type of analysis, Lean methodology allows the creation of a standardized work process to create the most efficient and effective flow of services. While originally developed as a tool to improve efficiency in manufacturing, Lean has been applied in customer and financial service industries, government agencies, and increasingly, in the health care industry [12–14]. In the outpatient clinic setting, waste primarily occurs while the patient is waiting for care: the waiting room in physicians' offices is an example of waste in the process. Gynecologic Oncology patients undergoing chemotherapy require frequent care visits. At the University of Virginia (UVA), each care visit for chemotherapy involves appointments at multiple locations within the Cancer Center, including the laboratory, the physician's office, and the infusion unit. This physical discontinuity can lead to considerable variability in patient flow as well as decreased care efficiency, with a resulting less positive patient experience. In the setting of appropriate clinical care that follows accepted national guidelines, one important way to improve patient satisfaction is to improve clinic efficiency, thereby eliminating waste and improving patient waiting times [10,15–17]. In addition to improving patient satisfaction, improving efficiency, particularly in the Infusion Unit, will subsequently allow increased Infusion Unit utilization. At UVA, increased utilization opportunity would then allow us to expand our clinical trials program, a goal of the Cancer Center that is significantly impacted by current lack of Infusion Unit availability. We undertook the current study of our Gynecologic Oncology patients experiencing outpatient chemotherapy to decrease patient waiting times by eliminating waste from the process, using Lean techniques. Our primary objective was to identify the sources of waste in our process, with the secondary objective of eliminating waste to demonstrate an improvement in the process of patient flow. 2. Methods Lean methodology dictates that an important first step in process improvement is to document the current process state. In order to do this, Lean utilizes a value stream map of patient flow that details event location, personnel, information technology requirements, and
alternative pathways that can indicate variability within the system. We therefore created a value stream map of the process at the outpatient Clinical Cancer Center at UVA. We then followed patients through the process in order to document variability in the process as well as periods of waste. Finally, we introduced a change in the process designed to decrease waste and improve patient satisfaction. Our project group consisted of: an attending physician in Gynecologic Oncology, a nurse clinical research coordinator, an undergraduate student, and a Lean expert advisor. The study was performed as a quality project, and for that reason Institutional Review Board (IRB) review was not required. UVA Human Subjects Research IRB (HSR-IRB) permission was subsequently obtained for a retrospective analysis of the prospectively collected data for the purposes of publication. 2.1. Building the value stream map The value stream map was generated with the assistance of clinic leaders and staff. Patient registrars, medical assistants, nursing coordinators, physicians and Cancer Center leadership gave input into all of the separate steps required for a patient to be seen for a chemotherapy infusion encounter. Excel software was utilized to create the value stream map. 2.2. Part I: tracking patients through the process Eligible patients were women with at least three scheduled appointments on a particular day at the UVA Cancer Center: one for laboratory work, one with the physician, and one to receive treatment in the infusion unit. Each patient was given a survey to fill out throughout their day, with a focus on recording the time expected for each appointment and the time the actual appointment service was provided. The surveys were verified and supplemented by clinical time stamps. The times recorded were: scheduled appointment; front desk registration, visit start at treatment access center (TAC, where intravenous lines are placed and laboratories are drawn), Women's clinic, Infusion Center; and specific incidents at the Infusion Center including arrival at infusion chair and hanging of infusion medications. The computerized clinical tracking system (TRACKS) was utilized to confirm registration times. The electronic medical record (EPIC) was used to pull data regarding the time that chemotherapy orders were signed and released, and the time that chemotherapy was administered. Clinic days were chosen randomly and all patients receiving chemotherapy on that day were approached for trial participation. The data were analyzed using SPSS version 21. Steps in the process that contributed to inefficiencies were identified by patient flow analysis. We also analyzed the data for variation between patients and between process steps. 2.3. Part 2: altering and re-measuring the process Following the data collection in Part 1 detailed above, the work group met to identify the best step(s) in the value stream map for intervention. It was determined that the group had the most control over the physician (MD) portion of the visit as the Infusion Unit was run by a separate entity in the Cancer Center. From our group's standpoint, therefore, an intervention with respect to the Women's Clinic appointment would have the highest yield. Since the MD also was responsible for signing chemotherapy orders and we could impact this step in the process, we anticipated that additional attention to the MD order signing delay would impact the infusion administration wait time. The decision was therefore made to implement an intervention in the way patients were roomed in the Women's Clinic. In this setting, “roomed” meant that the patient was moved from the waiting area to the examination room to see the physician. The process in the office prior to the intervention was as follows: the medical assistant roomed
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a patient, marked the room number on the clinic list posted in the physician workroom, and highlighted the patient's name using a yellow highlighter. For the process change, the medical assistants were instructed to room patients being seen for chemotherapy immediately upon their arrival to the waiting room, assuming a room was available, and to alert the physician that a chemotherapy patient had been roomed. The chemotherapy patients were marked on the patient list in the physicians work room with a pink highlighter so that all providers were aware that a chemotherapy patient had been roomed. This process was practiced for 3 months prior to re-survey of the patients in order to ensure that all providers were aware of and comfortable with the new patient flow. Additionally, attending physicians were encouraged to sign chemotherapy orders immediately after seeing the patient in clinic and reviewing her laboratory values. Following the 3 month period, chemotherapy patients were resurveyed in order to establish the impact of the intervention. Due to the nature of the patients being surveyed, it was not possible to survey the same patients from Part 1. The data were then analyzed via the identical methods as the original analysis. Two-sample independent t-tests were used to determine if a statistically significant difference in wait time had resulted, and a test for proportions was used to determine differences in percent MD time.
3. Results The value stream map is shown in Fig. 1. We identified five waiting times in the process: Treatment Access Center (TAC), Women's Center (time in waiting area), MD visit (time in examination room waiting for provider), Infusion Center, and Infusion Medication. Based on our value stream map, we then combined relevant waiting times for the subsequent analysis: total waiting time for Infusion was defined as Infusion Center plus Infusion Medication (Infusion Total), and total waiting time for the physician was defined as Women's Center plus MD visit (MD total).
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3.1. Study, part 1 Twenty-one patients receiving chemotherapy for gynecologic cancer at the UVA Cancer Center were identified and asked to participate in the study. All four Gynecologic Oncology attending physicians in the practice allowed their patients to be surveyed in this part of the study. No patients declined participation. Of the 21 patients, 2 patients had an extra step in the process: there was a computed tomography (CT) scan appointment inserted between the TAC visit and the MD visit. Both of these patients were on a clinical trial that required CT scan prior to treatment to assess treatment response. The CT scan required a separate wait for the CT scan itself as well as a wait for the radiology read. Since CT scanning is often included in the care of our chemotherapy patients (in this series, in 10% of our patients) we chose to leave the data in the analysis, recognizing that it might affect results. The three waiting times identified in the value stream map were analyzed for all 21 patients (Table 1). The infusion total time contributed to the largest proportion of wait time (55%). Overall, patients were on time for the beginning of the process. Only 3 of 21 patients (14%) were more than 5 min late for their first appointment (8 min, 12 min, and 15 min). However, of these 3 women, only two were subsequently late for the next appointment in the process (MD total) and these two women also had a CT scan scheduled between TAC and MD visit that contributed to the delay. Therefore it did not seem that patient related late arrival contributed to the overall wait time. An important part of the infusion medication wait time is physicianrelated: the chemotherapy orders must be signed by the physician before they can be released by the treating nurse and then prepared by the pharmacist. In phase I of the study, 8 patients (38%) had their chemotherapy orders signed by the MD after the infusion appointment time. One of these patients was a patient who had a CT scan (the other patient who had a CT scan had progression of disease on her scan and so her infusion was canceled). For the remaining 7 patients, the mean delay in orders being signed was 39 min (range 11–91 min).
Fig. 1. Value stream map. Legend: Red not value added, unambiguous. Green value added. Yellow value-enabling or mandatory. White decision point.
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Table 1 Current condition (baseline measures).
TAC MD Infusion Total
Mean wait time (minutes)
Range (minutes)
SD (minutes)
% of total wait time
12 42 65 119
0–22 20–83 38–108 81–154
6 21 23 26
10.1% 35.3% 54.6% 100%
TAC = Treatment Access Center, SD = Standard Deviation, MD = physician.
Also significant to the infusion wait time is the time from order signing to release of the order to the pharmacy by the nurse. In the majority of patients (62%), the orders were signed either on time or before the scheduled infusion appointment. Delay in nurse release was calculated from the time the orders were signed until the nurse release occurred. The mean delay in release was 37 min (range 11–122 min, SD 34 min). Finally, all patients waited for drug administration: the mean delay from order release to drug administration was 69 min (range 34–114 min, SD 26 min). 3.2. Study, part 2 Part 2 of the study was initiated after a change in process had been made. As indicated in the Methods section, a change was instituted in order to decrease the physician-related wait time. We then surveyed an additional 18 patients and repeated the analysis detailed above. The results for the three waiting times identified in the value stream map are shown in Table 2. All results are presented as part 1 vs part 2 of the study. TAC wait time remained essentially the same (12 vs 13 min, part 1 vs part 2, p = 0.912). The total MD wait time was significantly decreased from 42 min to 21 min (p = 0.001). The mean wait to go from the infusion waiting room to an infusion chair remained the same (25 min vs 23 min, p = 0.636) but the infusion administration time decreased (39 min vs 25 min, p = 0.044) resulting in a total infusion time decrease (65 min vs 48 min, p = 0.037). Total mean wait time in phase 2 of the study was 82 min, which was a significantly shorter wait than the total mean time in part 1 of 119 min (p = 0.001). The total MD wait time contributed to 26% of the wait time, compared to 35% in part 1 of the study (p = 0.358). 4. Discussion As health care providers, we are always exploring how to improve the quality of the care of our patients. From a provider standpoint, following national guidelines and practicing evidence-based care are the best methods to assure quality cancer care. From the patient standpoint, however, the perception of quality cancer care is different. Important patient factors with respect to quality include a compassionate and caring provider and environment, trust in their physician [18,19], and a minimum of waiting time [8,20], particularly during days that include long infusions. It should be noted that all patients, not just those receiving chemotherapy in the outpatient setting, place a significant negative value on long waiting times, and this negative value has been shown to negatively impact patient satisfaction scores. Table 2 New condition (change in process).
TAC MD Infusion Total
Mean wait time (minutes)
Range (minutes)
SD (minutes)
% total wait time
13 21 48 82
0–39 9–38 16–100 39–129
12 9 27 34
15.9% 25.6% 58.5% 100%
TAC = Treatment Access Center, SD = Standard Deviation, MD = physician.
As providers, we should aim to improve our overall customer service, not just to improve reimbursement, but more importantly to improve our patients' overall quality of care and quality of life. The incentive to improve quality of care by decreasing wait times has resulted in an effort to improve efficiency in the health care system in general and in the outpatient clinics in particular. Patient wait times can be decreased by increasing our own efficiency in providing care, but this must be done in the setting of decreased health care resources. Other investigators have used Lean methodology to improve efficiency in a variety of clinical settings [14,21–26]. One study utilized Six Sigma methodology to improve efficiency in a resident's women's clinic at an academic center [27]. Six Sigma is a similar process evaluation to Lean, and in fact a combination of the two methodologies is often used in process evaluation. The investigators were able to significantly decrease wait times for new visits and decrease waiting room times in the clinic. As a result of improved efficiency, the clinic also experienced an increased total number of patient visits, resulting in increased productivity. Patient satisfaction scores increased. There is limited data with respect to improving efficiency in an oncology clinic setting, particularly at an academic institution serving a diverse range of patients such as UVA. Skeldon et al. focused on urooncology patients, a similar patient population to our own, and utilized Lean methodology to improve clinic efficiency in the academic setting [14]. As in our study, they prepared a value stream analysis, followed by changes to process, including patient check-in, work areas, and nursing face time. They were able to demonstrate a shorter patient cycle time as well as a shorter time to initial patient assessment. An Australian chemotherapy unit utilized Lean methodology to decrease wait time and improve access to their 19 chair infusion unit [28]. Through analysis of their process and subsequent process refinement, they were able to decrease median wait time by 38%, as well as reduce the number of days required for a new patient to gain access to the unit for treatment. There is no doubt that the process in many hospitals and academic clinics are challenged by workflow inefficiencies. In addition, the entire process is not managed or owned by a single entity, which limits our ability to impact efficiency. In our process, the Gynecologic Oncologist is responsible for evaluating the patient with respect to appropriateness for treatment, and for prescribing the treatment. In contrast, the TAC and the Infusion Unit are run by the Cancer Center under separate management. The process as a whole is not owned by a single entity, though the Gynecologic Oncologist is technically in charge of the process. In this sense, the Gynecologic Oncology process in an academic institution is one that presents unique challenges and is worthy of further study. The addition of an active clinical trials mechanism (with the need for addition of frequent CT scans as a separate step in the process) makes the process more complex. Additionally, the inefficiency of our Infusion Unit impacts our ability to improve patient access. Expanding our clinical trial program, a goal of our Cancer Center, becomes limited by the inability of our clinical trial patients to access the Infusion Unit. Thus we are motivated to improve efficiency not just to improve our patients' experience and quality of care, but also to allow growth in our clinical trial program. Strengths of this study include the working group, which was composed of multiple stake-holders in the process and included a Lean expert, and a motivated Gynecologic Oncologist and nurse team. Weaknesses include the small sample size, the lack of patient satisfaction scores and the failure to include the Infusion unit in the process improvement. While patient satisfaction scores are collected by the outpatient clinic, scores specific to this project could not be obtained or correlated with our results. Our group is hopeful that our data will be convincing enough to the nurse manager of the infusion unit to work together to improve the process as a whole. Moreover, patient satisfaction scores, particularly with respect to waiting times, will be crucial to determine how valuable improvement in this arena will be for our practice. However, it is clear from prior studies that improving
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