Geographic Cohorting – An Industrial Engineering Approach to Reducing Waste
Tze Chao Chiam, PhD[1]
Lori Pelletier, PhD[2]
Richard Forster, MD[3]
ABSTRACT
This study at UMass Memorial Medical Center (UMMMC) investigates various models and configurations to improve placement of general medicine patients from the emergency department (ED) and other sources into acute-care units in the hospital. After applying rules based on hard constraints, such as the needs of surgical patients and specialty patients, the general medicinepatients are assigned randomly to available beds throughout the hospital. Physician teams are assigned to these patients without consideration of either theirgeographic location or the locations of the teams’ current patients. This practiceintroduces several potentialinefficiencies and wastes to the system,including increasing physicians’walk time to provide care, decreasing their “touch time” with patients, decreasingthe team’s communication with nursing staff regarding patients’ plan of care, and delayed discharge planning.
Geographic cohortinginvolves placing general medicine patients and their physicians at specific geographic locations in the facility.It has been proposed as a possible solution to several challenges,while decreasing system waste. However, it could also potentially increase the use of the ED as a “holding spot” for several types of patients. The implementation of geographic cohorting poses several challenges, including assuring nursing competencies in the acute-care units, balancing admission quotas of physician teams in the teaching hospital, and managing bed and physician reassignment for patients needing transfers between acute-care units due to changes in their medical conditions.
This study investigates the effectiveness of the various levels of “cohortness” through the use of a discrete-event simulation which was subsequently implemented at the UMMMC site. Results from both the simulation andthe implementation demonstrated promising trends includinga decrease in physicians’ average walk time, a decrease in the average time between ED admission to arrival in acute care,anda decrease in patients’ length of stay.
1
Geographic Cohorting – An Industrial Engineering Approach to Reducing Waste
Journal of the Society for Healthcare Improvement Professionals © 2013
515 South Figueroa Street, Suite 1300 •Los Angeles, CA 90071 •Phone +1-213-538-0700 •
Introduction
Healthcare delivery is plagued with wastes that impactpatient care. A Lean culture for process improvement has been pursued in nearly every industry and has generated remarkable gains in quality and efficiency by reducing unwanted variation and eliminating non-value added activities or wastes (Imai, 1986; Liker, 2004; Parks, 2003; Sharrock, 2007; Wood, 2004). Additionally,there is increasing recognition of the opportunity that industrial engineering and operations research tools may play in improvinghealthcare delivery by providing better decision support and data-based analysis (Benneyan, 1997; Khurma, Bacioiu & Pasek, 2008; Lawrence, 2005; Lee & Yih, 2010; Wu, Lehto & Yih, 2011).
This paper will focus on wastes associated with the processes of delivering care to general medicine patients being admitted to an inpatient setting, and the timeliness of care provided by hospital medical teams. A case study at UMass Memorial Medical Center (UMMMC) in Worcester, Massachusetts, has demonstrated that the inability to put the right patient in the right bed under the right caregiver is a contributing factor to extended inpatient wait times. General medicine patients, who constitute over 50% of the overall inpatient population, are geographically spread out across the large medical center for various reasons, including an inefficient algorithm for patient-bed assignment and patient-physician assignment. As a result, each physician team is responsible for patients who are scattered across various locations, and physician teams consistently need to walk long distances to provide treatment to the patients under their care. This geographybarrier results in delaying treatment or care plan disposition andthus directly contributes to the problem of hospital overcrowding.
In this research, simulation is employed to evaluate different cohort levels and the effects on the wastes of unnecessary movement and waiting. It is believed that with a high level of cohort, patients belonging to the same general medicine team will receive more timely care and be less geographically scattered. The time saved from walking could potentially be allocated to increase patient-physician touch time or enable a more timely evaluation of patients boarding in the ED.
Background
As a result ofovercrowding in hospitals there is often a delay in placing the right patient in the bedmost appropriate for the patient’s medical needs. As a result, some patients become “boarders” in the ED while others are assigned to an available bed, regardless of whether the bed is appropriate for the patient’s diagnosis. Studies at a large multi-site acute-care teaching hospital in Ontario, Canada have shown that an ED length of stay greater than 12 hours is associated with a 12.4% longer inpatient hospital length of stay and 11% greater inpatient cost, translating into an estimated 2,183 additional inpatient days and a corresponding increase in inpatient costs (Huang, Thind, Dreyer & Zaric, 2010).Many studies have attempted to alleviate this patient flow issue through various solutions, including the addition of more hospital beds (Bazarian, Schneider, Newman & Chodosh, 1996; Moloney et al., 2006; Ross, Naylor, Compton, Gibb & Wilson, 2001) and more staff (Bucheli & Martina, 2004). However, with increasing demand tobringdown costs and new reimbursement guidelines, hospitals are reluctant to acquireadditional resources.
In this study, a computer simulation model was created to provide decision makers with an understanding of the extent of waste present in their system (in this case, waiting and unnecessary movement); different options to alleviate the waste and thereby provide more value to the patient; and a mechanism for continuous improvement after implementation.
Methodology
This study used data provided by UMMMC, a private, non-profit integrated healthcare system in central Massachusetts. The study was initiated in April 2011 as part of a Lean project with the goal to reducewaiting and unnecessary movement. A discrete event simulation was used to perform a feasibility study of geographically cohorting general medicine patients. Performance metrics that are important to the UMMMCinclude physicians’ walk time, discharge time, and the time between when a patient is admitted fromthe ED to an acute-care unit and when the patient is physically in theacute-care bed (referred to as “ED admit to head-in-bed” in this paper). These metrics are chosen because it is believed that with a reduction of walk time, physicians will be able to provide more value-added activities with their patients (i.e., treatment time). An earlier discharge time could also potentially indicate the ability to make a timely bed-assignment for a newly admitted patient. Also, the delay between ED admit to head-in-bed could have a direct impact on patient safety. (Fordyce et al., 2003; Kulstad et al., 2010). For this study,Arena Simulation package was used.
There are nine physician teams at the University campus. Each team typically consists of an attending physician, a resident and two interns. Each team has a patient panel that consists of a maximum of 20 patients, with approximately half of the patient panel belonging to each intern to ensure a balanced workload.
At the beginning of a typical day, residents and interns who work during the day shift attend a night sign-out session in a designated night sign-out room from 7 to 7:30a.m., where hand-off of patients is performed between the night-shift physician and the day-shift physicians. From 7:30 to 8:15a.m., residents attend morning report, which is an educational session where the night-shift resident presents a patient case from the night before. During this time, the interns start seeing their patients and executing the relevant tasks associated with each patient.At the beginning of each round, each intern is aware of his/her patient location and diagnosis as well as general information such as the demographics of each patient. From 8:30a.m. to noon, attending physicians and residents join the interns on their team for rounds. At noon, residents and interns attend a conference session at a designated conference room until 1p.m., after which they continue to round on their patients and follow-up on any necessary tasks. The geographical location of each activity as well as the patients directly impacts the walking time and distanceof the physicians.
A three-week study was performed to examine the geographical locations of patients of all nine physician teams. The typical percentage distribution of patients is shown in Error! Reference source not found..
Unit / Percent distribution3E / 28.13
3W / 7.59
4Adm / 9.38
4E / 2.68
4W / 0.89
5P / 1.34
6E / 19.64
6W / 8.03
7E / 10.71
7W / 5.36
ED / 6.25
Table 1: Percent distribution of patient locations
for general medicine physicians based on three-week study.
Each unit has its ownnurse staff, with the exceptionof 7E and 7W, which shares a common team. As a result 7E and 7W are represented as one unit in the simulation. Since adult general medicine patients typically are not placed in unit 5P as 5P is a pediatric floor, patients on this floor are removed in the simulation, and the percentage distribution of patient location is normalized as shown in Error! Reference source not found.. This distribution of patient locations is used as the base case in the simulation.
Unit / Percent distribution3E / 28.51
3W / 7.69
4Adm / 9.51
4E / 2.72
4W / 0.91
6E / 19.9
6W / 8.14
7E / 16.29
ED / 6.33
Table 2: Normalized percent distribution of patient locations
for general medicine physicians in simulation.
1
Geographic Cohorting – An Industrial Engineering Approach to Reducing Waste
Journal of the Society for Healthcare Improvement Professionals © 2013
515 South Figueroa Street, Suite 1300 •Los Angeles, CA 90071 •Phone +1-213-538-0700 •
Patient type / Priority level / Percent distribution / Associated tasksUnstable patient / 7 / 5 / Calling consults, ICU transfer
Simple discharge patient / 6 / 5 / Gathering information, completing forms, family updates
Night admit in ED / 5 / 10 / Calling consults, gathering information, completing forms, performing history and physical assessments for admissions
Acute-care patient (regular) / 4 / 39 / Writing daily orders, calling consults, gathering information, completing forms, writing progress notes, family updates
Complex discharge patient / 3 / 15 / Gathering information, family updates, complex discharge paperwork
New morning admits / 2 / 10 / Calling consults, gathering information, completing forms, performing history and physical assessments for admissions
Acute-care patient (minimal care needs) / 1 / 20 / Writing progress notes
Table 3: Patient types, priority levels and associated tasks performed by physicians.
In the simulation, every patient is assigned a priority to be seen by the physician team. This priority and the percent distribution of each patient type are shown in Error! Reference source not found.. The percent distributions, indicating the volume of each patient type and the tasks performed by physicians associated with each patient type are obtained through interviews with physicians. Process times for the execution of each taskare also obtained through interviews with physicians as well as through time studies.
Since the activitiesduring rounds along with the geographic distribution of patients across all nine teams can be generalized, thesimulation is setup to study the impact of the patient cohort on one team. The patient geographical locations are assumed to follow the distribution in Error! Reference source not found..
A distance matrix is setup in the simulation using relative unit distances among various units based on actual physical locations of the units. It is assumed that the distance is minimal if a physician travels from one unit to the same unit (the case in which both patients are on the same unit) as shown in Error! Reference source not found.. There is no distance associated with traveling from night sign-out room to night sign-out room or from conference room to conference room as no patient care is provided in these rooms.
1
Geographic Cohorting – An Industrial Engineering Approach to Reducing Waste
Journal of the Society for Healthcare Improvement Professionals © 2013
515 South Figueroa Street, Suite 1300 •Los Angeles, CA 90071 •Phone +1-213-538-0700 •
ED / 3E / 3W / 4Adm / 4E / 4W / 6E / 6W / 7E / Night sign-out room / Conference roomED / 2 / 6 / 6 / 8 / 8 / 8 / 12 / 12 / 14 / 12 / 12
3E / 2 / 3 / 4 / 4 / 4 / 8 / 8 / 10 / 8 / 8
3W / 2 / 4 / 4 / 4 / 8 / 8 / 10 / 8 / 8
4Adm / 2 / 3 / 3 / 6 / 6 / 8 / 6 / 6
4E / 2 / 3 / 6 / 6 / 8 / 6 / 6
4W / 2 / 6 / 6 / 8 / 6 / 6
6E / 2 / 3 / 4 / 3 / 3
6W / 2 / 4 / 3 / 3
8
7E / 2 / 4 / 4
Night sign-out room / 0 / 0
Conference room / 0
Table 4: Relative distances among units.
ED / 3E / 3W / 4Adm / 4E / 4W / 6E / 6W / 7E / Night sign-out room / Conference roomED / 1 / 3 / 3 / 4 / 4 / 4 / 6 / 6 / 7 / 6 / 6
3E / 1 / 2 / 2 / 2 / 2 / 4 / 4 / 5 / 4 / 4
3W / 1 / 2 / 2 / 2 / 4 / 4 / 5 / 4 / 4
4Adm / 1 / 2 / 2 / 3 / 3 / 4 / 3 / 3
4E / 1 / 2 / 3 / 3 / 4 / 3 / 3
4W / 1 / 3 / 3 / 4 / 3 / 3
6E / 1 / 2 / 2 / 2 / 2
6W / 1 / 2 / 2 / 2
7E / 1 / 2 / 2
Night sign-out / 0 / 0
Conference room / 0
Table 5: Time traveled between units.
A walk time matrix is also setup to capture the walk times by the physicians in the simulation as shown in Error! Reference source not found.. The walk times are average walk times captured through time studies, rounded to the nearest whole number.
Four scenarios are studied in the simulation against the base case. In order to be prepared for worst-casescenarios, all cases are setup to simulate the processes during a day when the hospital is operating at high census (all acute-care beds are occupied at the beginning of the day, i.e., 7a.m.) and when the physician team is at a maximum load with a patient panel of 20. The simulation was run until all tasks were completed in each replication. A total of 30 replications were run, and the data was collected from each replication.
In the base case, patient geographical distribution is depicted in Error! Reference source not found.. Patient priority levels, percent distribution of each priority level, and associated tasks are described in Error! Reference source not found.. Relative distances among various units are shown in Error! Reference source not found..
In all four scenarios it is assumed that 6.33% of the team’s patients are night admits who are still in the ED at the beginning of the day, as indicated by historical data. In Case 1, 93.07% of the patients are cohorted at unit 3E, whereas cases 2, 3 and 4 assume 80%, 60% and 40% of the patients are cohorted at unit 3E, respectively. The geographical distribution of the remaining patients in each case is obtained by normalizing the remaining percent distribution of each case against the base case distribution in Error! Reference source not found.. Unit 3E is arbitrarily chosen as the unit of choice to study the effect of cohorting patients in this paper. Error! Reference source not found. shows the percent geographical distributions of patients in cases 1, 2, 3, and 4.
Unit / Base case / Case 1 / Case 2 / Case 3 / Case 43E / 28.51 / 93.67 / 80 / 60 / 40
3W / 7.69 / 0 / 1.61 / 3.97 / 6.33
4Adm / 9.51 / 0 / 2.00 / 4.91 / 7.83
4E / 2.72 / 0 / 0.57 / 1.41 / 2.24
4W / 0.91 / 0 / 0.19 / 0.47 / 0.75
6E / 19.9 / 0 / 4.17 / 10.28 / 16.39
6W / 8.14 / 0 / 1.70 / 4.21 / 6.7
7E / 16.29 / 0 / 3.42 / 8.42 / 13.42
ED / 6.33 / 6.33 / 6.33 / 6.33 / 6.33
Table 6: Percent geographical distribution of patients in cases 1, 2, 3 and 4.
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Geographic Cohorting – An Industrial Engineering Approach to Reducing Waste
Journal of the Society for Healthcare Improvement Professionals © 2013
515 South Figueroa Street, Suite 1300 •Los Angeles, CA 90071 •Phone +1-213-538-0700 •
Results
Simulation Results
Simulation shows that a 93.67% successful patient cohort at unit 3E will result in several positive outcomes. The average daily walk time by the physicians decreased by 51.4%, from 37.67minutes in the base case to 18.29minutes. Sensitivity analyses show that there is a decrease in average walk time for cases 2 and 3 as well, as shown in Error! Reference source not found..Significance tests show that the decrease of average daily walk time in cases 1, 2 and 3 compared to the base case is statistically significant. Case 4, with 40% cohorted at 3E, did not show a statistically significant decrease compared to base case. Based on the analysis in this metric, the threshold above which geographically cohorting patients will bring positive outcome is a cohort of 40%.
Error! Reference source not found. shows the decrease of average time between an ED admit to head-in-bed at a pilot unit. Although the decreases in all cases compared to the base case are not statistically significant at the α=0.05 level, the data shows a desirable trend.
Implementation Results
Based on the simulation outcome as well as other analyses, the leadership team at UMMMC decided to implement the inpatient cohorting project on May 8, 2012.
Although the medical center experienced a few high-census days during the data collection period, the overall census during the preliminary study was not as high as the worst-casescenarios in the simulation. Thus,it is expected that the data collected from the preliminary study will appear more superior to the outcome from the simulation.
Data from implementation is analyzed. The outcomes of interest to this study include time between ED admit to head-in-bed at the pilot units, patients’ lengthofstayand discharge times.
Results show that there is a 20% decrease in the average time from ED admit to head-in-bed in an acute-care unit. This decrease also is statistically significant at α = 0.05 level.Discharge time is chosen as a metric because an overall earlier discharge will allow admitted patients to be transferred to the acute-care beds earlier in the day, thus reducing the time between admit to head-in-bed. Results show that the discharge time from acute-care units also has been moved earlier in the day as shown inError! Reference source not found.. This improvement is also statistically significant at α = 0.05.
Changes in performance measureAverage time from admit to head-in-bed / 20.0% reduction
Average discharge time / 24 minutes earlier in the day
Average length of stay (day) / 12.2% reduction
Table 7: Implementation results.
Figure 1: Average daily walk time.
Figure 2: Average time for ED patients getting an inpatient bed.
Analysis shows that there is a decrease in average lengthofstayby 12.2%. A decrease in length ofstay also indicates freeing up beds to admit new patients. This decrease is attributable to the process changes that allow for efficient communication among care providers, specifically the physicians, nurses, and case managers.