ioe481/ioe481_past_reports/16F03.docx · Web viewThe team then observed current processes involved in accepting patients into EC3 and held informal staff interviews with Nurse TLs,

University of Michigan Health SystemProgram and Operations Analysis

Analysis of Emergency Critical Care Center UnderutilizationFinal Report

To: Jennifer Gegeheimer-Holmes, Director of Operations, Emergency Department

Renee Havey, Clinical Nurse Specialist, Emergency DepartmentSam Clark, Senior Industrial Engineer, Program and Operations Analysis

Colby Foster, Management Engineer Fellow, Program and Operations AnalysisDr. Mark Van Oyen, Professor, University of Michigan

From:IOE 481 Project Team #3

Max ColterCassie CookPragya Sinha

Michael Szocik

Date: December 13, 2016

Executive Summary

The Emergency Critical Care Center (EC3) at the University of Michigan Health System (UMHS) is an area in the adult emergency services department that is used for short-term Intensive Care Unit (ICU) stays. Since opening in 2015, EC3 has struggled to maintain a high utilization rate. Currently EC3 has a bed utilization rate of 40.9%, but the goal is to maintain a bed utilization rate of 85%, or having an average of 7 of the 9 beds occupied. Therefore, the Director of Operations for the Emergency Department asked a student team from IOE 481 at the University of Michigan to determine the root causes of bed underutilization in EC3 and propose a solution to remedy the problem, as well as examine nurse staffing levels in detail. To determine the root causes, the student team observed EC3, interviewed Nurse Team Leads (TL), Attending Providers, and a Physician Assistant (PA), and analyzed survey information, encounter data, patient movement data, and EC3 occupancy data. After completing the observations, interviews, and data analysis, the student team developed recommendations to improve EC3 bed utilization and determined appropriate nurse staffing levels.

BackgroundThe primary goal for EC3 is to have, on average, seven out of the nine beds filled. The majority of patients who are admitted to EC3 have Type 1 Diabetes, gastrointestinal bleeds, need for dialysis, terminal illnesses or other critical conditions. EC3 does not see trauma or burn patients.

The standard patient-to-nurse ratio is two patients to every one nurse. Based on this ratio, EC3 should have at least 5 nurses on duty. The ratio does not include the Nurse TL. There is often uncertainty as to how many nurses are needed to provide proper care for patients in EC3 since there are a wide variety of cases and types of patients that EC3 can accept. EC3 can end up being understaffed if there are patients that require a 1:1 patient-to-nurse ratio, and there are even some patients that require more than one nurse to ensure proper care. Because of the uncertainty in how many nurses will be needed for care and are currently available in EC3, EC3 may choose to not accept new patients even though they may only have one or two patients at a time.

MethodsThe student team performed seven tasks to complete this project.

● Literature Search. The team researched and reviewed eight academic papers to gain additional insight into the problems facing EC3. Of the eight papers researched, the team used three scholarly articles that aligned most with nurse staffing and bed utilization issues.

● Observations of Current Process and Staff Interviews. The student team observed EC3 in order to understand the decision making process involved with accepting new patients into EC3, and the patient flow through EC3. The student team interviewed six Nurse TLs and four Attending Providers during the months of October and November.

● Analyzed Nurse Staffing Survey. The nurse staffing survey detailed staffing levels for EC3 in four-hour increments between August 16 and September 30, 2016. The survey was developed and distributed by the Clinical Nurse Specialist. The team analyzed the data to determine if nurse staffing levels were appropriate.

● Analyzed EC3 Utilization Survey. The EC3 utilization survey was distributed amongst Emergency Department employees prior to the beginning of the project. The survey asked what barriers existed to filling EC3. The Clinical Nurse Specialist provided the team with the responses to the survey. The team analyzed the results to develop final recommendations.

● Analyzed EC3 Encounter Level Data. The encounter level data documented every patient admitted into the Emergency Department between January 1 and September 30, 2016. The data was filtered to look at EC3 patient populations, and was used to determine gaps in patient acceptance into EC3.

● Analyzed Adult Emergency Services Patient Movement Data. The adult emergency services patient movement data documented patient movement for those that entered the Emergency Department between January 1 and September 30, 2016. The data was analyzed to see the relationship between the length of stay in the Emergency Department and the sequence step in which the patient was admitted into EC3.

● Analyzed EC3 Occupancy Rates by Provider. Hourly snapshots of EC3’s occupancy were combined with the Attending Provider schedule to determine if there were differences in the occupancy rates between different Attending Providers. The student team also accounted for different shift times into their analysis.

Findings and Conclusions Upon completing the tasks detailed in the previous section, the student team developed a series of findings and conclusions. The findings and conclusions helped the student team to determine why EC3 is underutilized and appropriate nurse staffing levels, as well as develop recommendations to improve utilization of EC3.

● Literature Search. The literature search yielded information pertaining to methodologies used for nurse staff scheduling. The review of three relevant academic papers suggested that increasing the number of EC3 trained nurses in the overall ED nursing pool would help with staffing flexibility.

● Observations of Current Process and Staff Interviews. The observations of the current process showed that there is inconsistency in how a patient is accepted into EC3. The interviews with the Nurse TLs and Attending Providers confirmed the belief that nurse staffing and patient acuity are seen as major barriers to filling EC3 as well as the Attending Provider working in EC3 in that time frame.

● Analyzed Nurse Staffing Survey. The team analyzed results for 200 four-hour shifts. Based on the nurse staffing survey, the team determined that nurses are understaffed 8% of the time and are overstaffed 43% of the time. Our findings show that EC3 has adequate staffing, however this is because they are hesitant to admit new patients.

● Analyzed EC3 Utilization Survey. There were 103 total responses to the EC3 utilization survey from Emergency Department nurses, Physician’s Assistants, Resident Physicians, and Faculty Physicians. Of those that responded, 54.37% work in EC3. The surveys further solidified that the nurse staffing levels are a prominent barrier to filling open beds.

● Analyzed EC3 Encounter Level Data. The 58,968 records were filtered to show EC3 data only. The filtered data showed that approximately 40% of patients going to EC3 were EC3 status and over 96% of EC3 status patients went to EC3. This data shows that providers are regularly taking in EC3 status patients. However, the data may be inaccurate because the interviews conducted with the Nurse TLs suggest that many EC3 status classifications are processed after the Attending Provider decides to admit the patient. In addition, the data showed that there were 570 patients who did not have an EC3 classification, but were admitted to an ICU, which equates to 18.5 % of all EC3 patients. EC3 has an opportunity to obtain more patients through this population.

● Analyzed Adult Emergency Services Patient Movement Data. This data showed that the length of stay (LOS) of a patient is much shorter if EC3 was the first area of the Emergency Department that a patient was sent to, compared to if the patient went to 1, 2, or 3 areas prior to EC3. The mean total hospital LOS for a patient who visited EC3 first was approximately 9 hours, compared to 140 hours if EC3 was the second area the patient was sent to.

● Analyzed EC3 Occupancy Rates by Provider. The data showed that there is a significant difference in the number of patients in EC3 based on who the Attending Provider was. The highest average occupancy was 5.68 and represented the average occupancy for Attending Provider I, while the lowest average was 3.21 and belonged to Attending Provider Q.

RecommendationsThe student team recommends that the following five changes be implemented in order to reach the goals set forth by the project:

● Staff five nurses at all times in EC3● Ensure providers are aware of and following EC3 admittance policies and procedures ● Implement a visual trackboard to show potential EC3 patients for the entire ED ● Ramp-up EC3 cross training for nurses● Implement an ED flex staff module

The above recommendations have been formulated with respect to all the knowledge acquired over the course of this project, and the team believes that implementing these recommendations will improve the bed utilization of EC3.

Table of ContentsIntroduction 1

Background 1

Goals and Objectives 2

Key Issues 2

Project Scope 2

Methods 2Literature Search 3Observation of Current Processes and Staff Interviews 3Surveys and Data Studies – Nurse Staffing Survey 3Surveys and Data Studies – EC3 Utilization Survey 3Surveys and Data Studies – EC3 Encounter Level Data 4Surveys and Data Studies – Adult Emergency Services Patient Movement Data 4Surveys and Data Studies – EC3 Occupancy Rates by Provider 4

Findings and Conclusions 5Mathematical optimization methodologies are useful for nurse scheduling 5Observations and staff interviews uncover root causes of the underutilization issue 6

Nurse Team Lead Interviews 6Attending Provider Interviews 7Physician Assistant Interview 7

Nurse staffing survey reveals that EC3 is not understaffed 7Employees believe that main barriers to filling beds are staffing levels and acuity 8EC3 can gain more patients by targeting patients admitted directly to other ICUs 9Patient LOS is shorter if they are sent to EC3 first rather than second or third 9The number of patients in EC3 varies by Attending Provider 11

Conclusions 12

Recommendations 13

Expected Impact 14

References 15

Appendices 16Appendix A: Data Dashboard 16Appendix B: Nurse Team Lead Interview Questions 17Appendix C: Attending Provider Interview Questions 18Appendix D: Physician’s Assistant Interview Questions19Appendix E: Nurse Staffing Survey Table20Appendix F: EC3 Utilization Survey Questions 21

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Appendix G: ANOVA Test for EC3 Occupancy by Attending Provider, All Shifts 22Appendix H: ANOVA Test for EC3 Occupancy by Attending Provider, Night Shift 24Appendix I: ANOVA Test for EC3 Occupancy by Attending Provider, Day Shift 26

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List of FiguresFigure 1: Breakdown of Nurse Staffing Levels 8

Figure 2: Interval Plot of Total LOS (Hr) 10

Figure 3: Interval Plot EC3 LOS (Hr) 10

Figure 4: Analysis of Variance 11

Figure 5: Interval Plot of EC3 AvgOccup vs Provider 12

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IntroductionThe Emergency Critical Care Center (EC3) at the University of Michigan Health System (UMHS) is an area in the adult emergency services department that is used for short-term Intensive Care Unit (ICU) stays. UMHS aims to keep EC3 85% full, but has struggled to achieve this goal. The Director of Operations for the Emergency Department would like to know how to improve bed utilization in EC3 as well as how to determine appropriate nurse staffing levels for EC3. Therefore, the Director of Operations for the Emergency Department asked an IOE 481 student team from the University of Michigan to gather data to identify areas of improvement to increase the number of patients utilizing beds in EC3 and to examine nurse staffing for EC3 in detail. In addition, the Director of Operations for the Emergency Department asked the team to develop recommendations to ensure that, on average, seven out of nine beds are utilized in EC3.

BackgroundSince opening in February 2015, EC3 has struggled to maintain a high utilization rate. The goal for EC3 is to have, on average, seven out of nine beds occupied, which equates to an 85% utilization rate. In reality, EC3 has been averaging seven patients per day with an average length of stay (LOS) of 11 hours, which equates to a 40.9% utilization rate, as shown in the Data Dashboard in Appendix A. EC3 staff have noticed that the types of patients that commonly come into the area are patients that have Type 1 Diabetes, gastrointestinal bleeds, need for dialysis, terminal illnesses and other critical conditions. EC3 would like to see every patient that is ultimately dispositioned to an ICU. Currently, about 50% of Adult Emergency Services (AES) critical care admissions that were ultimately dispositioned to an ICU did not go through EC3 beforehand.

EC3 has a goal of maintaining a 2:1 patient-to-nurse ratio, which is lower than the typical 3:1 patient-to-nurse ratio seen in the rest of the Emergency Department (ED). In EC3, this ratio means that on average, five nurses should be assigned to EC3, anticipating that each nurse can handle the care of two patients. The current staffing plan involves five Resident Nurses (RN) and a Nurse Team Lead (TL) staffed from 11AM to 7AM and only three RN and one Nurse TL staffed from 7AM to 11AM. During the 7AM to 11AM time frame, EC3 would not meet their 2:1 patient-to-nurse goal if there were more than six patients in EC3. The EC3 staffing requirement does not account for very critically ill patients that can require multiple nurses for proper care. There is often uncertainty as to how many nurses will be needed to provide proper care (i.e. patient acuity) for patients in EC3 since there are a wide variety of cases and types of patients that EC3 can accept. EC3 can end up being understaffed if there are patients that require a 1:1 patient-to-nurse ratio, and there are even some patients that require more than one nurse to ensure proper care. Because of the uncertainty in how many nurses will be needed for care and are currently available in EC3, EC3 may choose to not accept new patients even though they may only have one or two patients at a time.

Goals and ObjectivesTo determine why EC3 is underutilized, develop a solution to increase the bed utilization in EC3, and determine appropriate nurse staffing levels, the student team has completed the following tasks:

● Conducted a literature search of scholarly articles and student projects related to the project scope and focus

● Observed EC3 and interviewed EC3 Nurse TLs and Attending Providers, and a Physician Assistant (PA)

● Analyzed EC3 nurse staffing surveys, an EC3 utilization survey, ED patient encounter level data, AES patient movement data, and EC3 occupancy rate data

After completing the tasks listed above, the student team created recommendations to improve bed utilization in EC3 such that on average seven out of nine beds are utilized and determined appropriate nurse staffing levels for EC3.

Key IssuesThe following key issues were the drivers for this project:

● EC3 Attending Providers are not being consulted about admittance of potential patients● EC3 staff is concerned with not meeting patient acuity● EC3 is seeing an average of 7 patients per day when the goal is to see an average of 17

patients per day● There is suspicion that the number of patients seen in EC3 is dependent on the Attending

Provider in EC3● EC3 is only seeing 48.4% of ICU admissions through the Emergency Department

Project ScopeThe scope of this project included examining patient flow for patients who have the potential to be admitted into EC3. These types of patients may or may not be in need on an ICU. This will include the initial decision to classify a patient as EC3 status, and the decision of where to send the patient. This project also included an examination of nurse staffing levels within EC3. This project will not include examination of trauma and burn patients in need of specific ICUs.

MethodsThe student team completed three major types of tasks to complete this project. First, the team conducted a literature search of scholarly articles and previous IOE 481 student projects to find useful information related to the project scope and potential recommendations. The team then observed current processes involved in accepting patients into EC3 and held informal staff interviews with Nurse TLs, Attending Providers, and a PA. Finally, the team analyzed surveys and data studies related to nurse staffing, EC3 utilization, patient encounter and movement data, and EC3 occupancy rates by provider.

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Literature SearchThe team researched and reviewed eight academic papers, including two previous IOE 481 projects and six scholarly articles that discussed issues associated with nurse staffing and patient flow. The student team used information from three of the eight articles in the project.

Observation of Current Processes and Staff InterviewsTo understand the patient flow through EC3 and the decision making process to determine whether a patient should be admitted into EC3, the student team observed EC3 by shadowing Nurse TLs and Attending Providers. The student team collectively observed EC3 for a total of 50 hours between the dates of October 4, 2016 and November 28, 2016. Observations were completed in four-hour time-blocks, and took place between the hours of 7am and 11pm on weekdays, with the peak hours (in terms of patient volume) for the ED being between 3pm and 7pm.

While observing, the team interviewed the Nurse TL or Attending Provider who was being observed. The team also set-up an interview with an Emergency Department PA. In total, six Nurse TLs, four Attending Providers, and one PA were interviewed. The interview questions were designed to receive staff feedback as to what barriers exist for keeping EC3 from reaching the utilization goal, as well as any ideas that they have regarding the root cause of the problem. The full list of questions for the Nurse TLs can be found in Appendix B, the full list for Attending Providers can be found in Appendix C, and the full list for the PA can be found in Appendix D.

Surveys and Data Studies - Nurse Staffing SurveyThe nurse staffing survey was designed by the Clinical Nurse Specialist and distributed from August 16th to September 30th, 2016. EC3 Nurse TLs completed the survey after each four-hour shift, resulting in a sample size of 200 entries to the survey. The survey documented the number of nurses staffed in EC3, the number of nurses needed to meet an appropriate patient acuity level, the number of EC3 and non-EC3 status patients in EC3, the name of the staffed charge nurse and EC3 Attending Provider, and the number of nurses pulled to or from EC3 to meet appropriate patient acuity levels. The survey was designed to determine if EC3 is adequately staffed for the number of patients that were in EC3 for each four-hour block. A sample of data obtained from the surveys can be found in Appendix F.

Surveys and Data Studies - EC3 Utilization Survey The Clinical Nurse Specialist provided the team with EC3 utilization survey results. The EC3 utilization survey was distributed to the ED staff prior to the team beginning the project. The surveys asked staff members what they believe are barriers to filling EC3, what parts of EC3 work well, what barriers exist to accepting transfer patients to EC3, and what can be done to improve flow in EC3. There were 103 total responses to the EC3 utilization survey from ED nurses, physician’s assistants, resident physicians, and faculty physicians. Of those that

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completed the survey, 54.37% of them worked within EC3. The student team used the results from this data to develop recommendations. A sample of this data has been included in Appendix E.

Surveys and Data Studies - EC3 Encounter Level DataThe Senior Management Engineer provided the team with the Emergency Department encounter level data. The encounter level data documented the date, time and destination of patient’s arrival, transfer, and discharge; duration of a patient’s EC3 status classification; EC3 length of stay (LOS); ICU LOS; and total LOS for every patient admitted to the ED from January 1st, 2016 to September 30th, 2016 for a total of 58,968 entries. The data was imported to Microsoft Access for analysis. The student team used the encounter level data to determine if there were patients that were not admitted to EC3 that could have been to improve utilization of EC3.

Surveys and Data Studies - Adult Emergency Services Patient Movement DataThe Senior Management Engineer provided the team with Adult Emergency Services patient movement data. The data documented the movement between different areas in the ED for a patient that was admitted into the ED between January 1 and September 30, 2016. The data detailed which areas of the ED the patient had visited, the sequence order of areas, (i.e. Resus to EC3 to the Critical Care Medicine Unit), and the LOS for each area a patient was sent to, and total LOS. The data included the patient movement for 48,016 patients. The movement data was filtered using queries in Microsoft Access to include records of patient cases that had gone to EC3 at any point during their stay at UMHS. The filtration resulted in a sample size of 1,444 records. This data was then exported to Microsoft Excel and used to compare the effects the EC3 sequence number (i.e. whether a patient who had gone to EC3 was dispositioned there first, second, etc.) had on the total LOS of the patient.

Surveys and Data Studies - EC3 Occupancy Rates by ProviderThe Senior Management Engineer provided the team with the occupancy of the ED and EC3 for each hour between July 3rd and October 31, 2016. The occupancy data showed the average number of patients in EC3 and the ED for each hour of the day. The Director of Operations for the Emergency Department provided the student team with the Attending Provider schedule for the months of July, August, September, and October 2016. Using Microsoft Access, the team was able to create a new table from the occupancy data and Attending Provider schedule, which showed the date, hour, Attending Provider, and average occupancy of EC3 for each hour of each day in the specified date range. This data was then used to conduct an analysis examining how individual variation among Attending Providers may influence the average occupancy of EC3. The total sample size for the occupancy rate by provider was 2,896 data points; 25 Attending Providers were included in the analysis.

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Findings and Conclusions Upon completing the tasks detailed in the previous section, the student team developed a series of findings and conclusions. The findings and conclusions helped the student team to determine why EC3 is underutilized and appropriate nurse staffing levels, as well as develop recommendations to improve utilization of EC3.

Mathematical optimization methodologies are useful for nurse scheduling.After reviewing previous IOE 481 student projects and scholarly articles, the team determined that the three most applicable articles were related to scheduling nurse staffing assignments. Based on A Primary Shift Rotation Nurse Scheduling Using Zero-One Linear Goal Programming [1], Nurse staffing, scheduling, and reallocation in the hospital [2], and A 0-1 goal programming model for nurse scheduling [3] the team learned of useful methodologies used in determining optimal nurse staffing allocations and scheduling procedures. The team sought to use this information to develop recommendations, specifically pertaining to staffing assignments.

Nursing human resource decisions fall into four categories: staffing decisions that determine the number of full time equivalent (FTE) nurses with different skill sets to be staffed for each unit, scheduling decisions which determine when nurses will be on and off duty for each shift and the minimum nursing requirements for each shift, allocation decisions that determine a pool of “floating nurses” to accommodate fluctuation in nursing requirements or absenteeism, and assignment decisions which assign individual nurses to each shift [1], [2].

The University of Michigan Hospital utilizes a program called ANSOS One-Staff to develop their nursing schedules, which fall under the assignment decisions category. Their current process starts with nursing staff filling out planning sheets to request their preferred schedule. Secretaries for the department collect the sheets and enter the information into One-Staff and balance the schedules according to staffing minimums for each day in the planning period. These staffing minimums are based on average census data and is ultimately decided on by administrators. The exact type of information that secretaries input into the program was not available to the student team because of time constraints. Nurses are then assigned to EC3 manually by the secretarial staff. The fairness of the distribution of staffing in EC3 is kept track of manually by the secretaries, meaning that the secretaries manually ensure that one nurse is not sent to EC3 more than others. The methods used for developing staffing schedules in ANSOS One-Staff was not publically available. However, optimization methods using linear programming are very popular techniques used to develop staffing schedules for nursing and from the information publically available it is likely that it uses some form of optimization combined with a simulation program.

Linear programming methods are well suited for determining when nurses will be on and off duty in scheduling decisions and assignment decisions. Staffing decisions for the floating nurse pool, the number of FTE nurses staffed, and the minimum nursing requirements for each shift are

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generally determined by the hospital administration [3]. The student team has determined that the project scope excludes an analysis of the ANSOS One-Staff software due to privacy restrictions on intellectual property. Instead, the student team believes that the project scope falls under administrative decisions for nurse pool allocation decisions, the number of FTE nurses staffed in EC3 and the minimum nursing requirements for each shift. When asked how many nurses were able to work in EC3, the Clinical Nurse Specialist stated that approximately 80 out of the 250 nurses staffed in the ED had been certified to work in EC3. It was also mentioned that there is an ongoing training effort to increase the proportion of ED nurses who are able to work in EC3. Increasing the number of EC3 certified nurses in the ED staffing pool would increase staffing decision flexibility for EC3.

Observations and staff interviews uncover the root causes of the underutilization issue. The student team conducted observations in order to fully understand how EC3 work. The student team observed patient movement to and from EC3, shadowed Nurse TLs and Attending Providers to understand their roles and responsibilities in EC3. Observations of the current process in EC3 showed that the EC3 electronic consult is often skipped. The formal protocol is that a consult page should be sent to the Attending Provider in EC3 from a potential patient’s provider, followed by the Provider going to the patient in question (or holding a discussion with the patient’s current provider) and determining if the patient is a good fit for EC3. Upon conclusion of the consult, the patients who are deemed EC3 status are then moved to an available bed within EC3. Patients deemed not EC3 status by the Attending Provider remain under the care of the patient’s current staff. However, the student team observed that nurses, Nurse TLs and Attending Providers will often discuss amongst themselves whether they believe a patient should come to EC3 prior to a consult being sent.

Nurse Team Lead InterviewsDuring the informal interviews, the Nurse TLs revealed that the most likely barriers to filling open beds include low nurse staffing levels, patient acuity, and anticipation of future critical patients. EC3 was designed to have a 2:1 patient-to-nurse ratio; however, the acuity of EC3 patients often does not allow for EC3 to accept potential new patients. Patient acuity refers to the level of care needed for a given patient, and because of the nature of EC3 patients it is often difficult to predict the level of care a patient will need during their stay.

During the day, EC3 typically has five nurses staffed, and one Nurse TL. However, between the hours of 7am and 11am, EC3 only has 3 three nurses and one Nurse TL. Due to the anticipated shortage of nurses in the morning, the Nurse TLs stated that they felt as if patients were not as readily accepted throughout the night in order to ensure that EC3 will not be understaffed in the early hours of the day.

Nurse TLs feel as if Attending Providers may not take an admit hold or a soft EC3 status patient in anticipation of a high acuity patient arriving. The Nurse TLs also stated that they believe

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providers try to choose patients who are low risk and uncomplicated so they can be treated quickly, and that they believe the Attending Provider influences the number of patients EC3 admits.

Attending Provider InterviewsThe interviews with the Attending Providers offered additional insight into the problems facing EC3. As mentioned previously, the Attending Providers are the ones who decide whether to admit a patient into EC3. When asked how they make the decision to accept or refuse a patient, all four Attending Providers generally said the decision came down to three factors: EC3’s ability to care for the patient, EC3 staffing levels, and whether they believe accepting the patient is an appropriate use of EC3 resources.

EC3 is intended to be used for critically ill patients, however the student team has found through data analysis that there are not enough critically ill patients in UMHS to keep EC3 85% utilized. The student team asked the providers what types of patients they accept if there are no critically ill patients in the ED that will be a good fit for EC3. Some of the Attending Providers stated that they will first accept potential EC3 status patients, then accept low acuity or minor care patients, and lastly, will accept admit holds if there is a need to do so in the ED. However, some of the Attending Providers stated that they do not accept admit holds or minor care patients as they are not an appropriate use of EC3 resources. This variability between providers lead the student team to examine EC3 occupancy by provider, which is detailed further in the section comparing the average number of patients in EC3 by provider.

Physician Assistant InterviewWhile the student team did not get the chance to observe any PAs, the team conducted an interview with the Lead PA. The interview with the PA raised similar concerns as the Nurse TLs and Attending Providers. The PA commented that they had perceived a difference between Attending Providers and occupancy of EC3, as the Nurse TLs had said earlier. The PA also stated they believed that Attending Providers were reluctant to take new patients between 7am and 8am, and 7pm and 8pm, as this is the shift changeover time and would require a significant amount of paperwork to be completed before the shift change.

The PA also brought up some ideas that they had for getting more patients into EC3. The PA believes that having visual cues, such as a trackboard, readily available for all EC3 staff to view labor intensive patients and potential EC3 status patients will make it easier for Attending Providers to see the need use beds in EC3, thus increasing the utilization of the space.

Nurse staffing survey reveals that EC3 is not understaffed.Based on the nurse staffing survey, the team determined that EC3 nurses are understaffed 8% of the time and are overstaffed 43% of the time (based on the ability to meet patient acuity for each four-hour block). Figure 1 below shows a pie-chart breaking down the percentage of time that

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EC3 is overstaffed, understaffed, or adequately staffed. Based on the results, EC3 is adequately staffed or overstaffed 92% of the time. This is contradictory to what the team learned from staff observations and interviews.

The team believes that the data may not provide a clear representation of staffing levels needed in EC3. The data was recorded once every four hours, and was filled out at the end of the shift. The data does not reflect any changes that occurred during the four-hour time block; the number of patients in EC3 can change at an hourly rate or less. There are also gaps in the data in which the nurse staffing levels were not recorded for a given four-hour block. However, based on the nurse staffing survey, there is flexibility to bring in more patients into EC3 so that the percentage of time that EC3 is overstaffed drops.

Employees believe that main barriers to filling beds are staffing levels and patient acuity. The EC3 utilization survey indicated that 103 staff members have similar ideas as to what barriers exist to keeping EC3 utilized. The survey indicated that the main barrier to filling open beds are low nurse staffing levels and patient acuity. Other barriers that are believed to exist include:

● Inconsistent patient classification● Inability to clearly define who should be going to EC3 ● Lack of consult with EC3 as to whether or not the patient should be sent to EC3

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Figure 1 shows the breakdown of nurse staffing levels in EC3.

Understaffed9%

Overstaffed43%

Staffed Ad-equately

49%

Breakdown of Nurse Staffing Levels n = 200 four hour shifts

There needs to be better communication between staff members, and a set of clear guidelines for what constitutes EC3 classification. The employees were also asked what barriers there are to transferring patients to EC3. Some of the responses received include:

● Time and people required for moving patients into and out of rooms takes ● Inconsistent hand-off between shifts● Tedious paperwork

The survey indicates the issues relating to meeting EC3 utilization are multifactorial. These include communication issues, inconsistencies between providers, and insufficient time at the end of shifts for activities related to accepting or transferring patients. EC3 can gain more patients by targeting patients admitted directly to other ICUs. The student team filtered the encounter data to capture information on patients who were classified as EC3 status. The filtered patient cases showed that approximately 40% of patients going to EC3 were EC3 status (sample size = 3073). The data also showed over 96% of EC3 status patients went to EC3 (sample size = 1890). This suggests that providers are regularly taking in EC3 status patients. However, this data may be biased as the interviews conducted with the Nurse TLs suggested that many EC3 status classifications are processed after the Attending Provider decides to admit the patient or the patient has been admitted to EC3. The data also showed that there were 570 patients who were not classified as EC3 status, but were admitted to an ICU; this amount equates to 18.5% of total EC3 patients. Within that population, close to one out of every six of patients had an ICU LOS less than 24 hours. EC3 has an opportunity to obtain more patients through this population as opposed to EC3 status patients; the majority of whom are already dispositioned to EC3 at some point in their stay.

Patient LOS is shorter if they are sent to EC3 first rather than second or third. The AES patient movement data showed that patient’s LOS for EC3 status patient was much shorter if EC3 was the first area of the Emergency Department that a patient was sent to, compared to if the patient went to 1, 2, or 3 areas prior to EC3. The interval plot for total LOS for a patient based when in the room sequence the patient visited EC3 can be viewed in Figure 2. The average total LOS for a patient that visited EC3 first was 9.92 hours. The average total LOS for patient that visited EC3 second, third, or fourth had total LOS of 140.88 hours, 146.76 hours, and 117.15 hours respectively.

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4321

1501209060300

EC3 Room Sequence

Tota

l LOS

(Hr)

Individual standard deviations are used to calculate the intervals.

95% CI for the Mean; n = 1438

n=223

n=1015 n=180n=19

Interval Plot of Total LOS (Hr)

Figure 2 shows that the total LOS for EC3 status patients in shorter if EC3 is the first area the patient is sent.

Figure 3 shows the average EC3 LOS for a patient that visited EC3 first, second, third, or fourth during their stay. EC3 LOS for a patient that was sent to EC3 first was 2.925 hours. If EC3 was the second, third, or fourth location a patient was sent to, EC3 LOS was 9.862 hours, 10.410 hours, and 13.891 hours respectively.

4321

20

15

10

5

0

EC3 Room Sequence

EC3

LOS

(Hr)

Individual standard deviations are used to calculate the intervals.

n=223

n=1015 n=180n=19

95% CI for the Mean; n = 1438Interval Plot of EC3 LOS (Hr)

Figure 3 shows that EC3 LOS is shorter is EC3 is the first area the patient is dispositioned to.

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The number of patients in EC3 varies by Attending Provider.The student team compared the average occupancy of EC3 for each Attending Providers using Minitab. A one-factor ANOVA test was completed to see if there is a statistically significant difference in average occupancy of EC3 for each Attending Provider. The null hypothesis assumed that the average occupancy of EC3 was the same for each Attending Provider. According to the Minitab output, the P-value for the test was 0.000, meaning that the null hypothesis is rejected. Figure 4 shows the analysis output of the test.

Figure 4 shows that the null hypothesis should be rejected based upon the ANOVA test statistic.

The ANOVA test shows that there is a difference amongst Attending Providers and the average occupancy for EC3. The interval plot for the test can be viewed below in Figure 4. Looking at Figure 4, it appears that Provider I, N, and R have a higher number of patients in EC3 on average, and Providers D, M, Q, T, and W have a lower number of patients in EC3 on average. The data confirms the suspicion that there is a difference in the number of patients admitted into EC3 based on who the Attending Provider is. The full ANOVA test results can be found in Appendix G.

The busiest time for the ED is between the hours of 3pm and 7pm, meaning that there typically are more patients admitted during the day shift than the night shift. To ensure that there is a significant difference between providers independent of the fact that the provider is working a day or night shift, the same ANOVA test was run for just day shift occupancies and just night shift. The conclusions from the original ANOVA test hold true in both scenarios, leading the student team to conclude that there is a significant difference in the average number of patients in EC3 by Attending Provider. Based on Figure 5, it appears that Provider I had the highest average occupancy in EC3 and Provider Q had the lowest average occupancy in EC3. The full ANOVA test for the night shift can be found in Appendix H, and the full ANOVA test for the day shift can be found in Appendix I.

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Figure 5 shows that there is substantial variation between EC3 providers.

ConclusionsUpon completing the project with the University of Michigan’s Emergency Critical Care Center, the team developed a series of findings and conclusions. The following bullet points summarize the key takeaways from the findings section of the report:

● The information found in the student team’s literature search indicated that decisions regarding nurse staffing should focus on minimum shift requirements and creation of a floating pool

● Observations and staff interviews helped uncover the root causes of underutilization. Some barriers to filling open beds include: low nurse staffing levels, patient acuity, anticipation of future critical patients, staff changeover, and appropriate use of resources.

● Nurse staffing surveys reveal that EC3 is not understaffed. However, this data does not account for the nurse staffing levels during the four hour gaps that data was not collected.

● The utilization survey indicates the issues relating to meeting EC3 utilization are mostly related to communication issues, inconsistencies between providers, and insufficient time at the end of shifts for accepting or transferring patients.

● EC3 can increase the number of admitted patients by targeting patients admitted directly to other ICUs. Patients can be examined in EC3, which allows an ICU bed to remain open.

● Patient length of stay is shorter if they are sent to EC3 first. When EC3 is the second or third area they visit, their length of stay increases. This includes both total length of stay and EC3 length of stay.

● The Attending Provider makes a difference on the number of patients admitted into EC3. This is reflected by the statistically different values obtained for average occupancy of EC3 when examined by provider.

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RecommendationsThe student team recommends that the following five changes be implemented in order to reach the goals set forth by the project:

● Staff five nurses at all times in EC3● Ensure providers are aware of and following the policies and procedures for EC3

admittance● Implement a visual trackboard to show potential EC3 patients for the entire ED ● Ramp-up cross training for nurses● Implement an ED flex staff module

Currently, EC3 has three patient care nurses between the hours of 7am and 11am. All other times of the day have five patient care nurses. The staff interviews indicated that EC3 is reluctant to take new patients if there is doubt that the patient will be properly cared for in EC3. As there is a known shortage of nurses between the hours of 7am and 11am, EC3 staff members indicated that EC3 is more hesitant to take new patients in the early hours of the morning. Having five nurses staffed 24-hours day in EC3 would alleviate the fear of being short staffed in EC3 and could potentially lead to more patients being accepted into EC3. These minimum staffing levels would have to change in the ANSOS One-Staff software the hospital uses to schedule nurse staffing.

The interviews with the Attending Providers made the team aware of several disconnects between what administration believed was happening in EC3 and what was actually happening. As noted in the discussion on observations and staff interviews, several procedural steps (i.e. the electronic consult order) believed to be followed by the staff were actually not being adhered to. This is not to say that staff were not doing their jobs, but that a clear disconnect exists between the perception of how EC3 is run and how it is actually run. The student team also discovered through staff interviews that some Attending Providers do not accept minor acuity patients into EC3, as they do not want to bill the patient for a critical care bed when the patient was a minor case. This is not true; patients are billed for the care they receive, not for the bed they are placed in. For this reason, the team believes it would be beneficial to re-educate all attending provider that work in EC3 of what the proper protocols and procedures are for EC3 admittance.

According to the interviews conducted with various staff members, it was brought to the team’s attention that there is not a good tool in EC3 for visualizing potential EC3 patients within the Emergency Department. In light of these comments, the team recommends that a visual tracker is developed for EC3, which when finished would provide EC3 staff with a quick and easy way to search for potential EC3 patients in other areas of the hospital. Ideally, this visual trackboard would be located somewhere within EC3 where all relevant staff members can easily see it.

From discussions with the Clinical Nurse Specialist, the team was informed that currently, ~80 of the 250 ED nurses have been trained to work in EC3. This represents only just over 30% of all potential nurses that could be eligible to work in EC3. With this in mind, and knowing efforts are

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already underway, the team recommends that cross-training within the ED should be ramped up in order to increase the number of nurses eligible to work in EC3. Based on the information gathered from the literature search, increasing the number of EC3 trained nurses (i.e. available nurses with that specialty training) will allow for more flexibility in nurse scheduling for EC3. It would also make the next recommendation more feasible to implement.

Finally, the team found that a consistent barrier to filling beds in EC3, according to staff members, was the fear of being short staffed if a high acuity patient is admitted or a current patient’s acuity rapidly increases. To counteract this, the team proposes the use of flex staffing. Ideally, this “flex staff” would consist of several nurses working in other units of the ED that would be available (a type of inter-unit on-call system) should EC3 accept a patient that requires exceptionally high levels of nursing care. These “flex” nurses would then return to their home unit once the high acuity EC3 patient is stabilized and patient acuity for the unit can be met with the assigned EC3 staff.

Expected ImpactThe overall expected impact of the implementation of the recommendations made by the student team are as follows:

● Improved nurse staffing levels● Reduced EC3 patient classification confusion● Higher numbers of patients accepted into EC3● Improved flexibility in nurse staffing to care for high acuity patients

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References

[1] Huarng, F. (1999, May). A Primary Shift Rotation Nurse Scheduling Using Zero-One Linear Goal Programming. Computers In Nursing, 17(3), 135-144.

[2] Warner DM. Nurse staffing, scheduling, and reallocation in the hospital. Hospital and Health Services Administration 1976a;21(3):77-90

[3]M. Azaiez and S. Al Sharif, "A 0-1 goal programming model for nurse scheduling", Computers & Operations Research, vol. 32, no. 3, pp. 491-507, 2005.

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Appendix A - Data DashboardAppendix B - Nurse Team Lead Interview Questions

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1. How do you decide to bring someone into EC3?2. What determines if they are EC3 status?3. How do you decide if a patient from the waiting room should be pulled?4. How do you decide whether or not to take admit holds?5. What determines nurse to patient ratio?6. How often do you feel like you can't take a patient? At what point are you overwhelmed?7. Barriers to keeping beds full?8. What are the factors that are used to determine if a patient should not be taken?9. Work/load balance?

Appendix C - Attending Provider Interview Questions

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1. How do you decide to bring someone into EC3?2. What determines if they are EC3 status?3. How do you decide if a patient from the waiting room should be pulled?4. How do you decide whether or not to take admit holds?5. What determines nurse to patient ratio?6. How often do you feel like you can't take a patient? At what point are you overwhelmed?7. Barriers to keeping beds full?8. What are the factors that are used to determine if a patient should not be taken?9. Work/load balance?

Appendix D - Physician Assistant Interview Questions

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1. How does the role of PA fit into EC3? 2. Trends between Attending Providers?3. What barriers exist to keeping EC3 full?

Appendix E - Nurse Staffing Survey Table

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Appendix F - EC3 Utilization Survey Questions

1. What is your job description?2. Do you work in EC3?3. What barriers exist to receiving patients in EC3?4. What parts of EC3 work well?5. What barriers exist to transferring patients to EC3?6. What can be done to improve the flow in Resus and to EC3?7. What suggestions do you have to improve EC3 utilization going forward?

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Appendix G - ANOVA Test for EC3 Occupancy by Attending Provider, All Shifts

One-way ANOVA: EC3 AvgOccup versus Provider

Null hypothesis All means are equalAlternative hypothesis At least one mean is differentSignificance level α = 0.05

Equal variances were assumed for the analysis.

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Factor Information

Factor Levels ValuesProvider 25 A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y

Analysis of Variance

Source DF Adj SS Adj MS F-Value P-ValueProvider 24 715.8 29.826 11.05 0.000Error 2871 7751.5 2.700Total 2895 8467.3

Model Summary

S R-sq R-sq(adj) R-sq(pred)1.64314 8.45% 7.69% 6.87%

Means

Provider N Mean StDev 95% CIA 96 4.182 1.666 ( 3.853, 4.511)B 144 4.234 1.628 ( 3.966, 4.503)C 192 4.331 1.581 ( 4.098, 4.563)D 96 3.477 1.257 ( 3.148, 3.805)E 108 4.546 1.626 ( 4.236, 4.856)F 300 4.222 1.761 ( 4.036, 4.408)G 120 4.504 1.800 ( 4.210, 4.798)

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H 156 4.399 1.674 ( 4.141, 4.657)I 108 5.681 1.622 ( 5.371, 5.991)J 96 4.435 1.737 ( 4.106, 4.764)K 96 4.411 2.039 ( 4.083, 4.740)L 84 4.372 1.398 ( 4.020, 4.724)M 60 3.679 1.871 ( 3.263, 4.095)N 96 5.391 1.548 ( 5.062, 5.719)O 96 4.695 1.237 ( 4.366, 5.024)P 132 4.549 1.816 ( 4.269, 4.830)Q 24 3.219 2.111 ( 2.561, 3.876)R 12 5.292 0.437 ( 4.362, 6.222)S 84 4.827 1.909 ( 4.476, 5.179)T 108 3.285 1.581 ( 2.975, 3.595)U 96 4.654 1.259 ( 4.325, 4.982)V 112 4.429 1.295 ( 4.124, 4.733)W 96 3.443 1.721 ( 3.114, 3.772)X 120 4.054 1.905 ( 3.760, 4.348)Y 264 4.4678 1.4985 (4.2695, 4.6661)

Pooled StDev = 1.64314

Appendix H - ANOVA Test for EC3 Occupancy by Attending Provider, Night Shift




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Factor Levels ValuesProvider 23 A, B, C, D, E, F, G, H, I, J, K, M, N, O, P, Q, S, T, U, V, W, X, Y



Model Summary


MeansProvider N Mean StDev 95% CIA 12 6.542 0.818 (5.669, 7.415)B 36 4.493 1.661 (3.989, 4.997)C 96 4.286 1.608 (3.978, 4.595)D 36 3.583 1.604 (3.079, 4.087)E 24 5.573 1.299 (4.956, 6.190)F 144 3.990 1.921 (3.738, 4.242)G 60 5.067 1.642 (4.676, 5.457)H 108 4.697 1.415 (4.406, 4.988)I 24 4.854 1.078 (4.237, 5.471)

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J 48 5.188 1.479 (4.751, 5.624)K 24 4.073 0.796 (3.456, 4.690)M 12 6.396 0.670 (5.523, 7.269)N 96 5.391 1.548 (5.082, 5.699)O 36 4.167 1.054 (3.663, 4.671)P 72 4.760 1.763 (4.404, 5.117)Q 12 1.438 1.315 (0.565, 2.310)S 84 4.827 1.909 (4.497, 5.157)T 84 3.211 1.340 (2.881, 3.541)U 24 4.906 0.969 (4.289, 5.524)V 112 4.429 1.295 (4.143, 4.714)W 48 2.729 1.592 (2.293, 3.166)X 36 3.861 1.313 (3.357, 4.365)Y 216 4.588 1.530 (4.382, 4.794)


Appendix I - ANOVA Test for EC3 Occupancy by Attending Provider, Day Shift




Factor Information

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Factor Levels ValuesProvider 22 A, B, C, D, E, F, G, H, I, J, K, L, M, O, P, Q, R, T, U, W, X, Y



Model Summary


Means

Provider N Mean StDev 95% CIA 84 3.845 1.472 (3.496, 4.195)B 108 4.148 1.616 (3.840, 4.457)C 96 4.375 1.560 (4.048, 4.702)D 60 3.413 1.003 (2.999, 3.826)E 84 4.253 1.597 (3.903, 4.603)F 156 4.436 1.575 (4.179, 4.692)G 60 3.942 1.788 (3.528, 4.355)H 48 3.729 2.007 (3.267, 4.192)I 84 5.917 1.677 (5.567, 6.266)J 48 3.682 1.661 (3.220, 4.145)

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K 72 4.524 2.303 (4.147, 4.902)L 84 4.372 1.398 (4.022, 4.722)M 48 3.000 1.391 (2.537, 3.463)O 60 5.013 1.237 (4.599, 5.426)P 60 4.296 1.859 (3.882, 4.710)Q 12 5.000 0.819 (4.075, 5.925)R 12 5.292 0.437 (4.367, 6.217)T 24 3.542 2.248 (2.888, 4.196)U 72 4.569 1.337 (4.192, 4.947)W 48 4.156 1.553 (3.694, 4.619)X 84 4.137 2.110 (3.787, 4.487)Y 48 3.927 1.221 (3.465, 4.390)


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Documents

ioe481/ioe481_past_reports/16F03.docx · Web viewThe team then observed current processes involved in accepting patients into EC3 and held informal staff interviews with Nurse TLs,