Max Colter, Cassie Cook, Pragya Sinha, Michael Szocik Analysis of Emergency Critical Care Center (EC3) Underutilization Final Presentation Max Colter, Cassie Cook, Pragya Sinha, Michael Szocik IOE 481 Team 3 December 13, 2016

Current EC3 Utilization Background Current EC3 Utilization Ideal EC3 Utilization

Background: Patient Flow Process Check in Screening Triage Workup and Consultation EC3 Transfer Background: Patient Flow Process

Goals and Objectives Goal: Determine why EC3 is being underutilized and propose a solution to increase bed utilization Objectives: Increase the number of patients admitted into EC3 Increase bed utilization in EC3 Prevent nurse staffing shortages Say if status is same or changed

2:1 Patient to Nurse Ratio Key Issues ATP Consultation 2:1 Patient to Nurse Ratio 7 Patients/Day Provider Variability 48.4% ICU Admissions EC3 charge nurses are not being consulted about reception of potential new patients EC3 staffing is not meeting their ideal 2:1 patient to nurse ratio The EC3 unit sees average of 7 patients/day, goal is see average of 17/day Variability amongst providers and TLS that result in various numbers of patients admitted EC3 is only seeing 48.4% of ICU admissions through the emergency department EC3 classification reduces the number of cases seen by residents = impacts educational opportunities

Project Scope Project Included: Project Excluded: Admittance procedures for EC3 patients Decision of where to send the patient Examination of nurse staffing within the EC3 Analysis of EC3 occupancy rates Project Excluded: Trauma and burn patients Project included: Admittance procedures for patients who are determined to be ICU eligible Initial decision to classify a patient as ICU eligible Decision of where to send the patient An examination of nurse and attending physician staffing within the EC3. Project Excluded: Not deemed to be ICU eligible Trauma and burn patients.

Methods and Findings Literature Search, Observations and Interviews

Literature Review “Nurse staffing, scheduling, and reallocation in the hospital” “A 0-1 goal programming model for nurse scheduling” Mathematical optimization methodologies ANSOS One-Staff Nurse staffing flexibility for special skill sets Maybe add 65-85% utilization stat from lit search The 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 three of these articles in their project.

Observations Completed: 50 hours total 28 hours with 6 EC3 TLs 22 hours with 4 Attending Provider 1 hour with Physician’s Assistant 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, 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 are not always moved into an EC3 bed, but this is usually the case. Patients deemed not EC3 status by the Provider remain under the care of their current area. 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 ever being sent.

Observations EC3 electronic consult is skipped Consult page sent to provider Provider determines if patient is good for EC3 EC3 Status patients moved to EC3 Non EC3 Status stays in current area Observations EC3 electronic consult is skipped Nurse TL and Provider discuss EC3 status acceptance 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, 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 are not always moved into an EC3 bed, but this is usually the case. Patients deemed not EC3 status by the Provider remain under the care of their current area. 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 ever being sent.

Staff Interviews Question Categories Patient flow Patient classification Barriers to filling beds Staffing/workload The peak hours, or the busiest hours, in the ED are on weekdays from 3 pm -7pm Each team member will be conducting observations in the EC3 and shadowing the nurses for approximately 20 hours each during the month of October. We will also be conducting staff interviews so that we can understand the flow through the emergency department Why did you choose this particular patient to bring over as an admit hold? Why did you choose this specific patient from the waiting room? What is your plan to fill the beds in the area? What are the barriers to filling the beds that are open? Additional questions pertaining to specific events witnessed by the team are also being asked, and their responses recorded.

Staff Interviews Continued… Nurse Team Lead Interviews Barriers: low staffing levels, acuity, future patients 7 am -11 am : 3 nurses, 1 TL Attending Provider Interviews Patient Acceptance: Ability to care Staffing levels EC3 resources Physician Assistant Interview Provider variability Shift changeover Visual track board 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 ATPs during the months of October and November. 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 ATPs confirmed the belief that nurse staffing and patient acuity are seen as major barriers to filling EC3 as well as the Nurse TL and ATP working in EC3 in that time frame.

Methods and Findings Surveys and Data Studies The peak hours, or the busiest hours, in the ED are on weekdays from 3 pm -7pm Each team member will be conducting observations in the EC3 and shadowing the nurses for approximately 20 hours each during the month of October. We will also be conducting staff interviews so that we can understand the flow through the emergency department Why did you choose this particular patient to bring over as an admit hold? Why did you choose this specific patient from the waiting room? What is your plan to fill the beds in the area? What are the barriers to filling the beds that are open? Additional questions pertaining to specific events witnessed by the team are also being asked, and their responses recorded.

Nurse Staffing Survey Team collected and analyzed ~2 months of data from a nurse staffing survey. Findings indicate the EC3 is understaffed only ~8% of shifts in that time period. Staffing level based on ability to meet patient acuity. Nurse staffing survey reveals 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.

Utilization Survey Provided by the Clinical Nurse Specialist 103 responses ED nurses Physician Assistants Resident Physicians Faculty Physicians Questions regarding staff opinions on EC3 related issues 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 to EC3, and what can be done to improve flow in EC3There 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 low nurse staffing levels is a prominent barrier to filling open beds.

Utilization Survey Barriers 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 Patient Transfer Time and people required for moving patients Inconsistent hand-off between shifts Tedious paperwork 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 Time and people required for moving patients into and out of rooms takes Inconsistent hand-off between shifts Tedious paperwork

AES Patient Movement Data Provided by the Senior Management Engineer Collected July 1st, 2016 – October 31st, 2016 Sample size = 48,016 patient movement records

AES Patient Movement Data Key Data Fields Room and Treatment Area Duration (Hr) Room Sequence #

AES Patient Movement Data

AES Patient Movement Data

Encounter Level Data Provided by the Senior Management Engineer Collected January 1st, 2016 – September 30th, 2016 Sample size = 58,968 patient records

Finding:Encounter Level Data Continued… Key Data Fields Patient arrival date/time Patient EC3 classification status Length of stay Disposition location Care level

Encounter Level Data Continued… Dispositioned to EC3

Encounter Level Data Continued… Need to change this to updated data set

Occupancy Rates The 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 the EC3. The total sample size for the occupancy rate by provider was 2,896 data points; 25 Attending Providers were included in the analysis.

Observations & Interviews Conclusions Literature Search Observations & Interviews Nurse Staffing Survey EC3 Utilization Survey EC3 Encounter Data EC3 Movement Data Occupancy Rates Observations and staff interviews provided the team with the root causes of underutilization issues. The barriers to filling open beds are low nurse staffing levels, patient acuity, anticipation of future critical patients, staff changeover, and appropriate use of resources. Nurse staffing survey reveals 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 gain more patients by targeting patients admitted directly to ICU. Patients can be examined in EC3 and this also allows for an ICU bed to remain available. Employees believe that main barriers to filling beds are staffing levels and patient acuity. There also needs to be better communication between providers, Nurse TL’s and registered nurses during EC3 admittance and transfer.   Patient length of stay is shorter if they are sent to the EC3 first. When EC3 is the second or third area they visit, their length of stay increases. 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.

Recommendations Staff 5 nurses Fact or Fiction Visual Track board Increase Training ED Flex Staff Staffing 5 nurses at all times Retraining providers about fact or crap in EC3 Visual trackboard? Continue training or ramp up ED flex staff (nursing pool)

Expected Impact Improved staffing levels Reduced classification confusion Higher patient acceptance Improved flexibility in staffing improve nurse staffing levels Good refresher for providers, they will also all be on the same page High visibility of patients coming into EC3 More available staff who can help in EC3 when its busy (more flexible staffing) Pool of nurses who can help with EC3 but may work in ED (more flexible staffing)

Project Client: Jennifer Gegeheimer-Holmes, Director of Operations of the ED Project Coordinators: Sam Clark, Senior Industrial Engineer Colby Foster, Management Engineer Fellow Key Hospital Staff Members: Renee Havey, Clinical Nurse Specialist Dr. Ben Bassin, Director of Operations for EC3 Sangeeta Vijayagopalan, Administrative Fellow and Interim Division Administrator for Critical Care Vi-jay-go-pal-an

Thank You!