1. Pediatric Hematology, Oncology, BMT & Infusion Patient Flow
Final Presentation
December 16, 2016
IOE 481 Team 8
Andre Lilly
Madeline McCormick
Brendan Roeschel
Alexa Weiser
Maddie
Thanks for being here
Introduce
Clarification questions

2. Introduction Project Focus: Problem: Project Managers:
Patient Flow in Pediatric Hematology/Oncology Clinic and Infusion Center, September - December 2016
Problem:
Poor alignment between patient arrivals and nurse availability
Long patient wait times
Project Managers:
Nicole Haskett, Clinic Manager
Pediatric Hematology, Oncology, BMT & Infusion
C.S. Mott Children’s Hospital
Shelli Anuszkiewicz, Nurse Manager
Pediatric Hematology-Oncology, BMT & Infusion
Project Coordinators:
Kevin DeHority, Lean Transformation Director
Michigan Quality System
University of Michigan Health System
Carali Van Otteren, Industrial Engineer
Program & Operations Analysis
Maddie
Define IOE
Define IOE 481
client/coordinators

3. Overview Process Map and Current State Project Goals and Scope
Methods, Findings, and Conclusions
Recommendations
Challenges and Risks
Discussion about Findings
Maddie

4. Background: Current State
Infusion Clinic peak hours are between 9:00AM and 1:00PM
Patient wait time is between 30 and 45 minutes
Staff unable to have a break or lunch until peak hours end
Patients are consistently scheduled using Primary Nursing
Andre

5. Background: Process Map
Andre

6. Key Issues Patients have significant wait times during peak hours
Staff report feeling overwhelmed by patient flow
Patient volume is unknown at times throughout the day
Patient satisfaction is a concern
Andre

7. Project Goal and Objectives
Primary Goal:
Aligning patient arrivals to nurse availability
Objectives:
Understand the current patient scheduling process
Collect timestamps for Clinic and Infusion Center patient flow
Understand current care model
Identify key variables that affect patient arrival rate
Andre

8. Project Scope Include patients that:
Checked into and out of Infusion Center
Are seen on Mondays, Tuesdays, and Thursdays
Exclude patients that:
Did not receive an infusion
Received BMT or Sickle Cell treatments
Andre

9. Methods
Alexa

10. Patient Flow Form
Collected 53 accurate and complete forms between 10/24/16 and 11/15/16
Alexa

11. Findings: Patient Flow
Distribution of Patient Arrivals at Infusion Center
Peak arrival is between 9:00 and 11:00 am
Alexa

12. Findings: Patient Flow
Proportion of On-time vs. Late Patients
Patients are late as often as they are on-time to their appointments
Brendan

13. Findings: Patient Flow
Late patients wait less than on-time patients
Sample size: 53
Source: Patient flow forms
Data collection period: 10/24/ /15/16
Brendan
St Dev = 42.9
St Dev = 13.2

14. Findings: Patient Flow
Seeing a provider prior to treatment does not significantly delay arrival to infusion center
Sample size: 53
Source: Patient flow forms
Data collection period: 10/24/ /15/16
Brendan
St Dev = 14.5
St Dev = 44.3

15. Findings: Patient Flow
Wait time is independent of the day of the week
Sample size: 53
Source: Patient flow forms
Data collection period: 10/24/ /15/16
Brendan

16. Findings: Patient Flow
Wait time is independent from infusion appointment duration
(0, 156) : Short
(156, 312) : Medium
(312, ∞) : Long
Sample size: 53
Source: Patient flow forms
Data collection period:10/24/ /15/16
Brendan

17. Findings: Interview Data
Primary Nursing
“Patients can wait up to 3 hours to have their infusion appointment due to primary nursing.”
“Primary nursing causes massive delays and is a completely avoidable problem since all nurses and MAs receive the same training and have equal qualifications.”
“Many patients are unaware that they are waiting for a primary nurse.”
Brendan

18. Findings: Primary Nursing
Proportion of Patients who see Primary Nurses
41% of patients are waiting to see a primary nurse rather than seeing the first nurse available
Brendan

19. Modular Nursing
“Team Nursing” - a team of RNs, MAs and aides under the supervision of one nurse leader
Patient care is delivered through the coordination and cooperation of each team member
Assignments are delegated on a shift-by-shift basis with respect to the team member’s level of accountability and availability.
Advantage: Flexibility of team structure lessens consequences of unexpected events (late patients, uncooperative patients, absent nurses etc.)
Maddie

20. Findings: Literature Search
Primary Nursing:
Caring Perception Index:
Mean = 35.9
St Dev = 5.4
N=89
Modular Nursing:
Caring Perception Index:
Mean = 36.2
St Dev = 5.3
N=94
Source: The impact of primary and modular nursing delivery systems on perceptions of caring behavior. US National Library of Medicine. Feb
Maddie

21. Conclusions Variables to Rule Out:
Provider appointment has no impact on wait time
Day of the week has no impact on wait time
Length of appointment has no impact on wait time
Variables to Consider:
Patient arrivals are not evenly spread throughout the day
Half of the patients arrive late to their infusion appointment
On-time patients are currently waiting longer than late patients
Room for improvement in the current care model
Maddie

22. Recommendations
Incentivize patients to arrive on time or to call the Clinic if they are running late
Do not see patients before their scheduled time if they arrive early to their infusion appointment
Shift from the Primary Nursing Model to Modular Nursing Model
Increase communication between Clinic and Infusion Center
Immediate
Impact
Remind patients to arrive on time for their appointments
Distribute surveys to patients to understand why they are arriving late
Delayed
Impact
Alexa
Current patient flow in the Infusion Center has been quantified
Client has a quantitative understanding of why patients must be on time
Proposed recommendations will reduce patient tardiness and balance patient-to-nurse alignment
Switching from Primary Nursing to Modular Nursing will decrease patient wait time

23. Risks
Moving away from primary nursing model could affect patient satisfaction
Past studies indicating that there were no differences in patient satisfaction were done with adults, not children
Patients may not follow rule of calling to let the Clinic know when they are late
Could lead to the Clinic expecting to be warned and this offsetting the schedule further
Alexa

24. Challenges
Many incomplete or incorrect Patient Flow Forms due to change in staffing and lack of communication and clarity with forms
Insufficient amount of time to perfect data collection process
Cannot control or penalize lateness because of sensitivity of treatments
Maddie

25. Next Steps
Continue data collection and make necessary improvements to the process to reduce holes in the data
Create queuing model to predict future state
Investigate communication between Clinic and Infusion Center
Pilot recommendations and continue to quantify wait times to track improvements made
Maddie
Continue data collection bc of holes in data and small sample sizes
Originally we had planned on implementing a queueing model but were unable to without complete data

26. Thank you!

27. Questions?