Phlebotomy. Check-out. Leave clinic. ⢠Clinical reports were available for patients' appointment times from Aug/1/2012 to Jul/31/2013. ⢠Within two observation ...
Evaluating room allocation policies in a cardiovascular outpatient clinic using discrete event simulation
Graduate Engineering and Technology PhD Abstract ID: 191
Vahab Vahdatzad, Jacqueline Griffin, PhD Department of Mechanical and Industrial Engineering, Northeastern University
Modeling patient flow
Abstract Overcrowding in hospitals is an increasingly common scenario that affects patients’ satisfaction, quality of care, staff workloads, and clinical outcome. In order to address and examine overcrowding in a cardiovascular outpatient clinic, a discrete-event simulation model is constructed which focuses on patient flow, appointment room availability, and staffing. A comprehensive model is constructed using feedback from administration and time-study data collection. After testing the model validity, the impact of reallocation of a fixed number of beds among different physicians within a unit is investigated. Pooled room allocation and designated room allocation policies are compared with respect to patient’s wait time, length of stay, and resource utilization. The impact of a hybrid allocation policy, using a balance of both pooled and designated room allocation is also examined. This model may be used to inform real-time room allocation policies that harness the power of real-time location systems in hospitals.
Sample patient flow when a patient needs phlebotomy test after being visited by a physician.
4 5 3 2
4
1
5
Patient flow legend
3
1
6 6
OBJECTIVE: Build a simulation model of to gain a greater understanding of patient flow and to analyze the system efficiency from patient and hospital perspective. • Decreasing patients’ length of stay. • Increasing Medical Assistant efficiency and room utilizations
Waiting area
14 Rooms
9 physician
4
Phlebotomy
EKG machines
4MA
3 EKG
5 6
Check-out
TRIA(0.33,1.20,4.8) TRIA(9,15,54) TRIA(0,1,11)
TRIA(3,5,11) TRIA(0,2,40) TRIA(0,11,50) TRIA(1,3.5,18)
Second observation and model input TRIA(0.5,1.20,2.20) Calculated by model TRIA(2,4,11) TRA(0.75,1.5,2.25) TRIA(1,2,4) TRIA(4,6,11) TRIA(0.5,0.75,1.25) Calculated by model Calculated by model TRIA(1,3.5,12)
30 min 28%
Appointment time variation and probabilities
30 20 10 0 Waiting time before treatment initiation
Waiting for physician
Patient Legnth of Stay
Simulation output
Patient Length of stay division
Legend CI
CI
Waiting area
MA
Physician visit
Check-in
MA Medical Assistant
CO Phlebotomy
0
10
20
30
40 time
20 min 47%
50
60
70
80
Waiting for EKG 10% waiting for Physician 39%
Vital 20% EKG 70%
Physician visit 61%
• All procedures provided by MAs will be performed in designated room assigned to each physician’s patients. Effect of room allocation policy on waiting time 25.0000 20.0000
• Procedures provided by MAs will be performed in any available room. Pooled allocation policy leads
Pooled
Hybrid
7%
Check-in Waiting time Vital set (No EKG) Vitals EKG Prep EKG MA post operation Waiting time for EKG Waiting for Physician Check-out time
Leave clinic
Divisions
Designated
• Clinical reports were available for patients’ appointment times from Aug/1/2012 to Jul/31/2013 • Within two observation period, data are collected for other processes related to patient flow. 60min 15 min 40 min 5% • Modified distributions are used as input to the simulation. 13% First observation
40
Room availability 74%
Data collection
Process
50
Results
MA availability 26%
Resources Medical assistants (MA)
60
Actual data
Examination room
• Patient flow and bottlenecks from clinic perspective • Introducing room allocation policies
Room allocation policies
Physicians
70
CO Check-out
Limited Resources that cause overcrowding Exam rooms
• Simulation model are validated by clinic administrative and staff • We also verified the simulation results using t-test • No significant difference between actual data and simulation results(α = 0.05)
80
Check-in
2 3
Background Conducted at cardiovascular outpatient clinics • 3 clinics with 26 exam room s and over 300 clinicians • Over 45000 visit per year - Although most of the patients are scheduled, clinics are overcrowded and patients have to wait a long time.
Model validation
to less waiting time in order to initiate treatment
• Developing a Discrete-event simulation model • Elaborative details are considered in building the model - Room allocation policies - Physicians and MAs non patient related activities(e.g. Paper works, documentations, room preparation) are considered separately. - Staff schedules are added to the model.
5.0000
Waiting time for examination room
0.0000
• Simulation results can help administrators to predict impact of changing resource levels and cost-benefits tradeoffs. • Adding one physician decrease average length of stay (LoS) significantly, followed by adding one MA. Imapcts of changning bottlenecks' levels on LOS 79 77 75 73 71 69 67 65 63
Current LoS Adding EKG machine Adding MA Adding Physician
Current LoS
•
10.0000
• Although all rooms are assigned to specific physicians based on daily schedules, in case of overcrowding in waiting area, MAs perform EKG and Vitals in any available room to accelerate patient flow and reduce length of stay.
Model building
•
Total waiting time
15.0000
Number of runs: 255 days Clinic closing condition: If all patients have left the system and no paperwork/administration tasks are left for physicians or MAs.
Adding EKG machine
Adding MA
Adding Physician
