Using information and technology in the measurement and evaluation … of healthcare processes? : Using information and technology in the measurement and evaluation … of healthcare processes? Michael W. Carter
Health Care Resource Modelling Group
Mechanical and Industrial Engineering
University of Toronto
Outline: Outline Brief intro to Operations Research
A few applications:
Cardiac Bed Planning
I.S. vs O.R. vs Statistical Analysis Brief Intro to O.R.: Brief Intro to O.R. Started during WWII in UK
Quantitative analysis: math, physics, stats
Canadians involved from the start Optimisation in Health Care: Optimisation in Health Care Two main criteria:
average annual cost?
for the particular episode?
quality of life?
Have you ever counted them?: Have you ever counted them? Nuclear Medicine at William Ostler
Endocrinology at the Cleveland Clinc
Medical Imaging at a large hospital OR Scheduling Delays: OR Scheduling Delays Downtown acute care hospital OR suite
Address issues causing delays in turnaround
ORSOS data: two main factors
lack of recovery room beds
“Simple” solution Hospital Patient Simulation: Hospital Patient Simulation 1989: Nursing Crisis in Ontario
Ont. Min. of Health & Five Hospitals
Linda O’Brien-Pallas & Linda McGillis-Hall (Nursing) plus John Blake (IE Dalhousie)
1995: Efficient Use of Resources!
“What if?” Simulation tool
However, some of the results were “simple” Data Challenges: Data Challenges The CIHI discharge report - a mixed blessing
Takes two years to get reliable data!
Stakeholder buy-in and politics CHEO: Emergency Room: CHEO: Emergency Room Children’s Hospital of Eastern Ontario: Ottawa 1993
Paediatric Teaching Hospital
50,000 patient visits per year in the ER CHEO: Waiting Times (1993): CHEO: Waiting Times (1993) CHEO: Emergency Room: CHEO: Emergency Room 20 % of patients wait over two hours
Eleven suggestions by staff
Simulation used to evaluate scenarios
Fast track clinic
New Casualty Officer
Staggered start times Data Challenges: Data Challenges We had high level data (arrival time, prelim diagnosis, ED LOS, triage level, disposition, etc.)
We had zero data about the processes or durations … hard to collect data in ED!
Hard to tell which patient they were discussing
Activities speed up when busy! Cardiology at S&WCHSC: Cardiology at S&WCHSC Fourth year thesis topic
Dr. Eric Cohen, Director, Cardiac Cath Lab
Nadine Kerrigan & Maggie Le
“What is the benefit of one more bed on the Cardiovascular ward?” Data Challenges: Data Challenges Killer – when we finally got LOS data (after months of asking)
… it was in “days” … we needed hours/minutes
Shouldn’t be to hard to track!
Tied to the ancient “midnight census” Slide15: Causes and Relationships of Overcrowding and Waiting in Different Emergency Departments: The CROWDED study MW Carter1, DJT Fernandes1,2, MJ Schull2, GS Zaric3, G Geiger4 1 Healthcare Productivity Laboratory, Mechanical & Industrial Engineering, U of Toronto;
2 Institute for Clinical Evaluative Sciences;
3 Richard Ivey School of Business, University of Western Ontario
4Sunnybrook and Women’s College Health Sciences Centre Background: Background ED overcrowding and waiting - major problem
Most analysis based on LOS data
Statistical models extrapolate the past
A few simulation models - typically model LOS
Does not help us analyze improvements
Wanted to understand what happens in an ED The Hospital Partners: The Hospital Partners Academic
Sunnybrook & Women's
Quinte Health Corp
South Muskoka Community
Royal Victoria - Barrie
Windsor Regional Generalized Model: Generalized Model Build ten individual ED models
Use components to build a general model
Design an interface to allow user to create their own model
Approx. 2200 patients tracked Data Challenges: Data Challenges NACRS does not have detailed info
Docs difficult to track
Used charts or white board as backup
Docs can treat patients “remotely” (orders, tests, chart review, results)
“behind closed doors”
Layout problems Strategic Hospital Planning Model: Strategic Hospital Planning Model Mid 1990’s – 3 year cuts of 18%
John Blake Ph.D. thesis - Mt. Sinai Hosp
Understand relationship between revenues, costs, resources.
Goal Programming formulation Problem Statement: Problem Statement Identify a case mix for physicians that:
Enables the hospital to break even.
Provides physicians with a stable income.
Allows physicians, as much as is possible, to perform their target mix of cases. Two Goal Programming Models: Two Goal Programming Models Volume model:
Fix the cost of each CMG
Determine the case mix that meets targets
Fix the case mix (volume) for each CMG (at current levels)
Determine the cost reductions necessary to meet targets Slide23: Project Results Used during 1996 (plan for 11% cut)
Intuition at hospital:
Retain clinically important services (oncology)
Eliminate “unimportant” services (dental, ENT, ophthalmology)
decrease thoracic, oncology
Thoracic surgery was eliminated in 1997 Simcoe County CCAC: Simcoe County CCAC Services
Meals, bathing, dressing, cleaning, living skills ...
21 Long term care facilities – 1,763 beds Simcoe County CCAC: Simcoe County CCAC Therapies
Occupational therapy (OT)
Speech pathology (SP)
Social work (SW) Total Cost to Clear Wait List: Total Cost to Clear Wait List Estimating Waiting Time: Estimating Waiting Time Queueing Theory: Given the customer arrival rate , and the system service rate , we can analytically compute a number of statistics (expected wait time, expected number of patients waiting, etc.) for each service.
This can be extended to multiple priority queues Monthly Arrival & Service Rates: Monthly Arrival & Service Rates Decision Support Tools: Decision Support Tools Model 1: Given limits on the queue for each priority (in a service) compute the minimum service level.
Model 2: Given a fixed service level, compute the expected wait times. Data Challenges: Data Challenges Data was dreadful (getting better)
Priority changed (1, 1A,1B…) by scheduler
Dates did not agree
Problem: No one was really using the data Western Canada Wait List Project: Western Canada Wait List Project Wait lists are anecdotal!
Plus, every doc has his/her own priority
WCWL has developed standard priority instruments
But, how will that help reduce wait times?
Need to develop models of resources to predict impact on wait times. Cardiac Care Network of Ontario: Cardiac Care Network of Ontario Currently fund 110 surgeries per 100,000 pop.
What would happen to wait lists if they funded 120? 130?
Developing simulation model with CCNO and ICES (Jack Tu) Readings: Readings Operations Research and Health Care: A Handbook of Methods and Applications Series : International Series in Operations Research and Management Science , Vol. 70 Brandeau, Margaret L.; Sainfort, Francois; Pierskalla, William P. (Eds.) 2004, 872 p.