Michael Carter

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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 e-mail: carter@mie.utoronto.ca


Outline Brief intro to Operations Research A few applications: ED Simulation Cardiac Bed Planning Strategic 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: Minimize Cost per visit/episode? average annual cost? Maximize Quality 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 cleaners unavailable “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! Multiple surgeries 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”


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 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 Kingston General Sunnybrook & Women's London HSC Rural Quinte Health Corp Stevenson Memorial South Muskoka Community Royal Victoria - Barrie Sudbury Regional Markham-Stouffville 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) Missing data “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. Mathematical model 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 Cost model: Fix the case mix (volume) for each CMG (at current levels) Determine the cost reductions necessary to meet targets


Project Results Used during 1996 (plan for 11% cut) Intuition at hospital: Retain clinically important services (oncology) Eliminate “unimportant” services (dental, ENT, ophthalmology) Model recommendations: increase dental/eye/ENT decrease thoracic, oncology Thoracic surgery was eliminated in 1997

Simcoe County CCAC: 

Simcoe County CCAC Services Nursing Therapies Personal Support Meals, bathing, dressing, cleaning, living skills ... Placement Services 21 Long term care facilities – 1,763 beds

Simcoe County CCAC: 

Simcoe County CCAC Therapies Occupational therapy (OT) Physiotherapy (PT) Diet/Nutrition (NUT) 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 Missing data 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 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.

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