Presentation on theme: "Lot’s of O.R. in health care!"— Presentation transcript:
1Healthcare Engineering: Quantitative Decision Support Models for the Healthcare Industry Lot’s of O.R. in health care!My bib around 6000 papers just on OR applications to health careMy purpose: to encourage quantitative academics to work in health care ...-and to encourage health care industry to utilize OROR in healthcare is growingMichael W. CarterCentre for Research in Healthcare OperationsMechanical and Industrial EngineeringUniversity of Toronto
2Outline Brief Overview of the Health Care Industry Why do we need engineers?Some application examplesNo assumption about the audience (academic? Healthcare? General public?)I will assume that you don’t know much about health care- interested in applications
3The Importance of Health Care Health care is North America’s largest single industry.Estimated total spending in Canada was $183 billion (CN) in ($2.5 trillion in the US)In Canada, in 2009, $5,452 per person was spent on health care compared to $8,047 in US
4International TrendsCenters for Medicare & Medicaid Services is predicting 17.7% by 2012Before 1971, Canadian & US systems were the sameNote the sharp increase in expense in past three yearsOECD web site: Oct 2007
9Commonwealth Fund Overall Ranking 2007 AUST.CAN.GERN.Z.U.K.U.S.OVERALL RANKING (2007)3.55216Quality Care42.5Right Care3Safe CareCoordinated CarePatient-Centered CareAccessEfficiencyEquityLong, Healthy, and Productive Lives4.5Health Expenditures per Capita, 2004$2,876*$3,165$3,005*$2,083$2,546$6,102* 2003 dataSource: Calculated by Commonwealth Fund based on the Commonwealth Fund 2004 International Health Policy Survey, the Commonwealth Fund 2005 International Health Policy Survey of Sicker Adults, the 2006 Commonwealth Fund International Health Policy Survey of Primary Care Physicians, and the Commonwealth Fund Commission on a High Performance Health System National Scorecard.
10Systemic Hospital Issues: The Four Faces of Health Care* ContainmentCoalitionHealth care is a business, but...Multiple decision makers.Conflicting goals, incentives.Social “good”.No market, no manager.TrusteesCommunityManagersControlInsiderCoalitionStatusCoalitionDoctorsCureNursingCareKey issue in healthcare- Implementation of any project will only occur if all stakeholders win!ClinicalCoalition*Glouberman & Mintzberg, 2001
11The Four Faces of Health Care* The same divisions apply to the overall social health system!Health Authorities InsurancePublic ControlElected OfficialsCommunity InvolvementAcute HospitalAcute CureLTC, PrimaryCommunity Care*Glouberman & Mintzberg, 2001
12Some success stories Ontario Waitlist Forecast System Dynamics: Cardiac SurgeonsMinistry of Health and Long Term Care and the Local Health Integration Networks (LHINs)Cancer Care Ontario: Chemo Therapy CentresSurgical Planning: Orthopaedic
13Ontario Waitlist Initiative Target to reduce wait times to benchmarks for five priority areas:Cardiac, Cataract, Cancer, Hip & Knee Replacement, MRI/CTProblem: How many (cataracts) do we need to do to meet bench mark (90% wait less than 26 weeks) by March 2007?
14Data Requirements for Prediction Current Patient Arrival RateProjected Future Arrival RateCurrent WaitlistDistribution of Patients on Waitlist (Priority)Surgical Volumes (Service Rates)Future Funded Surgical VolumesIf you gave me all of these values, I could tell you how long the wait list will be in the futureIn fact, I don’t know any of these numbers.We even had problems getting precise values for funded surgical volumes (I will elaborate later)In our model, we estimated all of these from
15Observed Waitlist Approximation Cutoff PointUsed an algorithm developed by ICES. (Access Atlas)Using OHIP (01.IV.2001 to 30.XI.2005) and CIHI (01.IV.2001 to 31.III.2005) created datasets that tracked daily identified arrivals, waitlists, surgeries performed.Gave us the arrival rate (using the average of the mean arrival rate) - Also gave us the wait times.For every patient, we know how long they waited (in days) and we know the number of surgeries that were performed between the decision to treat and the surgery. Therefore, we can assume that those people were, on average, ahead of this patient on the wait list.Average wait list was around people. Assumed 34,133 in June 04. The 90th percentile waited for over 100,000 surgeries or over 1 year.Cutoff … Sept 04. Pretty sure almost everyone on list is finished
16In order to estimate the shape of the distribution, we observed that the 90th percentile was consistently around 2.3 times the mean (each week) Varied between 2.1 – 2.5)Therefore, if we want the 90th percentile under 26 weeks, we need the mean under 11 weeks!
17Average monthly pt arrivals (blue) Notice seasonal patterns …dip in summer, Christmas and end of fiscal year (white bars)Yellow – Our forecast model … takes seasonal variation and annual trends into accountOrange – Many patients get both eyes done (one pt., two surgeries)– plus we could not compute a decision date for all pts.Inflated our forecast by 28.7% to get forecast surgery demand rate.
18Recent Ontario Performance Oct./Nov./Dec (all priorities)Hips – 23 weeks (Ont. target 90% in 26 weeks)Knees – 26 weeks (target 26)Cataracts – 16 weeks (target 26)Breast cancer – 5 weeks (target 12)Colorectal cancer – 6 weeks (target 12)Cardiac Bypass – 8 weeks (target 26)MRI – 16.6 weeks (target 4)CT – 7 weeks (target 4)The aggressive cataract model recommended funding an additional 5400 cataract procedures by March 2007, while the conservative model recommended no additional surgeries total knee replacements and 1000 total hip replacements were recommended to be additionally funded before March 31, On September , the McGuinty government announced additional funding to improve access to five key healthcare services as part of its wait times strategy. Among them, 6,100 more cataract surgeries and a combined total of 3,008 more hip and knee joint replacements were announced.By March 31, 2007, wait times were markedly improved from the previous six months (182 days). Cataract surgeries had 90% wait times within 156 days, Hips within 244 days, and Knees within 326 days. Although the number of funded cases was based in part on CRHE’s forecast, the Ministry’s funding announcement was tempered by available capacity, human resources and time constraints. Hospitals were awarded additional funding for surgeries, if they were on target to meet base surgical volumes for the year, and had available capacity. Hospitals were required to maintain current volumes of other surgeries.Not all additional surgeries were carried out. Many hospitals found that they could not meet the target volumes do to resource or personnel constraints. However, a review of the number of actual surgeries performed, arrivals and wait list observations reveals that if the model recommendations had been followed, the 90th percentile wait times for Cataracts would have been 131 days, for Hips days and for Knees days.
20Modeling the Future of Canadian Cardiac Surgery Workforce Using System Dynamics Michael Carter1,Chris Feindel2,Timothy Latham2 & Sonia Vanderby11Centre for Research in Healthcare Engineering, University of Toronto2Canadian Society of Cardiac Surgeons
21In Canada only 5 out of 11 slots were filled in 2009 match
24Study Motivation Will there be a future shortage of surgeons? Specialty selection decisions being made based on current situationCurrent oversupply; unemployed gradsEducation Process > 10 years
25Causal Loop (Influence) Diagram CSCS model also includes feedbackDemand-Supply gap to productivity
26Scenario TestingInitial increase in unemployed pool (grads) –currently in the “pipeline”Reduces enrolmentUnemployed gradually hired, few new grads, surgeon shortage rapidly increases
27Other System Dynamics Projects Alberta Health & WellnessModel for demand for GPs for next ten yearsOntario MOHLTCModel impact of “Aging at Home” strategyModel of mental health strategiesMay 20, 2009Operations Research & Patient Flow
34Cancer Care OntarioHow many medical oncologists do we need in Ontario?Graham Woodward, Adriane Castellino,Matt Nelson & Mike Carter
35HHR Model How are teams of providers configured in chemo clinics? How are responsibilities/tasks distributed among providers? (i.e., Who does what?)Focus on functions that could be performed by more than one type of providerAre there differences among sites? Best practice
36I believe you are familiar with this process diagram Our intention is to expand on this … finer granularity … who does what?Some functions are specific to scope of practice for certain professionals.Other tasks could be carried out by more than one type of person.
37Data Collection Each centre has different people doing the tasks. Need rough estimate of time required for each task by type of patient (expert opinion)Only trying to get a high level sense of who does what to answer questions like:“How many Medical Oncologists do we need at this centre?”Times may be different by centre and by provider type
38Integer Programming Models Given current volume and mix of patients, determine “ideal” provider configuration.Given current set of providers, how many patients can be treated? (% of current volume)How many providers are needed under different models of care?How do sites compare to each other in terms of resource use? (Best Practice.)Models of care: e.g., (Grand River makes use of nurse practitioners?)
39Surgical Planning & Scheduling Sherry Weaver, Daphne Sniekers, Dionne Aleman, Solmaz Azari-Rad, Carolyn Busby & Mike Carter
40Several current projects Western Canada Wait List: Orthopaedic surgeryAlberta Bone & Joint Health Institute: Calgary, Edmonton, WinnipegBone & Joint CanadaGeneral Perioperative SimulationHamilton, UHN, St. Mike’s, Mt. Sinai, William Osler (Brampton Civic & Etobicoke General)Sunnybrook Health SciencesUrgent Ortho & Smoothing Resource Use
41Conclusions Health Care is major industry The current system is not sustainableQuantitative methods (Operational Research) can helpThe health care industry is beginning to recognize the value of systems thinking
42Opportunities for Operations Research? Watch your newspaper:Patient flow → Supply ChainED Wait times → Queueing/SimulationSurgical Wait Lists → Better schedulingInfectious Diseases → Logistics, ModellingHealth Human Resources → Forecasting