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BAMS 580B

BAMS 580B. Class 1 October 27, 2010. Canada Health Act - Principles. Public Administration Health care insurance plans are to be administered and operated on a non-profit basis by a public authority. Comprehensiveness

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BAMS 580B

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  1. BAMS 580B Class 1 October 27, 2010 www.chcm.ubc.ca

  2. Canada Health Act - Principles Public Administration Health care insurance plans are to be administered and operated on a non-profit basis by a public authority. Comprehensiveness The health insurance plans of the provinces and territories must insure allhospital, physician and surgical-dental health services. Universality One hundred percent of the insured residents of a province or territory must be entitled to the insured health services on uniform terms and conditions. Portability Residents moving from one province or territory to another must continue to be covered for insured health care services for up to three months. Accessibility Insured persons have reasonable access to hospital, medical and surgical dental services unimpeded by charges or discrimination on the basis of age, health status or financial circumstances.

  3. Canadian Health Care BackgroundCIHI – Health Care in Canada 2006 In 2005, Canada spent $142 billion on health care or $4,411 per person This represents approximately 10% of Canada’s GDP 30% is spent on hospitals; 17% on retail drugs 1.5 million people work in health care 1 out of 10 Canadians work in health care Nurses and Physicians are the largest groups The workforce is rapidly aging

  4. International ComparisonsOECD - 2005 data Source: Stats.oecd.org

  5. Health System Challenges Reducing wait times Meeting the increased health care demands of an aging population Replacing an aging and diminishing workforce Using costly new technologies and therapies appropriately Delivering high quality and safe care

  6. Five questions • How do we know whether patients are flowing well? • Why does it matter? • How can we improve flow? • How do we know that what we did improved flow? • How do we maintain our improvements? www.chcm.ubc.ca

  7. Why is health care OM different than usual OM?

  8. “If you’re not keeping score, you’re just practicing” Vince Lombardi

  9. Metrics • Queue lengths • Waiting times • Percent who meet target • Patient Satisfaction scores • Staff Satisfaction scores www.chcm.ubc.ca

  10. Marty’s Levers • Manage Demand • Manage Resources • (Manage Efficiency) Use Resources Better www.chcm.ubc.ca

  11. Derek’s Equation • Variability + Fixed Capacity + High Utilization = Trouble www.chcm.ubc.ca

  12. Scheduling Unplanned Arrivals:Appointment Scheduling

  13. Topics The Appointment Scheduling Game Measuring Wait Times The “Capacity equals Demand” Fallacy Levers for Managing Capacity Operations Research and Surge Capacity Intelligent Scheduling What We Are Doing Now Concluding Comments

  14. Appointment Scheduling Game +

  15. The Appointment Scheduling Game Provide a timeline and process diagram for the scheduling clerk’s task. What is realistic and what is unrealistic about this game? What scheduling rule are you using? How is it performing?

  16. Metrics • Demand by color • Lateness • Percent served in time by color • Days late • Capacity Utilization • Wait time by color

  17. Scheduling Rules • First Come First Served • Reservation Policy

  18. Appointment Scheduling Game – More Questions • What will be tomorrow’s demand? Next week’s? • How should you set capacity? • What levers do you have to regulate this process? • In what situations is this type of scheduling relevant? • What information are you keeping track of? • What does this assume about appointment lengths and start times? • What if the system is unavailable on a given day? • What if an extremely urgent case has to be slotted in ASAP?

  19. Return to game – measure performance +

  20. Appointment Scheduling Game Issues Quantifying performance Forecasting demand Urgency classes and criteria Scheduling rules Surge capacity Scheduling within a day Breakdowns/Cancellation by Provider No Shows by patients Simulation

  21. Performance Metrics • Patient Wait Times • How to measure? • Averages • Min, Max; Percentiles • Service levels • Capacity Utilization • Overtime • ?

  22. Challenges in measuring wait times Patients are not homogeneous Different priority classes of patients face different wait times. Wait times, as currently measured, do not tell the whole story. Wait Time equals the length of time between when request for service and service delivery. Ignores upstream process steps and delays.

  23. Challenges in measuring wait times Averages do not tell the whole story Wait times vary between patients, over time and between sites. Performance measures must account for variability Wait time distributions are skewed. Recommended Metrics “Proportion of patients of a specific priority class who receive the service within a specific clinically desirable time” These provide meaningful guarantees to decision makers and system users. Reliable and complete wait time data is often not available Variability and performance cannot be determined

  24. Scheduling GameSimulation Results

  25. Simulation Output is Variable

  26. Policy Comparison

  27. Simulation Summaries

  28. VGH CT Scanning Study 28 www.chcm.ubc.ca

  29. VGH CT Scanner Questions Determine whether an additional scanner was needed and if so, where should it be located? Define and measure current waiting times for CT scans at VGH Identify system bottlenecks and inefficiencies Identify strategies for eliminating current backlogs and compare the short term and long term costs and benefits of each Propose ways to expand analyses to other sites and explore improvements in booking and centralized planning 29 www.chcm.ubc.ca

  30. Step1: The appointment system 30 • Process • Requisition arrives • Info – scan type and urgency • Clerk assigns date • Clerk contacts patient • Clerk records date • We will investigate management of appointment systems like this in depth this afternoon! www.chcm.ubc.ca

  31. Data Challenges 31 • How do we determine if the system is performing well? • Performance Metrics • Urgency Levels • What data is required? • Time stamps • Requisition received • Scan completed • Upstream measures • Where do we get it? • Databases • Appointment systems • ? www.chcm.ubc.ca

  32. More on Data 32 • Perspective vs. Retrospective data • Perspective – from now going forward • Based on appointment data • Retrospective – from now going back • Based on scan date • Historical • Complete records • What are the strengths and weakness of each type of data? • What we did - Obtain booked requisitions at the end of each day • Copy them • What are the shortcomings of the approach we used? www.chcm.ubc.ca

  33. Sample Wait Time Data Clients Jul-04 Jul-05 Jul-06 Mar-04 Mar-05 Mar-06 Mar-07 Jan-04 Nov-04 Nov-05 Nov-06 Jan-07 Nov-03 Jan-05 Jan-06 May-04 May-05 May-06 Sep-04 Sep-05 Sep-06 Calendar Time 33

  34. Data Summary - Outpatient Waiting Time Outpatient Categories OP1 OP2 OP3 Recommended WT < 1 wk < 2 wks < 4 wks (RWT) Actual WT 1.6 3.6 6.3 Average (wks) 6.6 10.4 13.9 Max 0.0 0.0 0.1 Min 42 86 103 Sample Size % scanned after 50.0% 68.6% 74.8% RWT CT Wait Times for Outpatients at VGH (Priority OP1: < 1 week) 20 Scheduled after 1 week: 50.0% 15 Frequency 10 5 0 0 1 2 3 4 5 6 7 Weeks CT Wait Times for Outpatients at VGH CT Wait Times for Outpatients at VGH (Priority OP2: < 2 weeks) (Priority OP3: < 4 weeks) 20 20 Scheduled after 2 weeks: 68.6% Scheduled after 4 weeks: 74.8% 15 15 Frequency Frequency 10 10 5 5 0 0 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Weeks Weeks 34 www.chcm.ubc.ca

  35. Step 2: the scanning process 35 • Key steps • Check in • Get ready • Enter room • Scan • Check scan • Out of room • How do we assess its performance? • What data do we need? • How do we get data? www.chcm.ubc.ca

  36. On Site Observations - VGH Scanners are not being used efficiently; capacity is wasted 36 www.chcm.ubc.ca

  37. Possible impediments to flow • System starved • No patient available • Why? • Porter delays • No exams scheduled during tech lunch breaks • Excessive time scheduled for an exam • Outpatients arrive late • Outpatients do not arrive (no shows)l • System congested • Exam times too short • Patients not picked up • Reformatting of images • Maintenance • Difficult IVs 37 www.chcm.ubc.ca

  38. Outcomes of study Added dedicated porter to CT area Management accepted idea of overbooking Hired lunch time technologist Commissioned in-depth study of porter services at VGH VCHA hired several grads Generated further research Allocation of capacity between outpatients and inpatients Jonathan Patrick’s PhD Dissertation on scheduling 38 www.chcm.ubc.ca

  39. Scheduling Rules

  40. Scheduling Rules • Earliest available slot • a.k.a. first come first served • Reserve capacity • For most urgent cases • For each class • Patrick - Puterman rule • Fill tomorrow • Then book as late as possible without exceeding target • If exceed target, use overtime or surge capacity • Based on complex stochastic optimization model • Markov decision processes

  41. “Current Practice” – Simulation results 53% of OP1, 36% of OP2, 25% of OP3 booked late!

  42. Reservation Policy – Simulation Results Cost: 21% of OP3 demand overtime, 50% of booked OP3 late.

  43. Patrick Puterman Rule – Simulation Results Cost: 1.5% of OP1 demand removed from the queue but only at special times

  44. Comparative Results

  45. Policy Insights In a system where demand is close to capacity, the judicious use of a small amount of overtime coupled with intelligent patient scheduling can meet wait time targets Overtime gives the resource manager the ability to deal with spikes in demand Without this ability, once the system is behind, it can’t catch up This reduces the need for excess base capacity Booking demand later and later merely compounds the problem Best to address the problem directly through the judicious use of overtime

  46. Forecasting Demand

  47. Forecasting Demand • What is the arrival rate per day? • How variable is it? • Two ways to find this • Theory (at least for dice) • Empirical or data driven • Means or medians • Standard Deviations and Quantiles • What data to use

  48. Forecasting Forecasts are necessary for effective decision making Forecasting, planning and control are interrelated Forecasts are usually (almost always) wrong Quantifying forecast variability is as important as determining the forecast; it is the basis for decision making. Rare events happen and can have significant impact on forecasts Scientific methods improve forecasting “Don’t predict the future, invent it!” Alan Kay

  49. Quantitative Forecasting methods Naïve: Last Period or Same Period Last Year Regression Extrapolation Causal Exponential Smoothing Simple Trend / Damped Trend Holt-Winters ARIMA models Simulations These require software and analyst expertise

  50. Data Patterns

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