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Waitlist Management in Nova Scotia: Policy and Practice

Waitlist Management in Nova Scotia: Policy and Practice. John T. Blake, Peter VanBerkel, Matthew Campbell. Department of Industrial Engineering Dalhousie University PO Box #1000 Halifax, NS B3J 2X4 CANADA. Nova Scotia. Nova Scotia Canada’s 2 nd smallest province (55,000 km 2 )

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Waitlist Management in Nova Scotia: Policy and Practice

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  1. Waitlist Management in Nova Scotia:Policy and Practice John T. Blake, Peter VanBerkel, Matthew Campbell Department of Industrial EngineeringDalhousie UniversityPO Box #1000Halifax, NS B3J 2X4CANADA

  2. Nova Scotia Nova Scotia • Canada’s 2nd smallest province (55,000 km2) • About 950,000 people • Principle cities: Halifax (310k) and Sydney (110k) • Principle industries: Government services, finance, retail, manufacturing, forestry • About 5800 km from Wroclaw • About 60 south of Wroclaw

  3. How’s The Weather? Wroclaw - Winter 2005 www.pbase.com/tygrys50/your_favorite Halifax - Winter 2005

  4. Healthcare in Nova Scotia • 43 hospitals • 1 adult tertiary (on two sites) • 1 paediatric • 9 regional/mid-size centres • 2000 physicians • 700 medical residents and interns • 60% on fee for service • Budget of $2.6 billion ($Can) • Provides tertiary services to patients from 3 Atlantic provinces.

  5. Problem Statement • Perception amongst docs & patients that wait times for elective procedures are long. • There has been a lot of “buzz” around wait times in Canada in general and in Nova Scotia. • There have been some recent rumblings in the press and policy areas about wait times. • More recently, there has been a supreme court ruling that may (or may not) change the nature of the CHA.

  6. Some Anecdotal Evidenceon Wait Lists CBC (Online Edition): Jan 23, 2003 www.npdcaucus.ns.ca

  7. Recent supreme court ruling ties wait time to charter freedom Appears to allow the introduction of private insurance or services Could signal a major shift in Canadian health care Most provinces have adopted wait time or access management commissions The Latest Wrinkle… Canadian Medical Association June 9/05

  8. Finally Some Action?Federal Advisor on Wait Times • Under a funding “bump” in 2004, the provinces and the feds agreed to some interesting mechanisms to manage waits. • The central idea is that research is required to understand the root causes of waits and cost-effective methods of resolution • Areas for review include: • Development of benchmarks for access, suitable to the Canadian context • Develop criteria for appropriateness • Identify the nature and causes of wait times, including physical capacity, process flow efficiency, spatial-geography issues, and barriers to care • Use operational research (!) to improve productivity and quality • Examine the impact of organizational design, policies, and incentives on wait times • Look at the impact of the media on the perception of wait times

  9. So what do we know about waits? With rare exceptions, waiting lists in Canada, as in most countries are non-standardized, capriciously organized, poorly monitored, and (according to most informed observers) in grave need of retooling As such, most of those currently in use are at best misleading sources of data on access to care, and at worst instruments of misinformation, propaganda, and general mischief McDonald, Shortt, Sanmartin, Barer, Lewis and Sheps (1998)

  10. Positives Equity: Time is more equally distributed than cash Broadly seen as equitable within a societal context Discourages consumption where social costs outweigh social benefits Negatives Masks mismatches between supply and demand Those who receive may not be most deserving Efforts to stratify by need subject to capriciousness System can gamed; wealthy more able to access care or bypass system Rationing by Wait List:Are Waits Always Bad? Wait times represent a non-price form of rationing healthcare

  11. Summary of Canadian Wait List Initiatives • Medical community views “wait list” initiatives as: • Registry of patients waiting for surgery • Prioritization scheme for ranking patients • Management tool (i.e. data base) • Since Canada lacks IT infrastructure to collect objective data, self-reported survey data is commonly used • Most effort (and cash) has been expended on prioritization or triage tools • The understanding of the need for objective data and an underlying conceptual model for wait times is just developing

  12. Registry Methods: BC A 2005 audit found ~8,000 of 68,000 patients either redundant, double counted, dead, or no longer in need of surgery

  13. More Policy QuestionsCan We Believe What we See? • Are wait lists inherently biased? • Physician induced demand: Docs may have an incentive to over prescribe, particularly if funded on FFS • Backwards bend supply curve: Longer wait lists seen as a sign of prowess • Your money or your life: Inflating wait lists may be a way to secure additional funding or hospital resources • Double counting: Patients may be double counted, dead, or no longer in need of services • Gate keeper: Ultimately physicians, not patients, make decision about who is/is not on wait list

  14. Which Numbers are Correct?

  15. The Dilemma: Getting bang for your Buck • Even if we spend more, there is an inconsistent relationship between spending and performance metrics • Latent Demand (or “A built bed is a filled bed”): • As we provide more resources, barriers to entry are lowered • Wait time, typically, decreases • This allows the procedure to be more widely prescribed • Thresholds for appropriateness drop • Gradually the system returns to its congested state • A number of Fraser Institute reports suggest that wait time is not correlated with increased spending • Increased institutional spending actually increased wait • Wait was seen to decrease with increased physician spending • Similar findings are reported in the UK

  16. International Experience: UK • The UK has perennially had issues with wait time • Over the past five years, however, wait for elective procedures has dropped • Reductions appear to be in response to a fiat on maximum waiting times • There is some indication that long waits have decreased, but average waits are largely unchanged Source: King’s Fund Trust

  17. The Operational Research Perspective: This is an easy problem! Isn’t it?!? • People have been studying line ups for about 100 years. • Much of the original work was done in relation to telephone switches. • With some assumptions we can fully define the operation of a queue with three or four pieces of data: Server Rateμ(Customers/Hour) Queue Size(usually infinite) Arrival Rateλ(Customers/Hour) Queue Discipline(usually FIFO) Number of Servers (s)

  18. Some Basic Results from OR

  19. The $64k Question:Why Isn’t OR in Greater Use in Healthcare? • Timing/Project Cycle: • Simulations typically take a long time to build and validate • Issues tend to be “front burner” for institutions • Cost • Simulation requires specialist knowledge & software • Data Availability: • It isn’t • IT systems are designed for clinical and administrative purposes; patient flow hasn’t been a design issue • In the Canadian context, process management is seen as administrative overhead.

  20. Why Ask Us? • Like most places, Nova Scotia currently lacks complete data to make an accurate determination of wait • It does have integrated billing and discharge data and is relatively compact • We’ve been asked to look at efficiency aspects of access • We do have some experience in orthopaedics • I’ll talk about some of our work in DI, surgery, and a provincial model CDHA Ortho (’04)

  21. Invest in efficiency improvements first Streamline and simplify the process Develop patient centred care Reduce no-shows Make DHA’s accountable for improving timely access to care Adopt evidence based decision-making Measure clinical and administrative outcomes Manage access to services better Standard triage tools Centralized wait lists Communicate access data with the general public Invest in IT strategies Increase capacity only when efficiency gains have been exploited. Develop integrated health human resources plans Nova Scotia Access Plan

  22. DI Issues • Some of our contributions have been logistical in nature • Implemented better data collection methods • Adapted QC tools for analysis • Some of our contributions are in the area of models • DEA analysis of providers • A general rant on the 3rd available appointment slot(ongoing) • The province now uses this tool to identify institutions that are out of control • Results are reported back to managers and institution CEOs • Implemented quarterly meetings with DI managers to review results

  23. Diagnostic Imaging DEA Study • There are 36 providers in NS – 2 tertiary; 9 regional; 27 rural • We run separate analysis for each band • Potential inputs include • Budget √ • Staff √ • Numbers & types of machines • Potential outputs include • Number and types of tests √ • Workload units √ • We produce both efficiency scores & comparator institutions • We think we are the first people to apply DEA across DI departments within a province • Implementation of the CCRD-I model with constant returns

  24. DEA Example: Rural Units and institutions coded

  25. DEA Results • Tertiary – not enough sites for meaningful analysis • Regional – Identified a single institution as benchmark • Rural – A bit more difficult • We did identify two institutions in one DHA, managed by the same team that shows up as efficient on most subsets of inputs and outputs • Implementation • Oddly, DI managers are a bit reluctant to talk efficiency – especially in front of the province • Would really just like more money (If I’m efficient does that mean I won’t get a new MRI machine?) • However, we are in the process of cleaning up data issues and establishing a benchmark procedure

  26. Surgical Issues • Our contributions are largely in the area of models • Evaluation of guaranteed waits for surgery to meet federal benchmarks • An analysis of general surgery at the QEII (the largest hospital in the province) • The development of a general acute care model for all hospitals in the province

  27. Guaranteed Waits • A number of ideas have been floated to deal with the impact of the supreme court ruling on waits. • One of the more popular idea is the “guaranteed wait” • Patients would be separated into three broad bands. • After a fixed amount of time patients would be “upgraded” into the next higher band • Some plans call for an automatic jump to the top band • Is this likely to be an effective policy?

  28. Guaranteed Waits With Guarantee Without Simple M/M/s model with an assumed exponential decay for utility We conclude that guaranteed waits may result in greater lost utility – should address capacity issues up front. Guaranteed waits are particularly dangerous if ρ > 1

  29. General Surgery Wait Time • A discrete event simulation model using • Modular design elements • Self building concepts • Excel interface to model elements • Data elements derived from three local sources • Subject to a substantial level of cleaning and organizing • Validated against a two year data sample for: • Occupancy rate • Expected wait time • Patient LOS

  30. General Surgery: Bottleneck Analysis • Two-way design to look at factors limiting patient flow • Analysis shows that beds, rather than OR time is the limiting factor • However, system is sensitive to reductions in OR time • Analysis showed a number of process issues – turn around being the most obvious

  31. Provincial Flow Model: Objectives Conventional wisdom claims that the ED is backed up because inpatient beds are used by people who should be in nursing homes. There has been a renewed call in the province for greater LTC beds We have been asked to determine best bang for buck in terms of resources. Should we invest in: • Long term care beds • Acute care beds • Emergency services • All three And if so, in what proportion? Is a system wide fix required, or do local conditions dictate local approaches

  32. We are developing a simulation model of the entire province This is probably unique in Canada The model will runs on a DAD abstract for 2004/05 We have detailed models of acute care, with simple extensions for LTC and (eventually) ED A phased approach to model building, testing, and development will be necessary We have developed a Phase 1 model (right) and are now working on extensions. The model is based on ARENA templates to reduce coding and repetition Project Methodology

  33. Provincial Model: Data Items • Of the 98,000 discharges only 2253 had any ALC days • ~88,800 ALC days in the province out of 820,000 inpatient days • ~138,000 days consumed by patients who ultimately end up with an ALC day Institution names obscured

  34. Is ALC the Only Factor? This chart suggest that while ALC bed days are an issue, so too are “conservable” days

  35. Nevertheless LTC Admissions are tight Overall, only 3 patients per day can be transferred from Acute Care to LTC in NS

  36. Some Interesting Notes • This is, to the best of my knowledge, the only system wide in existence in Canada or anywhere. • We are modelling at a high level, but the framework is very flexible and easily extended • The model should be seen as an evolutionary entity – we are starting simple and building up confidence and capability in the model and its results

  37. Phase 1: Model • A single bed complement for each acute care facility • A single patient type with a common LOS distribution • All ALC patients transfer to LTC facilities • Assume a single LTC facility for each DHA • LTC facilities take admissions from community and acute care institutions • All model widgets are “self-contained” instances of a generalized process

  38. Phase 1: ValidationHow do we test the model is working? • We test the averages for: - Arrival rates - Inpatient length of stay - Transfers to LTC using standard statistical tests (t-test) and compare model results against samples from DAD • We also test variance (σ2) using standard statistical techniques (χ2test) • We have no problem in reproducing admission numbers and appropriate lengths of stay.

  39. Now for the bad news… • Having completed a 1st model, we know our admissions are correct • We know that the LOS is correct • However, our bed utilization numbers are too low • In almost all instances, our model does not show a bottleneck • Reasons could include • Home care excluded • OR time excluded • Inadequate patient categorization • Fluctuations in bed availability over the year • Transfers between institutions

  40. Patient disposition is also an issue • Interestingly, 26% of ALC bed days are consumed by patients who ultimately go home.

  41. Issue: Appropriate Patient Types • There are statistically significant differences between med/surg and all other admission types • Mental health, in particular, has a very long LOS • We’ve decided to eliminate neonates – triggered by maternal admissions

  42. Issue: Admit Category(All significant except Mental Health)

  43. Issue: Transfers Out ofProvince A B H C G D Other E F Rehab Homecare NursingHome

  44. Phase II • Home Care • We have added extensions to simulate home care • We are collecting and validating capacity and length of stay data • Acute Care • We are implementing the model on a DHA by DHA basis • This allows us to vet assumptions and include local conditions • We have added capacity for different patient types and admission categories • We are now working to establish appropriate bed numbers and include surgical process capacity over time • Long Term Care • One of our more difficult jobs at present is validating LOS assumptions

  45. Future Plans • Validate acute care modules • Expand model to include ED • Expand to model specific services (i.e. Ortho) • Include a specific widget to represent OR time and master surgical schedule • “Package” simulation widgets • Develop a platform for local use of simulation models • Target date: January 2007

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