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Data Makes the Difference! SAS for Decision Support in Continuing Care Kevin Druhan Continuing Care Nova Scotia Departm

Data Makes the Difference! SAS for Decision Support in Continuing Care Kevin Druhan Continuing Care Nova Scotia Department of Health. Agenda. Overview of Continuing Care in NS SAS reports for decision-making Planning Policy / Program Evaluation Performance Management.

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Data Makes the Difference! SAS for Decision Support in Continuing Care Kevin Druhan Continuing Care Nova Scotia Departm

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  1. Data Makes the Difference!SAS for Decision Support in Continuing CareKevin DruhanContinuing Care Nova Scotia Department of Health

  2. Agenda • Overview of Continuing Care in NS • SAS reports for decision-making • Planning • Policy / Program Evaluation • Performance Management

  3. A Snapshot of Continuing Care in Nova Scotia • Provide services primarily for seniors • Home Care • Long-Term Care • Protection for Vulnerable Adults • Funded by NS Provincial Department of Health • Currently provide service to about 21,000 clients. • 6,600 residents living in Nursing Homes, Residential Care Facilities, and Community-Based Options. • Continuing Care Strategy released in 2006 • 10-year plan to enhance and expand Nova Scotia’s continuing care system • More LTC beds, new programs, program expansions, increased entitlements…

  4. Evidence-Based Decision-Making • Strong need to use evidence to allocate provincial health resources most effectively • Resource allocation • Evaluation of current programs • Understanding how operational activities contribute to client outcomes. • Access to Continuing Care Services (Nursing Homes, Home Care services) has become a key political issue in Nova Scotia • Limited investment in Continuing Care capacity • LTC wait times have increased dramatically • Healthcare system financially unsustainable in Nova Scotia! • Efficiency must be improved to provide same quality care at lower cost • First wave of baby-boomers retiring in 2011 The Challenge How can we use data to make the best decisions about how we fund health care services?

  5. Data Source – ‘SEAscape’ • Continuing Care is very information-intensive business! • Computerized Single Entry Access system implemented province-wide in 2002 (SEAscape) • Laptop-based application used by 250+ Care Coordinators, Intake, and Placement staff daily to manage client access to Continuing Care Services. • Built around electronic RAI-HC Assessment tool • Data housed in a centralized database. • Contains comprehensive information about all Continuing Care clients and the services they receive. • SAS used to extract data directly from Oracle database tables for reporting. Referral Assessment Home CareService Plan LTCWaitlist • Referral through 1-800 phone line • Client assigned to a Care Coordinator (Case Manager) • Case Worker assesses client: • Functional status • Cognitive status • Case Worker develops a plan for care, eg. • bath 2x per week • housekeeping biweekly • If LTC required, client care level determined, then client goes on LTC waitlist

  6. SAS Reports for Decision-MakingCase Studies

  7. 1. PlanningLTC Bed Planning • Background • Continuing Care Strategy announced 1320 new beds by 2015 • 832 new beds by 2010 (~70$ million) • How many LTC beds should be built provincially? • What level of care is needed in different areas of the province? • Where in the province are beds needed most? Decision Points

  8. Provincial Resource Allocation Strategy DeterminedStakeholder TenaciousStakeholder Provincial resources ($)up for grabs

  9. LTC Bed Allocation Approach • Population projections (75+) • Frailty (Resource Intensity Weighting) • Current continuing care services • Long term care bed planning ratios Continuing Care Planning Model Health Districts • Current / historical LTC waitlist information • RAI-HC demographic / client ID data • Home Support utilization data • Alternate Level of Care (Acute) data Model Validation • Consultation with district and service delivery staff • LTC demand / referral patterns • Program / staffing, capital and operational costs, integration with existing services Counties Refining bed allocations to the local context Communities

  10. Differences in Senior’s Frailtyby Health District

  11. Example – DHA 1 (South Shore) Bed Distribution

  12. LTC Bed Allocation by County (Census Division) 0 152 22 82 25 0 0 142 3 112 71 118 12 0 115 0 40 12

  13. 1. PlanningDiscussion • Evidence-based methodology provided a defensible framework for allocating LTC beds • SAS used for data extraction,integration of data from many different sources, and analysis • Strong stakeholder buy-in with results publicly announced Feb 2007 • Advocacy and political factors were managed more effectively • High-impact project ($70 million) promotes the use of health data for evidence-informed decision-making in NS • Improve transparency and accountability in planning • Facilitate discussions around ‘Why?’ • Other jurisdictions interested in adopting the Nova Scotia approach • View a more complete description of the methodology on the web: https://www.gov.ns.ca/health/ccs/ccs_strategy/Sector_Briefing_2007.pdf

  14. 2. Policy / Program EvaluationClient Reassessment Policy • Background • Policy • Clients should be reassessed every 90 days by their Care Coordinator • Idea was that if a client wanted to move to a long-term care facility (eg. nursing home), we would always have an up-to-date picture of the client to provide to the facility (prepare room, evaluate needs, etc) • What percentage of CC’s are following the policy, and where is policy not being followed? • Should policy be changed? Decision Points

  15. 2. Program EvaluationDiscussion Policy clearly not being followed!

  16. 2. 90-Day Reassessments – DHA 8 • In DHA 8, during the period Jan 2005 – July 2006: • Of the 178 clients who required 2 reassessments • 68%(121 clients) had no reassessments • 24% (42 clients) had only one reassessment • Only 8% (15 clients) had the required two reassessments! • Similar results in other health districts • Conclusion • Reassessments were frequently not done every 90 days, and often not for much longer periods of time!

  17. 2. Policy / Program EvaluationDiscussion • Result of analysis • New policy • Clients must now only be reassessed 90-days prior to admission to a LTC facility. • How do we operationalize the new policy? How do we know when a client is likely to be placed soon? • Use approach in SAS to calculate a ‘window of opportunity’ for likely placement. Decision Points

  18. 2. Policy / Program EvaluationDiscussion • Calculate median wait times for each LTC facility • Determine an estimated date of placement for each client based on the wait time for their preferred LTC facility. • Subtract 90 days from estimated placement date to obtain ‘assessment window start date’ Date client placed on LTC waitlist Mar 13, 2007 + Median wait time For LTC facility 319 days = Estimated LTC placement date Jan 26, 2008 - Pre-admissionAssessment Timeframe 90 days Assessment Required Oct 28, 2007

  19. 3. Performance ManagementCare Coordinator Work Activities • Background • District Managers and Supervisors of Care Coordinators requested support to better understand and evaluate the activities of their staff • Goal was toidentify and develop reports that would support management decision-making at the operational level

  20. Understanding workload How might this be used? • Approximate the number of initial assessments that need to be performed monthly. • Help establish a ‘baseline’ number of assessments that must be completed. • Provide support for additional FTE.

  21. Understanding workload • In DHA 9, January and June are consistently busy; October is slower. How might this be used? • Could be used to plan training, professional development, scheduling vacation…

  22. Caseload Distribution How might this be used? • Provide support for additional FTE

  23. Active Clients per Care Coordinator 2. Understanding caseload distribution

  24. 4. Measuring staff activity

  25. Monthly MDS-HC Assessments per Care Coordinator 4. Measuring staff activity Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

  26. Caseload Distribution • Report estimates caseload intensity by identifying 3 likely indicators of increased case management time* (Cognitive Performance Scale >1, Behavioral CAP triggered, IADL Difficulty Score >2) How might this be used? • When combining this information with the number of active clients, completed service plans, and completed assessments, it gives a better picture of a staff case activities. • Also generate for each care coordinator to estimate their caseload intensity? *based on research undertaken at Manitoba Centre on Aging

  27. 3. Performance ManagementDiscussion • SAS reports enables managers to make informed decisions around management of staff • Discussion point for staff performance appraisals • Monitoring workload and state of office • SAS program can be saved and re-run at any time in the future • results easy to duplicate • macros • Easy to send quarterly reports

  28. Conclusion • SAS is a very versatile, powerful tool for analysis and reporting in government. • Only basic SAS procedures required for most reports (grade 8 audience)! • PROC FREQ – how many clients are xxxx? • PROC MEANS – what does a typical client’s experience look like? • PROC TABULATE – make it pretty for me! • Presentation is important! • KISS principle (KeepItSimple for Public Servants) • Having good evidence is critical to make the best decisions and most appropriate use of provincial resources

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