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What happens to all that data? Role of the ACAP National Data Repository

What happens to all that data? Role of the ACAP National Data Repository. Yvonne Wells Lincoln Centre for Research on Ageing. Content. What is the NDR? How the data are handled What the data are used for Data quality How can data quality be improved?. What is the ACAP NDR?.

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What happens to all that data? Role of the ACAP National Data Repository

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  1. What happens to all that data? Role of the ACAP National Data Repository Yvonne Wells Lincoln Centre for Research on Ageing

  2. Content • What is the NDR? • How the data are handled • What the data are used for • Data quality • How can data quality be improved?

  3. What is the ACAP NDR? • Commissioned and funded by DoHA • Currently housed at Lincoln Centre, La Trobe Uni • Since late 2003 • Reports directly to DoHA • Quarterly • Annually • Ad hoc • The destination for all data collected on ACAP assessments • Australia-wide • 200,000 records per year

  4. Flow of information Individual assessments ACAT data (Team level) Evaluation Units (State-level) State departments NDR (National) Department of Health and Ageing

  5. How big is your state’s share? 0.5% 3% 1% 9% 8% 35% 17% 26%

  6. The quarterly cycle (Qs 1 – 3)

  7. The quarterly cycle (Q4)

  8. So what? • Delays at any stage prevent the NDR keeping its contractual commitments to DoHA • Delays in data entry matter • ACATs can fix their data between the first and updated downloads • Numbers • Quality • Do you get to see the validation reports?

  9. What’s in the validation reports? • Errors • Clearly wrong • Relatively easy to correct • e.g., dates in the wrong order, non-existent codes • Changes • NDR makes sure that all missing fields are filled • e.g., for assessor profession • Warnings • Inconsistencies between data fields • Not so easy to see what is right • e.g., carer availability and carer co-residence

  10. The report lists: • N records • N records validated • N errors • N changes • N warnings • N records for each team • List of errors and warnings by team • List of errors with team and client ID • List of warnings with team and client ID

  11. An example (Q2, Tas): 1

  12. An example (Q2, Generic): 2

  13. Example (Q2 various): 3

  14. Why are the data important? • The data are used by • ACAP Managers • State government • Commonwealth government (DoHA) • To chart performance • Against KPIs (nationally and state-level) • In some jurisdictions, against targets (ACAT-level) • To inform policy • To answer ministerial questions

  15. Some examples • Routine monitoring • assessment numbers • timeliness • Examining the effects of change in policy • Ad hoc data queries

  16. Assessments, ACAP, 1995-96 - 2006-07

  17. Monitoring assessment waiting times • Critical information for all levels of management • Focus of program performance • COAG 2006 initiative to improve performance

  18. Mean days to face-to-face contact

  19. What is the impact of policy changes? • Structural reforms (1997-98) • Introduction of MDS v2 (2003) • Change in policy regarding low-to-high level clients (2004)

  20. Impact of policy changes Recommendations for all clients MDS v2 Low-to-high Ageing in place

  21. Some recent ad hoc requests • Approvals for young clients, 2007 • Number of out-of-catchment assessments, 2007 • Indigenous clients, Feb 2008 • Critical to identify Indigenous status where possible • Duration of assessment by ACAT, location and priority, March 2008 • Data quality report, May 2008

  22. Comparison of Indigenous clients

  23. Duration of assessment, Other loc, by Priority Median days from referral to delegation date, by jurisdiction

  24. Data quality 1: Updates • Use of new items and codes • Victoria, Tasmania and ACT had implemented all required changes by Quarter 2, 2007-08 • including care coordination data • Other jurisdictions have some way to go

  25. Data quality 2: Validation • Comparison of validation reports (Q1 and Q2) with previous year • Most jurisdictions downloaded more records • Both the number and the proportion of fully validated records increased

  26. Data quality 3: Missing data • Very little missing data in sets from Tas and WA • Most states >1% missing data on • Indigenous status • Priority category • Respite care used & Government services used • Recommended assistance with activities, Recommended government services, & Recommended respite • Missing includes UTD and incorrect use of NA as well as missing values

  27. Data quality 4: Incorrect use of NA • Correct coding for • Living arrangements • Carer availability • Government services at assessment • Respite care at assessment • Current assistance with activities depends on the client’s Usual accommodation setting • For community clients, NA should not be used • For clients in residential care, NA should be used • <1% incorrect coding on all the above for Vic, Qld, WA, Tas

  28. Data quality 5: Care Coordination

  29. Impact of the e-ACCR Heidelberg Blacktown Swan * Pro-rated for the same size data set

  30. The e-ACCR is coming • What can you do? • Prepare • Improve data quality in the interim • Correctly complete the e-ACCR • Get it right the first time • We can help by providing • Detailed reports on data quality • Explanation of errors/warnings • Support for EUs

  31. Lincoln Centre for Research on Ageing Australian Institute for Primary Care La Trobe University Victoria 3086 +61 3 9479 3700 aipc@latrobe.edu.au

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