Methodological and statistical consulting to policy makers in health care finance

1. Methodological and statistical consulting to policy makers in health care finance Jules Ellis Radboud University Nijmegen

2. My style • Listen • E.g. Stork manager • Determine who advise and who decides • Compare advice to students • Ask the substantive context • People often start in abstract statistical terms • (E.g. is X correlated to Y -> gender is related to aggression) • Change the question • To something that can be done (toolbox) • To something that you think will be interesting for the client (E.g. is group program correlated with well-being -> does a group program influence well-being) • Offer alternatives • E.g. PCA versus ADF

3. Be pragmatic • Time, money • Prior concepts (e.g. clusters vs. factors groups vs scores) • Prejudice • There must be 7 clusters • Dichotomization is good / bad • Nobody/ everybody is doing this • Client must understand it within available time • Explain at right level (common sense) • Write • After consult (compare physician) • After analysis • Educate

4. Organizational hierarchy

5. Tasks in the hierarchy

6. Old cost model • Depends on location of client • Nursing homes: € x per client / day • Assisted Living Facilities (Caring homes): € y per client / day • Home Health Care (Extramural care) € z per client / day • Adult Day Care Services

7. Limitations of old cost model • Ignores client differences in need • Ignores overlap in client populations • Emphasis on somatic care • Supply driven, not demand driven

8. Time line • Pilot: 10 homes (2000 clients) • Test: 100 homes, automating • Benchmark 1: 1000 homes • Benchmark 2: 1000 homes

9. Assessment of client needs • Two central caring employees observe client during two weeks • Both fill in one questionnaire of 24 items about client • Two sum scores are computed: • Sumlic: Somatic need for care • Sumpsy: Psycho-social problems

10. Items for client needs

11. Assessment of caring time • Pilot: Observers make one round every 20 minutes (-> chaos) • Later: Employees receive a handheld computer • Beeps every 20 minutes, at random moment, whole week 16 hour / day • Employee records own behavior in one of 32 actions, + client • Total caring time in various categories per client per day is estimated from this

12. Direct Client Related Individual Client Related General daily life support (eat, wash, …) Assistence in preparing food and/ or drink Individual treatment Communication with family Housekeeping for individual Preserved actions Collective Client Related Collective medication Collective treatment House keeping for group Indirect Client Related Individual Client Related Coordination for individual Collective Client Related Coordination for individual Organization Related Waiting Break Education Travelling Employee related Missing Personal time Items for caring time

13. Categories: Client related Direct Individual Collective Indirect Individual Collective Organization related Employee related Functions: Housekeeping services Personal care Nursing Supportive assistance Activating assistance Treatment Time categories

14. Other data • Clients • 24 services • On which days present • Membership of collective time groups • Employees • 35 professions • contracts • On which days present

15. Analysis scheme

16. Question 1:Cluster the clients on basis of needs • 7 or 8 clusters • Homogenous in time and cost • Include psychological needs • Applicable in Nursing Homes and Assisted Living Facilities. • “Recognizable”

17. Answer to question 1 • Select items on inter observer agreement, and variance > 1 • Factor analysis on the items -> 2 factors • Define 2 subscales: somatic and psycho-social. • Reliability analysis (internal consistency) • Divide the scores of each subscale in 4 quartiles (Light, Medium, Serious, Very serious) • Result: 4 x 4 = 16 groups

19. Reasons • Cluster analysis -> only somatic factor • We have already 7 groups for nursing homes alone, which were formed as 3 x 3 groups and combining some of these. • The partition on the somatic axis should contain at least these 3 groups (to satisfy the nursing homes) + another one to accomodate the Assisted Living Facilities. • Similarly for the psychosocial axis. • So we need 4 x 4 groups to begin with -> 16 types • Without time & cost data we cannot know how to combine these groups and respect homogeity. • But perhaps we can combine some groups later.

20. First evaluation • Initially accepted with reluctance • As of 2006: There are still 16 client types • It took years to accept that

21. Question 2: Compute reference times • Compute mean individual client time (ICT) for each client type • The mean can be used in the cost model, e.g. to compute the total ICT that the home should deliver. • How reliable is this?

22. First answer to Question 2 • Using client types is an unnecessary loss of information • Conduct a multiple regression analysis, with dependent variable = ICT, independent variables = somatic need, psychosocial need • Compute the predicted ICT score for each client • Take the mean of the predicted scores for each home. • This was not accepted: Too difficult (3 parameters?!); there had to be groups.

23. Second answer to Question 2 • Use answer 1 but change the presentation a little • Compute the predicted ICT score for each client • Compute the mean of the predicted scores for each client type in each home • Compute the mean in each home

24. example

25. First evaluation • Sceptical first, embraced the idea later • Reliability of estimates • More consistent output • Employees in nursing homes typically argue that their client type X is a little more serious than in other homes • Reply: Indeed, and therefore we didn’t use the mean time of other homes but corrected it. • Negative beta-weight of psychosocial problems • Assistance needed in further research • Assistance needed automation

26. Question 3: How reliable are the time measurements?  = 153 min t = 6 * 20 min x = 6, n = 10, p = 0.6

27. Existing answer • Why this formula? (the method was bought) • Sometimes extreme large n needed. Why? • How many weeks observation are needed?

28. Answer to question 3 • Make a distinction between absolute unreliability (AU) and relative unreliablity (RU) • Require only that AU is small. • For example, if t = 1  0.5 minute, then RU = 50% but AU = 30 sec (who cares)? • The average time can be reliable even though the composing times are unreliable. • Distinguish also other forms of unreliability (sampling of clients, homes, weeks)

29. (Details) • Note that t = pT, where T is the total observation period (fixed). • AU(p) = 0.5 length of confidence interval for p • RU(p) = AU/p • AU(t) = AU(p)*T • RU(t) = RU(t)

30. First evaluation • Relevant people understood and accepted the idea immediately (why?) • One week of observation would be enough if every week is the same • Helped to convince nursing homes

31. The aftermath • Elections • Sector wide model (including handicapped, psychatric patients, etc.) • Association of nursing homes refused to make the data available to others • PWC -> KPMG, VLP -> ? , CCC ->  • 16 Client types -> 9 Care Heaviness Profiles • Reference times -> Indication times

32. Conclusions • Prior concepts and prejudices are persistent • Large deviations from this will be rejected • Small deviations are accepted if they are explained at common sense level • Write it down with relevant examples • “A good method is a method that makes my life easier”?

33. Methodological and statistical consulting to policy makers in health care finance Comments by Marieke Timmerman, University of Groningen, The Netherlands