Public Reporting of Long Term Care Quality: The US Experience

1. Public Reporting of Long Term Care Quality:The US Experience Vincent Mor, Ph.D. Brown University

2. Background • Long history of scandals regarding long term care quality, particularly nursing homes • While preference for and supply of “community based” alternatives have grown in US, all acknowledge residentially based long term care must be part of any system • Home Health less scrutinized but many worry about care adequacy since hard to inspect

3. Background • Institute of Medicine Report in 1987 served as basis for nursing home reform also adopted by home care • Uniform Resident Assessment Instrument created in 1991 and became the basis for the creation of performance measures designed to stimulate quality competition through public reporting • Home Health Outcome and Assessment Information Set (OASIS) emerged independently

4. Background (cont.) • Using RAI Nursing Home Quality Measures tested, revised and published as “Nursing Home Compare” since 2002 • More recent efforts to create composite measure incorporating Inspection results, Staffing Levels and Quality Measures have been widely promulgated • Home Health Quality Measures developed and tested and published as Home Health Compare since 2004

5. Purpose • Summarize US Experience with Development of Long Term Care Quality Measures • Review Conceptual and Technical Issues Facing the Construction of Long Term Care Quality Measures • Review Literature on Effects of Public Reporting of Quality Measures in Long Term Care

6. The Nursing Home Resident Assessment Instrument (RAI) • 1986 Institute of Medicine Report on Nursing Home Quality Recommended a Uniform RAI to Guide Care Planning --MDS • OBRA ‘87 Contained Nursing Home Reform Act Including RAI Requirement • A 300 Item, Multi-Dimensional RAI Tested for 2 Years • Mandated Implementation in 1991

7. Clinical Planning Basis of the MDS • Assessment Profile in Given Domain “Triggers” Potential “Risk” Status • Resident Assessment Protocol Reviewed to Determine Presence of Problem or High Risk of Problem • Care Planning and Treatment Directed to the Problem • Data Quality Contingent upon conduct of Clinical Care Planning Process

8. MDS Background • MDS Version 2.0 Introduced in 1996 • Admission, Short Term and Quarterly Reassessments done on all Residents • Inter-State Variation with some requiring additional data • Since 1998 all MDS records are computerized and submitted to Centers for Medicare & Medicaid

11. MDS: Putting Practice into Research

12. CMS Quality Measures • “The quality measures, developed under CMS contract to Abt Associates and a research team led by Drs. John Morris and Vince Mor, have been validated and are based on the best research currently available. These quality measures meet four criteria. They are important to consumers, are accurate (reliable, valid and risk adjusted), can be used to show ways in which facilities are different from one another, and can be influenced by the provision of high quality care by nursing home staff.” CMS Web Site

13. CMS Quality Measures - Long Term • Percent of Long-Stay Residents Given Influenza Vaccination During the Flu Season • Percent of Long-Stay Residents Given Pneumococcal Vaccination • Percent of Residents Whose Need for Help With Daily Activities Has Increased • Percent of Residents Who Have Moderate to Severe Pain • Percent of High-Risk Residents Who Have Pressure Sores • Percent of Low-Risk Residents Who Have Pressure Sores • Percent of Residents Who Were Physically Restrained • Percent of Residents Who are More Depressed or Anxious (Looks back 30 days) • Percent of Low-Risk Residents Who Lose Control of Their Bowels or Bladder • Percent of Residents Who Have/Had a Catheter Inserted and Left in Their Bladder • Percent of Residents Who Spent Most of Their Time in Bed or in a Chair • Percent of Residents Whose Ability to Move in and Around Their Room Got Worse • Percent of Residents with a Urinary Tract Infection (Looks back 30 days) • Percent of Residents Who Lose Too Much Weight (Looks back 30 days)

14. Physical Functioning- October/December 2009

15. Psychotropic Drug Use- October/December 2009

16. CMS Quality Measures – Short stay • Percent of Short-Stay Residents Given Influenza Vaccination During the Flu Season • Percent of Short-Stay Residents Who Were Assessed and Given Pneumococcal Vaccination • Percent of Short-Stay Residents With Delirium • Percent of Short-Stay Residents in Moderate to Severe Pain • Percent of Short-Stay Residents With Pressure Sores

17. Home Health Quality Measurement • OASIS began as a cooperative effort between home health agencies and researchers to develop simple “outcome” measures to track patients’ rate of improvement while in care • University of Colorado researchers worked with large Visiting Nurse Services to develop and test • CMS then funded multiple large demonstrations to implement the tool and use for quality measurement and case-mix reimbursement

18. Outcome Based Quality Improvement • Distinct measures of change in patient functioning, resolution of symptoms and ability to manage independently collected at the start and “end” of care (OR every 60 days) • Most Medicare home health is short term • Measures tested and revised with extensive case mix adjustment to allow for comparison across agencies and states

19. Risk-adjusted Home Health Outcome Report for Improvement of Activities of Daily Living • EXAMPLE: • Percent of Patients in Home Health Care whose ability to [Groom, Bathe, Dress Upper and Dress Lower Body] themselves improves between start of care and discharge

20. CMS OASIS Report – 2009Rates of Improvement in ADL

21. Risk-adjusted Home Health Outcome Report for Utilization Outcomes • Percent of patients who have received emergency care prior to or at the time of discharge from home health care. • Percent of patients who are discharged from home health care and remain in the community • Percent of patients who are admitted to an acute care hospital for at least 24 hours while a home health care patient.

22. Risk-adjusted Home Health Outcome Report

23. Conceptual Issues Inherent in Applying Quality Indicators • Requires “shared” interpretation of Quality • Assumes all Providers have same goals • Assumes Measured Quality Domains are Important • Indicators are NOT Quality per se, BUT often used as evidence in and of themselves • Assumes Facilities Accountable for most of the variation in the Indicator (e.g. outcomes) • Assumes Facilities Know how to Change Practice

24. Technical Issues That Can Compromise Validity of QI’s • Reliability & Validity of the data • Multi-dimensionality of Quality & Indicators • Stability of Estimates Sensitive to Sample Size • Ranks can Overestimate Differences • Patient Level Risk Adjustment Complex • Differences in Assessment Practices Influence QI Scores & Comparisons

25. Reliability Studies: NH • 219 of 462 (47.4%) facilities approached chose to participate in full study (52.4% for HB and 45.6% for non-HB); • Non-participants were more likely to be for-profit, less well staffed and with more regulatory deficiencies • 5758 patients (ave. 27.5/facility) included in reliability analyses; • 119 patients assessed twice by research nurses • Patients resemble traditional US nursing home patient

26. Reliability of “Gold Standard” Nurses • Of 100 items, only 3 didn’t reach Kappa>.4 • 50%+ items had Kappa >.75 • Pct. Agreement high even for ordinal items with variance

27. Reliability of Facility RNs to “Gold Standard” • Of the 100 data items 28 had Kappa <.4 and 15 had Kappa >.75 • Worst Kappa items were rare binary items like “end stage”, didn’t use toilet, recurrent lung aspirations, etc. • ADLs and other Functioning items had Kappa values above .75

28. Reliability of Constructed Quality Indicators: NH • Quality Indicators are composites of several RAI items; a definition of the denominator and of the conditions required to meet the QI definition • The inter-rater reliability of a QI is a function of the reliability of all the component items defining the algorithm

29. Avg. QI Prev rate Facility Ave SD of QI Prev rate Ave Kappa for Items used in QI % Agree Resch & facility RNs on QI QI Kappa Behavior Problems High & Low Risk Combined .20 .10 .71 89.8 .61 Little no activities .12 .12 .28 65.3 .23 Catheterized .07 .05 .71 92.5 .67 Incontinence .62 .13 .88 91.4 .78 Urinary Tract Infection .08 .05 .53 89.1 .45 Tube Feeding .08 .05 .73 98.1 .83 Inadequate Pain Management .11 .08 .85 86.5 .87 Prevalence and Inter-Rater Agreement and Reliability of Selected Facility Quality Indicators  [N=209 homes]

30. Facility QI Reliability Variation: Bladder/Bowel Incontinence

31. Facility QI Reliability Variation: Inadequate Pain Management

32. Reliability Studies: Home Health • Fewer inter-rater reliability studies of OASIS • More expensive to send two nurses at separate times on the same day to do the same assessment • Largest Reliability Study done as part of research to develop case-mix reimbursement system • ADL and other function items yield high levels of reliability; symptoms achieve “ok” reliability

33. Selected Inter-Rater Reliability Results from OASIS test

34. OASIS Reliability Results: Function

35. Validity of the Data & Measures • Validity of the data shown by the extent to which items and measures behave as expected relative to “gold standard” variables or “hard” outcomes • Compared MDS diagnoses to Hospital discharge diagnoses • Looked at MDS predictors of survival • Related to MDS measures to research scales

36. MDS vs. CMS Hospital diagnoses • Neurological Cerebrovascular disorders (ICD-9: 432, 434, 436, 437) • PPV = 0.73 Parkinson’s disease (ICD-9: 332) • PPV = 0.86 Alzheimer’s disease (ICD-9: 331) • PPV = 0.68 Brain degeneration (ICD-9: 331.0, 331.2, 331.7, 331.9) • PPV = 0.84

37. One Year Survival by Gender & Cognition Level Women (CPS 2-4) Men (CPS 0-1) Months

39. Construct Validity: Cognitive Performance Scale & Correlates • Cognitive Performance Scale (CPS) Derived from 5 MDS Items • Strong (>.85) Correlation with MMSE • High Kappa with Global Deterioration Scale (.76) • Percent Patients with Dementia Increases as CPS Declines • MDS Communication Correlated (.85) with MMSE • ADL, CPS Symptoms & Select Diagnoses Related to Survival

40. Sample Size and QI Stability • Providers and Consumers want QI to reflect not just what WAS but what WILL BE; SO • QI stability is desired • QI must be based upon minimum # observations • Correlation between quarters among QIs varies • Correlation among prevalence based QIs is high because same individuals assessed each quarter • Correlation between quarters among incidence and change based QIs lower and VERY sensitive to sample size

41. Residents’ Expected Rates of Change on Quality Indicators • Over 90 day period 77.1% of residents still in facility do not change on ADL, 14.7% decline and 8.2% improve. • Over 12 months 58% of residents in home don’t change and 30.2% decline. • Similar pattern for Communication, Cognition and individual ADL items • Means that rates of decline are low and many residents are needed to estimate a home’s rate of ADL decline with confidence.

42. Facility Size Number Residents Decline Estimate Residents Expected to Decline 20th Pctile Expected Residents Declining 80th Pctile Expected Residents Declining 20 Beds 12 1 <1 1 30 Beds 16 1 <1 3 50 Beds 28 2 1 4 80 Beds 45 4 2 6 100 Beds 56 5 2 7 150 Beds 83 7 4 11 200 Beds 117 9 5 14 Estimated Sample Size for Change

43. Long Term Predictability of Quality

44. Quality Fluctuation: Seasonality

45. Transforming QI Scores into Ranks • Many QI score distributions are skewed; many facilities with little or no problem and few facilities with many residents experiencing the problem. • Median facility might be very similar to the “best” (the one with fewest problems) • Transforming to ranks means saying there is a difference between the 10th and 40th percentile when there is little difference

46. Pressure Ulcer Prevalence Facility Distribution: Meaning of Ranks

47. Variability in Ranking Distributions Anti-psychotics: Median Ranks Persistent Pain: Median Ranks

48. Complexity of Determining Appropriate Risk Adjustment • Risk Factors May not be Measured Independent of the Provider (tx) Effect • Potential for Over Adjustment as Great as Under Adjustment • How to Adjust for Socio-Economic Differences Known to Affect Health Behavior or Clinical Characteristics (e.g. PU not “seen” on African American NH pts until at Stage 2 OR Pain Harder to “see” in Cognitively Impaired & Oldest pts)

49. Risk Adjustment Complexity

50. Why Adjust QIs • Facilities should be compared on ‘level playing field’, acknowledging differences in • Types of residents admitted • Ability to ameliorate clinical characteristics thought to predispose to poor outcomes irrespective of care quality • Variability in measurement acumen of assessors