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Comparative hospital performance: new data, borrowed methods, more targeted analysis?

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Comparative hospital performance: new data, borrowed methods, more targeted analysis?

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Comparative hospital performance: new data, borrowed methods, more targeted analysis?

  1. 1. Comparative hospital performance: new data, borrowed methods, more targeted analysis? Professor Jonathan Karnon
  2. 2. Why do we measure hospital performance? 1. To ‘sack the Board’  Aggregate, or aggregated performance measure  Under direct control of those being assessed – Costs OR Process indicators  Not so interested in magnitude of difference – Identify general poor performance 2. To inform target areas for quality improvement  Condition-specific  Not necessarily under direct control – Costs, outcomes, and processes  Value of improvement important
  3. 3. Task: Improve population health Option 1: Fund new technologies Option 2: Improve use of existing technologies Show me the money
  4. 4. Increasing interest in quality improvement 0 100 200 300 400 500 600 1983 1988 1993 1998 2003 2008 2013 PubMed"qualityimprovement"in titlepapers Year
  5. 5. Not a new idea “a programme of work not only to identify causes of variation at specific local level, but also to prioritise those variations and causes that have the most important impact on equity, effectiveness, efficiency and patient health outcomes”? Variations in health care: The good, the bad and the inexplicable, The King’s Fund 2011
  6. 6. Outline  Evidence of variation;  The information: costs, outcomes, and processes  The incentives: potential strategies for using the information
  7. 7. Variation in process  RAND Corp (McGlynn et al, NEJM 2003) – 30 acute and chronic conditions and preventive care – 10 to 80% participants receiving recommended care  CareTrack (Runciman et al, MJA 2012) – 22 common conditions – 32 to 86% compliance with appropriate care
  8. 8. Variation in costs Duckett & Breadon, Controlling costly care: a billion-dollar hospital opportunity, Grattan Institute 2014 $ per admission (2010/11)
  9. 9. Variation in outcomes *adjusted by age, sex, indigenous origin and Diagnostic Group Hierarchical Condition Category (HCC) risk score 30-Day Readmission Rates by Area Health Service of Residence (NSW: 1 July 2005 – 30 June 2008)*
  10. 10. Policy/Practice Relevance?  Appropriate processes in theory ≈ cost-effective care in practice? – Costs? Timeliness?  Best outcomes? – Meaningful outcomes? At what cost?  Lowest cost? – With what outcomes?  Which providers are providing cost-effective care, and how are they doing it?
  11. 11. Using comparative condition-specific health service data  Systematic review – Feedback of comparative performance data alone does not work  Anecdotal – Service change works best when common recognition of a problem  Theory – Costs and post-discharge outcomes data demonstrates problems  Despite risk adjustment  ‘my patients are sicker’ syndrome – Process data provides  Additional rationale for the existence of a problem  Starting points for identifying solutions 11
  12. 12. Case study: ED chest pain presentations  Four public hospital in South Australia  Clinical context, underlying diagnosis could be: – ST Elevated MI – Non-ST Elevated MI – Unstable angina – Non-cardiac chest pain  Aims: – Identify benchmark performer(s) on basis of costs and outcomes – Assess the potential value of improved performance at non- benchmark hospitals – Inform targets for investigation – variation in clinical pathways 12
  13. 13. The data  Clinical data extracted from common data warehouse – key procedures, pathology test results, movement between hospital departments and wards, etc. – automated linkages to population-based mortality data.  Administrative data, linked to index events to identify other inpatient separation (episodes) at all South Australian hospitals – age, gender, and postcode (SEIFA), co-morbidities 13
  14. 14. Missing data  ED capacity – ED beds, personnel – Other presentations: rates and severity  Inpatient capacity – Bed occupancy rates  Cardiac  Non-cardiac – Staffing ratios 14
  15. 15. The dependent variables  Costs: Bottom-up patient-level costs available for all inpatient separations  Outcomes: 30 day/12 month related admission (unstable angina, MI, or stroke) or mortality  Process, not quality indicators – Pr(admission) – Time to admission – Pr(PCI | angiogram) – Inpatient LoS 15
  16. 16. The analysis  Separate multiple regression models fitted – Cost, outcome, and process variables  Hospital interaction terms tested – Identify patient sub-groups driving variation  Mean covariate values used to generate predicted outputs – Bootstrapping, stratified by hospital, to represent uncertainty 16
  17. 17. Patient characteristics Hospital 1 Hospital 2 Hospital 3 Hospital 4 Difference (p value) No. patients 1997 1527 2368 2058 Age 60.2 62.5 59.1 57.9 <0.001 Male 0.54 0.53 0.53 0.54 0.768 SEIFA decile 5.4 4.0 6.0 2.6 <0.001 Positive troponin test result 0.17 0.13 0.12 0.11 <0.001 Existing circulatory disorder 0.37 0.33 0.37 0.33 0.002 Cancer 0.03 0.03 0.03 0.02 0.189 COPD 0.03 0.03 0.03 0.03 0.706 Renal disease 0.07 0.08 0.09 0.07 0.398 Diabetes 0.07 0.06 0.09 0.10 <0.001 Dementia/Alzheimer's 0.01 0.02 0.01 0.01 0.716 After hours presentation 0.61 0.62 0.59 0.58 0.082 Weekend presentation 0.24 0.25 0.23 0.24 0.636 17 SEIFA – SocioEconomic Indicator For Areas: 1=lowest decile
  18. 18. Inpatient costs per presenting patient Sub-group N Hosp 4 Hosp 1 - Hosp 4 Hosp 2 - Hosp 4 Hosp 3 - Hosp 4 All patients 7950 $2,868 $42 $630 ($307 to $989) $510 ($299 to $713) Existing circ., Out- of-hours 1706 $4,539 $229 $1,542 ($786 to $2365) $510 ($299 to $713) No existing circ., Out-of-hours 1082 $1,579 -$451 $136 (-$73 to $361) $510 ($299 to $713) Existing circ., In- hours 3050 $4,640 $643 $1,265 ($513 to $2111) $510 ($299 to $713) No existing circ., In-hours 2112 $1,610 -$6 -$91 (-$318 to $143) $510 ($299 to $713)
  19. 19. N Hosp 1 Hosp 2 Hosp 3 Hosp 4 Pr(event) RR (95%CI) 12m readmission or death All patients 7950 0.045 1.59 (1.27 - 1.93) 1.06 (0.80 - 1.35) 1.16 (0.91 - 1.47) Young males 2281 0.043 1.59 (1.27 - 1.94) 1.09 (0.75 - 1.51) 1.16 (0.91 - 1.47) Old males 1979 0.102 1.54 (1.25 - 1.85) 1.50 (1.18 - 1.93) 1.14 (0.91 - 1.43) Young females 1701 0.025 1.61 (1.28 - 1.97) 0.76 (0.47 - 1.20) 1.16 (0.90 - 1.48) Old females 1989 0.086 1.55 (1.26 - 1.87) 1.09 (0.81 - 1.42) 1.15 (0.91 - 1.44) 12m mortality All patients 7950 0.02 1.42 (0.93 to 2.05) 0.82 (0.47 to 1.23) 1.05 (0.83 to 1.2) 12m readmission All patients 7950 0.03 1.72 (1.27 to 2.27) 1.22 (0.84 to 1.66) 1.30 (1.01 to 1.92)
  20. 20. C-E Acceptability Planes value_avoiding_death_000s 0.00 value_avoiding_readmission_000s 50.00 prhosp_3_cost_effective 100.00 0.00 50.00 100.00 0.00 0.17 0.35 value_avoiding_death_000s 0.00 value_avoiding_readmission_000s 50.00 prhosp_1_cost_effective 100.00 0.00 50.00 100.00 0.02 0.39 0.76 value_avoiding_death_000s 0.00 value_avoiding_readmission_000s 50.00 prhosp_4_cost_effective 100.00 0.00 50.00 100.00 0.16 0.56 0.97 value_avoiding_death_000s 0.00 value_avoiding_readmission_000s 50.00 prhosp_2_cost_effective 100.00 0.00 50.00 100.00 0.00 0.01 0.03
  21. 21. Pr(Admitted) N Hospital 1 Hospital 2 Hospital 3 Hospital 4 Pr(admitted) RR (95%CI) All patients 7950 0.77 0.87 (0.83 - 0.91) 0.92 (0.89 - 0.96) 0.92 (0.88 - 0.96) Troponin +ive, Existing circulatory condition 842 0.97 0.99 (0.98 - 1.01) 0.97 (0.94 - 0.99) 0.97 (0.94 - 0.99) Troponin +ive, No existing circulatory condition 216 0.87 0.88 (0.79 - 0.95) 0.89 (0.78 - 0.97) 0.89 (0.78 - 0.97) Troponin -ive, Existing circulatory condition 1946 0.89 0.98 (0.94 - 1.03) 0.97 (0.95 - 0.99) 0.97 (0.94 - 0.99) Troponin -ive, No Existing circulatory condition 4946 0.62 0.71 (0.65 - 0.77) 0.90 (0.84 - 0.96) 0.90 (0.83 - 0.96) 21
  22. 22. Pr(PCI | Angiogram) 22 N Hospital 1 Hospital 2 Hospital 3 Hospital 4 Pr() RR (95%CI) All patients 7950 0.14 2.40 (1.71 - 3.36) 1.79 (1.31 - 2.44) 1.57 (1.14 - 2.15) Troponin +ive, Weekend 279 0.30 2.18 (1.36 - 3.44) 1.07 (0.62 - 1.87) 0.94 (0.54 - 1.53) Troponin +ive, Weekday 779 0.22 2.03 (1.44 - 2.79) 1.22 (0.83 - 1.74) 1.72 (1.26 - 2.28) Troponin -ive, Weekend 1632 0.17 2.76 (1.48 - 4.74) 1.72 (0.96 - 2.93) 0.93 (0.49 - 1.64) Troponin -ive, Weekday 5260 0.12 2.36 (1.56 - 3.41) 2.02 (1.35 - 2.92) 1.90 (1.32 - 2.64)
  23. 23. Length of Stay as inpatient 23 N Hosp4 (hrs) Hosp1 - Hosp4 Hosp2 - Hosp4 Hosp3 - Hosp4 All patients 5373 37.2 7.4 (4.46 - 10.32) 4.5 (0.79 - 8.38) 10.5 (7.58 - 13.42) Troponin +ive, Out- hours presentation 561 56.4 3.3 (-7.28 - 14.39) 18.0 (2.52 - 34.19) 16.3 (5.84 - 27.24) Troponin +ive, In- hours presentation 325 51.1 7.0 (-3.44 - 17.45) 13.8 (-1.55 - 29.42) 15.2 (4.33 - 26.11) Troponin -ive, Out- hours presentation 2702 33.8 6.5 (3.29 - 9.52) 3.8 (-0.13 - 7.87) 9.9 (6.50 - 13.03) Troponin -ive, In- hours presentation 1785 35.3 10.2 (5.88 - 14.53) -0.4 (-5.02 - 4.19) 8.7 (4.76 - 12.46)
  24. 24. How to compare larger numbers of providers?  Stratify by clinical process and identify best performing strata? – Hypothesis driven? – Process mining driven?  Group hospitals by performance and compare processes? – Empirical stratification
  25. 25. Informing action  “Publicising the existence of unwarranted variations and their causes does not guarantee that they will be tackled.”  “local health organisations… be required to publicly justify and explain in a consistent way their relative position on key aspects of health care variation.  …it may also be necessary to explore the development of harder- edged, locally focused incentives to encourage action to deal with unwarranted variation.” Variations in health care: The good, the bad and the inexplicable, The King’s Fund 2011
  26. 26. The incentives 26 1. Sticks a) Public Reporting b) Mandated action plans 2. Carrots a) Pay-for-Performance? b) External Services Improvement Fund  Mindful of the NHS Improvement Fund…  1b + 2b – Externally identified areas for improvement – Externally reviewed and supported applications to fund improvement projects
  27. 27. Prioritisation criteria 27  Expected Value of Removing Variation (EVRV) – Hospital 2, 1527 patients per annum – Costs per patient could reduce by $630 – Mortality could decrease by 1% (x $200,000?) – Readmissions decrease by 2% (x $50,000?)  Annual EVRV = 1527 x $(630 + 2000 + 1000) = $5.5 million  x5? x10?
  28. 28. Zombies and Paradoxes • “political paradox of rationing” • “the appeal for transparency in medical decision-making is like a zombie, an idea that refuses to die despite its limited utility” • Do the benefits of open and explicit quality improvement outweigh the:  Financial costs and Political risks of unrealistic expectations? Oberlander et al, Rationing medical care: rhetoric and reality in the Oregon Health Plan, CMAJ, 2001; 164(11):1583-7
  29. 29. Summary 29  Huge scope for service evaluation and improvement – Electronic data systems – Linkage facilities  Post-improvement evaluation: ICER estimates to inform the – Design of future improvement processes – Balance of spending on new technologies and existing services
  30. 30. Acknowledgements  Clarabelle Pham, Andrew Partington, Orla Caffrey, Jason Gordon, Brenton Hordacre, David Ben-Tovim, Paul Hakendorf, Maria Crotty  Funders: SA Health, NHMRC, HCF Foundation

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