Using a Standardized Cost Master to Study Variation in Hospital Care - a US based study

1. Using a Standardized Cost Master to Study Variation in Hospital Care - a US based study Raj Srivastava, MD, FRCP(C), MPH Associate Professor of Pediatrics, University of Utah School of Medicine Chair, Executive Council, Pediatric Research in Inpatient Settings (PRIS) Network Fellow, Institute for Healthcare Delivery and Research, Intermountain Health Care Activity Based Conference 2013, Sydney, Australia May 15th, 2013

2. Outline • Some of the problems we’re facing in the U.S. Health Care System • A joint venture between a large hospital research network and the CEOs of children’s hospitals • Relevance of pediatric specific methodology and implications for setting a National Efficient Price in pediatric hospital care

3. What are the some of the problems in the US Health Care System?

4. Our health care system is complex

5. We have some waste in health care

6. Delivering the right care to the right patient at the right time is challenging

7. What are some of the pediatric challenges facing children’s hospitals? • What are the important conditions/diseases? • Too much variation in resource utilization for clinical care • Difficult to track outcomes • Disconnect between hospital administration and clinicians and investigators • Where is the data we need to make the right decisions?

8. What are the solutions? • Prioritize conditions • Communicate to stakeholders • Measure variation and track outcomes • Get the right additional data • Include patient-centered outcomes • Incorporate trainees

9. Pediatric Research in Inpatient Settings (PRIS) Network • PRIS is an independent hospitalist research network founded through a collaborative effort of three organizations: the Academic Pediatric Association (APA), the American Academy of Pediatrics (AAP), and the Society for Hospital Medicine (SHM) • Over 700 hospitalists from 86 centers Core Principles

10. Mission • Improve the health of and healthcare delivery to hospitalized children and their families

11. Membership

12. What are the solutions? • Prioritize conditions • Communicate to stakeholders • Measure variation and track outcomes • Get the right additional data • Include patient-centered outcomes • Incorporate trainees

13. Almost two-fold differences in spending across hospital referral region • Explained almost entirely by differences in volume of health care services received by similar patients • Problem with overutilization

14. There is variation in Pediatrics • High variation in resource utilization across children’s hospitals for a variety of inpatient conditions: • Osteomyelitis • Pneumonia • UTI • Appendicitis

15. Overutilization leads to opportunity • In setting of strong evidence base, excessive variation signals a need for standardization of care • Reduce unwarranted variation  reduce costs, improve outcomes • Excessive variation may be a symptom of evidence gaps, and thus signals a need for more comparative effectiveness research (CER)

16. Overutilization leads to opportunity • In setting of strong evidence base, excessive variation signals a need for standardization of care • Reduce unwarranted variation  reduce costs, improve outcomes • Excessive variation may be a symptom of evidence gaps, and thus signals a need for more comparative effectiveness research (CER)

17. Aims To develop a screening methodology for identifying conditions that should be prioritized for CER or QI • Cumulatively expensive and/or • Prevalent • Display high inter-hospital variation in resource utilization • Strong evidence base  QI • Weak evidence base  CER

18. Study Design and Data Source Design: • Retrospective descriptive study Data Source: • Pediatric Health Information System (PHIS) • Hospitals located in 17 of the 20 major metropolitan areas in the US • Detailed billing data from 43 freestanding children's hospitals • Calendar years 2004-2009 • n=3,482,709 admissions

19. Pediatric Health Information System (PHIS) hospitals • Detailed billing data 43 children’s hospitals • 3.5 million admissions from 2004 – 2009

20. What’s collected on each patient encounter in PHIS

21. Creating a clinically meaningful condition grouper Generated lists of ICD-9 CM primary discharge diagnosis codes associated with hospitalizations (inpatients, ambulatory surgery, and observation unit) that accounted for: • 80% of admissions (high prevalence) and/or • 80% of total hospital charges (cumulatively expensive) • 701 ICD-9 CM codes Grouped into 502 distinct “conditions” “Was the initial clinical evaluation and management of the diagnoses are the same across individual codes?”

22. Further Cohort Refinement • Stratified conditions in ICD9 Grouper into • Medical: >80% with no surgical procedure • Surgical: >80% with surgical procedure • Med/Surg: 20-80% with surgical procedure • Excluded admissions for medical condition if unrelated surgical procedure performed • Included only admissions for surgical condition if related surgical procedure performed • Conducted by team of 8 pediatric hospitalists in PRIS Executive Council

23. Variation in unit costs persists using usual health services methods • High variation in unit costs across hospitals  excess noise in analysis

24. Cost Master Index Created a “cost master index”—a standard cost for EVERY CTC code—i.e. every billable item (n>22,000) Use hospital data (charges and RCCs) to calculate item costs in every record Standardized item cost = median of hospital median costs (based on RCC) Recalculate hospital bills: # Units x CMI Cost All costs inflated to 2009 US $ CPI M

25. Standardizing unit costs • Median cost for CBC = $32 • 2 CBC’s: cost = 2 x $32

26. Analysis Identify conditions with extreme variation in inter-hospital standardized costs • Look at overall distribution of cost/admission • Exclude admissions that account for top 1% of hospital admission costs • Define overall quintiles of cost/admission • Compare distribution (by quintile) of cost/admission across hospitals (bin-plot) • Compare range of cost/admission across hospitals (box-plot), adjusted for patient factors • Count number of hospitals that have >30% of admissions in the lowest quintile or >30% of admissions in highest quintile: Summary measure of outlier hospital count • Calculate intra-class correlation coefficient cost/admission across hospitals: Summary measure of inter-hospital variation

27. Results • Conditions • 255 medical • 231 surgical • 16 medical/surgical

28. Example • ACUTE APPENDICITIS WITHOUT PERITONITIS • 540.9 : ACUTE APPENDICITIS WITHOUT MENTION OF PERITONITIS

29. Appendicitis without Peritonitis 99th %ile = $16,836

30. Appendicitis without Peritonitis 20th %ile = $ 4,571 40th %ile = $ 5512 60th %ile = $ 6,502 80th %ile = $ 7,889 Distribution of costs for ALL hospital admissions

31. Bin Plot, Standardized Cost/Admission 7 8

32. Box Plot, Standardized Cost/Admission ICC = 0.19 Out of 50 most prevalent and 50 most costly conditions (77 in total), 26 had ICCs > 0.10 and 5 had ICCs > 0.30

33. Conditions Sorted by Standardized Cost Rank

34. Conditions Sorted by Prevalence Rank

35. Prevalence Rank, Cost Rank, and Variation in Adjusted Standardized Cost Per Admission for 50 Most Prevalent and 50 Most Costly Conditions* * Only conditions with an ICC >0.1 or more than 10 outlier hospitals are numbered

37. Limitations • “Mixed bag” conditions need to be tossed or broken out • No clinical outcomes data: High resource utilization may be warranted if it leads to better outcomes • Residual data issues • Missing billing data • Missing RCCs • Unreasonably high unit count at specific hospitals resulting in high mean standardized cost/admission

38. Conclusions • Novel approach for prioritizing conditions for CER and quality improvement work • Directionally accurate but still requires validation for individual hospitals and specific conditions • Have identified a limited set of conditions that are high cost, high prevalence, and for which there is a lot of variability in resource utilization across hospitals

39. Next Steps • Select conditions for further investigation of extreme variation: “drill down” • Identify and correct coding issues • Identify sources of variation (by clinical category e.g. radiology) • Identify practice patterns resulting in high resource utilization • Explore relationship between resource utilization/practice patterns and outcomes (e.g. complications, readmissions)

40. Implications Relevance of pediatric specific methodology and implications for setting a National Efficient Price in pediatric hospital care

41. Acknowledgements