Improving Administrative Data for Public Reporting

1. Improving Administrative Data for Public Reporting Anne Elixhauser Joe Parker Michael Pine Roxanne Andrews September 9, 2008

2. Outline • Background and rationale • Summary of two prior studies: • Potential safety events present on admission? • Adding clinical information to administrative data • Problems in POA coding – California example • Screens for detecting these problems • Supporting the enhancement of administrative claims data through state pilots

3. Administrative, or Billing Data Patient demographics (age, sex) Diagnoses & procedures (ICD-9-CM, DRG) Expected payer Length of stay Patient disposition Admission source & type Admission month Charges UB-92 (UB-04) Billing Form

4. 12 States Use AHRQ QIs for Hospital Reporting to the Public Oregon Wisconsin (part of state) Vermont New York Massachusetts Iowa Ohio Utah Colorado Kentucky Texas Florida

5. Limitations of Administrative Data • Lack clinically important information • Limited to ICD-9-CM diagnosis codes • Do not distinguish between diagnoses present on admission (POA) and those that originate during the hospital stay • Questions regarding use of only administrative data for hospital-specific reporting • Inadequate risk adjustment – additional data needed to predict individual patient’s risk of mortality • Concern about penalizing providers with the sickest patients

6. Tension Between Value of Data and Cost of Obtaining the Data • New York and California provide POA coding for diagnoses – now required for Medicare patients and many states will collect for all • Pennsylvania hospitals provided chart-abstracted clinical detail • Hospital concern about costs of medical record abstraction • Electronic medical records not yet poised to provide data efficiently • Exception: Lab data

7. How Often are Potential Patient Safety Events Present on Admission? • Study aimed at using POA information to determine what effect it will have on AHRQ Patient Safety Indicators • Examined face validity of POA coding in two states – California (CA) and New York (NY) • Study reported in … • Houchens R, Elixhauser A, Romano P. How often are potential “patient safety events present on admission?” Joint Commission Journal on Quality and Patient Safety. March 2008.

8. Percent of patient safety events remaining after POA diagnoses were removed* * Based on California data.

9. Impact of Adding Clinical Data to Administrative Data • Assess impact of incrementally adding: • POA codes for diagnoses • Lab values on admission • Increased number of diagnosis fields • Improved documentation (ICD-9-CM codes) • Vital signs • More difficult to obtain clinical data

10. Study Reported in … • Pine M, Jordan HS, Elixhauser A, et al. Enhancement of claims data to improve risk adjustment of hospital mortality. JAMA 2007; 267(1):71-76. • Jordan HS, Pine M, Elixhauser A, et al. Cost-effective enhancement of claims data to improve comparisons of patient safety. Journal of Patient Safety 2007; 3(2) 82-90. • Fry DR, Pine M, Jordan HS, et al. Combining administrative and clinical data to stratify surgical risk. Annals of Surgery 2007; 246(5): 875-885. • Pine M, Jordan HS, Elixhauser A, et al. Modifying claims data to improve risk-adjustment of inpatient mortality rates. Medical Decision Making (forthcoming)

11. Mortality Indicators AAA repair CABG surgery Craniotomy AMI CHF Cerebrovascular accident GI hemorrhage Pneumonia Post-operative patient safety events Pulmonary embolism/deep vein thrombosis Physiologic/metabolic abnormalities Respiratory failure Sepsis Indicators Studied

12. Data Used in Incrementally More Complex Models

13. C-Statistics for Mortality Models

14. pH (11) PTT (10) Na (9) WBC (9) BUN (8) pO2 (8) K (7) SGOT (7) Platelets (7) Albumin (5) pCO2 (4) Glucose (4) Creatinine (4) CPK-MB (4) Numerical Lab Data • Results of 22 lab tests entered at least one model • Results of 14 of these tests entered four or more models:

15. Vital Signs and Other Clinical Data • All vital signs entered four or more models • Pulse (8) • Temp (6) • Blood pressure (6) • Respirations (5) • Ejection fraction and culture results entered two models • Composite scores entered four or more models • ASA classification (6) • Glasgow Coma Score (4)

16. Abstracted Key Clinical Findings • 35 clinical findings entered at least one model • Only three findings entered more than two models • Coma (6) • Severe malnutrition (4) • Immunosuppressed (4) • 14 of these clinical findings have corresponding ICD-9-CM codes (e.g., coma, malnutrition)

17. Summary of Analyses • For some measures, POA coding has a significant impact on whether conditions are considered patient safety events • Administrative data can be improved at relatively low cost by: • Adding POA modifiers • Adding numerical lab data on admission • Improved ICD coding

18. Other Enhancements • Link to vital statistics • Link across settings • Readmissions • Episodes of care Today’s focus: POA and lab data