Improving Administrative Data for Public Reporting (Text Version)

Slide Presentation from the AHRQ 2008 Annual Conference

On September 9, 2008, Anne Elixhauser, Joe Parker, Michael Pine, and Roxanne Andrews, made this presentation at the 2008 Annual Conference. Select to access the PowerPoint® presentation (330 KB; Plugin Software Help).

Slide 1

Improving Administrative Data for Public Reporting

Anne Elixhauser
Joe Parker
Michael Pine
Roxanne Andrews
September 9, 2008

Slide 2

Outline

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

Slide 3

Administrative, or Billing Data

The slide shows a sample copy of a UB-92 (UB-04) Billing Form.

• Patient demographics (age, sex).
• Diagnoses & procedures.
• (ICD-9-CM, diagnosis-related group [DRG]).
• Expected payer.
• Length of stay.
• Patient disposition.
• Admission source & type.
• Admission month.
• Charges.

Slide 4

12 States Use AHRQ Quality Indicators (QIs) for Hospital Reporting to the Public

The slide shows a map of the United States with the following states highlighted:

• Vermont
• New York
• Massachusetts
• Ohio
• Kentucky
• Florida
• Wisconsin—part of state
• Iowa
• Texas
• Colorado
• Utah
• Oregon

Slide 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.

Slide 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

Slide 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.

Slide 8

Percent of patient safety events remaining after POA diagnoses were removed*

The bar graph measures the percentage for "Anesthesia cx," "Respir. failure," "Accidental puncture," "Hemorrhage," "Metab. Derang.," "Sepsis," "Pneumothorax," "Infection," "Foreign body," "Transfus. rxn," "PE/DVT," "Hip fx," and "Decub. ulcer." The graph shows "Anesthesia cx" with the highest percentage at 100%. The percentages for the others in the list slowly decrease, with "Decub. ulcer" showing the lowest percentage at approximately 12%.

Slide 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.

Slide 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).

Slide 11

Indicators Studied

• Mortality:
  • Indicators.
  • Abdominal aortic aneurysm (AAA) repair.
  • Coronary artery bypass graft (CABG) surgery.
  • Craniotomy.
  • Acute Myocardial Infarction (AMI).
  • Ccongestive heart failure (CHF).
  • Cerebrovascular accident.
  • Gastrointestinal (GI) hemorrhage.
  • Pneumonia.
• Post-operative patient:
  • Safety events.
  • Pulmonary embolism/deep vein thrombosis.
  • Physiologic/metabolic abnormalities.
  • Respiratory failure.
  • Sepsis.

Slide 12

Data Used in Incrementally More Complex Models

The table shows the "Types of Data Elements" for each "Model."

• ADM-8: Age, sex, principal diagnosis, up to 8 secondary diagnoses, selected surgical procedures.
• POA-8: ADM-8 + diagnoses present on admission.
• POA-24: Increased secondary dx to 24.
• POA-ICD: POA-24 + secondary dx present on admission in clinical database, but not reported using ICD codes.
• LAB: POA-24 + laboratory data.
• LAB-ICD: POA-ICD + laboratory data.
• FULL: LAB-ICD + vital signs + lab data not in LAB (e.g., blood culture results) + key clinical findings abstracted from medical records + composite clinical scores (i.e., ASA Classification).

Slide 13

C-Statistics for Mortality Models

The slide shows a graph with a vertical axis, average C-statistics, going from 0.76 to 0.90 and a horizontal axis citing various models: ADM-8; POA-8; POA-24; POA-ICD; LAB; LAB-ICD; and FULL.

Slide 14

Numerical Lab Data

• Results of 22 lab tests entered at least one model.
• Results of 14 of these tests entered four or more models:
  • 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).

Slide 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).

Slide 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).

Slide 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.

Slide 18

Other Enhancements

• Link to vital statistics.
• Link across settings.
• Readmissions.
• Episodes of care.

• Today's focus: POA and lab data.