Screening Administrative Data To Assess the Accuracy Of Present-on-Admission Coding Michael Pine, M.D., M.B.A. Michael Pine and Associates, Inc. Chicago, Illinois

© 2008 Michael Pine and Associates, Inc. Overview  Rationale for Development of POA Screens  Developmental Database and Selection of Cases  Description and Aggregate Performance of 12 Screens  Evaluation of Coding By Individual Hospitals  Computation of Composite Scores for Hospitals

© 2008 Michael Pine and Associates, Inc. Rationale for Development of POA Screens  POA Code Identifies Hospital-Acquired Complications Important in Computing Rates of Adverse Outcomes Important in Risk-Adjusting Performance Measures  Accurate Coding Requires Expertise and Teamwork  Inaccurate Coding Affects Assessments of Clinical Quality Affects Reimbursement  Chart Reviews to Detect Coding Errors Are Expensive  Well-Designed Screens Can Detect Problems Efficiently

© 2008 Michael Pine and Associates, Inc. Developmental Database  New York State SPARCS Data from 2003 through 2005  8,388,179 Discharges from 246 Hospitals  Secondary Diagnosis Codes Have POA Modifiers “1” = Present on Admission “2” = Hospital-Acquired “9” = Status on Admission Unknown

© 2008 Michael Pine and Associates, Inc. Selection of Cases for Screening  High-Risk Conditions By Principal Diagnosis 33 Categories (e.g., septicemia, respiratory failure) Mortality = 9.2%; 70% of Deaths; 22% of Discharges  Elective Admissions for Selected Surgical Procedures 7 Procedures (e.g., hysterectomy, knee replacement) Principal Diagnosis Consistent with Procedure Operation During First 2 Days of Hospitalization  Inpatient Childbirth By Diagnosis or Procedure Codes

© 2008 Michael Pine and Associates, Inc. Diagnoses Almost Always Present on Admission  231 Diagnosis Groups (e.g., malignancy, osteoporosis)  Analyzed for Each of the 3 Sets of Cases Screened  Aggregate Data for Each Set: Data Set# Codes% Inpatient% Unknown High-Risk Conditions5,506, %5.75% Elective Surgery588, %4.52% Inpatient Childbirth112, %8.93%

© 2008 Michael Pine and Associates, Inc. Complications in High-Risk Conditions  Chronic Diagnoses with and without Acute Components 21 Pairs (e.g., hernia with and without obstruction) Rates At Which Coded As Hospital-Acquired Chronic without Acute: 1.06% of 1,612,079 Diagnoses Chronic with Acute: 3.34% of 222,641 Diagnoses  Diagnoses Frequently Hospital-Acquired (e.g., anuria) 3 Categories Based on Frequency Hospital-Acquired 27 Diagnosis Groups in Category A; 59 in B; 54 in C Category A % of 172,472 Codes Hospital-Acquired Category B % of 469,970 Codes Hospital-Acquired Category C % of 772,049 Codes Hospital-Acquired

© 2008 Michael Pine and Associates, Inc. Mortality with Hospital-Acquired Complications  Only for High-Risk Conditions  Mortality Greater When Diagnosis Hospital-Acquired 3 Categories Based on Ratio of Mortality Rates 66 Diagnosis Groups in Category A; 54 in B; 64 in C Aggregate Data for Each Category: Category# POA Dx% Dead# Hosp Dx% DeadOdds Ratio A348, %27, %2.57 B747, %80, %1.87 C1,335, %247, %1.64

© 2008 Michael Pine and Associates, Inc. Complications in Elective Surgical Admissions  Diagnoses Frequently Hospital-Acquired Complications 64 Diagnosis Groups (e.g., septicemia, shock) Of 138,655 Codes, 68.3% Hospital-Acquired  Chronic Diagnoses with and without Acute Components 21 Pairs (e.g., asthma with and without exacerbation) Rates At Which Coded As Hospital-Acquired Chronic without Acute: 0.39% of 187,453 Diagnoses Chronic with Acute: 18.72% of 2,174 Diagnoses

© 2008 Michael Pine and Associates, Inc. Risk-Adjusted Post-Op Lengths of Stay  High Rates of Prolonged LOS in Uncomplicated Cases  Develop Predictive Equations for Routine Post-Op LOS Compute Observed Minus Predicted Post-Op LOS For All Live Discharges at Each Hospital Create XmR Control Charts of OBS minus PRED LOS Remove Outliers with Prolonged Post-Op LOS Repeat Process Until No Further Outliers Identified Set Upper Bound at Median Outlier Rate for All Hospitals Repeat Process Using Only Uncomplicated Cases Compute Outlier Rates for Each Hospital Identify Hospitals with Rates Greater Than Upper Bound

© 2008 Michael Pine and Associates, Inc. Risk-Adjusted Post-Op Lengths of Stay Live Discharges with and without Reported Complications

© 2008 Michael Pine and Associates, Inc. Risk-Adjusted Post-Op Lengths of Stay Live Discharges without Reported Complications

© 2008 Michael Pine and Associates, Inc. Complications in Obstetrical Admissions  Diagnoses Usually Present on Admission 7 Diagnosis Groups (e.g., multiple gestation) Of 448,242 Codes, 5.19% Hospital-Acquired  Fifth Digit Codes Incompatible with Inpatient Delivery 737,125 Inpatient Deliveries Fifth Digit = “0” or “3” or “4” in 0.27%  Inpatient Post-Partum Complications 74,669 Cases with Obstetrical Fifth Digit = “2” No Diagnosis Coded As Hospital-Acquired in 36.5%

© 2008 Michael Pine and Associates, Inc. Initial Analyses of Hospital Coding  226 Hospitals Screened with One or More Measures  22 Hospitals Have More Than 10% Unknowns  Diagnoses Almost Always Present on Admission Less Than 2% of Diagnoses Hospital-Acquired Data Set# Hospitals% Meeting Criterion High-Risk Conditions % Elective Surgery % Inpatient Delivery4845.8%

© 2008 Michael Pine and Associates, Inc. Hospital Coding for High-Risk Conditions  Chronic Diagnoses with Acute Components Hospital-Acquired Rate Greater Than 2% AND Greater Than Twice Rate for Chronic Codes Of 145 Hospitals, 71.7% Met Criteria  Diagnoses Frequently Hospital-Acquired Hospital-Acquired Rate Greater Than 15% for Category B Diagnoses AND Rate Monotonically Decreasing from Category A to Category C Of 181 Hospitals, 83.4% Met Criteria

© 2008 Michael Pine and Associates, Inc. Hospital Mortality Rates for High-Risk Conditions  Compute Predicted Mortality Rates Indirect Standardization within Each Category Based on Rates for Diagnoses Present on Admission  Odds Ratio of Observed to Predicted Mortality Rates Greater Than 1.60 for All Diagnoses OR Greater Than 1.30 for All Diagnoses AND Greater Than 1.60 for Diagnoses in Categories A and B  Of 184 Hospitals, 82.6% Met Criteria

© 2008 Michael Pine and Associates, Inc. Hospital Coding for Elective Surgical Admissions  Diagnoses Frequently Hospital-Acquired Complications Hospital-Acquired Rate Greater Than 65% Of 175 Hospitals, 61.1% Met Criterion  Chronic Diagnoses with Acute Components Compute 2 Standard Deviation Lower Bounds for Hospital-Acquired Rates Hospital-Acquired Rate Greater Than 12% AND Greater Than Three Times Rate for Chronic Codes OR Lower Bound Greater Than Twice Rate for Chronic Codes Of 93 Hospitals, 96.8% Met Criteria

© 2008 Michael Pine and Associates, Inc. Prolonged Risk-Adjusted Post-Op Length of Stay  Median Outlier Rate for All Live Discharges = 5.36%  Outlier Rates for Uncomplicated Cases Less Than Upper Bound: In 81.5% of 178 Hospitals In 98.4% of 64 Reference Hospitals In 71.9% of 114 Remaining Hospitals

© 2008 Michael Pine and Associates, Inc. Hospital Coding for Obstetrical Admissions  Diagnoses Usually Present on Admission Hospital-Acquired Rate Less Than 3% Of 134 Hospitals, 63.4% Met Criterion  Fifth Digit Codes Incompatible with Inpatient Delivery Less Than 0.5% of Obstetrical Codes Incompatible Of 134 Hospitals, 87.3% Met Criterion  Cases with Inpatient Post-Partum Complications Less Than 20% without Hospital-Acquired Diagnosis Of 123 Hospitals, 41.5% Met Criterion

© 2008 Michael Pine and Associates, Inc. Composite Hospital Scoring  Range of Points Assigned to Each Measure Range from 1 to N with N = 4, 5, 8, or 10 Score Only for 204 Hospitals with Adequate Data Score Measure Only When Volume Criteria Met  For Each Hospital, Compute: Total of Points Scored for Each Measure Maximum and Minimum Possible Points  For Each Measure, Compute Average of Points Scored  Obtain Final Adjusted Hospital Scores By Interpolation

© 2008 Michael Pine and Associates, Inc. Final Adjusted Hospital Scores HospitalTotalMaximumMinimum Adjusted Score Adjusted Score (%) AVG % A % B % C % D % E % F %

© 2008 Michael Pine and Associates, Inc. Screening and Improvement of POA Coding POA Screening Identification of Opportunities for Improvement Performance Evaluation Process Analysis Intervention in Process Plan for Improvement