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,

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, Illinois773-643-1700

[email protected]

© 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


Rationale for Development of POA Screens

POA Code Identifies Hospital-Acquired ComplicationsImportant in Computing Rates of Adverse OutcomesImportant in Risk-Adjusting Performance Measures

Accurate Coding Requires Expertise and Teamwork Inaccurate Coding

Affects Assessments of Clinical QualityAffects Reimbursement

Chart Reviews to Detect Coding Errors Are Expensive Well-Designed Screens Can Detect Problems Efficiently


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


Selection of Cases for Screening

High-Risk Conditions By Principal Diagnosis33 Categories (e.g., septicemia, respiratory failure)Mortality = 9.2%; 70% of Deaths; 22% of Discharges

Elective Admissions for Selected Surgical Procedures7 Procedures (e.g., hysterectomy, knee replacement)Principal Diagnosis Consistent with ProcedureOperation During First 2 Days of Hospitalization

Inpatient Childbirth By Diagnosis or Procedure Codes


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 Conditions 5,506,043 1.13% 5.75%

Elective Surgery 588,874 0.63% 4.52%

Inpatient Childbirth 112,987 1.85% 8.93%


Complications in High-Risk Conditions

Chronic Diagnoses with and without Acute Components21 Pairs (e.g., hernia with and without obstruction)Rates At Which Coded As Hospital-Acquired

Chronic without Acute: 1.06% of 1,612,079 DiagnosesChronic with Acute: 3.34% of 222,641 Diagnoses

Diagnoses Frequently Hospital-Acquired (e.g., anuria)3 Categories Based on Frequency Hospital-Acquired27 Diagnosis Groups in Category A; 59 in B; 54 in C

Category A - 63.5% of 172,472 Codes Hospital-Acquired Category B - 34.7% of 469,970 Codes Hospital-Acquired Category C - 24.8% of 772,049 Codes Hospital-Acquired


Mortality with Hospital-Acquired Complications

Only for High-Risk Conditions Mortality Greater When Diagnosis Hospital-Acquired

3 Categories Based on Ratio of Mortality Rates66 Diagnosis Groups in Category A; 54 in B; 64 in CAggregate Data for Each Category:

Category # POA Dx % Dead # Hosp Dx % Dead Odds Ratio

A 348,860 12.6% 27,406 27.0% 2.57

B 747,172 15.3% 80,856 25.2% 1.87

C 1,335,879 21.2% 247,144 30.5% 1.64


Complications in Elective Surgical Admissions

Diagnoses Frequently Hospital-Acquired Complications64 Diagnosis Groups (e.g., septicemia, shock)Of 138,655 Codes, 68.3% Hospital-Acquired

Chronic Diagnoses with and without Acute Components21 Pairs (e.g., asthma with and without exacerbation)Rates At Which Coded As Hospital-Acquired

Chronic without Acute: 0.39% of 187,453 DiagnosesChronic with Acute: 18.72% of 2,174 Diagnoses


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 LOSFor All Live Discharges at Each Hospital

Create XmR Control Charts of OBS minus PRED LOSRemove Outliers with Prolonged Post-Op LOSRepeat Process Until No Further Outliers IdentifiedSet Upper Bound at Median Outlier Rate for All Hospitals

Repeat Process Using Only Uncomplicated CasesCompute Outlier Rates for Each HospitalIdentify Hospitals with Rates Greater Than Upper Bound


Risk-Adjusted Post-Op Lengths of StayLive Discharges with and without Reported Complications

-10

0

10

20

30

40

50

60

1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511

Sequence Identifier

O

BS

LO

S m

inus

PR

ED

LO

S (d

ays)

Average 3 Std Dev Normal LOS Long LOS w Cpl Long LOS w/o Cpl


Risk-Adjusted Post-Op Lengths of StayLive Discharges without Reported Complications

-10

0

10

20

30

40

50

60

1 38 82 129 180 225 270 310 354 398 448 491

Sequence Identifier

O

BS

LO

S m

inus

PR

ED

LO

S (d

ays)

Average 3 Std Dev Normal LOS Long LOS w/o Cpl


Complications in Obstetrical Admissions

Diagnoses Usually Present on Admission7 Diagnosis Groups (e.g., multiple gestation)Of 448,242 Codes, 5.19% Hospital-Acquired

Fifth Digit Codes Incompatible with Inpatient Delivery737,125 Inpatient DeliveriesFifth Digit = “0” or “3” or “4” in 0.27%

Inpatient Post-Partum Complications74,669 Cases with Obstetrical Fifth Digit = “2”No Diagnosis Coded As Hospital-Acquired in 36.5%


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 200 91.5%

Elective Surgery 123 89.4%

Inpatient Delivery 48 45.8%


Hospital Coding for High-Risk Conditions

Chronic Diagnoses with Acute ComponentsHospital-Acquired Rate Greater Than 2% AND

Greater Than Twice Rate for Chronic CodesOf 145 Hospitals, 71.7% Met Criteria

Diagnoses Frequently Hospital-AcquiredHospital-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


Hospital Mortality Rates for High-Risk Conditions

Compute Predicted Mortality RatesIndirect Standardization within Each CategoryBased on Rates for Diagnoses Present on Admission

Odds Ratio of Observed to Predicted Mortality RatesGreater Than 1.60 for All Diagnoses ORGreater 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


Hospital Coding for Elective Surgical Admissions

Diagnoses Frequently Hospital-Acquired ComplicationsHospital-Acquired Rate Greater Than 65%Of 175 Hospitals, 61.1% Met Criterion

Chronic Diagnoses with Acute ComponentsCompute 2 Standard Deviation Lower Bounds for

Hospital-Acquired RatesHospital-Acquired Rate Greater Than 12% AND Greater

Than Three Times Rate for Chronic Codes ORLower Bound Greater Than Twice Rate for Chronic Codes

Of 93 Hospitals, 96.8% Met Criteria


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 HospitalsIn 98.4% of 64 Reference HospitalsIn 71.9% of 114 Remaining Hospitals


Hospital Coding for Obstetrical Admissions

Diagnoses Usually Present on AdmissionHospital-Acquired Rate Less Than 3%Of 134 Hospitals, 63.4% Met Criterion

Fifth Digit Codes Incompatible with Inpatient DeliveryLess Than 0.5% of Obstetrical Codes IncompatibleOf 134 Hospitals, 87.3% Met Criterion

Cases with Inpatient Post-Partum ComplicationsLess Than 20% without Hospital-Acquired DiagnosisOf 123 Hospitals, 41.5% Met Criterion


Composite Hospital Scoring

Range of Points Assigned to Each MeasureRange from 1 to N with N = 4, 5, 8, or 10Score Only for 204 Hospitals with Adequate DataScore Measure Only When Volume Criteria Met

For Each Hospital, Compute:Total of Points Scored for Each MeasureMaximum and Minimum Possible Points

For Each Measure, Compute Average of Points Scored Obtain Final Adjusted Hospital Scores By Interpolation


Final Adjusted Hospital Scores

Hospital Total Maximum Minimum Adjusted Score

Adjusted Score (%)

AVG 77.8 96 12 77.8 81.1%

A 96 96 12 96.0 100%

B 61 61 8 96.0 100%

C 66 96 12 66.0 68.8%

D 61 68 8 82.7 86.2%

E 54 57 7 88.8 92.5%

F 48 82 10 55.7 58.0%


Screening and Improvement of POA Coding

POA Screening

Identification of Opportunities for

Improvement

Performance Evaluation

ProcessAnalysis

Interventionin Process

Plan forImprovement

Documents

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,