Methods Applying AHRQ Quality Indicators to Healthcare ... - HCUP

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APPENDIX E:COMPARISON OF INPATIENT AND EMERGENCY DEPARTMENT USEFOR SELECTED PQIs AND PDIsFor the 2011 NHQR, HCUP data were used to examine national and regional differences ininpatient and emergency department (ED) use for selected AHRQ Prevention Quality Indicators(PQIs) and related Pediatric Quality Indicators (PDIs). Table E-1 in this appendix contains a listof PQIs and PDIs examined.The PQIs are measures of quality associated with processes and outcomes of care thatoccurred in an outpatient or an inpatient setting. The PQIs rely solely on hospital administrativedata and, for this reason, are screens for examining quality that may indicate the need for morein-depth studies. Experts have suggested that using both inpatient and emergency room datamay give a more accurate picture of avoidable visits/admissions for some ambulatory caresensitive conditions which can be identified by certain PQIs and PDIs.Two HCUP databases were used for the analysis:The HCUP Nationwide Emergency Department Sample (NEDS), a nationally stratifiedsample of hospital-based EDs from HCUP States that contribute ED data (28 States inthe 2008 NEDS).The HCUP Nationwide Inpatient Sample (NIS), a nationally stratified sample of hospitalsfrom HCUP States that contribute inpatient data (42 States in the 2008 NIS).The 2008 NEDS contains approximately 28.5 million ED events from 980 hospital-based EDs.The NEDS includes information on ED visits that do not result in an admission (i.e., treat-andreleasevisits and transfers to another hospital) as well as discharge information on patientsinitially seen in the ED and then admitted to the same hospital. For 2008, the NIS containsroughly 8.2 million inpatient discharges from more than 1,000 hospitals. Discharge-levelweights included with the NEDS and NIS are used to produce national estimates.Several steps were taken to prepare the HCUP databases: (1) QI software review andmodification, (2) acquisition of population-based data, (3) general preparation of HCUP data,and (4) identification of statistical methods.1. QI Software Review and Modification. A modification of PQI Version 4.1 was used.The PQIs were developed for use with hospital inpatient discharge data. No guidelinesfor applying the AHRQ QIs to emergency department data were available when thisanalysis began. Some of the events in the NEDS are visits for patients initially seen inthe emergency room and then admitted to the same hospital (an “ED admission”), andsome NEDS events are ED visits that do not result in an inpatient admission (e.g., treatand-releasevisits and transfers to another hospital). About 16 percent of records in the2008 NEDS represent an ED admission. The PQIs rely on the first-listed diagnosis code(DX1) to identify cases with the outcome of interest. For ED admissions, DX1 is theprincipal diagnosis code and reflects the condition established to be chiefly responsiblefor a patients’ admission to the hospital. Unfortunately, principal diagnosis is not clearlydiscernible for ED visits that do not result in admission. Coding instructions for outpatientdata specify that the first-listed diagnosis is supposed to be the "reason for visit," whichis different than the principal diagnosis. Even though DX1 in ED data is not necessarilythe principal diagnosis, using DX1 preserves the concept from the PQI algorithm that theMethods for HCUP Data in 2011 NHQR and NHDR E-1December 9, 2011
first code has higher priority than others. Therefore, this analysis used the first-listeddiagnosis in both the inpatient and ED data analyses.2. Acquisition of Population-Based Data. The next step was to acquire data for thenumerator and denominator populations for the QIs. A QI is a measure of an event thatoccurs in a hospital, requiring a numerator count of the event of interest and adenominator count of the population (within the hospital or within the geographic area) towhich the event relates.For the numerator counts of the PQI or PDI, we used the HCUP NEDS to create nationalestimates of all ED visits, ED visits resulting in admission to the same hospital, and allother types of ED visits. We used the HCUP NIS to create national estimates ofinpatient admissions including those admitted through the ED. For the denominatorcounts, population ZIP-Code-level counts from Claritas (a vendor that compiles andadds value to the U.S. Bureau of Census data) were used for all reporting categories.Claritas uses intra-census methods to estimate household and demographic statistics forgeographic areas (Claritas, Inc., 2008). We also used the Claritas population data forrisk adjustment by age and gender.3. Preparation of HCUP Data. Next, the HCUP NEDS was modified to create an analyticfile consistent with the NIS which is already used for other measures in the NHQR. TheNEDS consists only of hospital-based EDs from community, non-rehabilitation hospitalsand includes discharge weights to the universe of hospital-based ED visits to the U.S. asdefined by the American Hospital Association Annual Survey Database. For missingage and gender data that occurred on a small proportion of discharge records, a “hotdeck” imputation method (which draws donors from strata of similar hospitals andpatients) was used to assign values while preserving the variance within the data.4. Statistical Methods. Age-gender adjustments were made for age and genderdifferences across population subgroups and were based on methods of directstandardization (Fleiss, 1973). Age was categorized into 18 five-year increments.5. Masking Rates for Statistical Reliability, Data Quality, and Confidentiality. PQI andPDI estimates were included in this analysis if they reached a threshold defined by arelative standard error less than 30% and at least 10 unweighted cases in thedenominator. Estimates that did not satisfy these criteria were set to missing.Methods for HCUP Data in 2011 NHQR and NHDR E-2December 9, 2011
Page 1: HCUP Methods Series
Page 6 and 7: Assign Additional Measures for Repo
Page 8 and 9: For the discharge-based PDIs, risk
Page 10 and 11: Data Elements for Exclusions: Three
Page 12 and 13: In a subsequent analysis, we invest
Page 17 and 18: QI No.IQI 22IQI 23IQI 24IQI 25IQI 2
Page 19 and 20: QI No.PSI 19PSI 21PSI 22PSI 23PSI 2
Page 21 and 22: Data Sources for the HCUP Nationwid
Page 23 and 24: Table 4. Use of Secondary Procedure
Page 25: Table 6. Number of Diagnosis and Pr
Page 28 and 29: APPENDICES
Page 30 and 31: HCUP Race DataHCUP coding includes
Page 32 and 33: Primary expected payer2008 Disparit
Page 34 and 35: Median income of Patient’s ZIP Co
Page 36 and 37: Typically, most States have no more
Page 38 and 39: . Standard error estimates for age/
Page 40 and 41: inpatient population distribution f
Page 42 and 43: IQI CompositeWeightNHQR/NHDRSummary
Page 46 and 47: Table E-1. List of PQIs and PDIs Us
Page 48 and 49: "clinical grouper" makes it easier
Page 50 and 51: APPENDIX G:STATE-LEVEL BIVARIATE AN
Page 52 and 53: AHRQPreventionQualityIndicators(PQI
Page 54 and 55: AHRQPreventionQualityIndicators(PQI
Page 56 and 57: APPENDIX H:PATIENT SAFETY IN HOSPIT

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