Validating Health Information Exchange Data for Quality Measurement

Research Forum Abstracts

using the standard needle versus 0% using the insulin needle. Failure to obtain bloodby a single puncture was observed in 7 (14%) punctures by the standard needle butonly 3 (6%) by the insulin needle.

Conclusions: Radial arterial puncture performed using the 29G common insulinsyringe is associated with less pain, fewer immediate complications and fewerprocedural failure rates.

Association Between Race and the Administration

Dickason M, Dalawari P, Chauhan V, Ibler E, Kuehnle S, Mahoney DM, Mor A,Armbrecht E/Saint Louis University School of Medicine, Saint Louis, MO;St. Louis University Center for Outcomes Research, Saint Louis, MO

Study Objective: To identify any association between patient race and theadministration of analgesia in the emergency department (ED) for three specificdiagnoses.

Methods: This retrospective chart review included patients presenting to the EDof a single Midwestern academic institution with a primary ICD-9 diagnosis of one ofthe following: back pain/strain (ICD-9 724), migraine (ICD-9 346), or extremityfracture (fracture of the humerus ICD-9 812, femur ICD-9 821, or tibia/fibula ICD-9 823) from 2007 to 2011. Trained and blinded staff collected data from ED chartsabout analgesia type (opiate or nonopiate), administration (given in ED or given ahome prescription) as well as demographics. The primary outcome was theproportion of African-Americans (AAs) who received analgesia compared toCaucasians. A Pearson’s chi-squared test was used to compare proportions bydiagnosis category. A multiple logistic regression model was developed to estimatethe effect of patient and physician characteristics on analgesia type for those withback pain/strain, the condition which most relies on the subjective assessment ofneed for analgesia.

Results: Of 2461 patients within the time period, 2136 patients were included inthe analysis: 1568, 216, and 352 cases of back pain, migraine, and extremity fracture,respectively; 13% had no recorded race. There was no statistically significant racialdifference (AA vs. Caucasians) in the administration of analgesia (back pain: 86% vs.86%, p¼0.81; migraine: 83% vs. 73%, p¼0.085; extremity fracture: 94% vs. 90%,p¼0.166). Of those patients who received analgesia, there was no statisticallysignificant racial difference in the administration of an opiate for migraine orextremity fracture (migraine: 49% vs. 62%, p¼0.11; extremity fracture: 97% vs.98%, p¼0.485). However, African-Americans who presented with back pain wereless likely to receive an opiate than Caucasians (50% vs. 72%, p<0.001). The logisticregression model estimated an odds ratio of 2.3 (95%CI 1.75, 3.04) for Caucasians,adjusting for age, acuity, physician sex, and physician training in emergencymedicine; indicating Caucasians were 2.3 times more likely to receive an opiate incomparison to AA patients. Secondary outcomes showed that male ED physiciansand ED physicians who had completed an EM residency were more likely toprescribe opiates. Neither patient-physician race similarity nor sex similaritycorrelated with opiate administration.

Conclusions: There was no race-based disparity in analgesia administration for thethree diagnoses studied, nor for opiate administration for extremity fracture ormigraine; however, African-Americans were less likely to receive an opiate for back painthan Caucasians. Many factors may contribute to this disparity, from physician bias topoor communication between physicians and patients of dissimilar races.

Validating Health Information Exchange Data for Quality

Study Objectives: Previous work has shown that measuring 72-hour returns andfrequent emergency department (ED) users with health information exchange (HIE)data captures more patients than data from a single hospital. We compare 2 years ofHIE data to electronic health record (EHR) data to assess the validity and concordanceof the data sources.

Methods: Site-specific HIE data for an academic urban hospital were providedby the New York Clinical Information Exchange (NYCLIX), and EHR data wereprovided directly by the same hospital. Data elements from both sources were obtainedfor all ED visits from 3/1/09 to 2/28/11 including visit number (denotes a uniqueencounter), medical record number (MRN – denotes a unique patient), admission

S94 Annals of Emergency Medicine

and discharge date and time, date of birth and sex. Data were merged on visit numberto evaluate concordance of unique encounters, and on MRN to evaluate concordanceof unique patients. Data were de-identified in accordance with New York Staterequirements for research using HIE data. Descriptive statistics were used to explorethe differences between the two data sets. Specific rules were applied to correct fordifferences between the data sets at the encounter level by counting as a match if: 1)difference in age < 1 year, 2) sex of male or female in one and “unknown” in the other,or 3) difference in admit or discharge date/time < 6 hours. Finally we compared countsof 72-hour returns and frequent ED users (� 4 visits in 30 days) between the two datasets using Chi square, hypothesizing that the results are the same.

Results: Discrepancies in the number of unique visits and patients between theEHR and HIE were negligible (<1%) (Table 1).

Table 1. Counts of patients who were in the EHR only, the HIE only or both theEHR and HIE.

Concordance EHR only HIE only EHR and HIE

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# of Unique encounters (Visit #)

Further analysis revealed additional discrepancies for the remaining variables, butwhen the rules above were applied these became negligible (Table 2).

Table 2. Analysis of discrepancies for age, sex, and admit and discharge date/time stamps before and after data cleaning rules applied, which allowed nearmatches to count as matches.

Data Cleaning Match Non Match Adj Match Adj Non Match

Age

Finally, we found no statistical difference between the two data sets for 72-hourreturns and frequent ED users (Table 3).

Table 3. Compares the counts for both frequent ED user and 72-hour returnmeasures applied to the two data sets. (* Denotes no statistical differencebetween EHR and HIE counts using chi square with alpha set to 0.05.).

EHR EHR Unique HIE HIE Unique

cto

P-value*

FrequentUsers

1,204(1.1%)

109,015

109,953

72 HourReturns

8,299(7.6%)

109,015

109,953

Conclusions: This study validates the use of HIE data for this purpose compared tohospital EHR data. The results show that the concordance between EHR and HIEdata for unique encounters and patients, age and actual date/time tamps is good, andthat when we apply 72-hour return and frequent ED measures to the two data sets, theresults are not statistically different. This analysis supports the hypothesis that EDquality measures for 72-hour returns and frequent ED use can be applied to an HIEdata source to improve our ability to detect these patients for quality measurement andcare improvement. Because the results may vary from site to site, a similar analysisacross multiple sites in the HIE will be conducted.

Printer Alarm for Notification of Time-Sensitive Results

Study Objectives: The Joint Commission recognized improved reporting ofcritical values as a National Patient Safety Goal. Institution-wide definition and

ber 2013