1. Abstract Background Objectives Summary & Conclusions Methods Data Collection & Processing for OQUIN C.Shaun Wagner, John Dodson, Wei Ding OQUIN IT Team The job of collecting data from OQUIN network practices and importing them into the OQUIN database for reporting is complex. By analyzing each step of the process, we hope to find ways to streamline our efforts and improve data capture quality, timeliness of reports and improved National Committee for Quality Assurance (NCQA) scores. Each clinic in the OQUIN network is unique in the way they capture and store patient data, even if they utilize the same Electronic Health Record System (EHR). This presents challenges to standardizing data collection and importing methods. Identify areas where NCQA scores are poor and search for solutions. Improve the number of providers who qualify for each of the NCQA standards we report. After identifying a clinic with low NCQA scores. The database is analyzed. If the numbers seem to be accurate to what is reported, the next step is to look at the raw data files to look for a problem with importing. If no problem is found then we have to search in the clinic’s database to see if the data is being stored. If data is found, it is collected and imported and reports are re run to see if the scores improve. To improve quality of data in the OQUIN database, feedback reports and NCQA scores, network clinics and providers should concentrate on the following: Enter the correct ICD9 and CPT codes for visits Check that medications prescribed accurately Timely ordering of patient labs Results The OQUIN team identified a very large clinic with low scores for smoking status/cessation advice or treatment under the NCQA standards for Heart/Stroke Recognition. When the database was queried, we found that the data we had collected and imported was being reported correctly. We utilized our Virtual Private Network (VPN) connection with this clinic to look through the EHRs database. It was discovered that smoking status and cessation information was being stored in a table that wasn’t currently being collected. After the team collected and imported this data the Heart/Stroke score for smoking advice or treatment went up 20%. This increase was not enough to get to the 80% goal for score. Improved coding of smoking cessation should improve their score further. This same clinic also had no reported use of aspirin or another antithrombotic. It was discovered that most of this information is being stored in a table that the OQUIN team does not have access to. This table is going to be available when the conversion to a new database is complete. We were able to identify quite a number of aspirin that were not being imported correctly. The percentage of aspirin use went from 0% to 38%. Many other clinics received the same amount of analysis and most of the low scores can be attributed to not coding visits in the EMR efficiently for scoring, capturing laboratory results on paper or scanned and misspelling or inaccurate medication information. Copyright OQUIN 2012