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Using a data quality assessment focused on data relevancy, interpretability, and believability, the study shows that removing these templates yields a more realistic view of 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was the main reporting problem studied in the outpatient setting?","Question",{"text":63,"@type":64},"Customization of an Epic EHR report produced confusing roll-up results when appointment schedule templates were included, affecting how utilization was interpreted.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was the report evaluated for quality?",{"text":68,"@type":64},"The report was measured using a data quality assessment covering data relevancy, interpretability, and believability.",{"name":70,"@type":61,"acceptedAnswer":71},"What change improved the utilization reporting results?",{"text":72,"@type":64},"Removing the appointment schedule templates enabled the team to show a more realistic picture of utilization through the roll-up metrics.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},393246,1791004072,{"code":4,"msg":82,"data":83},"success",[84,89,94,99,104,109,114,119,124],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":86,"show_sort_weight":87,"slug":88},11,"Presentations",90,"presentations",{"id":90,"doc_module":22,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},12,"Resumes",80,"resumes",{"id":95,"doc_module":22,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},14,"Invoices",70,"invoices",{"id":100,"doc_module":22,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},15,"Posters",60,"posters",{"id":105,"doc_module":22,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},16,"Social 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Arkansas at Little Rock & Senior Technical Project Manager, University of Arkansas for Medical Sciences Cancer Service Line  \n[Ammorse2@uams.edu](Ammorse2@uams.edu)  \nAbstract: With the surge in adoption of Electronic Health Record (EHR) systems, Healthcare Providers and Administrators anticipated the ability to receive timely and accurate reports upon implementation. Many EHR vendors provide out of the box reports which are impressive on demonstration, yet in the same presentation these vendors frequently encouraged their customers to customize the EHR based on their needs. While a much needed feature, this can create a mighty web of challenges in regards to reporting.  \nIn this study, I examined the challenge of customization in EHR systems as it relates to reporting. I reviewed a customer developed report which was based on best practices by the EHR vendor yet the customization of the EHR resulted confusing results in the roll up format. This report was measured using a data quality assessment for Data Relevancy, Interpretability and Believability. The interpretation of the referenced report resulted in confusing roll up data due to inclusion of appointment schedule templates which were by design to be used on an as needed basis. By removing these templates, we were able to show a more realistic picture of utilization through the roll up metrics.  \nIntroduction and Background  \nBetween August 2013 and May 2014, the University of Arkansas for Medical Sciences (UAMS) transitioned to a new integrated EHR system called Epic. Due to the complexity of this implementation, UAMS chose to utilize a two-phased implementation process (Gamble, 2016) . Epic is one of the largest providers of Health Information technology used throughout the world. As of 2016, Epic reports over 190 million users (SearchHealthIT, 2016) . Epic is based on the Massachusetts General Hospital Utility MultiProgramming System (MUMPS) programming language and relies on Cache as a core technology for its software (Monegain, 2015) . The 32 Epic software modules implemented replaced an estimated 24 of 86 disparate systems utilized throughout the medical center inpatient, outpatient clinics, laboratories, radiology and more. This system implementation brought together 89+ clinic locations, 35+ care areas across 21 inpatient units and 10 hospital outpatient departments (Lane, 2017) . The new EHR had many features considered of value to the organization including full integration of the patient experience which was longa desire for both patients and hospital administration.  \nWhile the benefits were many, there were equally as many challenges to overcome with some being anticipated yet others unforeseen. Over the years, UAMS had utilized another vendor product for outpatient clinic scheduling. This product had been extensively customized over the years in an effort to support the practice nuances for most clinics. Specifically in the Cancer Service Line, our clinics are comprised of multiple cross-discipline providers, each who require specific scheduling parameters to meet the needs of the patient population. These providers can be scheduled in multiple clinics and surgical areas throughout a work week. Epic introduced UAMS to the terminology of a template for building provider schedules. A  \ntemplate provides a framework by which a physician’s schedule is built on. While other Service Lines have similar templates throughout their clinics, each of our clinics required a unique build with multiple exceptions to be managed per clinic.  \nAlong with the complexity of the legacy system, UAMS decided on a very quick implementation schedule. This presented considerable challenges. Thousands of hours of discovery, training and IT development we","cbCaidARuvfHl7It","https://ap.wps.com/l/cbCaidARuvfHl7It","pdf",1430668,"English","# Abstract\n# Introduction and Background\n## EHR transition and Epic implementation\n## Outpatient scheduling challenges and go-live impact\n## Optimization phase and ongoing issues","[{\"question\":\"What was the main reporting problem studied in the outpatient setting?\",\"answer\":\"Customization of an Epic EHR report produced confusing roll-up results when appointment schedule templates were included, affecting how utilization was interpreted.\"},{\"question\":\"How was the report evaluated for quality?\",\"answer\":\"The report was measured using a data quality assessment covering data relevancy, interpretability, and believability.\"},{\"question\":\"What change improved the utilization reporting results?\",\"answer\":\"Removing the appointment schedule templates enabled the team to show a more realistic picture of utilization through the roll-up metrics.\"}]","Improving Data Quality in the Outpatient Setting: A Case Study of Provider Template Evaluation and Optimization in the Cancer Service Line | PDF",1790381501]