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Sharing Adverse Drug Event Surveillance Results Using Business Intelligence Technology Monica M. Horvath, Ph.D., Heidi Cozart, R. Ph., Jeffrey Ferranti,

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Presentation on theme: "Sharing Adverse Drug Event Surveillance Results Using Business Intelligence Technology Monica M. Horvath, Ph.D., Heidi Cozart, R. Ph., Jeffrey Ferranti,"— Presentation transcript:

1 Sharing Adverse Drug Event Surveillance Results Using Business Intelligence Technology Monica M. Horvath, Ph.D., Heidi Cozart, R. Ph., Jeffrey Ferranti, M.D., M.S., Duke Health Technology Solutions - Duke University Health System, Durham, North Carolina INTRODUCTION RESULTS CONCLUSIONS & LESSONS LEARNED ABSTRACT Introduction: At Duke University Health System (DUHS), the analysis and reporting of adverse drug events (ADEs) using a combination of computerized surveillance and targeted chart review is a core component of medication safety. Currently, significant resources are allocated to analyze this information in an aggregate and longitudinal manner. Multiple data streams are combined into static spreadsheets. Reports cannot be altered in a fast, accurate, or scalable manner. A better solution is needed. Methods: Business intelligence (BI) approaches allow organizations to create and distribute reports quickly. We developed an extract, transform, and load process to bring ADE surveillance data into the DUHS data warehouse. Cognos BI 8.2 was used to create census-adjusted ADE aggregate rate reports. Extensive prompt screens allow end users to filter data by date, nursing station, severity, and drug category. Drill-down functionality enables navigation from aggregate rates to ADE anecdotes by interaction with report graphics. Results: Three highly interactive core reports were created. At first implementation, this system provides quality improvement and patient safety officers fast, accurate, and scalable reports. Distribution using BI has greatly enhanced communication of medication safety data at DUHS to where ADE-S Cognos reports will be included on the DUHS balanced scorecard. Conclusions: To ensure project success, an appropriate team member is required to bridge communications between clinical and technical staff during development. Design and development of the Cognos model must reflect the eccentricities of operational systems. REFERENCES 1.Nebeker JR, Hoffman JM, Weir CR, Bennett CL, Hurdle JF. High rates of adverse drug events in a highly computerized hospital. Arch Intern Med May 23;165(10): Institute of Medicine. Preventing Medication Errors: Quality Chasm Series. Washington, DC: National Academy Press; Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc Mar-Apr;10(2): Classen DC, Pestotnik SL, Evans RS, Burke JP. Computerized surveillance of adverse drug events in hospital patients. Jama Nov 27;266(20): Ferranti J, Horvath M, Cozart H, et al. Re-evaluating the safety profile of pediatrics: A comparison of computerized adverse drug event surveillance and voluntary reporting in the pediatric environment. Pediatrics 2008;121:e1-e7. ACKNOWLEDGEMENTS The authors thank Julie Eckstrand, David Leonard, Andrea Long, Julie Whitehurst, Ira Togo, and the DUHS data warehouse team for technical support and valuable discussions. Adverse Drug Events and Patient Care 1.5 million adverse drug events (ADEs) occur yearly in the US, affecting >25% of inpatients at tertiary care teaching hospitals. (1,2) Aggregate data on medical mistakes and adverse events should be reported back to those responsible for quality improvement. ADEs should be analyzed longitudinally in light of hospital visit information, hospital census, and patient demographics. Computerized adverse drug event surveillance (ADE-S) is recognized as an effective approach to monitor ADEs in key risk areas over time. (3,4) Aggregate ADE-S reporting at DUHS is a manual, time-consuming process. Disparate data extracts must be combined and operational eccentricities removed. Current aggregate ADE rate reports cannot be automatically updated. QI officers cannot drill down into the ADE-S data to examine patient subpopulations without new, ad hoc reports. METHODS Part 1: Integrate ADE Surveillance Data into the DUHS Data Warehouse 2) In BI development, an intermediary is required between the clinical customers and the technical data warehouse staff to ensure success. Much of this projects success lay in having a dedicated research analyst and a clinical pharmacist with health system experience to act as intermediaries between clinical staff familiar with the ADE-S operational system and the technical staff that maintain the DSR. This model ensured that no matter the technical challenges presented, solutions were approached in a manner consistent with clinician needs. Without these individuals, critical design flaws may not have been corrected prior to full implementation, resulting in an inferior end product. We would not embark on any future project that seeks to warehouse and report the information from clinical operations without allocating such resources. 3) Although the needs of the ADE-S operational system will differ from those of the warehoused data, the design of both systems must complement each other. The ETL must be sophisticated enough to detect when older ADE-S data may have been changed in the operational system (CLINAPP). Some aspects of the ADE-S operational system was redesigned as to prevent the warehousing of dirty data. 4) Clinician-friendly BI tools and a reporting infrastructure can inject new creativity and enthusiasm into QI initiatives. Cognos provides safety reports with a uniform look and feel to aid in cross- comparison and analysis. Most clinicians were immediately comfortable with navigating a web-based format. Cognos reporting deployment caused a paradigm shift in patient safety leaders level of excitement and energy. After gaining only a basic understanding of BI features, a new culture of creativity emerged. Leaders wanted to ask new and innovative questions about their safety data in hopes of identifying interventional opportunities, which were previously unexplored. Part 2: Aggregate Report Creation in Cognos BI 2.0 Summary Reporting Performance Using Cognos BI 2.0 CognosAd-hoc reporting SpeedReport generation in 5-10 minsReport generation in 1-2 hrs AccuracyBeing completely automatic, errors only arise if there are issues with the source data. Comparison to Cognos reports identified numerous errors in ad hoc aggregate reports. ScalabilityElaborate prompting screen gives flexibility in report output. Clinicians can independently obtain additional report views, such as changing the date range. Interactive drill downs enable clinician navigation from aggregate data to individual event stories. Reports are static. A research analyst must be resourced to generate each new report view. Incorporation of patient demographics and hospital encounter data requires a database technician. An ETL process brought ADE-S data into the DUHS data warehouse, the DUHS archive. ADE-S data was extracted from the operational system (CLINAPP) and converted to standardized fields. New data warehouse relationships were created to merge ADE-S with existing demographics and hospital visit database tables. In this architectural phase, several warehouse tables, such as hospital census, were enhanced to accommodate the needs of ADE-S reporting. With this new architecture, extensive encounter detail became available on each ADE. Transformed data is organized into packages within Cognos, which are defined groups of logically linked metadata and business objects. Packages can be linked to permit construction of package-spanning reports. Our final report authoring platform included four packages: Patient, hospital encounter, ADE-S event details, and census. A research analyst underwent Cognos training in Report Studio, which is a web tool to create reports that include prompts, lists, charts, cross-tabs, and drill down information. Reports are built through drag-and- drop of the data items in Cognos packages, which reside in the DUHS data warehouse. Only the most relevant items of each Cognos package were exposed to authors in Report Studio. Authors can include dynamic features such as conditional formatting which renders output differently according to report results. Report Description PromptsReport OutputDrill-downs ? ADE list report – event details Hospital Event date Event location (nursing station) Surveillance drug rule Surveillance drug category Event causality Event severity Medical record number Duke employee ID List report shows prompted items and: Lab value Intervention Event comments Gender Race Age DOB Admission date Discharge date Admitting service Discharge service Length of stay Length of stay to date Hyperlink on medical record number will rerun list report to show all historical ADEs for that patient ADE rates – by month, per drug category Hospital Event date Event location (nursing station) Surveillance drug category Event severity Charts and tables show ADEs rates (events per 1000 patient days or per 100 admissions) by month. Distinct series for each drug categories Charts: Clicking on bars gives the ADE list report (event details) for all ADEs that compose the bar. Tables: Clicking on hyperlinks give an ADE list report (event details) for all ADEs that correspond to that table cell ADE rates – by month, per nursing station Hospital Event date Event location (nursing station) Surveillance drug category Event severity Charts and tables show ADEs rates (per 1000 patient days or per 100 admissions) by month. Distinct series for each nursing station. Computerized ADE surveillance checks the clinical data systems for increasingly unsafe patient conditions at three DUHS hospitals. The surveillance engine looks for patient records matching specific logic-based rules, or triggers that considers factors such as orders, medications, and lab values. Potential adverse events are presented to clinical pharmacists in an operational, clinical web application (CLINAPP), where alerts are scored for severity and causality (5). Aggregate ADE analysis is completed manually and delivered to patient safety committees DUHS-wide as well as the DUHS balanced scorecard. Cognos Report: A web-enabled, dynamic, user- specified collection of database queries, prompts, layouts, and styles arranged by a report author. Reports may have multiple versions and output formats (pdf, html, xls) depending on the actions and selections of the report executor. Cognos Report View: A static data slice of the report driven by the end user (report executor). 1)BI Approaches greatly increase safety reporting performance at DUHS Core safety reports available through Cognos business intelligence Three core sets of reports were generated and made available to sixteen quality improvement and patient safety officers throughout the DUHS system. Each individual was given basic Cognos classroom training encompassing navigation of the Cognos user interface, step- by-step instructions on running prompted reports, and adequate time for ad hoc exploration of the tool. Manual ADE-S reports were still provided to facilitate comparison of the two reporting methods. Over a three month period, users were interviewed as to their opinions on the reports according to scalability, accuracy, and speed. Feedback was highly positive, and ADE-S Cognos reports are now in a pilot phase for incorporation into the DUHS Balanced Scorecard. Cognos Connection: Portal for Report Access Standardized Prompt Screens Dynamic Report Output (html, pdf, or MS Excel) Clicking on bars or hyperlinks launches detailed report *Protected health information removed


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