Appendix A: DARTNet Design Specifications Note: Prepared by the DARTNet Team, University of Colorado Denver Department of Family Medicine, Contract No. HHSA29020050037I TO2. A- 1 Appendix A Contents Introduction ................................................................................................................................. A-3 Overall Aims ............................................................................................................................... A-4 Technical Overview .................................................................................................................... A-4 Clinical Data Sources .......................................................................................................... A-5 Clinical Decision Support and Data Extraction .................................................................. A-7 Data Mapping With Each EHR........................................................................................... A-9 Grid Based Computing ............................................................................................................... A-9 Data Export and Presentation to the Grid ......................................................................... A-11 Quality Improvement ........................................................................................................ A-15 Data Additions Through Natural Language Processing ................................................... A-15 Directed Point-of-Care Data Collection .................................................................................... A-19 Summary ................................................................................................................................... A-19 References ................................................................................................................................. A-21 Appendix A-A: Criteria for Adding New Member Organizations to DARTNet ..................... A-22 Appendix A-B: Coding Dictionaries for DARTNet Data ......................................................... A-23 Appendix A-C: About the Globus Toolkit................................................................................ A-33 Grid Technologies ............................................................................................................. A-33 Globus Toolkit .................................................................................................................. A-35 X.509 Security .................................................................................................................. A-37 Open Grid Services Architecture – Data Access and Integration (OGSA-DAI) .............. A-37 A- 2 Introduction The Distributed Ambulatory Research in Therapeutics Network (DARTNet) is a prototype federated network of electronic health record (EHR) data from eight organizations representing over 500 clinicians and over 400,000 patients. The prototype system captures, codify and standardize over 150 unique data elements per patient for more than 48 months. DARTNet takes advantage of our team‘s expertise in analyzing large existing data sets and operating practice-based research networks (PBRN), and is proving to be an asset for the development of a new distributed research network of standardized clinical data from primary care clinicians‘ EHRs. Four current CO-DEcIDE partners were involved in the development of the first prototype for DARTNet: the University of Colorado Department of Family Medicine (CU-DFM), the University of Colorado School of Pharmacy (CU SOP), the American Academy of Family Physicians National Research Network (AAFP NRN) and the Robert Graham Center (RGC). Two technical partners joined in this effort: the University of Minnesota Center for Excellence in Primary Care (UMN) and Clinical Integration Networks of America, Inc. (CINA). The process of developing the DARTNet prototype actively explored how we can use existing EHR data to supplement data from large administrative datasets in order to answer questions concerning the safety and effectiveness of medications and medical devices. Furthermore, using our PBRN expertise assisted us to explore the ability to fill gaps in clinical data using point-of-care data collection techniques. A key requirement for DARTNet is the standardization of data elements across EHR products. We accomplished this using advanced clinical decision support tools already available from CINA. We used tools developed by CINA to access and export standardized data at each clinical organization into a relational data set that we refer to as a Clinical Data Repository (CDR). This standardized data set, which includes patient identifiers, was successfully transferred to a second database (the electronic Primary Care Network Gateway database— Gateway for short), de-identified and presented for query access through a secure Grid enabled web-portal. Both of these databases reside within each participating clinical organization. The movement of data from the CDR to the ePCRN Gateway database is based on the ASTM- standardized Continuity of Care Record (CCR). A full set of patient data never left the clinical sites where they are stored in this effort; however, the DARTNet team has the ability to query the de-identified federated databases in order to answer research questions that cannot be answered from existing administrative datasets. Furthermore, we explored the development of natural language processing (NLP) system to be used to unlock key data elements from EHR text. The DARTNet system is readily expandable using Grid-based local parallel processing and a two-stage data extraction and de-identification process. The DARTNet architecture will support a final system to accommodate at least two orders per year—of magnitude greater than this prototype with a single central technical support site. By adding additional central support sites (or supernodes) the network is essentially infinitely expandable. Furthermore, the data interfaces are not specific to primary care and can be expanded to include sub-specialty data where they are available electronically. When taken to scale, DARTNet will be able to explore both rare safety events in low usage medications and the safety and efficacy of commonly used ambulatory therapies. A-3 Overall Aims Aim 1: Develop a federated network of 200+ primary care clinicians who use EHRs, while examining the following issues. a. Establish a governance system that supports access to federated data while allowing members to maintain control of their data. b. Create a data extraction approach that will allow virtually any clinicians with EHRs to join the network as desired. c. Examine the ability of an existing National Institutes of Health (NIH) supported software package to meet the distributed query needs of the network. Aim 2: Analytically demonstrate how existing large-scale data sets can be enhanced by patient- level data from the federated primary care network to inform and expand knowledge of effective and safe medical therapeutics. a. Use existing large datasets (e.g., Ingenix) to evaluate medical therapeutics safety and effectiveness from a population based level. b. Examine what additional information can be obtained from existing patient level data available through DARTNet. c. Determine what information will only be available through direct data collection from clinicians or patients. Aim 3: Demonstrate the ability to collect specific data from clinicians or their staff on a clinically defined set of patients to enrich the EHR data set and answer effectiveness and safety questions concerning medical therapeutics. a. Demonstrate the ability of the federated system to use clinical and administrative data to identify patients from whom additional data might be collected. Technical Overview The Distributed Ambulatory Research in Therapeutics Network (DARTNet) builds on several best-in-breed technologies to create a true distributed clinical data repository for data acquisition and other activities. The system is currently based in primary care practices. It is not dependent on any particular electronic health record (EHR) for data access. Systems are in place to encourage high quality data collection: improved care processes (which increase the likelihood that selected data elements are captured), multiple data interfaces, data standardization, a data repository, and a GRID presentation for distributed query activities. Figure A-1 below summarizes the relationships between data sources and data access points. We will highlight elements of this figure throughout this document. A- 4 Figure A-1. Relationship of data sources and the DARTNet architecture within a single organization DARTNet Translation Interface Other* CDR CCR Gateway Research Portal EHR Web POC NLP Services Queries and Data Transfers *Other data sources include billing data, hospital data, SureScripts data, and other third party databases. This document is organized from the point of contact between patient and physician during routine clinical care, moving toward use of the data for research and quality improvement. Beginning with the source of clinical data within the patient-centered medical home, this report will provide a technical description of the following critical data collection and processing components of DARTNet: . Collecting EHR data from the primary care practice . Interfaces to secure laboratory, radiology, and medication data . Interfaces to secure hospital data