A Simple Strategy for Implementing Standard ...

A Simple Strategy for Implementing Standard Reference Terminologies in a Distributed Healthcare Delivery System with Minimal Impact to Existing Applications Omar Bouhaddou PhD1, Michael J. Lincoln MD1,2, Sarah Maulden MD MS1, Holli Murphy MS1, Pradnya Warnekar RPh, MS1, Viet Nguyen MD1, Siew Lam, MD, PhD1, Steven H Brown, MD1, Ferdinand J. Frankson MS1, Glen Crandall1, Carla Hughes RN, Roger Sigley1, Marcia Insley1, Gail Graham1 1 2

Department of Veterans Affairs Office of Information, Salt Lake City, Utah USA University of Utah Department of Medical Informatics, Salt Lake City, Utah USA

Abstract The Veterans Administration (VA) has adopted an ambitious program to standardize its clinical terminology to comply with industry-wide standards. The VA is using commercially available tools and in-house software to create a high-quality reference terminology system. The terminology will be used by current and future applications with no planned disruption to operational systems. The first large customer of the group is the national VA Health Data Repository (HDR). Unique enterprise identifiers are assigned to each standard term, and a rich network of semantic relationships makes the resulting data not only recognizable, but highly computable and reusable in a variety of applications, including decision support and data sharing with partners such as the Department of Defense (DoD). This paper describes the specific methods and approaches that the VA has employed to develop and implement this innovative program in existing information system. The goal is to share with others our experience with key issues that face our industry as we move toward an electronic health record for every individual. Background Standard reference terminologies are the key to a technology that will allow our industry to fulfill the promise of a complete electronic health record for every individual. The benefits of standard terminologies for data quality, data sharing, and decision support have been well documented [1]. Today, the search for star terminologies is rapidly passing. Instead, the National Center for Vital and Health Statistics (NCVHS) and Consolidated Health Informatics (CHI) have designated many terminology standards that federal agencies and the private sector can adopt to build institution-based and

interoperable medical records. Critical challenges remain to find simple strategies to retrofit these standards into existing clinical information systems and to evolve a more sophisticated solution for continued use of these standards in next-generation systems. This paper describes the VA experience with these challenges. The VA has the largest medical system in the US, with more than 1300 care sites and 7.4 million enrolled patients. It is recognized as a leader in care coordination and computerized patient care [2, 3]. Over the past two decades, the VA has developed a MUMPS or M-based VistA system comprised of over 150 application packages, including Pharmacy, Radiology, Laboratory, Dietetics, Progress Notes, Discharge Summary, Consultations, Problem List, Billing, and Patient Administration. The Computerized Patient Record System (CPRS) was deployed eight years ago to provide the graphical front-end for the clinical record [4]. However, each VistA package team continued to manage its own reference data and terminology. Locally extended terminologies resulted in overlooked synonymy and semantic collisions among concepts, which in turn produced noninteroperable patient data. In 2003, the VA established the Data Standardization (DS) and Enterprise Terminology Services (ETS) projects to adopt or create standard reference terminologies according to CHI and other external requirements and implement them throughout the enterprise. The DS team approves standards for data content (e.g., use of SNOMED CT codes for Problem List documentation), standardizes data across existing VistA sites, and enforces compliance with these new standards. The ETS

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group provides a terminology modeling environment (e.g., formal description logic), deployment processes, and runtime access services for all clinical applications. In 2005, the VA’s national HDR project began aggregating standardized coded data. This required standardized terminology across all VA sites. (A separate “historical database” also began aggregating non-standard legacy data). In addition, the VA began to reengineer the VistA MUMPS-based applications using Java and relational database management systems. The resulting new clinical information system, called HealtheVet, was designed to provide more robust data aggregation and decision support functionality and also to interoperate with partners such as the DoD. This paper describes the concomitant DS and ETS programs and details the migration solutions that were developed for the initial implementation and ongoing maintenance of terminology data standards. It also describes the positive impact of terminology standardization on the creation of the national HDR, on consistent decision support throughout the enterprise, and on the VA-DoD computable patient data exchange project. Methods Data Standardization at the VA. The objective of Data Standardization is to present all VA sites with one national standards-based health language. The VA considered a mapping strategy where local differences would continue to exist at individual sites but would be mapped to a central standard prior to data storage into the national HDR [6]. However, this approach was rejected because it would require ongoing costly mapping, would be associated with lower quality data due to mapping errors, and would not improve standard documentation practices among clinical users. Standards Development. Data Standardization follows a domain-based approach (e.g., Vitals, Allergies, Pharmacy, Laboratory). The VA creates a Domain Action Team for each domain. Team stakeholders include data analysts, terminologists, architects, application developers, subject matter experts, and clinician end-users. The team analyzes required coded attributes for a given domain, relevant recommendations from CHI and NCVHS, and existing VistA reference data to create a candidate standard. The resulting candidate is

subsequently reviewed and approved by all stakeholders based on an internal balloting process. Terminologists then model the standard terms with their associated codes, relationships, and properties in a terminology database that uses commercial ontology management software. Standards Implementation. The Domain Action Teams pass the approved standards to Domain Implementation Teams, which include terminology analysts, developers, clinical application coordinators, and VistA package custodians. The Data Implementation Teams decide for each domain whether to map historical VistA data elements to the new standards or to proceed with implementation of standardized data after a given start date. For example, based on clinical and business rules, Vitals data is standardized from a certain date forward, while Allergies data is mapped to the standard from the earliest coded descriptors in the database. This allows some legacy data among VA sites to be identified and mapped to the standard when appropriate. But this is a one-time mapping process, not the ongoing mapping process that was previously rejected. VistA Application Changes. During implementation, as illustrated in Table 1, each site’s term file is compared to the new national standard. Based on computer and expert reviews, positive matches receive a VA Unique Identifier (VUID), an effective date, and an active status (active=1). Local terms that are not included in the standard also receive a unique VUID, but they receive an inactive status (inactive=0). Standard terms that are not found in a local file are added with an active status. Finally, all duplicate occurrences of a concept at different sites receive the same VUID. Table 1: Standard Implementation of allergy data at a site. The ‘Activation Status’ field may contain multiple values, since an entry can be activated and inactivated over time. Allergen Name pollen

VUID

‘mondays’

320101

201015

Activation Status Active Status 1 0 1 Active Status 0


Effective Date 01/20/2005 01/18/2005 01/01/1901 Effective Date 09/01/2005

Hence, implementation requires addition of new VUID and Activation Status fields, which are defined consistently across domains, as well as addition of new entries (active standard terms that are not already available in local files). This strategy requires only minor changes to VistA applications, which minimizes the impact on users and increases the likelihood of successful implementation [5]. VistA applications must read the new Activation Status field and limit user selection to active entries only. Inactive terms are still accessible but only to support display of legacy data. After a new standard has been deployed, the local files are locked to preserve standardization. File maintenance then shifts from the package developer to the Enterprise Terminology Services (ETS) group. Enterprise Terminology Services: Data Maintenance, Deployment, and Runtime Access. Following the process of data standardization, the enterprise is now ready for maintenance and deployment of standardized terminology. Each designated standard code set is loaded into the terminology content database to instantiate the domain semantic relationships and to enable maintenance and distribution to the sites. Terminology Creation and Maintenance. The ETS team reviews and analyzes existing terminology models as well as working implementations of these models [7, 8] to continuously refine its own. The VA currently uses Apelon’s Terminology Development Environment (TDE) software for content creation and maintenance and for description logic classification. Domain data stewards oversee the content work, including internal terminology development, Standards Development Organization (SDO) updates, and site-submitted requests for additions or modifications. Legacy VistA data is relatively ‘flat’ with a simple information model. In HealtheVet, new business requirements demand a richer semantic model with inter-concept and inter-terminology linkages. Data stewards author these enhancements. For example, the legacy VistA National Drug File (NDF) has migrated to a rich ontology called NDF-Reference Terminology (NDF-RT) [9]. The VA has adopted several SDO terminologies including ICD-9 CM, CPT, LOINC, SNOMED CT, and the HL7 vocabulary

tables. However, SDO data is used only within the VA after it has been represented by a VAspecific term and received a VUID through a unique ID distributor available to all data stewards. Existing and new concept relationships are imported or created as required by the clinical application consumers of that data. Subsets of terms can also be created using enumeration or queries (e.g., allergy reactions). Concepts, their textual descriptions, relationships, and subsets are the fundamental building blocks of the VA terminology model. Another important activity for terminologists is to ensure data interoperability by representing the mapping relationship from VA-specific concepts to CHI and NCVHS standards. For example, DoD to VA Pharmacy interoperability is enabled by the use of the National Library of Medicine RxNorm standard for clinical drugs. Eventually, national terminology standards will become stable and responsive, and the VA and others will use them “natively.” Finally, VA terminologists also host and manage the New Term Rapid Turnaround (NTRT) process. This process provides domain-specific Web forms that end-users or site application coordinators use to request needed but apparently missing concepts. The forms collect the concept, synonyms, and other metadata (e.g., proposed drug class) specific to the domain. Requests are then routed to the appropriate domain data stewards. Requestors can access the NTRT Web site to see the status of the request at any time. Requestors receive email notification when processing of requests is completed. Terminology Deployment. With appropriate board oversight, data stewards approve subsets of new or changed concepts for deployment. The changes are distributed to the VA’s 128 VistA sites using version 2.4 HL7 messages (RDF/RDT structure) that are routed through an interface engine using a VistA core utility to update the targeted VistA M files. The system supports verification of receipt through application acknowledgements, verification of update completion through MD5 checksum algorithms, and roll-back of deployment sets. Runtime Terminology Access Services. In legacy VistA, each package provides access to its domain term file to all other applications. HealtheVet will migrate to an enterprise-centric, standards-based approach to terminology


services using the HL7 Common Terminology Services (CTS) standard [10]. See Figure 1 below. Results Data Standardization Enables Creation of a National HDR. Following implementation of standardized data, all newly created VA patient data and some historical data are forwarded to the national HDR. This occurs through a trigger and push mechanism, via HL7 messages that contain standard VUIDs as the payload of observation segments. Existing data associated with an active VUID may also be transmitted to the HDR, while nonstandard data is transmitted in readable but non-computable form to a “historical data” repository. To date, Vitals, Allergies, Pharmacy, and Laboratory (Chemistry/Hematology) data from 128 VA sites has been standardized and aggregated into HDR. Data Standardization Expands Decision Support Capabilities. VistA decision support modules such as Drug-Allergy Interactions, Drug-Drug Interactions, and Duplicate Therapy checks are taking advantage of the newly available computable standard data from multiple sites. For example, the Drug Allergy-Drug Order check queries the HDR dynamically each time a prescription order is placed. This allows an alert to be generated based on a drug order that is placed at one site and a drug allergy that was recorded at a different site. This decision support capability can be extended to data that is exchanged across enterprises as shown below. Data Standardization and Interoperability: VADoD Clinical Data Repository/Health Data Repository (CHDR). CHDR allows sharing of computable patient data for patients that are seen both at the VA and the DoD. It is estimated that roughly 40 percent of veterans receive care outside of the VA system. In summer 2006, CHDR began providing computable outpatient Pharmacy and Allergies information to both partners to drive automated data review and decision support. Using the CHI-designated RxNorm, UMLS, and SNOMED CT standards, terminology for drug names, ingredients, classes, and allergy reactions is mediated between the VA and the DoD. For example, VA clinical drug identifiers are translated to RxNorm concept unique identifiers (RXCUIs) and then to their corresponding DoD identifiers. As a result, 95 percent of the medications that are stored at the national VA repository will be understood and

computable at the DoD and conversely. To preserve interoperability over time, the two agencies and the CHI SDOs are discussing a coordinated terminology content maintenance plan where updates to each system would be communicated to the other. Preferably, the deployment schedules would be harmonized so that updates to one system are communicated with the CHI standard and the other system before becoming operational. Today, this work is just beginning and is prone to errors and delays. Improved communication and workflow automation will be considered as requirements for the next phases of refinements to the CHDR project. Discussion Large-scale technology migration projects often fail for predictable reasons, including too-large scope, changing business needs, undocumented dependencies, and others [5]. Mindful of these lessons, the VA is migrating its clinical applications incrementally, domain-by-domain, to minimize impact on care and clinical documentation. Before the VA began standardizing data, each VistA application owned and managed its own clinical data. Local site changes over time increased variability; and reference terminologies and ontologies were not employed. Our eventual reliance on a (virtually) completely computerized patient record, combined with the national HDR and CHDR projects, made data standardization and reference terminology urgent priorities. Some of the “lessons learned” through the VA experience follow. First, the VA requires a robust standardization process and terminology development environment to scale up to the task. Sixty staff members including clinicians, informaticians, and developers work on 50 domains and manage terminology centrally. Second, deployment must start now but be carried out with minimal impact to currently running applications, especially when no paper record exists to act as a safety net. Third, a rapidly responsive new term request process was absolutely demanded by users and required for patient safety. Fourth, given the VA statutory requirement to retain medical records in usable form for 75 years and given the imperfect status of current standards, we derived the VA terminology from SDO terminologies as much as possible and implemented VUIDs that remain


address this technology and move closer to the consistent across domains and independent from goal of providing an electronic health record for any external, possibly ephemeral organization. every individual. We hope the VA experience with implementing national terminology standards will help others VA Data Standards Development Standardization Organizations (SDOs)

NTRT

Local Application Coordinator

Users

Terminology Development HL7 Messages

Database Replication

VistA

HealtheVet HL7 CTS-Compliant Terminology Server

M Reference Data Files M Applications

NTRT

New Java Applications

M Patient Data Files

Users

CHDR

HL7

HDR

DoD

Figure 1: Information flow diagram of standard terminology from a central Terminology Development environment to the existing VA clinical information system - VistA (HL7 messages updates to MUMPS files) and future HealtheVet system (database replication to an HL7-compliant Common Terminology Server). References 1.

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3.

Asch, S. M. et. al. Comparison of Quality of Care for Patients in the Veterans Health Administration and Patients in a National Sample. Ann Intern Med 2004;141:938-945

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Graham, G, et al. Information Everywhere: How the EHR Transformed Care at VA. Journal of AHIMA 74, no.3 (2003): 20-24

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Brodie ML, Stonebraker M. Migrating Legacy Systems: Gateways, Interfaces, and the Incremental Approach. Morgan Kaufman Series, San Francisco, 1995.

6.

Lau, LM and Shakib, S. Towards Data Interoperability: Practical Issues in Terminology Implementation and Mapping. In: HIC 2005: Thirteenth National Health Informatics Conference ; HINZ 2005: Fourth Health Informatics Conference; pages: [208213].

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Dolin RH, Mattison JE, Cohn S, et al. Kaiser Permanente's Convergent Medical Terminology. Medinfo. 2004;11(Pt 1):346-50.

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Cimino JJ. From Data to Knowledge through Concept-Oriented Terminologies: Experience with the Medical Entities Dictionary. J Am Med Inform Assoc. 2000 May-Jun;7(3):28897.

9.

Brown SH, Elkin PL, Rosenbloom ST, Husser C, Bauer BA, Lincoln MJ, Carter J, Erlbaum M, Tuttle MS. VA National Drug File Reference Terminology: a Cross-Institutional Content Coverage Study. MedInfo. 2004;11(Pt 1):477-81.

10. Health Level 7 Vocab TC CTS specifications: http://www.hl7.org/library/committees/ vocab/CTS%20II.ppt. Last accessed 3/16/05
