1 CDA IG for Genetic Testing Reports – Towards a Clinical Genomic Statement Shabo A. Abstract— Genetic tests have recently become an important tool in clinical care that further personalizes the care processes based on the patient's genetic profile or even individual genetic makeup. Genetic testing methods are diverse and span from testing for known germline mutations in the context of single-gene disorders, to full sequencing of genes in tumor tissues looking for somatic variations in cancer cells. Most of the genetic tests look for genetic variations or use cytogenetic methods but there is a growing use of gene expression testing kits in clinical care and other genomic research techniques slowly propagate to healthcare. The CDA Implementation Guide for Genetic Testing Reports is an attempt to suggest a standard framework for reports that summarize genetic testing results, targeting both clinicians and applications. The GTR has an extensive use of human readable content such as background information and recommendations which are necessary but hard to present in a definite way because of the relatively higher level of uncertainly and sometimes controversy of genetic testing results. This makes the narrative somewhat fuzzy and thus difficult to structure. With regard to the actual findings, these are represented as clinical genomic statements as further explained in detail. The GTR section outline is built on a single summary section at the top, along with multiple sections describing specific types of genetic tests (e.g., genetic variation, cytogenetics, etc.). The summary section consists of an overall interpretation summarizing the interpretations of each of the specific genetic tests (see figure 1). I. INTRODUCTION T he CDA [1] Implementation Guide (IG) for Genetic Testing Reports (GTR) is being developed by the HL7 Clinical Genomics Work Group (co-sponsored by the Structured Documents Work Group), using the OHT MDHT tool for CDA [2]. The GTR can be used by all producers of genetic testing reports such as genetic labs and clinical geneticists. As a consequence of genetic testing diversity and the constantly growing number of techniques yielding new result formats less familiar to the receiving party (e.g., referring physician), it is crucial to have a report format with emphasis on detailed but easy-to-understand interpretations of the testing results along with concrete recommendations. Nevertheless, background information on performed tests is important as well to allow the receiving party to assess the test reliability especially in relatively new tests. The GTR is part of an attempt to bridge the gap between the markedly different worlds of medical informatics and bioinformatics by referencing and building on the HL7 Clinical Genomics standards [3]. These standards cope with that gap by encapsulating raw genomic data along with the most clinically-significant data items (so-call bubbled-up) represented by HL7 objects possibly associated with phenotypic information. In contrast, the GTR consists of merely the bubbled-up information along with the displayable information. For example, full DNA sequences of tested genes can be encapsulated along with certain variations along these sequences that are clinically meaningful to the patient. Amnon Shabo (Shvo) is with the IBM Research Lab in Haifa (phone: 97254-4714070; fax: 972-4-8296116; e-mail: shabo@il.ibm.com). Fig.1. Section Outline of the CDA IG for GTR (screen snapshot taken from the Eclipse-based OHT MDHT tool for developing CDA IGs). There are several design challenges in the development of the GTR that originate in the genomic information complexity as well as in the need to make genomic data relevant to clinical care and more importantly readable and comprehendible to clinicians at the point of care. The following sections describe some of the main challenges. II. DESIGN CHALLENGES A. Granularity This implementation guide copes with the challenge of how fine grain clinical genomic data exchanged through proprietary or HL7 Clinical Genomics messages would be represented using the CDA model. The best example is a full sequencing test of several genes where specific segments of each gene were sequenced. Using existing HL7 Clinical Genomics 2 specifications (e.g., the Genetic Variation CMET) it is possible to convey this raw genomic data set, however for the sake of a human-readable report summarizing genetic test results, it seems better if that messaging granularity of data will not be modeled into the GTR, rather optionally be referenced when needed. It is important to note that CDA R2 is limited in its expressive power and it is the current basis of GTR. If GTR is upgraded to CDA R3 (when R3 becomes available), then this design decision will be revisited. B. Clinical Genomic Statement The challenge of the GTR's structured data is to come up with an abstract template for a 'Clinical Genomic Statement'. The template under ballot has, at its core, a genomic observation (e.g., a genetic variation) along with necessary information like time, method and performer. This observation is then associated with the indications of making this observation and with interpretations of that observation (see figure 2). Following the HL7 Clinical Genomics Domain, an important design principle is to disallow the use of the interpretationCode attribute of the genomic observation because it is important to have it as a separate observation where time, method and other attributes could be different than those of the genomic observation. Fig. 2. The Clinical Genomic Statement main structure. C. Universal vs. Specific Templates Another challenge is how to define a universal implementation guide that can accommodate the needs described above, which could then be further refined to specific genetic testing reports, either realm specific or method-specific etc. The design principle currently used is to have a Universal document template and derive specific templates from the parent Universal one, e.g., by binding to different codes required by each realm. It is expected to see implementation guides derived from the Universal GTR for geographic realms, e.g., a US Realm-specific GTR could mandate the use of LOINC codes developed for genetic testing results [4] and are used in the Universal spec as examples (see figure 3). It is also foreseen to have different GTR templates for research and pharmaceutical efforts, which follow specific work flows unique to the sponsoring organization (e.g., a pharma company) and the GTR needs to support it. Fig. 3. An example of a LOINC value set utilized in GTR. These value sets were initially developed as part of an HL7 v2 implementation guide [5]. III. INITIAL IMPLEMENTATIONS The GTR is balloted as a Draft Standard for Trial Use and is being experimented in several efforts, for example: (1) In a Korean Patient Empowerment System [6] where patients can manage their health data through a web portal, the system can import clinical data from sources such as EHR and PHR systems as well as from health monitoring devices and genetic labs. The system includes healthcare services such as the Personalized Adverse Drug Event alerts service, which also uses pharmacogenetic knowledge-bases and combines it with the patient clinical and genetic data. The system maintains the clinical data in HL7 CCD instances while the GTR is used for handling the genetic testing reports. (2) Translational Software is a startup company that is building a Web-based portal for ordering and interpreting genetic tests. The portal will implement GTR for interpretive results and to enable laboratories to deliver test results to Electronic Medical Record systems in HL7 standards using both narrative and structured data [7]. IV. CONCLUSION The CDA IG for GTR is an attempt to bridge from genetic lab messages with high-resolution genomic data, to human readable reports consisting of both narrative and structured entries. While the narrative accommodates the extensive background information and fuzzy recommendations, the structured entries represent the essential data following a 'clinical genomic statement' abstract template that can be used by decision support applications at the point of (personalized) care. ACKNOWLEDGMENT The development of the GTR is a joint effort of members of the HL7 Clinical Genomics Work Group supported by the entire community of HL7 as well as by the developers of the OHT MDHT tool for CDA. REFERENCES [1] [2] [3] [4] [5] [6] [7] R.H Dolin, L. Alschuler, S. Boyer, C. Beebe, F.M. Behlen, P.V. Biron, A. Shabo (2006). HL7 Clinical Document Architecture, Release 2. Journal of the American Medical Informatics Association. 13(1): 30-39. Open Health Tools (OHT) Model-Driven Health Tools (MDHT) Project for CDA [Online). Available: https://www.projects.openhealthtools.org/sf/projects/mdht/ HL7 Clinical Genomics Domain [Online]. Available: http://www.hl7.org Logical Observation Identifiers Names and Codes (LOINC) [Online]. Available: http://loinc.org/ HL7 Version 2 Implementation Guide: Clinical Genomics; Fully LOINC-Qualified Genetic Variation Model, Release 1. HL7; 2009. H. Roitman, Y. Mesika, Y. Tsimerman and Y. Maman. Increasing Patient Safety using Explanation-Driven Personalized Content Recommendation. Proceedings of the 1st ACM International Health Informatics Symposium, Arlington, VA, November 2010. TranslationalSoftware. Available: http://translationalsofware.com .