Jeremy L Warner1, Matthew J Rioth2, Kenneth D Mandl3, Joshua C Mandel3, David A Kreda4, Isaac S Kohane5, Daniel Carbone6, Ross Oreto6, Lucy Wang6, Shilin Zhu7, Heming Yao8, Gil Alterovitz9. 1. Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA jeremy.warner@vanderbilt.edu. 2. Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. 3. Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. 4. Independent Consultant, New York, NY, USA. 5. Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Pediatric Endocrinology, Boston Children's Hospital, Boston, MA, USA. 6. Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA. 7. Department of Electrical Engineering and Information Science, University of Science and Technology of China, Hefei, China. 8. Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA. 9. Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Abstract
BACKGROUND: Precision cancer medicine (PCM) will require ready access to genomic data within the clinical workflow and tools to assist clinical interpretation and enable decisions. Since most electronic health record (EHR) systems do not yet provide such functionality, we developed an EHR-agnostic, clinico-genomic mobile app to demonstrate several features that will be needed for point-of-care conversations. METHODS: Our prototype, called Substitutable Medical Applications and Reusable Technology (SMART)® PCM, visualizes genomic information in real time, comparing a patient's diagnosis-specific somatic gene mutations detected by PCR-based hotspot testing to a population-level set of comparable data. The initial prototype works for patient specimens with 0 or 1 detected mutation. Genomics extensions were created for the Health Level Seven® Fast Healthcare Interoperability Resources (FHIR)® standard; otherwise, the prototype is a normal SMART on FHIR app. RESULTS: The PCM prototype can rapidly present a visualization that compares a patient's somatic genomic alterations against a distribution built from more than 3000 patients, along with context-specific links to external knowledge bases. Initial evaluation by oncologists provided important feedback about the prototype's strengths and weaknesses. We added several requested enhancements and successfully demonstrated the app at the inaugural American Society of Clinical Oncology Interoperability Demonstration; we have also begun to expand visualization capabilities to include cancer specimens with multiple mutations. DISCUSSION: PCM is open-source software for clinicians to present the individual patient within the population-level spectrum of cancer somatic mutations. The app can be implemented on any SMART on FHIR-enabled EHRs, and future versions of PCM should be able to evolve in parallel with external knowledge bases.
BACKGROUND: Precision cancer medicine (PCM) will require ready access to genomic data within the clinical workflow and tools to assist clinical interpretation and enable decisions. Since most electronic health record (EHR) systems do not yet provide such functionality, we developed an EHR-agnostic, clinico-genomic mobile app to demonstrate several features that will be needed for point-of-care conversations. METHODS: Our prototype, called Substitutable Medical Applications and Reusable Technology (SMART)® PCM, visualizes genomic information in real time, comparing a patient's diagnosis-specific somatic gene mutations detected by PCR-based hotspot testing to a population-level set of comparable data. The initial prototype works for patient specimens with 0 or 1 detected mutation. Genomics extensions were created for the Health Level Seven® Fast Healthcare Interoperability Resources (FHIR)® standard; otherwise, the prototype is a normal SMART on FHIR app. RESULTS: The PCM prototype can rapidly present a visualization that compares a patient's somatic genomic alterations against a distribution built from more than 3000 patients, along with context-specific links to external knowledge bases. Initial evaluation by oncologists provided important feedback about the prototype's strengths and weaknesses. We added several requested enhancements and successfully demonstrated the app at the inaugural American Society of Clinical Oncology Interoperability Demonstration; we have also begun to expand visualization capabilities to include cancer specimens with multiple mutations. DISCUSSION: PCM is open-source software for clinicians to present the individual patient within the population-level spectrum of cancer somatic mutations. The app can be implemented on any SMART on FHIR-enabled EHRs, and future versions of PCM should be able to evolve in parallel with external knowledge bases.
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