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Enabling Ubiquitous Personal Health Device Integration through Open Standards PowerPoint Presentation
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Enabling Ubiquitous Personal Health Device Integration through Open Standards

Enabling Ubiquitous Personal Health Device Integration through Open Standards

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Enabling Ubiquitous Personal Health Device Integration through Open Standards

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  1. Enabling Ubiquitous Personal Health Device Integration through Open Standards Steven A. Demurjian and Maifi Khan Computer Science & Engineering Department The University of Connecticut 371 Fairfield Road, Storrs, CT Thomas P. Agresta and Michael Blechner Family Medicine & Pathology Departments University of Connecticut Health Center 263 Farmington Avenue, Farmington, CT steve@engr.uconn.edu, mok11002@engr.uconn.edu agresta@nso1.uchc.edu, mblechner@uchc.edu

  2. Introduction • Today’s Applications and Systems Built around Multiple Technologies • APIs, Cloud Computing, Web Services, Data Mining, etc. • Alternative Data Structure Standards • XML, RDF, JSON, OWL, etc. • Meta-Systems that Share, Use and Exchange Information to fully function • XML as de-facto Standard • What are the Key Challenges? • Information Interoperation from Multiple Sources • Integration of Today’s and Emerging Technologies

  3. Interplay of Information in Healthcare RxTerms XML Schema MS Health Vault LOINC XML Schema C# Data MeSH XML DTD Harvard SMART EHR SNOMED XML Schema ASP.NET API JSON-LD XML-C XML-C REST API Secure XML UMLS XML DTD Standards Open mHealth Secure XML JSON XML-C RxNorm XML Schema USES USES Secure XML Global Security Policy and Control Health Information Exchange Secure XML EPIC LucyPHR Server XML PHA Patient App Mobile JAVA APIs Secure CDA OpenEMR EHR Server PHA Provider Mobile App Secure CDA XML Java APIs SMARTSync App XML Converter GE Centricity EHRServer CCR Local Security Policy/Control WSDL PHI Secure PHI

  4. Personal Health Assistant PHAand SMARTSync • PHA: • Mobile App for Medication and Chronic Disease Management • iOS/Android Versions hooked to MS Healthvault • Patient Version to Enter Data/Authorize Providers • Provider Version to View Authorized Data • SMARTSync (Harvard SMART Platform) • SMART EMR and MS Healthvault • Medication Reconciliation • Identify: Overmedication, Adverse Interactions, and Adverse Reactions • Uses Standards and Online Resources NDR-RT and RxNorm

  5. PHA and SMARTSync Architecture

  6. PHA – Patient Version

  7. PHA – Patient Version

  8. PHA – Patient Version

  9. PHA – Provider Version

  10. SMARTSync and InteractionsYellow: Significant Red: Critical

  11. Proposed Apprach • Leverage and Extend our HIE Architecture • MSHV Supports • Medical Devices: blood pressure monitors, glucose meters, scales, medi-watches, pedometers • Exercise Data Types and API: Allow Information to be Stored – fitbit can download into MSHV • Objective: Support Clinical Decision Support on: • Medications, Observations of Daily Living • Chronic Disease Management, Exercise/Diet Logs • Aim for Trending and Potential to Alert Stakeholders (Patients, Insurers, Providers)

  12. Compatibility of our Approach • Interoperation Architecture Facilitates Communication Across Multiple Formats (XML, JSON, REST API) • Our Approach Consistent with Current Personal Health Devices • fitbit, withings, and bodymedia all Use REST API and Oauth • withings uses JSON to Return Responses • iHealth stores information in Cloud

  13. Planned Approach • Expand HIE Architecture to Wide Range of Sources • Personal Health Records (MSHV, WebMD) • Personal Health Devices (fitbits, withings, etc.) • Electronic Health Records • CIGNA Data Sources • Propose and Analyze Other Possible Architectural Alternatives • Focus on Current and Emerging Platforms • Rapid Changes in Mobile Technologies • Identify Relevant Analytics for CIGNA Stakeholders

  14. Team • Steven Demurjian • Software/HIE Architectures, Access Control • Maifi Khan • Cloud Computing, Real-time Health Care Monitoring, Wireless Sensor Networks • Thomas Agresta • Clinical Practice, Analyzing Clinical Data, HIE • Michael Belchner • Pathology Info Systems, HIE, i2b2/Data Analytics

  15. S. Demurjian BMI Research Interests • Collaborative Extensions to NIST RBAC • Model When and How Interactions Occur • Support PCMH and Collaborative Care • Security for XML • Medical Standards (HL7 CDA, CCR, etc.) • Customize XML Instances Delivered to Users • Health Information Exchange • Architectural Solutions for Interoperability • Medication Management & Reconciliation • Android/IOS Apps Linked to MS Health Vault • Reconciliation via Harvard’s Smart Platform • Working with openMhealth.org

  16. M. Khan BMI Research Interests Real-time Monitoring and Preventative Healthcare Real-time Access of Patients’ Sensor Data Cloud-based Storage Architecture Identification of “Early” Symptoms Reliability and Troubleshooting of Edge Clients Troubleshooting Low-power Sensor Devices Real-time Failure Diagnosis Software Architecture for Self-powered Devices Fail-safe Energy Management Algorithms Energy Harvesting Algorithms

  17. Thomas Agresta BMI Research Interests • Optimal HIT Solutions for Primary Care • Clinical Decision Support, Patient engagement • Health Information Exchange for Care Transitions • Organizational structures for supporting Primary Care Informatics • Clinical Informatics Education • Informatics Educational Methods and Strategies • Use of Simulation in Primary Care Education • Virtual patients, families & EMR use in clinical care • High- Tech, High-Touch Primary Care • Informatics Tools for Collaboration in Clinical Research Informatics • Secondary Use of Healthcare data for analysis and research

  18. M. Blechner BMI Research Interests • Health Information Exchange • “Leveraging An HIE Infrastructure To Build A Clinical Research Data Warehouse” • CICATS pilot grant for system development to capture clinical data from a Health Information Exchange (HIE) into a research data warehouse. • Intelligent Tutoring System Development • Collaborative e-learning environment leveraging natural language processing and medical ontologies (UMLS) to facilitate concept relationship discovery • Clinical Pathology • Data warehousing for business and clinical intelligence in the clinical laboratory