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CIGNA-1 Enabling Ubiquitous Personal Health Device Integration through Open Standards Steven A. Demurjian and Maifi Khan Computer Science & Engineering.

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Presentation on theme: "CIGNA-1 Enabling Ubiquitous Personal Health Device Integration through Open Standards Steven A. Demurjian and Maifi Khan Computer Science & Engineering."— Presentation transcript:

1 CIGNA-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

2 CIGNA-2  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 TechnologiesIntroduction

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

4 CIGNA-4  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 Personal Health Assistant PHA and SMARTSync

5 CIGNA-5 PHA and SMARTSync Architecture

6 CIGNA-6 PHA – Patient Version

7 CIGNA-7 PHA – Patient Version

8 CIGNA-8 PHA – Patient Version

9 CIGNA-9 PHA – Provider Version

10 CIGNA-10 SMARTSync and Interactions Yellow: Significant Red: Critical

11 CIGNA-11  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) Proposed Apprach

12 CIGNA-12  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 Compatibility of our Approach

13 CIGNA-13  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 Planned Approach

14 CIGNA-14  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 AnalyticsTeam

15 CIGNA-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 CIGNA-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 CIGNA-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 CIGNA-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


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