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Strategic Health IT Advanced Research Projects (SHARP) Secondary Use of EHR Data Principal Investigator: Christopher G. Chute, MD, DrPH Program Manager:

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Presentation on theme: "Strategic Health IT Advanced Research Projects (SHARP) Secondary Use of EHR Data Principal Investigator: Christopher G. Chute, MD, DrPH Program Manager:"— Presentation transcript:

1 Strategic Health IT Advanced Research Projects (SHARP) Secondary Use of EHR Data Principal Investigator: Christopher G. Chute, MD, DrPH Program Manager: Lacey Hart, MBA, PMP Mayo Clinic, Rochester, MN ORGANIZATIONCOLLABORATORS Agilex Technologies, Inc. Centerphase Solutions, Inc. Clinical Data Interchange Standards Consortium (CDISC) Deloitte Group Health Research Institute Harvard Childrens Hospital Boston IBM T.J. Watson Research Center Intermountain Healthcare Mayo Clinic Massachusetts Institute of Technology Minnesota Health Information Exchange (MN HIE) University at Albany - SUNY University of Colorado University of Pittsburgh University of Utah AREA 4 PROGRAM Mayo Clinic, long a leader in the science of health care delivery, is proud to be a recipient of the Area 4 Strategic Health IT Advanced Research Project award. The SHARP Program – part of the Office of the National Coordinator for Health Information Technology, is focused on improving quality, safety and efficiency of health care through Information Technology. Traditionally, a patients medical information, such as medical history, exam data, hospital visits and physician notes, are stored inconsistently and in multiple locations, both electronically and non-electronically. Mayo Clinics program will work towards creating a unified electronic healthcare record (EHR), allowing for the exchange of information among care providers, government agencies, and other stake holders. Through six projects, Mayo Clinics program will: 1. Standardize health data elements and ensure data integrity Patient information can be stored using several different abbreviations and representations for the same piece of data. For example, diabetes mellitus (more commonly referred to as diabetes), can be referred to in a patients medical record alternately as diabetic, and DM. The first phase of the project, called Clinical Data Normalization, will work towards transforming this non-standardized patient data into one unified set terminology. In this case, diabetes mellitus, diabetic, and DM would all be re-named diabetes. 2. Merge and standardize patient data from non-electronic forms with the EHR Some important patient information, such as that from physicians radiology and pathology notes, is stored in non-electronic, or free text form. This project will first work to merge the patient information in free texts with that in the electronic health care record. The next step, called Natural Language Processing (NLP), will work towards classifying certain tags, such as diabetic, DM and 57 year old male under specific categories, such as disease or demographics. NLP, in addition to clinical data normalization, will help improve the efficiency of patient care by reducing inconsistencies in patient data, giving physicians more accurate and uniform information in a centralized location. 3. Seek physically observable patient traits for further study Physically observable traits or phenotypes. These traits result from interactions between a patients genes and environmental conditions. Mayo Clinic will use a process called High-Throughput Phenotyping, which uses clinical data normalization and NLP to identify and group a particular phenotype, such as Type 2 diabetes. This process will enhance a physicians ability to identify and study individual or groups of phenotypes. 4. Find processes to make clinical data normalization, NLP and high-throughput phenotyping more efficient using fewer resources This part of the process will focus on building adequate computing resources and infrastructures to accomplish the previous steps. Called Performance Optimization, this system will allow for those seeking patient information to receive it quickly, increasing the efficiency of patient care. 5. Detect and reconcile inconsistent data Mayo Clinic will utilize high-confidence services, or data quality metrics, to identify and optionally correct inconsistent or conflicting data. 6. Evaluate the progress and efficiency of Mayo Clinics project This program will use an Evaluation Framework using the Nationwide Health Information Network (NHIN). NHIN is a set of standards, services and policies that enable secure health information exchange over the internet. COLLABORATION Learn more about Mayo Clinics SHARP Area 4 Program process at Health IT Pilot Communities through Recovery Act Beacon Community Program Principal Investigators: C. Michael Harper, Jr. M.D.; Christopher G. Chute, MD, DrPH; Douglas L. Wood, M.D. Program Manager: Lacey Hart, MBA, PMP Mayo Clinic, Rochester, MN PROGRAM ADVISORY COMMITTEE Suzanne Bakken, RN DNSc, Columbia University C. David Hardison, PhD, VP SAIC Barbara A. Koenig, PhD, Bioethics, Mayo Clinic Issac Kohane, MD PhD, i2b2 Director, Harvard Marty LaVenture, PhD MPH, Minnesota Department of Health Dan Masys, MD, Chair, Biomedical Informatics, Vanderbilt University Mark A. Musen, MD PhD, Division Head BMIR, Stanford University Robert A. Rizza, MD, Executive Dean for Research, Mayo Clinic Nina Schwenk, MD, Vice Chair Board of Governors, Mayo Clinic Kent A. Spackman, MD PhD, Chief Terminologist, IHTSDO Tevfik Bedirhan Üstün, MD, Coordinator Classifications, WHO SE MN POPULATION HEALTH SE MN community will embrace standards based HIE to improve access, quality and efficiency of health care delivery Childhood Asthma and Diabetes Reduce Emergency room visits Reduce unscheduled MD visits Reduce hospitalization Improve self-reported functioning Improve compliance with the treatment of asthma Improve school attendance Reduce days out of work – self-reported for Diabetes Improve compliance with Diabetes Conceptual Infrastructure BEACON COMMUNITIES Community Services Council of Tulsa, Tulsa, OK Delta Health Alliance, Inc., Stoneville, MS Eastern Maine Healthcare Systems, Brewer, ME Geisinger Clinic, Danville, PA HealthInsight, Salt Lake City, UT Indiana Health Information Exchange, INC., Indianapolis, IN Inland Northwest Health Services, Spokane, WA Louisiana Public Health Institute, New Orleans, LA Mayo Clinic Rochester, Rochester, MN Rhode Island Quality Institute, Providence, RI Rocky Mountain Health Maintenance Organization, Grand Junction, CO Southern Piedmont Community Care Plan, Inc., Concord, NC The Regents of the University of California at San Diego, San Diego, CA University of Hawaii at Hilo, Hilo, HI Western New York Clinical Information Exchange, Inc., Buffalo, NY Learn more about Mayo Clinics Beacon Program at Extend advanced health IT & exchange infrastructure Leverage data to inform specific delivery system & payment strategies Demonstrate a vision of the future where: Hospitals, clinicians, & patients are meaningful users of health IT Communities achieve measurable & sustainable improvements in health care quality, safety, efficiency, and population health Fillmore county Dodge county Freeborn county Goodhue county Houston county Mower county Olmsted county Rice county Steele county Wabasha county Winona county Nursing homes Public health HospitalsHospitals Emergency rooms Home health SchoolsSchools Out-patient clinics Mayo Clinic Olmsted Medical Mayo Health System Winona Health Map data © 2010 Google, INEGI VAVA MN Dept Employee Relat. MN Dept of Health MN Dept Human Services AgilexAgilex MN Cnty Comp Coop MN HIE StratisStratis MayoMayo Olmsted Medical Cntr Olmsted Mayo Health System System WinonaHealthWinonaHealth EMRsEMRs MN HIE exchange Public health (NHs, schools, home health) Public health (NHs, schools, home health) PortalPortal PortalPortal Population management Analysis (and reporting to practice) PortalPortal PortalPortal PortalPortal PortalPortal PortalPortal PortalPortal


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