SHARPn Tools in Beacon Laboratory

1. Principal Investigator: Christopher G. Chute, MD, DrPH Program Director: Lacey Hart, MBA, PMP® SHARPn Tools in Beacon Laboratory Project 1 – Clinical Data Normalization Services and Pipelines Project 2 – Natural Language Processing (AP Photo/Seth Wenig) Project 3 – High Throughput Phenotyping Project 4 – Scaling Capacity Area 4: Program (SHARPn) A phenotype is an observable set of characteristics about a patient, such as a diagnosis, demographics, and a set of lab results. Identifying a cohort of patients with a similar phenotype is the first step in asking a question about what their health outcomes were or for intervention. Data normalization is at the heart of secondary use of clinical data. If the data is not comparable between sources, it can’t be aggregated into large datasets and used reliably to answer research questions or survey populations from multiple health organizations. Mayo Clinic, a longtime 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. Collaborating Partners: 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 Beacon Laboratory • The Beacon Communities are looking for ways to use their HIT infrastructure to provide better, more efficient care. By creating tangible tools, services, and software for large-scale health record data sharing, the SHARPn project will ultimately help improve the quality and efficiency of patient care through the use of an electronic health care record. • This research is being applied in the Southeast Minnesota Beacon ‘laboratory’ where the framework of open-source services can be dynamically configured to transform EHR data into standards-conforming, comparable information suitable for large-scale analyses, inferencing, and integration of disparate health data for the region. • The SE Minnesota Beacon needs to identify high-risk diabetes patients in its population. This Community consists of multiple sites, with multiple EHR systems. • The SHARPn pilot in Southeast Minnesota will utilize… • Natural language processing • High-Throughput Phenotyping using a diabetes algorithm, and • Deep Question Answering on the UIMA platform • …to identify high-risk patients in the Beacon population and effectively target resources. • Mayo Clinic’s SHARP normalization (SHARPn) is committed to open-source resources that can industrially scale to address barriers to the broad-based, facile, and ethical use of EHR data for secondary purposes. SHARPn will make these artifacts available to the other Beacon communities as consumers of secondary EHR data use as open source tools, services, and scalable software. • HTP stands for high-throughput phenotyping. To phenotype patients in the past meant a laborious paper chart review. Now, using the tools developed by SHARPn and its collaborators, the majority of this task can be completed using electronic data much more efficiently. • The SHARPn HTP project will allow clinicians and investigators to identify patients from their EHR data. The project is developing: • phenotyping processes • algorithms for specific diseases • tools to incorporate data from multiple sites • Clinical data comes in all different forms even for the same piece of information. • For example, age could be reported as 40 years for an adult, 18 months for a toddler or 3 days for an infant. • Without normalization, data can’t be used as a single a dataset. Mission • Leveraging Health Informatics To: • Generate New Knowledge • Improve Care • Address Population Needs • Support Community of EHR Data Consumers by Developing: • Open-source Tools • Services • Scalable Software Natural Language Processing (NLP) systems can extract structured information from these notes that allows the information contained there to be searched, for example for a diagnosis, compared, perhaps to find common co-morbidities with a certain diagnosis, and summarized. SHARPn leverages "Deep Question Answering" of IBM’s Unstructured Information Management Architecture’s (UIMA) robust technology. UIMA lets the Watson supercomputer play Jeopardy and make sense of EHR data. Area 4: Program Advisory 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 Federal Steering SubCommitttee: Janet Woodcock (FDA) Ken Buetow (NCI/NIH) Milt Corn (NLM/NIH) Laura Conn (CDC) Ram Sriram (NIST) Watson expects the science to help extend the power of advanced analytics to make sense of vast quantities of structured and unstructured data where it could help clinicians to diagnose and treat patients. UIMA software is available through Apache, an open license. • Why Natural Language Processing? • …because a lot of clinical data is captured in free-text notes. • Extracting structured information facilitates… • Searching • Comparing • Summarizing • …to enable research, improve standards of care and evaluate outcomes easily. • Clinical Text Analysis and Knowledge Extraction System (cTAKES) is open-source software for natural language processing. One application of cTAKES(3) is a medication annotator. By processing a clinical note, the annotator can extract what medication is being taken, when, how, for how long, and how often. It can also determine if the medication is no longer being taken. Goals I’ll take “Informatics Acronyms” for $1000, Alex. References Research that will enable aggregation of different types of healthcare data collected from a variety of sources. Services to identify diseases, risk factors, eligibility for clinical studies, or adverse events in clinical and population-based settings. Tools to understand and improve data quality. What do SHARPn and Jeopardy! have in common? • SHARP Area 4: http://sharpn.org • Southeast Minnesota Beacon. www.semnbeacon.org • cTAKES Tool: https://cabig-kc.nci.nih.gov/Vocab/KC/index.php/OHNLP_Documentation_and_Downloads • UIMA: http://uima.apache.org/ The answer is… “UIMA” Right again, WATSON!  2011 Mayo Foundation for Medical Education and Research