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Semantic Retrieval of EMR Semantic Retrieval of Medical Records Related to Patient Symptoms Huimin Zhao (Joint work with Hemant K. Jain, David P. Klemer, and Carmelo Gaudioso) University of Wisconsin – Milwaukee Winter IS 2006, Salt Lake City Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Outline > > > > > Need for Symptom-based EMR Retrieval Related Work Proposed Framework Major Challenges and Research Issues Research Plan Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Movements towards EMR > Having EMR in doctor's offices and hospitals is > both crucial and long overdue There are many active movements towards EMR > “The Decade of Health Information Technology: Delivering > > > > Consumer-centric and Information-rich Health Care" Master Patient Index (MPI) research funded by the National Institute of Standards Integrating the Healthcare Enterprise (IHE) initiative sponsored by RSNA and HIMSS IBHIS in the UK CHIN in Europe Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Some Major EMR Systems > Vista > > > Used by the military and VA facilities > Office version will be offered to all doctors free of charge Cerner > Used by Aurora Epic > Used by Advanced Healthcare (network of 250 physicians in Southeast Wisconsin) > GE Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Need for Symptom-based Retrieval of EMR > Next challenge will be on effective EMR retrieval > > > Size of the EMR databases is enormous > Largely skewed distribution; "90 - 10" rule The current query/search functions in EMR systems are limited An intelligent retrieval system helps doctors identify EMRs relevant to a patient's symptoms Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Commonly-Encountered Scenarios > Emergency room encounters a patient for the first time, presenting with particular symptoms > The patient’s relevant medical history has significant impact on the approach to diagnosis and treatment > Family practitioner sees a new patient, who needs immediate refills for heart medications > Very specific subset of the patient’s records (medications and doses) is needed > Records related to possible condition needs to be accessed while unrelated records ignored Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Related Work > Simply word based Information Retrieval (IR) may not yield satisfactory performance, > due to the complex semantic relationships among symptoms and diagnostic/therapeutic processes > Enhancing free-text queries > using medical lexicons, semantic relationships, and domain knowledge > Most studies devoted to retrieving general medical documents, rather than EMR Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Methods for Query Refinement > EMR retrieval tools > > Medical lexicons (Baud et al. 2001) > Clinical Terms Version 3 (Brown and Sonksen 2000) Medical literature search tools > Domain-specific vocabularies (askMEDLINE, Fontelo et al. 2005) > UMLS (Medical World Search, Suarez et al. 1997; Leroy and Chen 2001; Plovnick and Zeng 2004; Liu and Chu 2005) > MeSH (Malet et al. 1999; Göbel et al 2001) Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Shortcomings in Existing Systems > Existing systems have used some semantic relationships (e.g., synonyms, hyper/hyponyms) in standard ontologies > Mismatches due to terminology discrepancies are reduced. > More complex relationships across symptoms, diagnoses, and therapeutics are not used > Cross references are not handled. Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Preliminary Proposed Framework Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Knowledge Base: the Key > Healthcare ontologies. > Dictionary, lexicon, thesaurus, terminology, classification, semantic network > > > E.g., Unified Medical Language System (UMLS) Domain knowledge elicited from experts > Both global and local knowledge Other heuristics > E.g., term co-occurrence and relevance feedback Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR UMLS > A leading medical ontology developed by the > > National Library of Medicine (NLM). UMLS Knowledge Source Server supports online queries and Java APIs. Consists of three knowledge sources > Metathesaurus > Semantic Network > SPECIALIST lexicon Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR UMLS Knowledge Source Server Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Metathesaurus > Consists of over 60 source vocabularies, > > including ICD, MeSH, SNOMED. Contains synonyms, narrower concepts (hyponyms), and broader concepts (hypernyms), about medical concepts E.g., "colon cancer” > Synonyms: “colorectal cancer”, “colon carcinoma” > Hyponyms: "malignant neoplasm of hepatic flexure of colon” > Hypernyms: “malignant neoplasms” > Can be directly used to expand a user query. Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Co-occurrence Statistics > Metathesaurus also contains term co> > occurrence info based on contributing sources > E.g., “colon”, “intestinal mucosa”, “rectum”, etc. Can be used as initial heuristics of term cooccurrence, refined in EMR databases Can be used as hypothetical related concepts in the domain knowledge base Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Semantic Network > Categorizes concepts in the Metathesaurus and > > relationships among concepts. > 155 semantic types > 54 relationships > E.g., “Antibiotic” “treats” “Disease or Syndrome” Schema only, no instances Can be used to design the schema of the knowledge base Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR SPECIALIST Lexicon > Contains syntactic, morphological, and > orthographic information about medical terms > E.g., pain, pains, pained, paining Can be used to standardize different forms of a term into a uniform basic form Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Major Challenges and Research Issues > Heterogeneity across local systems > Integration with Web services, industry standards (e.g., HL7) > Diversity of media types > Structured data, free text, and images (X-ray, cardiology, ultrasound), handwritten reports > The knowledge acquisition bottleneck > Knowledge engineering on global and local levels > Evolution of domain knowledge > Knowledge base needs to adapt to changes > Privacy, confidentiality, and security > Adequate policies, procedures, and technologies needed. Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Current Status > This research is still in an early planning stage > Formed an interdisciplinary research team, with > > IS researchers and healthcare experts Formulated a preliminary framework Installed Vista-Office and UMLS access Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Ongoing Work > Seeking collaboration with Advanced Healthcare > > > > and Independent Physician Organization. Designing a prototype system Designing a knowledge base for a few sectors (e.g., heart disease, diabetes, asthma) Will extend the knowledge base to other areas Applying for internal and external grants Jain, Zhao, Klemer, & Gaudioso UWM Semantic Retrieval of EMR Comments ! Jain, Zhao, Klemer, & Gaudioso UWM