Using Natural Language Processing To Analyze Electronic Health Records. Philip Poon PhD Data Scientist
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1 Using Natural Language Processing To Analyze Electronic Health Records Philip Poon PhD Data Scientist
2 Agenda Overview of UnityPoint Health and Electronic Health Record Natural Language Processing development Current Use Cases and Results
3 Overview Electronic Health Record (EHR) - Epic Software used to document care provided Accessible by all providers in UnityPoint Health (UPH) Contains structured (lab tests, medications, height, etc.) and unstructured data (physician notes, progress reports, etc.) 21.5M Free-Text Notes from 2015 (42GB) Natural Language Processing (NLP) is applied to turn free text into usable features for modeling and analysis
4 UnityPoint Health Map of UPH Here
5 UnityPoint Health 3788 Beds 155,334 Inpatient visits Hospital or Rehabilitation Stays 5.4M Outpatient visits Clinic visits 900+ Physicians Employees
6 IBM Watson Corpus: 200 million pages of structured and unstructured content (for Jeopardy) Including full copy of Wikipedia Not connected to the internet Has been commercialized for use in healthcare Build medical-specific terms into dictionaries. Develop character rules to recognize patterns of characters that represent specific types of information, (e.g. telephone numbers or vital measures). Develop parsing rules to identify patterns of text that represent particular concepts, such as person names. Unstructured Information Management Architecture (UIMA) pipeline directly to Watson NLP server and automatically create fields and facets that are mapped to the annotations. Analyze documents with a Watson Content Analytics pipeline and view the resulting annotations in ICA Studio.
7 Development Cycle Define Use Case Develop UIMA pipeline Apply to the large document set Analysis and Cross Validation
8 An Example JANE is taking her medications as prescribed.active Problems Allergic rhinitis (477.9) Benign essential hypertension (401.1); JNC 8 goal < 150/90 Degenerative disc disease (722.6); L4-L5 and L5 S1 Depression (311) Dyslexia (784.61) Fusion of spine of lumbar region (756.15) GERD (gastroesophageal reflux disease) (530.81) Glucose intolerance (impaired glucose tolerance) (790.22) Hip osteoarthritis (715.95) Hyperlipidemia (272.4) Hypothyroidism (244.9) Ventral hernia (553.20).PMH History of anemia (V12.3) History of migraine headaches (V12.49).PSH Arthrodesis Lumbar Colonoscopy (Fiberoptic); 2006 Dilation And Curettage Hysterectomy Tonsillectomy Wrist Surgery.Family Hx Benign essential hypertension: Mother,Father,Sibling Family history of alcoholism: Sister,Brother (V17.0) Family history of cataracts: Sister (V19.19) Family history of diabetes mellitus: Mother,Father,Sibling (V18.0) Family history of glaucoma: Sister (V19.11) Family history of malignant neoplasm of urinary bladder: Aunt (V16.52).Personal Hx Lives with parents Never a smoker
9 An Example Active problem: Benign essential hypertension (401.1), impaired glucose tolerance (709.22) PMH: magraine (V12.49) PSH: Arthrodesis Lumbar Colonoscopy (?) Family Hx: Benign essential hypertension: Mother,Father,Sibling; diabetes mellitus: Mother,Father,Sibling (V18.0). Personal history: Lives with parents. Never a smoker.
10 Another Example Have not been able to contact patient by phone 3 times. Per protocol, patient is dropped from COPD education program. Does this patient has COPD? Yes!
11 Challenges Coding Change from ICD9 to ICD10 on Oct unique entries to Messy data (symbols, formatting, misspelled terms) e.g., diabetes melitus
12 Use Case: Chronic Conditions Does our documentation accurately reflect a patient s complexity? Affects how much we re reimbursed for providing care Example: Diabetes Identified about 10% of 330,000 Medicare patients were not properly documented as diabetics Proper documentation would have resulted with big revenue saving.
13 Use Cases: Social Determinants Living arrangements, education, employment, income Can be used in building predictive models and providing actionable information for care givers
14 Thanks! Philip Poon
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