Mining of Clinical Notes to Aid in Drug Surveillance
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1 Mining of Clinical Notes to Aid in Drug Surveillance Nigam Shah, MBBS, PhD Bob Rogers, PhD DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.
2 Conflict of Interest Disclosure Nigam Shah, MBBS, PhD Has no real or apparent conflicts of interest to report HIMSS
3 Conflict of Interest Disclosure Bob Rogers, PhD Salary: Apixio Ownership Interest (stocks, stock options or other ownership interest excluding diversified mutual funds): Apixio 2013 HIMSS
4 Learning Objectives Explain how exchange of information will result in improved safety at lower cost nationally through big data analytics List three major adverse events that can be detected in textual clinical data Estimate the savings associated with identifying adverse events with big data analytics
5 Big Data in Healthcare
6 Unstructured Data in Electronic Records Prevalence of Data Types within the Patient Record >90% of patient data is represented in unstructured text and scanned documents Scanned Documents Text Structured Data
7 Clinical Data Hidden from Mission Critical Processes >63% of key clinical data is NOT coded 11/1/2012
8 Quality Measures: Required Clinical Facts Often Not Accessible in Coded Data CMS PQRS Measure Set CMS ACO Quality Measure Set 69% 31% Admin Data Based Clinical Data Required 65% 35% Admin Data Clinical Data Required HEDIS Measure Set* 37% Admin Data Based 63% Hybrid Measure (uses Clinical Data) 8/21/2012
9 Computer-Assisted Text Mining Example: Diabetic Foot Exam Clinical Encounter Document * Diabetic foot exam * CPT G0245 * Diabetes = 8/21/ Confidential Copyright 2012, Apixio Inc.
10 Optimized Quality Measurement Leveraging Text NQF 0062: Microalbinuria (Diabetes Urine Screening) Textual + Coded Data (with Text Mining) 45% Coded Data (without Text Mining) 22% Client Study Copyright 2012, Apixio Inc. Confidential
11 Optimized Quality Measurement Leveraging NQF 0067: Coronary Artery Disease (CAD): Oral Antiplatelet Therapy Prescribed for Patients Textual + Coded Data (with Text Mining) Coded Data (without Text Mining) 37% (26/71) Denominator Exclusions = 1 44% (47/106) Denominator Exclusions = 170 Client Study 8/21/2012
12 Heart Failure Heart Failure?
13 Or Chart Failure? Heart Failure No Heart Failure
14 The Healthcare Analytics Platform Processes clinical notes Analyzes entire patient history Provides real-time access to information Is scalable
15 Drug Safety Surveillance
16 Finding drug safety signals AERS (SRS) Medical records Clinical trials Biological DBs Chemical DBs PubMed Health forums Physician query logs Web search logs
17 A dumb algorithm with lots of data beats a clever one with modest amounts of it
18 Detecting associations at scale Clinical data warehouse Associations of interest A lot of the data is text!
19 From notes to patient feature matrix Ontologies provide features & dependencies BioPortal knowledge graph Procedures Devices Diseases Drugs Creating clean lexicons Term 1 : : : Term n Term recognition tool NCBO Annotator Annotation Workflow Negation detection Family History detection Text clinical note NegEx Patterns Terms Recognized Negation detection Simple text processing, Feature extraction Drugs Diseases Devices Procedures
20 T1 Tn P : : Pn Who is getting these drugs, conditions, etc? T1 Tn T : : Tn 1 Drug Safety Practice-based evidence P1 Pn P : : Pn 1 What is special about these patients? Comparative Effectiveness Predictions
21 Drug safety Analyzing the patient feature matrix for drug safety surveillance
22 Concomitant drug use More than 76% of older Americans are on two or more drugs
23 Polypharmacy The use of more than one drug used in combination to treat one disease e.g. cancer treatment e.g. diabetes treatment metformin + rosiglitazone metformin + pioglitazone e.g. Hypertension ACE inhibitor + something
24 Comorbidities Many diseases are correlated Diabetes and high cholesterol Hypertension and heart disease These patients are commonly on multiple drugs to treat their diseases
25 In-patients Patients admitted to the hospital are often on many drugs
26 Finding drug safety signals AERS (SRS) Medical records Clinical trials Biological DBs Chemical DBs PubMed Health forums Physician query logs Web search logs
27 Questions worth asking Diabetes Hypertensi on GERD Metformin Lisinopril Omeprazol Risky situation Sources: Coded data from the EMR AERS reports Literature Biochemical assays Text from the drug label Free-text from the EMR Lab values Patient blogs Search logs How many risky situations exist? How many of my patients are at risk? Am I in a risky situation? If yes, what are my choices?
28 Turning text into safety signals Text clinical note x 10 7 Patient-feature matrix rows = patients columns = medical concepts Can detect 7 out of 9 recalls in the past decade MI No MI Vioxx a b No Vioxx c d
29 Methods Temporal information: First mention Ind.-drug-event pattern Measures: OR, OR025-OR975 Threshold: OR025>1 Adjustments: First pass restrict cohorts by indication Second pass use PSM to adjust for age, gender, race, length of observation, and health status (# of prescriptions & # co-morbidities)
30 Time to signal Using STRIDE data and our annotation pipeline we were able to signal 6/9 ADRs earlier than withdrawal date (OR025>1). (rosiglitazon-mi, celecoxib-mi, cisapride-qt, pioglitzone-bladder cancer, valdecoxib-mi)
31 Performance by event
32 Gold Standard based on the EU-ADR validation set: 28 positive test cases 165 negative test cases 12 events 78 drugs Overall accuracy
33 Drug drug interactions from text Gold Standard from Medispan, DrugBank, NDF-RT: 466 positive test cases 466 negative test cases 10 events 333 drugs
34 Suggesting options Among patients treated with ACE inhibitors [ for heart failure...], the spironolactone prescription rate was 34 per 1000 patients in 1994 After the publication of RALES, it increased to 149 per 1000 patients by The rate of hospitalization for hyperkalemia rose from 2.4 per 1000 patients in 1994 to 11.0 per 1000 patients in 2001 The associated mortality rose from 0.3 per 1000 to 2.0 per 1000 patients. Publication of RALES was not associated with significant decreases in the rates of readmission for heart failure or death from all causes. Juurlink et al N Engl J Med 2004; 351: August 5, 2004 DOI: /NEJMoa Event drug1 drug2 Odds hyperkalemia spironolactone benazepril hyperkalemia captopril spironolactone hyperkalemia enalapril spironolactone Hyperkalemia spironolactone quinapril Hyperkalemia spironolactone lisinopril hyperkalemia spironolactone ramipril hyperkalemia spironolactone fosinopril Could we make a suggestion by data-mining?
35 True negatives are tricky
36 Dimethylarginine Dimethylaminohydrolase (DDAH) Metabolizes ADMA to preserve NOS activity NOS NO + citrulline ADMA Cardiovascular Risk Factors and Oxidative Stress DDAH Dimethylamine + Citrulline L-Arginine Ito et al Circulation 1999; Lin et al Circulation 2001; Stuehlinger et al Circulation 2003; Weis et al Circulation 2004
37 Investigating PPI safety PPIs increase ADMA. ADMA is associated with MACE. Do PPIs Increase the risk of MACE? What do the clinical notes say?
38 New discovery or false positive? Text clinical note x 10 7? Patient-feature matrix rows = patients columns = medical concepts
39 Text clinical notes x 10 7 Predict Depression
40 Acknowledgements Paea LePendu, Anna Bauer-Mehren, Srini Iyer Rave Harpaz, Ken Jung, Amogh Vasekar Tyler Cole, Sandy Huang John Cooke, Yohannes Ghebremariam NIH NCBO funding (PI, Mark Musen) NIH i2b2 funding (PI, Zak Kohane) DOM Start up funds STRIDE Team Todd Ferris Tanya Podchiyska Alex Skrenchuk, BMIR-IT
41 Nigam Shah, MBBS, PhD Thank You! Bob Rogers,
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