A Prototype Knowledge Base and    SMART App to Facilitate     Organization of PatientMedications by Clinical Problems     ...
Summarization Aims• Develop methodologies that:  – Model and summarize complex, chronically-ill    patients’ EHR data  – E...
Research Focus• Develop a knowledge base for linking  medications and problems• Evaluate the knowledge base with real  pat...
Ontology-Based Summarization• The Unified Medical Language System  Metathesaurus (UMLS)   – RxNorm, SNOMED CT, NDF-RT     ...
Ontology-Based Summarization• Generated over 7 million problem–  medication linksRxCUI                     Medication     ...
Evaluation Setting• Large, multi-specialty ambulatory  academic practice for adults, adolescents  and children• Electronic...
Evaluation Population• 5000 randomly selected patients  • At least one outpatient encounter during    July 1, 2010-Decembe...
Knowledge Base Utility• Knowledge base linked 5,251 medications  to problems  – 10,738 total links with manual provider li...
Knowledge Base Accuracy• Expert review of 25 patients with ≥ 3  problems and ≥ 5 medications• 82.1% positive predictive va...
SMART Architecture• Substitutable Medical Apps, Reusable  Technologies• Allows “iPhone-like” substitutability for  medical...
SMART App Development• SMART Javascript Library• Developed and tested in the SMART  Reference EMR  – 50 test patients  – M...
SMART App Demonstration                          12
13
14
Future Directions• Expand knowledge base to include medications  with “isa” relationship• Improve local mapping to standar...
Summary• Ontology-based summarization knowledge  base can assist problem-medication  linking• SMART app effectively utiliz...
Acknowledgments• Funding   – National Center for Cognitive Informatics and Decision     Making in Healthcare SHARP Program...
Thank YouEmail: allison.b.mccoy@uth.tmc.edu      Twitter: allisonbmccoy                                     19
A Prototype Knowledge Base and SMART App to Facilitate Organization of Patient Medications by Clinical Problems
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A Prototype Knowledge Base and SMART App to Facilitate Organization of Patient Medications by Clinical Problems

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Increasing use of electronic health records requires comprehensive patient-centered views of clinical data. We describe a prototype knowledge base and SMART app that facilitates organization of patient medications by clinical problems, comprising a preliminary step in building such patient-centered views. The knowledge base includes 7,164,444 distinct problem-medication links, generated from RxNorm, SNOMED CT, and NDF-RT within the UMLS Metathesaurus. In an evaluation of the knowledge base applied to 5000 de-identified patient records, 22.4% of medications linked to an entry in the patient’s active problem list, compared to 32.6% of medications manually linked by providers; 46.5% of total links were unique to the knowledge base, not added by providers. Expert review of a random patient subset estimated a sensitivity of 37.1% and specificity of 98.9%. The SMART API successfully utilized the knowledge base to generate problem-medication links for test patients. Future work is necessary to improve knowledge base sensitivity and efficiency.

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A Prototype Knowledge Base and SMART App to Facilitate Organization of Patient Medications by Clinical Problems

  1. 1. A Prototype Knowledge Base and SMART App to Facilitate Organization of PatientMedications by Clinical Problems Allison B. McCoy, PhD Adam Wright, PhD, Archana Laxmisan, MD, MA Hardeep Singh, MD, MPH, Dean F. Sittig, PhD
  2. 2. Summarization Aims• Develop methodologies that: – Model and summarize complex, chronically-ill patients’ EHR data – Enhance decision making with context- appropriate, evidence-based recommendations• To improve clinician decision-making under information overload and time pressure 2
  3. 3. Research Focus• Develop a knowledge base for linking medications and problems• Evaluate the knowledge base with real patient data• Implement a prototype app that utilizes the knowledge base to summarize patient data 3
  4. 4. Ontology-Based Summarization• The Unified Medical Language System Metathesaurus (UMLS) – RxNorm, SNOMED CT, NDF-RT NDF-RT “may_treat” NDF-RT SNOMED CTRxNorm CUI Preparation Disease Concept “isa” SNOMED CT Concept 4
  5. 5. Ontology-Based Summarization• Generated over 7 million problem– medication linksRxCUI Medication SNOMED Code Problem 198211 SIMVASTATIN 40 MG Oral (systemic) tablet 13644009 Hypercholesterolaemia 198211 SIMVASTATIN 40 MG Oral (systemic) tablet 302870006 Hypertriglyceridaemia 198211 SIMVASTATIN 40 MG Oral (systemic) tablet 3744001 Hyperlipoproteinaemia 198211 SIMVASTATIN 40 MG Oral (systemic) tablet 129589009 Endogenous hyperlipaemia … … … … 200345 SIMVASTATIN 80 MG Oral (systemic) tablet 13644009 Hypercholesterolaemia 200345 SIMVASTATIN 80 MG Oral (systemic) tablet 302870006 Hypertriglyceridaemia 200345 SIMVASTATIN 80 MG Oral (systemic) tablet 3744001 Hyperlipoproteinaemia 200345 SIMVASTATIN 80 MG Oral (systemic) tablet 129589009 Endogenous hyperlipaemia … … … … 5
  6. 6. Evaluation Setting• Large, multi-specialty ambulatory academic practice for adults, adolescents and children• Electronic health record utilization• Manual links enabled between a medication and a diagnosis within the patient’s clinical problem list 6
  7. 7. Evaluation Population• 5000 randomly selected patients • At least one outpatient encounter during July 1, 2010-December 31, 2010 – At least one active, coded clinical problem and medication – Included only problems and medications mapped to SNOMED CT and RxNorm 7
  8. 8. Knowledge Base Utility• Knowledge base linked 5,251 medications to problems – 10,738 total links with manual provider links – 4,763 medications not previously linked by providers (47% of total links) 8
  9. 9. Knowledge Base Accuracy• Expert review of 25 patients with ≥ 3 problems and ≥ 5 medications• 82.1% positive predictive value – Ex. Linking Prednisone 20 MG Tablet to Hypertrophic Scar – false due to wrong route• 37.1% sensitivity – Ex. Difference in precision of medication and problem entries in EHR compared to KB – Ex. Relationships within UMLS are incomplete – Ex. Incomplete medication and problem mappings in EHR to standards 9
  10. 10. SMART Architecture• Substitutable Medical Apps, Reusable Technologies• Allows “iPhone-like” substitutability for medical apps 10
  11. 11. SMART App Development• SMART Javascript Library• Developed and tested in the SMART Reference EMR – 50 test patients – Medications mapped to RxNorm – Problems mapped to SNOMED CT 11
  12. 12. SMART App Demonstration 12
  13. 13. 13
  14. 14. 14
  15. 15. Future Directions• Expand knowledge base to include medications with “isa” relationship• Improve local mapping to standardized terminologies• Compare accuracy with knowledge bases developed using data mining and other methods• Expand knowledge base to include other data types (e.g., lab values)• Implement knowledge base into live EHR• Evaluate use of summarization and knowledge base on clinical practice 16
  16. 16. Summary• Ontology-based summarization knowledge base can assist problem-medication linking• SMART app effectively utilizes knowledge base to display problem-oriented medication list 17
  17. 17. Acknowledgments• Funding – National Center for Cognitive Informatics and Decision Making in Healthcare SHARP Program Award (Grant No. 10510949) – Houston VA HSR&D Center of Excellence (HFP90-020) – NCRR Grant (3UL1RR024148) – UT Houston-Memorial Hermann Center for Healthcare Quality and Safety• Collaborators – SHARP-C Project 3 Members – UTHealth CDW Team – UTHealth SBMI IT Support – SMART Team 18
  18. 18. Thank YouEmail: allison.b.mccoy@uth.tmc.edu Twitter: allisonbmccoy 19

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