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

           Allison B. McCoy, PhD
   Adam Wright, PhD, Archana Laxmisan, MD, MA
    Hardeep Singh, MD, MPH, Dean F. Sittig, PhD
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
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
Ontology-Based Summarization
• The Unified Medical Language System
  Metathesaurus (UMLS)
   – RxNorm, SNOMED CT, NDF-RT

              NDF-RT       “may_treat”   NDF-RT    SNOMED CT
RxNorm CUI
             Preparation                 Disease     Concept


                                                        “isa”



                                                   SNOMED CT
                                                     Concept



                                                                4
Ontology-Based Summarization
• Generated over 7 million problem–
  medication links
RxCUI                     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
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
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
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
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
SMART Architecture
• Substitutable Medical Apps, Reusable
  Technologies
• Allows “iPhone-like” substitutability for
  medical apps




                                              10
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
SMART App Demonstration




                          12
13
14
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
Summary
• Ontology-based summarization knowledge
  base can assist problem-medication
  linking
• SMART app effectively utilizes knowledge
  base to display problem-oriented
  medication list



                                         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
Thank You

Email: allison.b.mccoy@uth.tmc.edu

      Twitter: allisonbmccoy




                                     19

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

  • 1. A Prototype Knowledge Base and SMART App to Facilitate Organization of Patient Medications by Clinical Problems Allison B. McCoy, PhD Adam Wright, PhD, Archana Laxmisan, MD, MA Hardeep Singh, MD, MPH, Dean F. Sittig, PhD
  • 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. 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. Ontology-Based Summarization • The Unified Medical Language System Metathesaurus (UMLS) – RxNorm, SNOMED CT, NDF-RT NDF-RT “may_treat” NDF-RT SNOMED CT RxNorm CUI Preparation Disease Concept “isa” SNOMED CT Concept 4
  • 5. Ontology-Based Summarization • Generated over 7 million problem– medication links RxCUI 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. 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. 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. 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. 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. SMART Architecture • Substitutable Medical Apps, Reusable Technologies • Allows “iPhone-like” substitutability for medical apps 10
  • 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
  • 13. 13
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  • 16. 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
  • 17. Summary • Ontology-based summarization knowledge base can assist problem-medication linking • SMART app effectively utilizes knowledge base to display problem-oriented medication list 17
  • 18. 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
  • 19. Thank You Email: allison.b.mccoy@uth.tmc.edu Twitter: allisonbmccoy 19