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OpenMRS Concept Management Tutorial

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OpenMRS Concept Management Tutorial presented on 9 Dec 2015 at the OpenMRS Worldwide Summit in Singapore. Presented by Andy Kanter and Ellen Ball. 4 hour presentation.

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OpenMRS Concept Management Tutorial

  1. 1. Andrew S. Kanter, MD MPH FACMIa,b,c a Intelligent Medical Objects, Inc., Chicago, USA b Department of Biomedical Informatics, Columbia University, New York, NY, USA c Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA Ellen Ball Partners In Health, Boston, USA OpenMRS Concept Management OpenMRS Worldwide Summit 9 December 2015 Singapore
  2. 2. Topics • Terminology 101 • OpenMRS data model and concepts • Controlled terminology and reference mappings • Management of concept dictionary • Usage on forms and reports • Future
  3. 3. Introduction and Disclosure • Andy (ask2164@cumc.columbia.edu) • OpenMRS Leadership (Terminology and Meta Data Lead) • Direct Columbia International eHealth Lab • Department of Biomedical Informatics • Department of Epidemiology/MSPH • Board Member/Director of Clinical Integration for Intelligent Medical Objects (IMO) •Ellen (eball@pih.org) •Implemented OpenMRS at Partners In Health Haiti, Rwanda, Lesotho, Malawi, Peru, Liberia, and Sierra Leone
  4. 4. Terminology 101 Terminology about terminology (independent of OpenMRS)
  5. 5. Why vocabulary matters… ● Clinical users of EHRs resist the constraints of structured documentation ● Users and administrators underestimate the complexity and difficulty of data mining ● Data is dirty, misplaced, and/or incomplete ● Humans think conceptually, systems store data literally ● Everything we want to do depends on how meaning is recorded in the information system. Clinical intent is paramount and you get one chance to capture it correctly!
  6. 6. Terminology Reporting/data mining Clinical data model Decision support Clinical data entry/review Informatics Infrastructure
  7. 7. The Interoperability-Adoption Tug-of-War ● Interoperability requires standards and limited scope ● Adoption favors customization and local preferences producing broad scope
  8. 8. Terminology about Terminology ● Concepts and Concept Dictionary ● Descriptions—strings, terms, lexicals, CONCEPT_NAME ● Words—keywords, index terms ● Relationships—maps, CONCEPT_REFERENCE_MAP ● Administrative codes ● Reference terminology ● Interface terminology ● Groups—value sets, convenience sets
  9. 9. Terminology about Terminology (cont) ● Domains ● Granularity—broader vs. more specific ● Pre-coordination ● Post-coordination
  10. 10. Concepts ● The actual meaning is a phrase or even a paragraph. ● Developed at the right level for the user ● Severe right knee pain ● Liver dysfunction ● Can have many different descriptions but all have the same meaning ● Assigned a non-sensical numeric identifier ● Meaning often developed through relationships to other concepts ● One description often flagged as the default name
  11. 11. Concepts ● Goal: default description (fully specified name) sufficient to understand the concept ● Unambiguously defined ● Have one domain ● Can provide more semantics around concept than default description ● Fully specified name includes appended domain, e.g., cough (finding) vs. cough (symptom)
  12. 12. Descriptions ● A collection of text strings or terms ● perennial allergic rhinitis ● seasonal allergies (hay fever) ● allergic rhinitis, seasonal ● hay fever ● perennial rhinitis ● perennial allergies ● …
  13. 13. Descriptions ● May need context for full understanding ● Fever ● Patient reported they felt feverish ● Patient reported they took their temperature with thermometer ● Healthcare provider took temperature and was elevated to… ● Acronym - Careful— ARV = “Anti-rabies vaccination” or “Antiretroviral”? ● Pragmatics ● Brain tumor ● malignant neoplasm of brain/Neoplasm of brain/Brain mass ● Breast CA ● Breast cancer / Breast carcinoma
  14. 14. Description attributes ● Unique code ● Audience ● MD, ancillary health, patients ● Length (cell phone, etc.) ● Search friendly (word order important) ● Display to user vs. recognize as mapped to concept ● Locale, language, country, etc.
  15. 15. Case style ● Right case** ● CHF (congestive heart failure) ● Sentence case* ● Spine fracture ● Title case ● Spine Fracture ● Upper case ● SPINE FRACTURE
  16. 16. Words ● Definition ● Not obvious ● Alphanumerics separated by non-alphanumerics ● What about apostrophes like Alzheimer’s or peau d’orange? ● Words ensure consistency with searching ● Not every concept will have a description with all misspellings or word variants ● Hepatic failure vs. liver failure
  17. 17. Relationships and Mappings ● One of the defining features of an ontology, i.e., relationships between concepts ● Drawing the lines between concepts or between concepts and codes ● Relationship types ● Can be more complex than parent-child (Is-A) ● “Severe anemia” is narrower-than Anemia ● Other examples, has-location, has-severity, has-laterality
  18. 18. User interface terminology (descriptions) AMI (alternate term) Myocardial infarction, acute (entry term) Acute MI (alternate term) Acute myocardial infarction (preferred) Reference terminology Acute ischemic heart disease Ischemic heart disease Structural disorder of the heart myocardial disease heart disease disease of cardiovascular system Myocardial infarction Mycardial necrosis Concepts Acute myocardial infarction Words Heart, cardiac, myocardium, myocardial, infarction, CV, attack, AMI, acute, …
  19. 19. Mappings (type of relationship) ● One or more external codes mapped to each concept ● ICD10 code B54.9 ● SNOMED code 2423424211 ● UMLS code C0018621 ● Need relationship type ● Is it broader than, narrower than, same as…? ● Important for inference
  20. 20. Mappings and Inference ● Malaria ● Same as SNOMED CT 61462000 (Malaria) ● Same as ICD-10 B54 (Unspecified malaria) ● Severe malaria ● Narrower than SNOMED CT 61462000 (Malaria) ● In both eyes ● Narrower than SNOMED CT 54485002 (ophthalmic use)
  21. 21. Concept-oriented terminologydescriptions concepts Named relationships Description attributes external codes
  22. 22. Administrative terminology ● Used primarily for classification ● Major examples include: ● ICD (ICD-10-WHO, ICD-10-CM, etc.) ● CPT® ● Not particularly good for capturing clinical data ● Often used for billing and reimbursement and some reporting
  23. 23. Administrative terminology ● ICD-10-CM is now mandated for use in the US as of 10/15 ● Differences between ICD-9-CM, ICD-10 and ICD-10-CM ● 13,000 ICD-9-CM to 68,000+ for ICD-10-CM ● 3-5 digits for ICD-9 compared to 3-7 for ICD-10 ● ICD-9 had only a few alpha codes, all ICD-10 codes start with a letter ● Combination codes for conditions and common symptoms or manifestations and for poisonings and external causes ● Added laterality
  24. 24. Reference terminology ● Concept-based ● Controlled medical terminology ● Often ontological ● Major examples include: ● SNOMED CT ● RxNorm
  25. 25. Interface terminology ● List of terms or phrases ● Supports clinician entry into electronic systems ● Multiple descriptions may mean the same “concept” ● May have unique identifiers ● Major examples include: ● IMO Problem (IT), Procedure (IT) ● Vanderbilt Terminology
  26. 26. Groups ● Used for providing a list for user selection ● Used for providing Allergen class-ingredients ● Can be published value set for quality reporting ● Extensional value sets used for meaningful use ● Asthma, active diagnosis with set list of ICD or SNOMED CT codes ● Can be programmatic for decision support ● Intensional value set based on logic such as ● All children of SNOMED code xxxxx ● Includes with children A, B, C but excludes D
  27. 27. Pre-coordination ● More user friendly ● Examples ● Acinar cell carcinoma of the pancreas ● Severe right knee pain ● Recurrent intravascular papillary endothelial hyperplasia of the right middle finger ● Recurrent intravascular papillary endothelial hyperplasia of the right ring finger……. ● Combinatorial explosion
  28. 28. Post-coordination ● Clinical concept assembled at point of care ● Core concept identified ● Location selected ● Optional severity
  29. 29. Examples Pre-coordination Post-coordination Acinar cell carcinoma of the pancreas carcinoma of pancreas + acinar cell carcinoma Severe right knee pain knee pain + right + severe Recurrent intravascular papillary endothelial hyperplasia of the right middle finger intravascular papillary endothelial hyperplasia + middle finger structure + right
  30. 30. Terminology Process 1. Core terminology content development including mapping to standards (code mapping) 2. Specialized domain content development (including subsetting of content, expansion of content, etc.) 3. Mapping of user requirements to specific concepts (field mapping) 4. Deployment of content within the software platform (including searching within forms, data capture tools, etc.) 5. Meta-data modeling and information modeling including schema design 6. Ontolological work including building of aggregate indicators and measures (including maps to standard quality measures, etc.) 7. Reporting/Analysis using common algorithms, formulae and concepts 8. Transactional translation or tagging for on-the-fly encoding of concepts including natural language processing
  31. 31. Class Introductions •Name, role, and organization •Goal for tutorial •Describe problem
  32. 32. OpenMRS Concepts and Data Model
  33. 33. OpenMRS concept dictionary •A collection of concepts •CIEL, PIH, Kenya, etc. •Forks, subsets, and supersets •Local or central management
  34. 34. Concept creation workflow Paper form, list of data fields, or indicators Concept analysis in existing forms Propose new concept in CIEL or use existing concept Add language, description, synonyms, and mappings
  35. 35. Which concepts?
  36. 36. What is an OpenMRS concept?
  37. 37. Data model: Concepts •concept_id •class •datatype •description •names • fully specified vs preferred name • synonyms • locale
  38. 38. Data model: Concept classes
  39. 39. Data model: Coded answers
  40. 40. Data model: Convenience sets, etc.
  41. 41. Data model: Concept data type
  42. 42. Example numeric concept
  43. 43. Data model: Concept name type
  44. 44. Data model: Locale ● ISO Language code (en, fr, es, ht, etc) ● Language+country Anemia (en-US), Anaemia (en-GB), Anémie (fr) ● UTF-8
  45. 45. OpenMRS Model Visit Encounter Obs Concept visit_id encounter_id obs_id person_id concept_id value_coded value_numeric value_text value_boolean value_drug value_datetime
  46. 46. OpenMRS Model: person table: cause_of_death concept concept.causeOfDeath = 9713 global_property table: property_value might be concept concept.cd4 = 5497 person_attribute_type table: name = Civil status format = org.openmrs.concept format_key = 1054
  47. 47. OpenMRS and Terminology Model Concept Names (Interface) Concept Codes (Interface) Reference Terms Reference Sources Reference Relationships ICD-10-WHO SNOMED CT LOINC IS-A Has … Concept Map Drugs (Interface)
  48. 48. Data model: Drugs
  49. 49. Leveraging Reference Maps Reasons for using shared concepts
  50. 50. Why not just use ICD-10 or SNOMED? • Admin/Reference terms change which require changing reports and forms • Clinicians don’t use terms like • Other disease of blood & blood-forming organ • SNOMED is post-coordinated • Hard to say fracture of RIGHT arm
  51. 51. So why should OpenMRS share concepts? • Interoperability of data between applications and between organizations • Ability to share forms, data collection tools • Ability to share reports • Ability to share decision support rules
  52. 52. Immunization Decision Support
  53. 53. Leveraging Maps for Reporting • There are multiple CIEL concepts mapped to the same ICD or SNOMED code • Use Reference_Reference_Map to build subsumption queries • CIEL/OCL to add map for particular value sets
  54. 54. Reporting using maps
  55. 55. Managing a concept dictionary Strategies, translation, etc.
  56. 56. Concept management scenarios Standalone All concepts managed locally PIH Malawi Master/Slave Concepts maintained on central server CIEL with subscription PIH Haiti with mds PIH Rwanda with sync Central Curation Open Concept Lab (OCL)
  57. 57. CIEL Concept Dictionary • Contains most diseases, procedures and medications (>49,000 concepts) • Mapped to SNOMED CT, ICD-10, 3BT, RxNorm, LOINC and CVX codes. • Several Languages: SNOMED CT 49,514 ICD-10-WHO 40,015 RxNORM 5,599 LOINC 390 3BT 7,703 68,275 en 4001 vi 62 bn 30 rw 32,630 es 2,737 fr 51 ru 29 ht 11,760 nl 242 sw 51 ti 13 am 7 om
  58. 58. CIEL Included in Appliances
  59. 59. 311 users in 40+ countries CIEL Dictionary via Dropbox
  60. 60. Dropbox has all versions
  61. 61. Terminology-related Modules • Metadata Sharing Module (MDS) • Validation Module • Terminology Service Bureau
  62. 62. Metadata Sharing Module (MDS)
  63. 63. Validation Module
  64. 64. Terminology Service Bureau- 50,000 concepts
  65. 65. Terminology Service Bureau
  66. 66. Terminology Service Bureau
  67. 67. Interface Terms for Africa SNOMED CT English French Kinyarwanda Swahili 271737000 Anemia Anémie Kubura amaraso Upungufu wa damu 87282003 Intestinal parasites Parasitoses intestinales Inzoka Minyoo 61462000 Malaria Paludisme Malariya Homa ya malaria 2492009 Malnutrition Malnutrition Indwara z’imirire mibi Utapia mlo 14189004 Measles Rougeole Iseru Ukambi
  68. 68. Working with forms HTML form entry, custom modules
  69. 69. Example form
  70. 70. HTML form entry
  71. 71. Searching DB using ICD or Text
  72. 72. Example form using set for UI
  73. 73. Data model: Convenience sets, etc.
  74. 74. Future Open Concept Lab, sustainability
  75. 75. Open Concept Lab- Jonathan Payne • Beta customer is Kenya EMR • Working with Kenyan Community and ITECH • 9 months behind schedule • Focusing on API then UI • Initial Beta testing complete
  76. 76. Open MRS OpenMRSSubscription Subscription Process • Create OCL user to get an OCL API token • Install OCL Subscription Module in your OpenMRS instance and configure to subscribe to a specific source • On first synchronization, pulls entire dictionary • On subsequent synchronization, pulls latest changes only (e.g. new concepts, updates, deletes, retires) • Does NOT overwrite local concepts or concept metadata (based on concept and concept metadata UUIDs) Open Concept Lab OCL API OCL Subscription Module
  77. 77. Open Concept Lab (OCL)
  78. 78. OpenHIE and Terminology Management Terminology Management Service 2 1 2 1 • OCL as source of content for the TS. • Requires local TS. • Appropriate for high- volume, real-time transactions (e.g. code validation, lookups, transformations, etc.). • OCL provides canonical source(s) to HIE, subscription service, & collaborative management tool. • NOT for real-time, high- volume transactions. • Alleviates need for local service.
  79. 79. Terminology Sustainability • Looking for additional community leadership (Judy, Jonathan, etc.) • Basic support and funding from Columbia is running out • Looking for sustaining support ($150K/y) • Partnering with OCL/IMO
  80. 80. Proposed OCL Sustainability Model FREE BASIC PREMIUM ENTERPRISE Target • Existing CIEL User-base • Researchers, harmonization, terminology geeks • Dictionary managers, e.g. AMPATH, PIH, CIEL • Governments or institutions managing terminology as a core service; require guaranteed level of service Features • Access to all OCL functionality for CIEL dictionary only • Limits on the number of subsets you can create/manage • OpenMRS Subscription to CIEL dictionary • Includes access to CIEL community content • Limited API access • Access to major terminology sources in addition to CIEL (ICD-10, LOINC, SNOMED, etc.) • No limit on collections • Ability to propose content for curation in one of the “managed” dictionaries (i.e. CIEL) • Create your own sources • Full API Access • Guaranteed level of service for terminology curation • Assistance importing local/proprietary terminology sources • Configuration of organizational workspace • Additional training and services available Initial User Base • OpenMRS + CIEL Subscriptions: >100 • MCL: 16k lookups/searchers; 2k unique visitors in last year • THRIVE/WHO • Partners In Health • Kenya Ministry of Health
  81. 81. OCL Roadmap 2015 Q3 • OCL Launched with Kenya MOH! • Basic functionality complete: –Full-text search –Create users and organizations –Build your own sources and create/edit concepts and mappings –Export of sources using AWS • CIEL dictionary imported • All functionality implemented through APIs • OpenMRS subscription to a single source (e.g. CIEL dictionary) 2015 Q4 • Begin implementing sustainability model and signing up paid clients • Optimized search (e.g. better weighting of search terms to improve likelihood of finding the correct result) • Full support for creating and managing collections (i.e. references to concepts from other sources) • Import WHO ICD-10 source • CIEL transition to managing dictionary on OCL instead of in OpenMRS • Secured access to OCL website and API (e.g. https encryption) • Stability and performance improvements (esp. imports, exports) Potential Future Features • FHIR API compatibility • Import additional sources, including SNOMED CT, LOINC • RSS feeds of changes to sources, collections, and concepts • Social functionality • Improved organization management - better control of access to content for members of an organization • Ability for users to "star" sources, collections, and concepts • Collection/source comparisons • Ability for users to "follow" organizations or other users
  82. 82. Resources ● Open Concept Lab (OCL) – http://openconceptlab.com ● Maternal Concept Lab (MCL) – http://maternalconceptlab.com ● ICD10 (2016) ○ English http://apps.who.int/classifications/icd10/browse/2016/en ○ French http://apps.who.int/classifications/icd10/browse/2016/fr ● LOINC - https://loinc.org/ ● SNOMED CT- http://http://browser.ihtsdotools.org/ ● OpenMRS modules - https://modules.openmrs.org ○ Metadata Sharing (MDS) ○ Validation ○ Groovy

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