Lesson 1Supporting dimension tables: provider, location, facility, code lookupsDis section explain supporting dimension tables such as provider, location, facility, and code lookups. E cover hierarchies, slowly changing attributes, and how well-designed dimensions improve filtering, grouping, and drill-down analyses in Sierra Leone context.
Provider and specialty dimensionsLocation and facility hierarchiesClinical code and value set lookupsManaging slowly changing dimensionsLesson 2Encounter/Visit entity: admit/arrival, discharge, visit type and timestampsDis section describe di encounter or visit entity, including admission, arrival, discharge, visit type, and timestamps. E cover linking encounters to patients, locations, and payers, and support length-of-stay and throughput metrics for us.
Encounter types and classificationsAdmission, transfer, and discharge timesLinking encounters to patientsVisit grouping and episode logicLesson 3Canonical patient entity: identifiers, demography, merges and survivorshipDis section define a canonical patient entity for analytics, covering identifiers, demographics, merges, and survivorship rules. E explain mastering across sources, handling duplicates, and preserving historical identity changes safely in our system.
Core patient identifiers and keysDemographic attributes for analyticsPatient matching and merge logicSurvivorship and source precedenceLesson 4Procedures and orders entities: procedure codes, order IDs, performing providerDis section cover modeling procedures and orders, including procedure codes, order identifiers, and performing providers. E explain linking orders to results, scheduling, and status, while supporting utilization, quality, and throughput analytics here.
Order header and line item structureProcedure and order coding standardsLinking orders, procedures, and resultsOrder status, timing, and priorityLesson 5Keys and relationships: patient_id, encounter_id, result linking and referential integrityDis section detail how patient, encounter, and result keys maintain referential integrity across clinical datasets. E cover natural versus surrogate keys, cascading rules, and strategies for handling late-arriving or corrected records in Sierra Leone.
Patient and encounter key designResult and order linkage patternsSurrogate keys vs natural identifiersCascades, deletes, and orphan recordsLesson 6Diagnoses and problem list entities: fields, code system, severity, onset and resolutionDis section focus on diagnoses and problem list entities, including fields for codes, status, severity, onset, and resolution. E address coding systems, chronic versus acute problems, and handling revisions or inactivation over time for us.
Core diagnosis and problem fieldsICD, SNOMED, and other code systemsOnset, resolution, and episode timingActive, historical, and resolved problemsLesson 7Lab result entity design: test code, specimen, collection time, result value, units, reference range, statusDis section detail lab result entity design, including test codes, specimens, collection times, values, units, reference ranges, and statuses. E address abnormal flags, panels, and handling corrected or repeated results for analytics in our labs.
Test, panel, and component structureSpecimen type and collection detailsResult value, units, and reference rangeResult status, flags, and correctionsLesson 8Schematic examples: star schema for analytics and entity relationship mappingDis section introduce star schemas for clinical analytics and contrast dem with entity-relationship diagrams. Learners go see how facts, dimensions, and relationships map to EHR concepts and support performant analytic queries for Sierra Leone.
Clinical fact and dimension tablesStar vs snowflake in healthcareMapping EHR entities to factsBridging many-to-many clinical linksLesson 9Principles of analytics data modeling vs transactional modelingDis section compare analytic and transactional data models in healthcare. E explain normalization, denormalization, query patterns, and workload characteristics, guiding choices dat balance performance, flexibility, and data quality for our context.
OLTP vs OLAP workloads in EHRsNormalization and denormalization tradeoffsSlowly changing clinical attributesModeling for longitudinal patient views
