Lesson 1Supporting dimension tables: provider, location, facility, code lookupsThis part explains supporting dimension tables like provider, location, facility, and code lookups. It covers hierarchies, slowly changing attributes, and how good designs improve filtering, grouping, and detailed analyses in Zimbabwean health data.
Provider and specialty dimensionsLocation and facility hierarchiesClinical code and value set lookupsManaging slowly changing dimensionsLesson 2Encounter/Visit entity: admit/arrival, discharge, visit type and timestampsThis part describes the encounter or visit entity, including admission, arrival, discharge, visit type, and timestamps. It covers linking encounters to patients, locations, and payers, supporting stay length and throughput measures in Zimbabwe.
Encounter types and classificationsAdmission, transfer, and discharge timesLinking encounters to patientsVisit grouping and episode logicLesson 3Canonical patient entity: identifiers, demography, merges and survivorshipThis part defines a standard patient entity for analysis, covering identifiers, demographics, merges, and survivorship rules. It explains mastering across sources, handling duplicates, and keeping historical changes safe in Zimbabwean records.
Core patient identifiers and keysDemographic attributes for analyticsPatient matching and merge logicSurvivorship and source precedenceLesson 4Procedures and orders entities: procedure codes, order IDs, performing providerThis part covers modeling procedures and orders, including codes, order IDs, and performing providers. It explains linking orders to results, scheduling, and status, supporting use, quality, and throughput analysis in Zimbabwe.
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 integrityThis part details how patient, encounter, and result keys keep referential integrity in clinical datasets. It covers natural vs surrogate keys, cascading rules, and handling late or corrected records in Zimbabwean systems.
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 resolutionThis part focuses on diagnoses and problem list entities, including fields for codes, status, severity, onset, and resolution. It addresses coding systems, chronic vs acute problems, and handling changes over time in Zimbabwe.
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, statusThis part details lab result entity design, including test codes, specimens, collection times, values, units, reference ranges, and statuses. It addresses abnormal flags, panels, and handling corrected results for analysis in Zimbabwe.
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 mappingThis part introduces star schemas for clinical analysis and contrasts with entity-relationship diagrams. Learners see how facts, dimensions, and relationships map to EHR concepts for effective queries in Zimbabwean healthcare.
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 modelingThis part compares analytic and transactional data models in healthcare. It explains normalization, denormalization, query patterns, and workload traits, guiding choices for performance, flexibility, and data quality in Zimbabwe.
OLTP vs OLAP workloads in EHRsNormalization and denormalization tradeoffsSlowly changing clinical attributesModeling for longitudinal patient views
