Lesson 1Supporting dimension tables: provider, location, facility, code lookupsThis part explains dimension tables that support analysis, such as for providers, locations, facilities, and code lookups. It discusses hierarchies, changes over time, and how good designs improve filtering, grouping, and detailed analyses in Eritrean contexts.
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, covering admission, arrival, discharge, visit types, and time stamps. It includes linking to patients, locations, and payers, supporting metrics like stay duration and flow in Eritrea.
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, including identifiers, demographics, merges, and survivorship rules. It covers mastering from various sources, handling duplicates, and keeping historical changes safe in Eritrean systems.
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, with codes, identifiers, and providers. It explains linking to results, scheduling, and status, aiding analysis of use, quality, and flow in Eritrean healthcare.
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 keys for patients, encounters, and results ensure integrity across datasets. It covers natural vs. surrogate keys, cascading rules, and handling late or corrected records in Eritrea.
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 lists, with fields for codes, status, severity, onset, and resolution. It addresses coding systems, chronic vs. acute issues, and managing changes over time in Eritrean records.
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 design for lab results, including test codes, specimens, collection times, values, units, ranges, and statuses. It covers abnormal flags, panels, and handling corrections for analysis in Eritrea.
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 compares them to entity-relationship diagrams. Learners see how facts, dimensions, and relationships connect to EHR ideas and aid efficient queries in Eritrean settings.
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 models in healthcare. It explains normalization, denormalization, query patterns, and workload traits, guiding choices for performance, flexibility, and quality in Eritrea.
OLTP vs OLAP workloads in EHRsNormalization and denormalization tradeoffsSlowly changing clinical attributesModeling for longitudinal patient views
