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