Forum Discussion
Healthcare Semantic Model
Hello all,
I have been having some difficulty trying make the most efficient semantic model I can for my report. I'm finding a classic star schema to be challanging due to the content of my data. I'll do my best to summarize the organization of the data, in hope it can generate some ideas.
General list of tables:
Person_MRN (MRN as PK and Person_ID as FK)
Person (Person_id as PK and wider table about the person)
Condition_occurrence (features Person_ID, Visit_occurrence_id, diagnosis vist info)
procedure_occurrence (features Person_ID, Visit_occurrence_id, procedural vist info)
visit_occurrence (visit_occurrence_id as PK and care_site_id as FK)
care_site (Care_site_id as PK)
The design of my report is just to identify the distinct count of MRN with a filter selected condition and/or procedure, and the care site breakdown of those meeting the filter requirments.
I am asking to map MRN -> condition_occurrence.person_id -> visit_occurrence.visit_occurrence_id -> Care_site.care_site_name
but I also need MRN -> procedure_occurrence.person_id -> visit_occurrence.visit_occurrence_id -> Care_site.care_site_name
since patients have more than one condition and/or procedure, I have to map through the visit_occurrence_ID's in order to capture the unique visits. There could even be a case where a distinct MRN with a distinct procedure was seen at more than one care site.
I tried to make views to coalesce the information, but I haven't found the perfect fit yet. Any thoughts are appreciated!
I am asking to map MRN -> condition_occurrence.person_id -> visit_occurrence.visit_occurrence_id -> Care_site.care_site_name but I also need MRN -> procedure_occurrence.person_id -> visit_occurrence.visit_occurrence_id -> Care_site.care_site_namedoesn't that defeat the purpose of the de-identification ?
Here are some guidelines to follow when designing a star schema, especially since you are dealing with healthcare data:
1. Your Fact table(s) should be normalized
2. Your Dimension tables should be de-normalized
Given the nature of healthcare data, I would almost say you absolutely need a EAV style Fact table. Relational database purists pooh-pooh EAV, but with the proper knowledge, you will come to really appreciate just how versatile they are. The number of attribute columns will be dictated by the number of Dimension tables. See below.
How many Dimension tables should you have? Six, a dozen? Well, you need to consider how users will want to slice the data and how heirachical those slices are.
The short rule is you need a Dimension table for each slicer e.g. Date, Product, Geography, etc, but you don't want seperate Dimension tables for Product Category and Product Subcategory because they live in the same slicer as a hierarchy.
The most intuitive example is a Date dimension table which has any number of hierarchies from day, week, month, quarter, etc. You de-normalize all that hierarchy into a single Date dimension table. The goal is to do the same for all your other dimension tables.
Now, it sounds like you may be dealing with many-to-many relationships (MMR) between your tables. In some contexts this is unavoidable, and knowing how to properly design a start schema for them is really important. The topic is far too complex to outline over a forum. My suggestion is you acquire a copy of The Definitive Guide to DAX. They have an entire chapter dedicated to MMRs and the various techniques available.
HTH
9 Replies
what's a MRN? Is that an event or a property?
MRN = Primary ID for a patient within a system. Patient_ID is a de-identified version for tracking throught the rest of the tables
I am asking to map MRN -> condition_occurrence.person_id -> visit_occurrence.visit_occurrence_id -> Care_site.care_site_name but I also need MRN -> procedure_occurrence.person_id -> visit_occurrence.visit_occurrence_id -> Care_site.care_site_namedoesn't that defeat the purpose of the de-identification ?
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