Category: Information Systems

Categories
Healthcare Analytics Information Systems OpenSource

OHDSI OMOP to FHIR mapper

Wikimedia commons: Copyright held by BAPS Swaminarayan Sanstha (web: www.baps.org, email: info@baps.org); Unknown photographer / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)

TL;DR Below is an open-source common-line tool for converting an OHDSI OMOP cohort (defined in ATLAS) to a FHIR bundle and vice versa.

Originally published by Bell Eapen at nuchange.ca on July 22, 2020. If you have some feedback, reach out to the author on Twitter,  LinkedIn or  Github.

OHDSI OMOP CDM is one of the most popular clinical data models for health data warehouses. The simple, but clinically motivated data structure is intuitively appealing to clinicians leading to its good adoption. In this respect, it has overtaken HL7-V3 which is more robust but has a steeper learning curve, especially for clinicians. The OHDSI OMOP CDM is widely used in the pharmaceutical industry for drug monitoring.

FHIR is emerging as the defacto standard for health system interoperability, owing largely to its simplicity and the use of existing and popular standards such as REST. As NoSQL databases become more and popular in healthcare, FHIR can also be a good persistence schema. It aligns well with search technologies such as elasticsearch.

As both standards are popular, conversion from one to the other may be commonly required. Researchers at Georgia Tech have an open-source tool – GT-FHIR2 – for mapping an existing OHDSI OMOP CDM database as FHIR endpoint. However, conversion between existing systems may not be easy with a full-stack solution. 

I have a simpler solution that I believe will be useful in the following scenarios:

  • To export a cohort to a FHIR based analytics tool.
  • To load new resources to OMOP CDM databases for incremental ETL.

Omopfhirmap is a command-line tool for mapping a OHDSI cohort, defined in ATLAS, to a FHIR bundle that can be optionally submitted to a FHIR server for processing. Conversely, it can process a FHIR bundle and add resources to an existing CDM database ignoring duplicates. Unlike GT-FHIR2, the OMOP on FHIR Project at Georgia Tech omopfhirmap does not expose OMOP database as FHIR endpoints. 

I have used spring-boot and JPA for easy wiring of services and abstraction of database and the hapi-fhir as it is an obvious choice for any java based FHIR applications. It is still work in progress and any help will be appreciated (Refer to CONTRBUTING.md).

OMOP <-> FHIR mapper
https://github.com/dermatologist/omopfhirmap
5 forks.
12 stars.
8 open issues.

Recent commits:

Categories
Information Systems OpenSource

OSCAR EMR and FHIR

OSCAR (Open Source Clinical Application and Resource) EMR is a web-based electronic medical record (EMR) system initially developed for primary care clinics in Canada. Oscar is a Java spring based web application with a relatively old codebase. OSCAR is widely used in the provinces of Ontario and British Columbia and is supported by many Oscar service providers.

Fast Healthcare Interoperability Resources (FHIR) is an HL7 standard describing data schema and a RESTful API for health information exchange. FHIR is fast emerging as the de-facto standard for interoperability between health information systems because of its simplicity and the use of existing web standards such as REST.

OSCAR being primarily designed for primary care clinics does not support interoperability with other systems out of the box. FHIR in its entirety is not supported by OSCAR. A partial implementation of FHIR to support the immunization dataflow as FHIR bundles is available. One of the requests that constantly pops up in the OSCAR community is the need for a full FHIR API implementation for OSCAR.

We had some initial discussions on how to go about implementing a FHIR API for OSCAR EMR. FHIR is a REST API exposing FHIR Resources such as Patients, Observations and CarePlan as JSON resources. The HAPI-FHIR java library defines all the FHIR resources and the associated functions. The first step in building the API is to map the relatively messy OSCAR data model to FHIR resources. The Patient resource has been mapped and is available in the OSCAR repository. This (/src/main/java/org/oscarehr/integration/fhir/model/Patient.java) can be used as the template to map other required resources.

The next step is to extend the REST API that is currently available to expose FHIR APIs after authentication. If you have some ideas/expertise/interest in this, please comment below.

Categories
Health Research Methodology Information Systems

Grounded Theory – QRMine: Qualitative Research support tools in Python.

Grounded theory (GT) emerged as a research methodology from medical sociology following the seminal work by Barney Glaser and Anselm Strauss. However, they later developed different views on their original contribution with their supporters leading to the establishment of a classical Glaserian GT and a pragmatic Straussian Grounded Theory. Constant comparison is central in Classical Grounded Theory, and it involves incident to incident comparison for identifying categories, incident to category comparison for refining the categories and category to category comparison for the emergence of the theory.

Grounded Theory ResearchGlaser’s Classical GT (1) provides guidelines for evaluation of the GT methodology. The evaluation should be based on whether the theory fits the data, whether the theory is understandable to the non-professionals, whether the theory is generalizable to other situations, and whether the theory offers control over the structure and processes.

Strauss and Corbin (2) recommended a strict coding structure elaborating on how to code and structure data. The seminal article by Strauss and Corbin describes three stages of coding: open coding, axial coding, and selective coding. Classical Grounded Theory offers more flexibility than Straussian GT while the latter may be easier to conduct especially for new researchers.

Open coding is the first step where data is broken down analytically, and conceptually similar chunks are grouped together under categories and subcategories. Once the differences between the categories are established, properties and dimensions of each are dissected. Coding in GT may be overwhelming, and scaling up of categories from open coding may be difficult. This leads to the generation of low-level theories. With natural language processing, information systems can help young researchers to make sense of the of data that they have collected during the stage of open coding. QRMine is a software suite for supporting qualitative researchers using NLP. Gtdict is a module that identifies Categories, Properties, and Dimensions in the interview transcript.

QRMine is opensource and is available here. Ideas, comments and pull requests welcome.

Last 3 commits to GitHub Repo:

References:

1.
Glaser BG. The Constant Comparative Method of Qualitative Analysis. Social Problems [Internet]. 1965 Apr;12(4):436–45. Available from: http://dx.doi.org/10.1525/sp.1965.12.4.03a00070
2.
Corbin JM, Strauss A. Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology [Internet]. 1990;13(1):3–21. Available from: http://dx.doi.org/10.1007/BF00988593 [Source]