Medical alarms have appeared on the Emergency Care Research Institute’s list of top medical hazards four times — twice in the number one spot. According to a recent FDA survey, bad sound design for medical devices accounted for 566 deaths over four years, mostly because the sounds can be so annoying that they get turned down so doctors and nurses can concentrate, leading to potentially deadly consequences.
In this TEDx McMaster talk from February 2021, Michael Schutz, an associate professor of music cognition and percussion, explains how his research with the Music Acoustics Perception Learning (MAPLE) lab is helping to create better alarms — and better patient outcomes.
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).
The COVID-19 pandemic brought to light many of the vulnerabilities in our data collection and analytics workflows. Lack of uniform data models limits the analytical capabilities of public health organizations and many of them have to re-invent the wheel even for basic analysis. As many other sectors embrace big data and machine learning, many healthcare analysts are still stuck with the basic data wrenching with Excel.
The OHDSI OMOP CDM (Common data model) for observational data is a popular initiative for bringing data into a common format that allows for collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies. Though OHDSI OMOP CDM is primarily for patient-centred observational analysis, mostly for clinical research, it can be used with minor tweaks for public health and epidemiologic data as well. We have written about some of the technical details here.
The OHDSI OMOP CDM is relatively simple and intuitive for clinical teams than emerging standards such as FHIR. Though the relational database approach and some of the software tools associated with OHDSI OMOP CDM are archaic, the data model is clinically motivated. There is an ecosystem of software tools for many of the analytics tools that can be used out of the box. The Observational Medical Outcomes Partnership (OMOP) CDM, now in its version 6.0, has simple but powerful vocabulary management. OHDSI OMOP CDM is a good choice for healthcare organizations moving towards health data warehousing and OLAP.
One weakness of OHDSI is the lack of tools for efficient ETL from existing EHR and HIS. Converting existing EHR data to the CDM is still a complex task that requires technical expertise. During the additional “home time” during the COVID pandemic, I have created three software libraries for ETL tool developers. These libraries in Python, .NET and Golang encapsulated the V6.0 CDM and helps in writing and reading data from a variety of databases with the V6.0 tables. The libraries also support creating the CDM tables for new databases and loading the vocabulary files.
These libraries might save you some time if you are building scripts for ETL to CDM. They are all open-source and free to use in your tools. Do give me a shout if you find these libraries useful and please star the repositories on GitHub.