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Diagnostic-data mobility initiative

Medicus is proud to announce a new initiative that aims to resolve the challenge of making the diagnostic data interoperable across different providers, countries, languages, systems and machines.

Medicus’ Diagnostic-Data Mobility Initiative (DDMI) aims to establish a platform and set of tools that can capture the diagnostic outcome from one source in any format and convert it into any other format while preserving as much of its crucial medical context as possible.

Putting it simply, the objective of this initiative is to be able to combine, compare and contextualise the test results of any patient from two separate labs from two separate countries, something which was not available to patients on a global level before.

Today Medicus is looking for partners to help bring this platform to full fruition, including but not limited to diagnostic labs, hospitals, electronic health record providers, IHS (information hospital system) providers, LIS (lab information system) providers, health insurance providers, public agencies that are involved with health and data, universities and research institutes tackling similar challenges.

Learn more:

Diagnostic data is generally one of the hardest types of health data to combine if they’re coming from different sources, due to the:

 

  • Different names and identifiers of test results
  • Different languages
  • Different reference ranges
  • Different test units
  • Different machines
  • Different calibrations (for the same machine)
  • Different medical guidelines

 

To add to that, there’s also the challenge of the different standards for clinical data structure, such as HL7 v2, HL7 v3, CDA, FHIR and the different coding standards for health data such as: LOINC, SNOMED, SNOMED-CT, UMLS.

 

All of this compounds the issue of trying to join two streams of health data from two sources and combine them in a way that makes sense for clinicians.

During our almost four years of existence, we have encountered this particular challenge with every single client we have worked with. We continue to hear the same refrain from all the key players across the industry and across geographies, and as a result concluded that this is one of the largest obstacles in making health data meaningful across multiple sources. Without unified, clean and meaningful data, no smart systems or algorithms can function and thrive.

 

Diagnostic data is generally one of the most complex types of health data to combine when derived from different sources, due to a number of factors, from differences in names and languages, to differing identifiers of test results and varying reference ranges and test units. To further complicate matters, different machines with different calibrations are evaluated against different medical guidelines resulting in a seemingly insurmountable barrier to interoperability.

 

Specifically, the German healthcare system has recently been moving towards implementing electronic health record solutions, where multiple providers from the private and public sector are trying to implement a system that will allow them to gather, store and access information from different diagnostic providers and make that available to the German population. This has brought this issue to the forefront for us, because without a system that allows interoperability, the data cannot work with each other, and diagnostic tests are the first to be affected by this.

 

We started tackling this challenge early on across multiple aspects and it is only lately (over the past 6 months) that we started to realize that putting all the different internal tools together and investing more time and resources into this problem can help us build something that can resolve this challenge for most cases.

In order to solve this challenge, the platform employs technologies that Medicus has been developing gradually over the past few years, including Medicus’ own diagnostic mapping solution, its databases of coding standards and diagnostic data, its health data API, NLP (natural language processing) and machine-learning.

 

The platform aims to establish a platform and set of tools that can capture the diagnostic outcome from one source in any format and convert it into any other format while preserving as much of its crucial medical context as possible.

 

Putting it simply, the objective of this initiative is to be able to combine, compare and contextualise the test results of any patient from two separate labs from two separate countries, something which was not available to patients on a global level before.

 

The last thing we want to do is introduce a new standard or a coding system; instead DDMI will act as a translation layer between different standards, starting with the most common and later expanding it to cover all available standards.

 

Features:

  • Compatible with the most common outcome delivery formats, such as HL7 v2, HL7 v3, CDA, FHIR, XML, JSON, CSV, or even text files
  • Compatible with the most common coding standards, such as LOINC, SNOMED, SNOMED-CT, UMLS.
  • Auto-mapping of all diagnostic data from one system to the next, including:
    • Names and key identifiers of test results
    • Different languages
    • Reference ranges – technically
    • Reference ranges – medically
    • Test units
  • Auto-mapping of user/patient profile details, including:
    • Basic profile: age, gender
    • Medical profile: conditions, medications, family history
    • Lifestyle profile: activity, diet
    • Any data fields that fails the auto-mapping will be transferred and displayed separately (even if there are duplicates)
    • Data fields will be exported/generated in a viewable format that allows for manual mapping

Diagnostic data is generally one of the most complex types of health data to combine when derived from different sources, due to a number of factors, from differences in names and languages, to differing identifiers of test results and varying reference ranges and test units. To further complicate matters, different machines with different calibrations are evaluated against different medical guidelines resulting in a seemingly insurmountable barrier to interoperability.

 

The platform employs the following technologies that Medicus has been developing gradually over the past few years:

 

  • Medicus’ own diagnostic mapping solution
  • Medicus’ different databases of coding standards
  • Medicus’ database of diagnostic data across 4 languages (and growing)
  • Medicus’ health data API (aggregator/filter)
  • NLP (natural language processing)
  • Machine-learning

 

This platform is currently an internal Medicus project that’s being developed continuously, with the ultimate aim to launch it publicly for anyone to make use of. Today the platform can be utilized by working directly with our team.

 

Get in touch to find out more: [email protected]

 

We’re looking for partners to help us bring this platform to full fruition, so if you feel that you can help add value and would like to also see such a platform out there, please get in touch.

 

Example of profiles we’d love to work with:

 

  • Diagnostic labs
  • Hospitals
  • Electronic patient record providers
  • Doctors and Doctor Associations
  • IHS (information hospital system) providers
  • LIS (lab information system) providers
  • Insurance providers
  • Public agencies involved with health and data
  • Universities and research institutes tackling similar challenges

 

Over the next six months, we will be busy looking for partners, working closely with them and making more progress on the platform development, and we’re planning to organize an event dedicated to this initiative in the first half of next year.

 

We’re working on a plan to take this platform public with the support of the partners and supply it with tools and documentation that allows anyone to use it with minimum to no support from our team.

 

We will be sharing periodic updates of the progress of the platform and the initiative overall, please leave your email below to sign up to get notified with our updates.

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