Experiences and examples of using openEHR at �Karolinska University Hospital

Erik Sundvall & Claudia Ehrentraut

Note: The animations in this Google presentation file are broken compared to the Powerpoint original. Watch the following 4-minute clip instead if you want to see all parts of slides and animations properly: https://drive.google.com/file/d/1J-4CHVmv7kIzaNNb3DRcJxdqndLZY7eP/view?usp=drive_link

HiGHmed Symposium,�Berlin, October 14, 2021

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Clinicians, patients and researchers

make decisions based on data…

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…bad data, bad decisions,

good data, (potential for) good decisions…

…thus: A data centric approach…

https://www.highmed.org/en/events-highmed-symposium-2021?hsLang=en

https://youtu.be/LinRtEsaLjs?t=13192 (Starting at approx 3h 40m)

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Operating model – focus on point of care

INVESTIGATION PHASE

TREATMENT PHASE

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�()

mostly for clinical data

mostly for administrative and logistical data (”PAS” etc)

LAS

PACS

Analytical datalayer

Patient monitoring

Used for data sharing, if the other

standards are not used for sharing

The main ”competition” in this zone is proprietary systems/data models, �not other standards

EHR

Specialist-systems

LIS

RIS

These standards can be combined with terminology content from e.g.

or/and

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Karolinska‘s approach to standardisation �(including openEHR)

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Clinical data�openEHR�CDR

Waveform data�TSDB

Images�MMA

Omics data�GDR

Patient reported�data openEHR�CDR

Production�data�EDW

Operational�data�Dem/FHIR

Specialized EHRs/EMRs

• OVERVIEWS
• DECISION SUPPORT
• PROCESS SUPPORT

• QUALITY
• RESEARCH
• INNOVATION

PRECISION-�MEDICINE

K@HOME

PRODUCTION�MANAGEMENT

LIMS

RIS/PACS

Patient Monitoring

PDMS

EDW

…

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Dataplatform

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Different parts stored in internal or external cloud services or traditional local storage. �The point is not exactly WHERE we store things, but rather that we are able to understand and to use our data freely – not limited by system suppliers’ systems and business models

Toolbox

integrations

Various views and applications. Purchased or developed by ourselves.

Different kinds of storage

Digital health platform�(optimized for healthcare)

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”Traditional” �application �(example)

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System supplier decides how data is stored and accessed. We want to avoid acquiring new systems like this one.

integrations

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Dataplatform

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Different parts stored in internal or external cloud services or traditional local storage. �The point is not exactly WHERE we store things, but rather that we are able to understand and to use our data freely – not limited by system suppliers’ systems and business models

Toolbox

integrations

Various views and applications. Purchased or developed by ourselves.

Different kinds of storage

Digital health platform�(optimized for healthcare)

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”Traditional” �application �(example)

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System supplier decides how data is stored and accessed. We want to avoid acquiring new systems like this one.

integrations

LIMS

Patient Monitoring

PDMS

RIS/PACS

RIS/PACS

Specialized apps

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New main EHR

…if partially or fully open/standardised,

then it will fit somewhere here

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Data will be copied/converted (see slide 19-24)

Storage for app data

Specialized EHRs/EMRs

Data should be copied/converted already from start

New main EHR

…if legacy monolith

Browse & search app for data from old EHR systems

TakeCare

Current main EHR

Shutdown� ~2030(?)

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• Patient reported data via patient application
• Symptom control before chemotherapy (operational since 2023)
• PROM-data
• Automatic transfer from healthcare systems to quality registries
• Medical oncology treatment (chemotherapy etc.)
• Cancer surgery
• Specimen-based diagnostics
• Pathology (breast & prostate)
• Chemistry lab
• Imaging diagnostics
• Radiology (prostate)

(Upcoming: Genomics referral & reporting in openEHR. Genomic sequencing & analysis, VCF-files etc., tracked in FHIR)

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• openEHR-force (Karolinska/regional internal)
• openEHR Sweden + national projects
• The international openEHR community (forums, CKM etc)
• European openEHR Network
• openEHR interested partners in other networks
• CCE (Cancer Core Europe)
• EUHA
• …

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Symphony project

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Project details

• Innovation project in EU + Turkey
• 4 use cases ​
• Sweden: Prostate cancer usecase

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Aim ​

• Create a vendor-independent architecture and implementation of platform components needed to fulfill requirements for overview that can be used during MDT* conference for prostate cancer, including automatic risk classification according to national guidelines​
• Reduce double documentation (record data once, use often) & information loss in care flow
• Provide real-time quality data, real-time feedback, etc.​

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Symphony project

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Symphony project

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Symphony project

• Common Over 400,000 new cases diagnosed annually in Europe

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• Broad risk spectrum Five-year survival at 97% but still the biggest cancer killer among Nordic men

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• Multidisciplinary and heterogeneous urologists, oncologists, radiologists, pathologists, nurses, and patients

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• Patient-Centered Treatment Balance Trade-off between cancer control and side effects that impact patients’ quality of life

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Symphony project

Patient reported data

Pathology response (macroscopic + microscopic today)

Radiology response�(soon also microscopic pathology)

Urology data

Lab result (PSA)

Clinical Data repository (CDR)

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openEHR archetype- template- and query- design tools

openEHR templates

openEHR templates

openEHR templates + stored query definitions

*) Cambio also provides similar services within the Symphony project

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• Patient reported data (IPSS, IIEF-5, health declaration)
• GitHub - regionstockholm/CKM-mirror-via-modellbibliotek at prostate-cancer-patient-reported-data
• Radiology response
• GitHub - regionstockholm/CKM-mirror-via-modellbibliotek at prostate-cancer-radiology
• Radiology response prostate - openEHR Clinical - Confluence
• Pathology request and response

Symphony project

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Data from Cuviva patient application

(imported, stored and visualised in Tietoevry platform using Better widget)

Cambio web application displayed in Tietoevry platform

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Access your old Systems’ data with a CDR + UI/visualisation toolkit

Copying/migrating data to openEHR/FHIR-format �from our old EHR system “TakeCare“ before shutting it down

Project description, see post #27 in: https://discourse.openehr.org/t/karolinska-stockholm-procurement-of-digital-health-platform-cdr-tools-services-consultants/4457/27

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Project description, see post #27 in: https://discourse.openehr.org/t/karolinska-stockholm-procurement-of-digital-health-platform-cdr-tools-services-consultants/4457/27

1 FTE consultancy from each of two expertise areas:

1. Informatics focus�[freshEHR won]
2. Integration & visualisation focus�[Tietoevry won]

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Project length:�3 months

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Conversion strategies

1. CKM-archetypes = international or national standardised openEHR structures
2. Integration archetypes/templates = locally/custom developed structures that copy the structure of the source system
3. Combination (of 1+2) = first converted using integration archetypes and in a later step, either immediately or (even years) later, some (or all) values are converted also based on CKM-archetypes
4. FHIR = international standard for integrations, used e.g. for some administrative data in Karolinska’s Digital Health Platform. There are (at least) three solution patterns:�4A. FHIR-resources in a FHIR-server (direct conversion, before storage)�4B. Store in a database copied form source system, expose via ”FHIR-facade”�4C. Store openEHR-integration-archetype-based in CDR expose via ”FHIR-facade”

Priority ordered* data from TakeCare, colour coded as planned at start of project:

• Medications, #1 – TC Exchange (XML), well defined API
• Clinical notes (forms), #2 – TC Exchange (XML), thousands of forms/templates and headings. Huge variations in structure/modelling.
• Clinical Chemistry, #1 – TC Juno (JSON), some modelling and partial mappings were available. Well defined API.
• Measurements, #3 – TC Juno (JSON), thousands of different legacy source templates. Some were converted to CKM-archetype-based
• Activities, #1 - TC Juno (JSON), variation in terms, fixed structure in TakeCare
• Appointment Bookings, #4 – TC Juno (JSON) raw data-dump, interesting to expose via FHIR ��*) We listed some more than we expected that the consultants would have time for, but it went surprisingly well! All types were mapped and converted. All were visualized in GUI except the last one (Appointment Bookings) before time ran out.

openEHR-CDR �CDR = Clinical Data Repository

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openEHR API & tools

Patient care Reading/browsing/ search-views. Text, visualisations and ”dashboards” for gerneral (or specific) purposes.

Research, quality control etc.

Query and report-tools are included in the platform (but not tested in PoC)

FHIR-�server

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FHIR API & tools

Variants of conversion/mapping used in PoC

Patient-id-info

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TakeCare data to CDR

TakeCare

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PoC

TakeCare

PoC

TakeCare

#1 CKM�ark.

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TakeCare data to CDR

TakeCare

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PoC

#2 �integr. �ark.

TakeCare

TakeCare

PoC

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#3 �integr �🡪CKM

#1 CKM�ark.

#2 �integr. �ark.

TakeCare

Direkt…

Direkt…

TakeCare

PoC

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Findings

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openEHR vs. other systems

• openEHR can coexist with other systems in many different ways
• openEHR-based systems can replace some legacy/proproetary systems (or parts of them)

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Involvment of healthcare professionals

• Greetings from Patrik: "IT is far too important to leave to the nerds at the IT department"
• Involving healthcare professionals is essential – we can't just buy something and belive that the vendor and IT department will develop and maintain it
• Decisions and development can be made by, or closer to, the health care professionals
• National and international medical expertise, rather than IT people & vendors

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A nice reusable pattern

• Source systems with form authoring parts can be upgraded to...
• ... accept openEHR templates as inital blueprints for new forms (and store template paths for fields) and
• ... easily send form content in openEHR format (vendors often chose simplified/flat openEHR JSON format)
• This simplifies fixing the problems at the source - instead of trying to do magic integrations later

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Collaboration with others

• Data and workload sharing (use existing archetypes and templates)

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• (Re)empower health care professionals to specify their need of data
• Decisions and development can be made closer to the health care professionals
• National and international medical expertise rather than IT people & vendors
• Vendor-independent, clinical models are valid regardless of IT systems
• Increased development speed (due to reuse)
• New/improved functionality for existing systems can be added quickly
• Reduce bottlenecks of local/regional IT organization and suppliers
• Standardized way to store data
• openEHR templates can serve as a configuration basis in existing (non-openEHR) systems
• Collaboration with others (data sharing, workload sharing)

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