Roadmap to Healthcare Integration Service: Architecture, Evolution, and Key Insights

Healthcare is evolving rapidly, but integration between various systems remains a persistent challenge for startups, SMBs, and rural healthcare providers.

Let us take a quick look into how we are building the Health Integration Service to address challenges of healthcare technology adoption for players in the healthtech space. We aim to simplify these challenges by offering an integration platform that combines APIs, data interchange support, and real-time analytics in a modular and scalable architecture. We will outline the architecture of the service, discuss insights from its development process, and review key features based on the source code and development logs.

High-Level Architecture

The architecture was designed with flexibility, scalability, and interoperability in mind. It adopts a modular approach to ensure that different modules can scale independently, enabling seamless integration with healthcare providers’ systems, IoT devices, and third-party platforms. The structure also allows to spin off modules into separate micro-services at a time when a microlithic architecture cannot be avoided. Here is an overview of the salient features we have in our roadmap.

A Consolidated view of current architecture roadmap

Modular API Layer

Our service expose a set of developer-friendly APIs for quick and secure integration with Electronic Health Records (EHRs), IoT health devices, and telemedicine platforms.

• Core Services: APIs for EHR/EMR integration, health device data processing, and patient management. Third-party players will have the option to integrate with us. The key players building services for patients can easily integrate their solution quickly with location-aware patient management service. This gives them eyes into the personalized patient pathways, their care history and realtime monitoring through various interfaces the patient chooses to integrate with their solution. This means services building ontop of our core services for patients who choose to use one or more care services for (tele-)consultation booking, telehealth services, fitbit apps, etc simply have to integrate with our API to enable those features in their solution and we will start gathering patient details in a compliant manner yet identifying the unique anonymized patient profile. This way various apps still get to manage the same patient with their offering while we build a more comprehensive profile of the patient.

• Data Interchange Standards: The solution will expand its compliance to adhere to FHIR, HL7, and DICOM standards to ensure seamless data exchange across multiple healthcare systems. So apps integrating to our core services which are plugged to several care providers using a vendor EHR solution can fetch or push patient data in a compliant standard where the vendor solution may have their own compliant datastore(s). Ultimately, we will aggregate from multiple data sources to support our Open Data Hub layer which will be a centralized platform to process, store and analyze non-PHI patient data consolidating them into a single storage layer. App providers will be able to gain comparative insights and personalized patient analytics using our analytics solution so they can provide rich experience to their users without worrying too much about implementing in-house analytics from day one.

• Open Data Hub: Our intent to build the service narrows down here. To democratize and open up the healthcare data in a compliant, safe and anonymous way to any healthcare agency, platform, healthtech service, research institutions which can help them rapidly produce vaccines, accelerate drug trials, help cure cancer, help tackle genomic data and correlate with various population segments, quickly monitor disease spread, make sense of forecasts and help prepare during a calamity like the last pandemic. The ODH is architecturally an Enterprise Data Hub that has unified access across all kinds of data and transforms it into standard health data format. The ODH is supposed to be open for audit, open for amendment and open to digital public goods consumption for other players to build more powerful services to cater to global healthcare.

Key Features

API Integration and Security

In our roadmap, a secure and flexible middleware layer for healthcare integrations, our mission is secure software by design that includes:

• OAuth 2.0 based SMART on FHIR Consent-based Workflow: Ensures that all API interactions are secure and compliant with privacy laws like HIPAA and GDPR. Everything in the middleware platform described above is inside the API service as described here:

  

  Integration of our Healthcare Integration API (HIAS) Service with an OAuth2 Authorization Server. The flow illustrates how third-party Client Apps interact with HIAS to request healthcare data on behalf of patients. The OAuth2 Authorization Server handles the client registration, consent approval, and token issuance. HIAS validates the access token with the OAuth2 server and retrieves healthcare data from the provider. The patient also has control over consent management, allowing them to revoke access through the Client App, prompting HIAS to update access rights accordingly.

• Environment Management: The code supports multiple environments, making it easy for developers to switch between production and sandbox modes during development. Our continuous deployment on render hosts bleeding edge dev environment. Developers auditing, building on-top of, forking or contributing to the project can locally test it using containerization.

Real-Time Data and Predictive Analytics

The platform offers real-time data analysis for patient journeys, predictive care management, and triage systems using our inhouse ML models leveraging resource optimization. Path prediction, Rating and Predicting consultation services leverages these models. For a premium, customers can opt-in for this service as part of our commercial offering. This is where we leverage the Open Data Hub layer.

• Patient Data Insights: The system uses real-time data from connected devices and health systems to provide actionable insights for care providers.

• Predictive Models: HIAS includes modules that offer predictive analytics, helping healthcare providers forecast patient admissions, optimize staffing, and manage supplies.

• Open Data Hub Pipeline: Multiple data pipelines can source in aggregated data on the ODH layer which could be accessed by systems integrated with healthcare facilities such as HMIS.
  

  

Care Recommendations

By analyzing health data and location, HIAS can provide personalized recommendations for care providers based on accessible and available resources.

• Location aware services: Patients are matched with care providers based on geographic proximity, real-time traffic, and availability of resources.

• Dynamic Care Routing: Our ML models determine the best course of action based on a patient's health data, directing them to the appropriate facility.

Data Normalization, Security, and Compliance

One of the most critical aspects of the platform is the handling of patient data securely and anonymously. Our implementation focuses on privacy-compliant data anonymization techniques to ensure that no Personal Health Information (PHI) is exposed.

• Data Anonymization: All health data from connected devices is anonymized before being stored or processed for analytics.

• Compliance: The platform complies with global standards like HIPAA and GDPR, ensuring that patient data remains private and secure.

Development Updates

SpatiaLite and SQLAlchemy Integration

One of the major features implemented was support for geographic data. We introduced a custom PointType field type for databases in SQLAlchemy models, defining spatial data which we use with Spatialite extension for sqlite to cache coordinates of healthcare facilities in local database for fast fetching and geometric computations.

class PointType(types.TypeDecorator):

    impl = types.Text

    def process_bind_param(self, value, dialect):

        return f'POINT({value[0]} {value[1]})' if value else None

This feature allows developers to work with spatial data while maintaining SQLAlchemy’s ORM benefits.

During development, I encountered challenges related to integrating SpatiaLite with SQLite, particularly when running tests. To handle this, I implemented a SetUpTestDatabase class for better management of test database lifecycles. This ensured tests could be executed consistently in isolation with less code complexity.

Developer -> SetUpTestDatabase : Runs Integration Tests

SetUpTestDatabase --> SQLAlchemy : Creates Test Database

Containerized Integration Testing

To facilitate smooth deployments and testing, we implemented container support with either docker or podman. The `docker-compose.yml` configuration orchestrates services in a containerized environment, which simplifies testing and deployment.

services:
    app:
        <<: *shared-config
        build: 
          context: .
          dockerfile: Dockerfile
          args:
            CACHEBUST: ${CACHEBUST:-$(date +%s)}

This setup ensures that our application can be deployed and tested in environments mimicking production, minimizing deployment friction. We plan to extend the services as we build our solution. The Dockerfile in this case is just a few lines:

FROM xcov19-setup AS run # leveraging base image

# Set the working directory
WORKDIR /app

COPY --chown=nonroot:nonroot --chmod=555 xcov19 xcov19/
COPY --chown=nonroot:nonroot --chmod=555 Makefile .
COPY --chown=nonroot:nonroot --chmod=555 *.sh .

USER nonroot:nonroot

ARG START_CMD="make test-integration"
ENV START_CMD=${START_CMD}
RUN if [ -z "${START_CMD}" ]; then echo "Unable to detect a container start command" && exit 1; fi
CMD ${START_CMD}

Most of the heavy lifting is setup by reusing the base image. Our docker orchestration is lightweight but allows us to quickly make changes to the source, wait and watch and reload or restart the containers. When unit testing, running container should not be required and with integration tests, our isolated tests in each container run ensures, that all IO configurations do not affect the host runner.

Developer-Focused API Integration

One of the core value propositions of HIAAS is its simplified API for developers. With the introduction of health-related endpoints, developers can easily integrate without extensive customization.

#### Sample Endpoint:

http

GET /api/v1/facilities

#### Returns a list of nearby healthcare facilities based on location data.

Conclusion

This is only the beginning of a skunkwork and laying foundations towards building HIAS from implementing high level architecture goals to enabling spatial database extensions to ensuring seamless deployment through containerization. The result is an idea taking shape in the form of a platform that significantly reduces the technical burden of healthcare integration for startups, offering advanced features like remote patient monitoring, accessiblity services, and easy-to-use APIs.

Stay tuned for more updates as I post updates and continue to refine.