The most impressive trait about the Jelvix team is that you can't give them a task or idea too large. No matter how grand a vision you may have, they'll always have a solution or means to accomplish it.
Thank you, Jelvix, making our vision into a reality. You executed, delivered and were responsive through the whole project. The finished product has an awesome look, feel and user experience that will change the way physical therapists and patients interact between visits.
Our application was finished and able to generate revenue within one year as the Jelvix team adhered to the required timeline efficiently and professionally. They were communicative, responsive, and always available to take on feedback and make tweaks or changes as required.
Jelvix delivered digital products that are fit for purpose and, in the case of the mobile apps, award-winning. Led by an engaged project manager, communication with the development team is smooth and purposeful. They contributed conceptually to the solutions and were excited to problem-solve.
Our goal is to build software solutions that truly matter, making the world a better place for everyone while helping entrepreneurs succeed in their journey.
For 16 years, Jelvix has been a strategic tech partner for global leaders and innovators. We leverage high-end design, rigorous QA, and expert consultancy to engineer scalable solutions that turn complex business challenges into market-leading products.
We built a secure data bridge for remote patient monitoring that integrates BP devices, wearables, and Epic FHIR without replacing existing infrastructure. This solution addresses the industry-wide challenges of data fragmentation and regulatory compliance through specialized healthcare software development.
The client is a healthtech SaaS company that oversees remote patient monitoring hypertension services in the provider network within the US and UK. This system caters to enterprises that need interoperability and ROI before signing any agreement.Their devices and tools, such as Bluetooth BP cuffs, wearables, and Epic EHR, were poorly interoperable within a single chronic disease management software. This project required expertise in enterprise software development and an understanding of FHIR standards, HIPAA constraints, and how AI behaves on clinical data.
Connecting data from the devices, the EHR, and analytics to one chronic disease management software without impacting clinical work was challenging.Data FragmentationDevice and EHR data were siloed in inconsistent formats (API, CSV, notes). Lack of standardization and deduplication caused record loss and fragmented patient history.
EHR Integration as a Growth BottleneckEpic FHIR integrations varied by facility, requiring custom engineering sprints for every new site. Analytics were delayed, trapped in inefficient batch-based ETL pipelines.Compliance and ScalabilityData lineage, consent, and DPIA gaps created HIPAA/GDPR exposure. AI training lacked consistency, stalling prediction accuracy and forcing a full reset with every new vendor.
Instead of redesigning the entire remote patient monitoring platform from scratch, Jelvix introduced a data bridge between devices, EHR, AI services, and the analytics warehouse by means of a canonical layer to which each system connects. This architecture also enabled scalable big data in healthcare processing for real-time analytics and predictive modeling.Unified Device Ingestion PipelineReusable connectors for BP cuffs and wearables performed the functions of webhooks, polling, and buffer ingestion through a single ingestion pipeline. The schema mapping process transformed all units, timestamps, and PHI boundaries into a health event. This process made the device onboarding faster by 70%.EHR IntegrationSMART on FHIR provided support for clinical processes as well as back-end cohort processing, whereas Epic FHIR R4 facilitated the bulk export of cohorts via pre-existing open.epic registration services. The Site Capability Matrix was used to identify what read/write capability sites had.AI Services: Adherence Scoring and BP Risk PredictionJelvix developed an algorithm based on AI predictive analytics in healthcare that evaluated patient adherence and assessed the risks associated with BP based on BP-related, adherence-related, and trend-related characteristics. Validation of the algorithm included verification using shadow mode, detection of data drifts, calibration testing, and retraining. The model was built with Explainable AI principles, where each risk score is traceable to its contributing features, giving clinicians and compliance teams a clear basis for clinical decisions and audit evidence. The client got 30% improvement in risk-scoring accuracy compared to the baseline.Compliance-by-Design ArchitectureRequirements for HIPAA, GDPR, and HIPAA compliant AI were implemented within the architecture of the platform. Patient PHI information was encrypted during transit and at rest, while access policies ensured visibility according to specific roles of users. We also ensured data lineage from FHIR Provenance.
Faster Onboarding
Higher Accuracy
Batch Delays
The architecture is made up of five domains, and each domain correlates to a certain layer in the integration stack.Device Ingestion DomainApplies Apache Kafka pipelines and connectors for the ingestion, deduplication, and normalization of raw telemetry data obtained from BP cuffs and wearable devices into health events streams.EHR Integration DomainUses SMART on FHIR and Epic FHIR R4 APIs for connecting provider EHR systems, allowing the adjustment of reading and writing functionalities depending on the site setup.AI/ML DomainApplies Python, scikit-learn, and MLflow technologies for computing adherence scores and predicting risks of BP, along with an automation of drift detection and model retraining using an MLOps pipeline.Compliance DomainInvolves in-transit and at-rest encryption, RBAC-based access controls, a consent ledger, and FHIR Provenance to provide complete data security and generate compliance reports.Infrastructure DomainUtilizes Kubernetes and AWS for the execution of auto-scaling microservices, blue/green deployments, load balancing, and monitoring through observability.
The project needed a team of experts in the areas of healthcare data engineering, FHIR platform, and clinical AI development:Solution Architect: Integration layer architecture, FHIR standards, compliance-by-design approach.Integration Engineers (2): SMART on FHIR implementation, Epic connectivity, device connector framework.Backend Engineer: Microservices, Kafka pipelines, API development, canonical data model.Data Scientist: Feature store design, adherence scoring, BP risk prediction, MLOps pipeline.QA Automation Engineer: Integration testing, FHIR conformance testing, compliance validation.DevOps Engineer: Kubernetes infrastructure, CI/CD pipelines, blue/green deploys, observability.Security and Compliance Lead: HIPAA/GDPR controls, DPIA templates, consent ledger, audit trail.
Jelvix delivered the project using a three-stage process, with the MVP data bridge ready for production after 8 weeks.Readiness and Blueprint (2 weeks)Site discovery consisted of audits of the API capabilities of devices and electronic health records on each target site to identify what capabilities each site supported before moving forward with any development work. This phase yielded a risk register, data contracts, a site capability matrix defining read-only, partial, and write-back per site, and compliance gap analysis.Data Bridge and AI (4 weeks)The first step involved the middleware layer and the device connector. The incremental synchronization reduced stress on the APIs. The synthetic load testing made it possible to discover flaws, inconsistencies, drifts, and failures even before actual data could pass through the system. The compliance layers, including encryptors, consent ledger, and FHIR Provenance layer, were also created concurrently. By the conclusion of this stage, the MVP data bridge was production-ready.Epic Enablement (3–5 weeks per site)The SMART on FHIR certification process was undertaken at each site in the clinical context prior to UAT to validate workflows before implementation. The write-back toggle functionality was enabled wherever possible, in accordance with each site’s capability matrix, to provide as much integration capability as was available. The analytics dashboard solution was delivered for the payers to meet their reporting requirements.
The technology stack was chosen in order to manage the various healthcare standards, event streaming, and compliance for all the connected systems:Healthcare Standards: FHIR R4, SMART on FHIR integration, HL7, FHIR Observations, Bulk FHIR exportsEHR Integration: Epic FHIR R4 endpoints, SMART on FHIR user and backend servicesEvent Streaming: Apache Kafka for device ingestion and canonical event processingAI/ML: Python, scikit-learn, feature engineering, MLflow, shadow deployment workflowsBackend: Python, TypeScript, microservices architectureInfrastructure: Kubernetes, PostgreSQL, AWS, autoscaling, blue/green deploymentsSecurity and Compliance: Encryption in transit and at rest, RBAC, consent ledger, FHIR Provenance, audit logging
Jelvix delivered full data connectivity across the client’s entire patient cohort portfolio.
Clinical teams got real-time visibility across all patient cohorts in one interface, without manual data collection from separate systems.
The enterprise payers got their cohort dashboards and drill-downs by adherence and BP risk.
Adding new devices happens through the connector framework, and adding new provider sites happens via Epic enablement.
A single audit trail, standardized DPIA, and access review process — ready for HIPAA and GDPR scrutiny without emergency remediation work.
Unified and real-time data processing across various systems is currently implemented for the RPM system for treating hypertension.
Connector reuse and the canonical model replaced per-vendor engineering sprints.
Enhancement in risk score using advanced retraining and feature management.
Analysis for cohorts migrated from batch-based ETL to near real-time dashboards.
Interoperability and AI predictive analytics in healthcare were delivered in eight weeks.
Complete data lineage and consent evidence are applied to every transformation via FHIR Provenance.
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