Forward Deployment Engineer, Aidence
HL7 Integration
$125-135k
Somerville, MA
More information about location
AI-powered clinical applications for the lung cancer pathway
Be an early applicant
AI-powered clinical applications for the lung cancer pathway
1-20 employees
Be an early applicant
$125-135k
Somerville, MA
More information about location
1-20 employees
Company mission
To provide AI that empowers healthcare and pharmaceutical professionals to deliver faster, more precise diagnostics and treatments.
Role
Who you are
- Education: Bachelor’s degree in Computer Science, Biomedical Engineering, Health Informatics, Information Systems or a related field, or an equivalent combination of education and experience (required)
- Experience: 8+ years of overall experience in healthcare IT integration or clinical systems (required)
- Experience: 5+ years of hands-on HL7 v2.x interface design and support in a customer-facing or implementation role, ideally in radiology or imaging (required)
- Expert-level experience with at least one integration engine; Mirth Connect / NextGen Connect strongly preferred (Rhapsody, Cloverleaf or Corepoint acceptable) (required)
- Strong understanding of radiology order-to-result workflow across RIS, PACS, modality worklist, reporting and EHR (required)
- Working knowledge of DICOM, including how HL7 order data maps to DICOM attributes and modality worklist (required)
- Proficiency in JavaScript (for interface transformers), SQL and message/log analysis (required)
- Demonstrated ability to work directly with customer IT, interface analysts and clinical stakeholders, including in escalated situations (required)
- Experience with FHIR R4 and IHE profiles (SWF, PIR, XDS) (preferred)
- Imaging informatics credential such as CIIP or PARCA (preferred)
- Experience in a regulated medical device environment (FDA 21 CFR 820, ISO 13485) and with ITIL practices (preferred)
- Microsoft Office experience required
- Communicates, cooperates, and consistently functions professionally and harmoniously with all levels of supervision, co-workers, visitors, and vendors
- Demonstrates initiative, personal awareness, professionalism and integrity, and exercises confidentiality in all areas of performance
- Follows all local, regional and country laws concerning employment
- Follows all DeepHealth policies and procedures
- Follows data privacy, compliance, safety and confidentiality standards at all times
- Practices universal safety precautions
- Promotes good public relations on the phone and in person
- Adapts and is willing to learn new tasks, methods, and systems
- Reports to work regularly as scheduled; consistently punctual with respect to working hours, meal and rest breaks, and maintains satisfactory personal attendance in accordance with DeepHealth guidelines
- Completes job responsibilities in a quality and timely manner
What the job involves
- The Forward Deployment Engineer, HL7 Integration helps customers adopt DeepHealth’s Diagnostic Suite correctly by owning how it integrates with their clinical systems
- The role combines HL7 and DICOM engineering depth, radiology workflow knowledge and customer communication to design customer integrations, coach implementation teams, resolve complex interface issues, and turn recurring integration needs into standard, configurable product capabilities
- The Forward Deployment Engineering (FDE) function, whose purpose is to help customers adopt DeepHealth products correctly sits at the intersection of the Technical Delivery Manager (TDM), Product, Service and Solution Delivery teams to ensure smooth customer adoption
- It transitions best practices to the Service and Solution Delivery teams and plans upcoming product changes with them to ensure commercial readiness
- Own integration solution design for assigned customers, mapping the radiology order-to-result workflow (RIS/EHR, worklists, PACS, Viewer, reporting, results) to HL7 and DICOM interface configuration
- Lead integration technical discovery with customers and implementation teams, covering interfaces, message types, code sets, migration data and cutover requirements
- Build, configure and validate interfaces in the integration engine (Mirth Connect / NextGen Connect), including mappings, transformers, routing and test message sets
- Partner with the assigned implementation counterpart for this workstream to adopt learnings and best practices into customer configuration and implementations; train and coach implementation teams and customer interface analysts on the standard interface specification and correct configuration; publish integration playbooks and baselines
- Serve as customer-facing technical lead for integration during go-lives, hypercare and escalations
- Provide implementation support as the escalation point for HL7 and integration issues during implementation, including root cause analysis and resolution with engineering (approximately 20–30% of time)
- Establish change control for site-specific interface customizations, including documentation, versioning and rollback plans aligned to product releases
- Convert recurring interface needs into standard, configurable product capabilities and requirements, reducing one-off custom integrations
- Own the AI adoption strategy for clinical data integration: define requirements, expected customer impact and success criteria, and feed them back to Product and Engineering
- Configure and support adoption of AI-enabled workflows that depend on clinical data integration (e.g., AI report drafting using prior and clinical data), coordinating rollout, site readiness and customer adoption with the Technical Delivery Manager and Product Manager
- Own escalation of engineering issues from assigned accounts: triage, document reproducible defects, track status with engineering and close the loop with customers
- Support site reliability (SRE) for HL7 in assigned accounts: monitor service health with observability tools, participate in incident response and drive reliability improvements with engineering
Our take
The medical technology market is expected to continue its growth due to innovative technological developments, the increasing pace of population ageing, chronic diseases, and trust in medical devices. Alongside this, advancements in AI and robotic solutions, cloud-based software and healthcare apps, are likely to boost industry growth in the future.
Radiologists agree that AI is necessary to compensate for the increase in medical images they need to assess. Without AI, the demand for scans would outgrow the human ability to review them timely and accurately. Aidence provides an AI solution for lung nodule detection and management, which helps improve lung cancer prognosis by detecting, measuring, classifying and tracking the growth of pulmonary nodules on patients' chest CT scans.
Aidence was acquired by US radiology firm RadNet in 2022. Its future goals include expanding its products to detect larger masses on chest CTs and using the data collected from scans to provide further insights into the effectiveness of lung cancer treatments.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Mar 2020
$2m
GRANT
Feb 2019
$11.3m
SERIES A
Total funding: $15.8m
Company HQ
De Weteringschans, Amsterdam, Netherlands
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