Healix AI Labs / India / Early stage
Radiologyworkflows,reimaginedfor India.
Healix AI Labs is exploring AI-assisted tools that help hospitals and diagnostic centres coordinate imaging workflows, reduce manual handoffs, and improve visibility from study intake to report delivery — with qualified clinicians in control.
Building toward more connected radiology operations.
Synthetic volume · not patient imaging
01 Vision
Better radiologystarts withbetter workflows.
Every imaging study depends on a chain of people and systems. When that chain runs on phone calls, registers and disconnected software, work waits in places nobody can see.
A study brings together many related slices. A report depends on coordinated handoffs between people and systems. Healix is exploring ways to make those workflows visible, connected and accountable.
- 01
Intake
Studies arrive from modalities with their order details.
Where work waits today: Registers, re-typed details
- 02
Assignment
Each study needs the right reader, at the right time.
Where work waits today: Phone calls, chat groups
- 03
Reporting
Reports are drafted, reviewed and signed by clinicians.
Where work waits today: Unclear ownership
- 04
Communication
Approved reports reach referring clinicians.
Where work waits today: Manual dispatch
- 05
Follow-up
Recommendations are tracked until someone closes them.
Where work waits today: No single owner
02 From study to report
Every handoffshould be visible.
The workflow we’re designing around follows one study through four stages. It is a concept we aim to validate with radiology teams, not a system in production today.
- Stage 01
Study received
A study arrives from the modality with its order details. The intent: it appears on a shared worklist the moment it lands, not when someone remembers to check.
T+00:00 Study received from modality
- Stage 02
Case assigned
The study is matched to an appropriate reader by modality, priority and availability. The assignment is recorded, so nobody has to ask who has it.
T+00:06 Assigned to Radiologist B
- Stage 03
Qualified clinician reviews and approves
A qualified radiologist reads the study and prepares the report. Any software assistance is shown as assistance; the clinical judgement and the signature stay with the clinician.
T+00:21 Report in clinician review
- Stage 04
Report delivered, follow-up tracked
The signed report is released to the referring clinician, and any recommended follow-up stays visible until someone closes it.
T+00:48 Approved report delivered
03 Product direction
Operational clarityfor radiology teams.
A coordination layer for the work around the image: who has a study, where it is, how long it has waited, and what happens next.
StatusProduct concepts we aim to develop and validate with radiology teams. Not generally available.
Illustrative product concept with fictional data. A new study, DEMO-MR-0419, enters the worklist, is assigned to a radiologist, moves into clinician review, awaits sign-off, is approved by the clinician, and becomes ready for delivery.
What we’re building toward
Tagged honestly by stage. Initial directions are where we expect to start; exploratory work may change shape, or not ship at all.
- 01
Unified reporting worklists
One shared view of studies across modalities, sites and reporting teams.
Initial direction - 02
Case-status visibility
Where each study stands: awaiting a reader, in review, awaiting sign-off, or released.
Initial direction - 03
Turnaround-time monitoring
Elapsed time tracked against targets your team sets, by modality and priority.
Initial direction - 04
Delay alerts and escalations
Studies at risk of missing their window surface early, before they turn into a phone call.
Initial direction - 05
Assignment and handoff tracking
Clear ownership at every step, with a record of each handoff between people and teams.
Initial direction - 06
Operational analytics
Patterns in volume, workload and bottlenecks to support staffing and process decisions.
Later direction - 07
AI-assisted reporting
Exploring structured drafting and workflow assistance under qualified clinician review.
Exploratory
In this example, DEMO-CT-0398 is 32 min past the fictional target. Suggested next step: notify the reporting lead.
- Front desk to TechnologistRegisteredT+00:00
- Technologist to Reporting queueImages completeT+00:09
- Reporting queue to Radiologist BAssignedT+00:14
- Radiologist B to Sign-offDraft ready for approvalT+01:02
- Sign-off to Referring clinicianAwaiting approval—
04 Clinician in control
Intelligentassistance.Clinicalaccountability.
We’re exploring AI as a way to take friction out of workflow and reporting tasks: organising, drafting, flagging. It is not a substitute for clinical judgement. Qualified professionals stay responsible for every clinical decision and every final report.
- Role of AI
- Assistive · under exploration
- Final authority
- A qualified clinician
- Report release
- Only after clinician sign-off
- Clinical claims
- None. No diagnostic performance is claimed.
- Structured draft from the reporting templateSuggested
- Relevant prior study flagged for comparisonSuggested
- Worklist priority suggestionSuggested
- Read, edited and decided by the radiologist
- Final report signed by the clinician
- Released to the referring clinician
Report statusApproved · Radiologist (demo)
05 Built for real operations
Technology should fitthe way healthcare works.
Every hospital and diagnostic centre has its own mix of equipment, software, staff and habits. We want to learn how yours actually works before building anything that touches it.
Integrations with PACS, RIS and specific vendors are not available today. Each one will be scoped and validated with design partners, site by site, before we claim it works.
- Imaging modalities
- PACS
- RIS
- Reporting tools
- HIS / EMR
- Phone, messaging, registers
- Front desk
- Technologists
- Radiologists
- Reporting leads
- Administrators
- Referring clinicians
Meeting teams where they are.
Radiology departments in India sit at very different points on this spectrum. A useful coordination tool has to work across all of it.
- Paper and phoneRegisters, calls, handwritten handoffs
- Spreadsheets and chatShared sheets, messaging groups
- Standalone systemsA PACS or RIS, used on its own
- PACS + RISConnected imaging and scheduling
- Integrated stackHIS, RIS, and PACS working together
- 01PACS / RIS environments
- We plan to understand each site’s existing setup before proposing how to connect to it. We don’t assume universal compatibility.
- 02Reporting handoffs
- Map where reports are drafted, reviewed, and released before changing anything about how that happens.
- 03Operational visibility
- Start with the status and timing signals teams already produce, rather than asking for new data entry.
- 04Secure data handling
- Data minimisation, role-based access, and audit trails as design requirements, built with India’s data-protection obligations in mind.
- 05Implementation
- No rip-and-replace. The intent is to start with one workflow at one site, and earn the next step.
- 06Technical maturity
- Useful where systems are partial or manual, not only in fully integrated departments.
06 Principles
Four commitments that guide how we build.
- 01
Clinician-led
Qualified professionals retain responsibility for clinical decisions. Software supports their work; it does not take their place.
- 02
Workflow-first
Solve operational friction before introducing complexity. If a feature doesn’t remove friction for a team, it waits.
- 03
Privacy-conscious
Build around appropriate data protection, permissions and responsible handling, from the first design decision.
- 04
Measured by usefulness
Prioritise meaningful operational outcomes over flashy demonstrations. The test is whether teams keep using it.
07 Design partners
Help shape whatradiology operationsbecome next.
We’re early, and we’d rather build with radiology teams than for them. If coordination, turnaround, or handoffs cost your team time, we’d like to hear how.
- Diagnostic-centre owners
- Hospital administrators
- Radiologists
- Radiology operations teams
- 01
A conversation
We start by listening: how studies move through your department today, and where they wait.
- 02
A shared focus
Together, we would agree on the workflow problem worth tackling first.
- 03
Early access with clear expectations
As prototypes become ready, we intend to invite partners to try them and share feedback. Scope and availability would be agreed in advance.
Please noteThis form is for organisations and professionals. Don’t send patient records, scans, medical histories, or patient identifiers.
Build a better wayfor radiology to work.
Healix AI Labs is exploring a more connected, accountable and efficient approach to radiology operations in India, with the clinicians who do the work.