Physicians spending 2-3 hours per day on clinical documentation that AI-assisted ambient capture could reduce to a post-visit review and edit?
Prior authorisation requests taking 2-3 days because the process is manual -- clinical staff pulling records, completing payer forms, submitting by fax, and following up by phone?
AI in Healthcare
AI in healthcare creates measurable value in specific places: reducing the documentation burden on clinicians, automating the administrative workflows that consume 30% of a physician's day, and surfacing clinical risk signals before they become adverse events -- not in replacing clinical judgement.
We build AI tools for health systems, digital health companies, and healthcare operators who need AI applied to a specific clinical or operational problem with explainable outputs, HIPAA-aware data handling, and a clear ROI case before development starts.
AI clinical documentation -- ambient conversation capture, structured note generation, and physician review workflow
Prior authorisation automation using clinical data extraction, payer criteria matching, and submission via payer API or portal
Predictive analytics for readmission risk, care gap identification, and population health management
HIPAA-aware AI architecture with explainable model outputs and audit logging for clinical use
RaftLabs builds AI software for healthcare organisations, digital health companies, and health systems. We develop AI-powered clinical documentation tools, prior authorisation automation, clinical decision support, patient triage chatbots, medical image analysis support, predictive analytics for care gaps and readmission risk, and AI-assisted coding and billing tools. All AI healthcare builds are HIPAA-aware with explainable model outputs and audit logging. Most AI healthcare builds deliver in 10-16 weeks at a fixed cost.
HIPAAAware design
·ExplainableModel outputs
·FixedCost delivery
·10-16Week delivery
Where AI creates real value in healthcare operations
Clinical documentation takes 2-3 hours of a physician's day on average, most of it transcribing what was said during the encounter into a structured note. Prior authorisations take 2-3 staff days per request and are denied 12% of the time on first submission, requiring appeal work. Patient no-shows cost a practice 5-8% of scheduled revenue and are predictable 48 hours in advance with a simple model. Care gaps -- preventive screenings overdue, chronic disease management not optimised -- are visible in the clinical data but not surfaced to the care team systematically. These are the AI use cases with a clear operational ROI before you write a line of code.
Every AI system in healthcare that processes protected health information (PHI) must be built with HIPAA technical safeguards -- encryption, access controls, audit logging, BAA coverage for every AI vendor and infrastructure provider that touches the data. Explainability matters for clinical AI too: a model that predicts readmission risk needs to show the clinical team which signals drove the prediction, not just output a score. We build AI healthcare software with these requirements built into the architecture from the start. We build for health systems adding AI to existing clinical workflows, digital health companies building AI-powered features into their platforms, and healthcare operators who need specific AI automation -- not a broad engagement.
What we build
AI clinical documentation
Ambient conversation capture using voice recognition to record the patient-physician encounter, with a structured clinical note generated from the conversation and presented to the physician for review and sign-off -- not a transcription, a structured SOAP or specialty-specific note with the right sections populated. Specialty-specific templates for primary care, emergency medicine, behavioural health, orthopaedics, and other disciplines. Physician review interface that shows the AI-generated note alongside the conversation transcript, with the ability to edit, accept, or reject specific sections. Direct write to the EHR via FHIR or HL7 interface when the physician approves. PHI handling designed so audio is not retained beyond the session without explicit consent and documentation.
Prior authorisation automation
Clinical data extraction from the EHR to populate prior authorisation requests: diagnosis codes, procedure codes, relevant clinical history, lab results, and prior treatment attempts. Payer criteria matching that checks the clinical data against the payer's published clinical criteria for the requested procedure. Auto-population of payer portal forms or submission via payer API (Gold Carding programmes, Epic Payer Platform integration, or direct API where available). Fax submission with structured data for payers that still require it. Status tracking and automated follow-up triggers when a submission has not received a response within the payer's stated turnaround time. Denial analysis with appeal template generation pre-populated with the relevant clinical evidence.
Clinical decision support
Rules-based and ML-based alerts surfaced to clinicians at the point of care: drug-drug interaction warnings, drug-allergy cross-checks, duplicate order detection, sepsis screening score calculation from vital sign trends, and evidence-based order set recommendations for common presentations. Alert design that reduces alert fatigue -- high-specificity alerts rather than high-sensitivity alerts, with clear differentiation between hard stops and advisory alerts. Integration with the EHR workflow so alerts appear in the right place in the clinical process rather than in a separate application. Alert response tracking so clinical leadership can see override rates and adjust alert logic accordingly.
Predictive analytics and population health
Readmission risk models that identify patients at high risk of 30-day readmission before discharge, so the care team can intervene with a targeted care transition plan. Care gap identification that surfaces patients overdue for preventive screenings, chronic disease monitoring labs, or specialist follow-up -- presented in a care manager workflow rather than a static report. No-show prediction that identifies appointments at high risk of non-attendance 48-72 hours in advance, enabling proactive outreach. Sepsis early warning models trained on vital sign and lab trends that alert the care team before the patient meets full SIRS criteria. All models with explainable output -- the clinical signals driving each prediction visible to the clinician, not a black-box score.
AI-powered patient triage and chatbots
Symptom assessment chatbots that collect structured symptom data from patients before their appointment or via an asynchronous telehealth channel, generating a pre-visit summary for the clinician. HIPAA-aware conversation design with PHI handling documented for your BAA. Appointment booking chatbot that handles scheduling, reschedules, and cancellation requests without a staff member in the loop. Patient intake automation that collects demographic, insurance, and medical history data before the visit and pre-populates the EHR registration record. Escalation logic to human staff when the patient's response indicates a clinical need that requires immediate attention.
AI medical coding and billing assistance
AI-assisted code suggestion that reviews the clinical note and recommends ICD-10 diagnosis codes and CPT procedure codes based on the documented encounter, with the specific text that supports each code recommendation highlighted for coder review. Query generation for clinical documentation improvement (CDI) when the note supports a more specific code than what the AI can confirm from the current documentation. Undercoding detection that identifies encounters where the documented complexity supports a higher E&M level than what was billed. Audit trail of AI suggestions and coder decisions for compliance review. Designed to assist coders -- not replace them -- by reducing the manual work of initial code assignment and focusing coder attention on ambiguous cases.
Frequently asked questions
Clinical documentation reduction is the most commonly measured: AI ambient documentation saves physicians 60-90 minutes per day, which translates to additional patient capacity or reduced burnout-related turnover. Prior authorisation automation reduces staff time per request from 2-3 days to same-day submission, at roughly $10-20 per manual request in staff cost. No-show prediction recovers 5-8% of scheduled revenue that would otherwise be lost to empty appointment slots. Care gap closure improves performance on value-based care contracts where the payment model rewards prevention and chronic disease management outcomes. We scope each project with an ROI case built on your data -- patient volume, staff cost, current denial rates -- before committing to a build.
HIPAA technical safeguards for AI systems include: encryption at rest and in transit for all PHI touching the AI pipeline, role-based access control with minimum necessary access to patient data, audit logging of every PHI access and model inference that uses patient data, and BAA coverage with every AI vendor and infrastructure provider that processes PHI. PHI is not used for model training without explicit de-identification or documented patient consent. Model outputs that contain PHI are treated as PHI for storage and access purposes. We deliver compliance documentation alongside the software so your compliance and legal teams can review what was built and how PHI is handled before go-live.
Transcription converts speech to text -- a medical transcriptionist or ASR (automatic speech recognition) service produces a word-for-word record of what was said during the encounter. AI clinical documentation produces a structured clinical note from the conversation: a SOAP note with the subjective, objective, assessment, and plan sections populated from what was discussed, not what was literally said. The difference is the structure: a transcription requires a clinician to read and reformat it into a note; an AI-generated note is already in the correct clinical format for physician review and sign-off. The physician reviews the AI-generated note, edits as needed, and signs it -- typically a 2-5 minute task rather than a 10-20 minute documentation task.
Yes. The standard integration paths are: FHIR R4 for reading patient context and writing clinical data back to Epic and Cerner, CDS Hooks for surfacing clinical decision support alerts within the EHR encounter workflow without the clinician leaving the EHR, and SMART on FHIR for launching AI-powered applications within the EHR context using the patient's identity and session. For older EHR systems that predate these standards, HL7 v2 feeds handle data exchange for documentation and results. The right integration approach depends on your EHR vendor, your version, and whether your organisation has an existing API agreement -- we confirm integration feasibility during scoping.
