AI for healthcare that gives clinicians back the hours documentation takes.

Ambient AI scribing works by capturing the clinical encounter conversation in real time using a microphone in the consultation room or an app on the clinician's device. A speech-to-text model transcribes the encounter. A clinical NLP model then structures the transcript into the relevant documentation sections: chief complaint, history of present illness, examination findings, assessment, and plan. The draft note is presented to the clinician for review and approval before it enters the EHR. The clinician edits what needs changing and signs off. The AI generates the first draft; the clinician retains full review and approval responsibility. The time saving is in the drafting step, which typically takes 15-30 minutes per encounter for documentation-heavy specialties. Across a full clinic day, this represents 2-4 hours of clinician time. The models can be configured for specialty-specific vocabulary: a cardiology clinic uses different terminology and documentation structure than a GP practice or a mental health service. We assess your EHR system and documentation workflows in discovery to determine the integration approach and configuration requirements.

Prior authorisation prediction models are trained on your historical authorisation submission data: submissions that were approved and submissions that were denied, along with the clinical and administrative features of each request. The model learns which combinations of payer, procedure code, diagnosis code, patient demographic, and clinical documentation patterns correlate with denials. Before submission, each pending authorisation request is scored. High-denial-risk requests surface to your team with the contributing factors: is it a payer-specific coverage exclusion, a missing clinical documentation requirement, or a diagnosis-procedure combination the payer typically disputes? Your team then has the option to strengthen the clinical documentation before submission, explore an alternative procedure code, or engage the payer's peer-to-peer process proactively. The goal is to shift denial management from reactive (the denial arrives, you appeal) to anticipatory (you address the likely denial reason before submission). Requires at least 12 months of prior authorisation submission history with outcomes to train effectively.

Patient readmission risk models use a combination of clinical and operational features available at the point of discharge to score each patient's 30-day readmission probability. Clinical inputs include primary diagnosis, comorbidity count, number of prior admissions in the past 12 months, medication count, lab values at discharge (for conditions where lab trajectory is predictive), and functional status. Operational inputs include discharge destination, whether a follow-up appointment was scheduled and how soon, and whether the patient has a documented primary care provider. Social determinants where captured in the EHR, such as housing instability or documented transportation barriers, also improve model accuracy significantly. Output is a risk score and the contributing factors for each discharged patient. High-risk patients enter an intensified post-discharge follow-up protocol: a phone call within 24 hours, an expedited outpatient appointment, and a pharmacy reconciliation check. The model helps allocate these resources to the patients who most need them rather than applying the same follow-up to everyone.

Care gap identification uses NLP and structured query analysis across patient records to find patients who are overdue for a preventive or chronic disease management intervention that their clinical history indicates they should have received. Examples: a diabetic patient who has not had an HbA1c in 12 months, a patient on a statin who has not had a lipid panel in 24 months, a patient aged over 50 with no documented colorectal cancer screening, or a hypertensive patient whose blood pressure readings in recent encounters suggest inadequate control. The model queries across your patient panel and produces a prioritised list of patients with identified gaps, filtered by gap type, patient risk tier, and time since last relevant intervention. This list feeds your care management team or generates outreach for patients to schedule the relevant appointment. For practices participating in value-based care arrangements or quality reporting programmes, care gap closure is directly tied to performance metrics and revenue. We assess which gap types are most clinically and financially relevant in your context during scoping.

Healthcare AI projects at RaftLabs are scoped and priced before development starts. A focused single-model system, such as a readmission risk scorer or a no-show predictor, typically runs from $40,000 to $80,000 depending on data availability, EHR integration complexity, and regulatory requirements. Ambient scribing platforms with specialty-specific configuration and EHR API integration are typically $80,000 to $150,000. We provide a fixed-price quote after the discovery phase so there are no surprises on the final invoice.

Yes. A HIPAA-compliant voice agent authenticates the patient, collects name, date of birth, reason for visit, and insurance information, and writes the completed record directly into the EHR via FHIR or HL7, with end-to-end encryption and no PHI retained in call logs beyond the minimum retention period agreed in the BAA. What took 7-12 minutes of front-desk time completes in 3-4 minutes with no manual entry. The same voice infrastructure runs medication-adherence check-ins and structured post-discharge symptom monitoring at 24, 48, and 72 hours, with clear escalation to a live clinician built into the dialogue design whenever a caller signals distress, never an attempt to handle a medical emergency.

Yes. HIPAA compliance requirements are scoped and addressed in week 1, not added before launch. We have shipped HIPAA-compliant systems for US healthcare clients including a remote patient monitoring platform that onboarded 150 patients in 12 weeks. Technical safeguards, audit controls, data integrity controls, and transmission security (HIPAA 45 CFR SS164.312) are applied throughout the data pipeline. De-identification using the Safe Harbor method is applied to any data used for model training outside the production EHR environment.