Clinicians spending more time on documentation than on patients, prior authorisations that delay care because the review is manual, and patients who readmit because discharge follow-up didn't reach them in time: these are the operational and clinical failures that AI can reduce.
We build AI systems for healthcare organisations: clinical documentation automation using ambient scribing, prior authorisation prediction, patient readmission risk scoring, AI-powered diagnostic image analysis support, revenue cycle optimisation, patient no-show prediction, drug interaction flagging, and care gap identification. Each system is scoped against your data, your workflows, and the specific clinical or operational outcome being targeted.
Clinical documentation time reduced with ambient AI scribing that drafts notes from the clinical encounter
Prior authorisation outcomes predicted before submission so your team focuses effort on likely denials
Readmission risk scores generated at discharge so follow-up resources go to the highest-risk patients
No-show probability scores that let your scheduling team fill slots before they go empty
RaftLabs builds AI systems for healthcare organisations including ambient AI scribing that saves 15--30 minutes per clinical encounter, prior authorisation prediction models that surface denial risk before submission, patient readmission risk scoring at discharge, diagnostic image analysis support tools, revenue cycle optimisation using claims pattern analysis, patient no-show prediction, and care gap identification from patient record data. Each system is scoped at a fixed price after a discovery phase that maps your EHR data, claims data, and clinical workflows to the specific AI capability being built.
Trusted by
Clinical and operational AI that reduces burden, not just cost
Healthcare AI at its best does two things: it gives clinicians back the time that documentation takes from direct patient care, and it surfaces the clinical signals that would otherwise arrive too late, the patient who will readmit, the prior auth that will be denied, the care gap that will become a complication. Both require AI built against your specific workflows, your EHR structure, and your patient population.
Capabilities
What we build
Ambient AI scribing
Real-time clinical encounter transcription and structured note drafting using speech-to-text and clinical NLP models. The AI captures the encounter conversation, structures it into documentation sections (HPI, assessment, plan), and presents a draft note to the clinician for review and approval before EHR entry. Configurable for specialty-specific documentation requirements. Integrates with your existing EHR via standard APIs. Reduces documentation time by 15-30 minutes per encounter while keeping the clinician in the review and approval loop.
Prior authorisation prediction
Classification models trained on your historical payer submission and outcome data. Scores each pending prior auth request by denial probability before submission. Surfaces the contributing denial risk factors: payer-specific coverage criteria, missing documentation requirements, diagnosis-procedure code mismatches. Gives your team time to strengthen the submission, explore alternatives, or initiate peer-to-peer review proactively rather than after a denial arrives. Shifts prior auth management from reactive appeals to anticipatory submission quality.
The model ingests prior auth history via HL7 v2 ADT and ORM message feeds or FHIR R4 Task and Claim resources depending on your EHR's integration capabilities. ICD-10 diagnosis codes and CPT/HCPCS procedure codes are extracted from structured claim fields; clinical notes attached to submissions are parsed using NLP (AWS Comprehend Medical or BioBERT fine-tuned on clinical text) to extract supporting clinical evidence, comorbidity mentions, and functional status language that payers weight heavily. Feature engineering includes payer-specific denial rate by procedure code from your historical data, average clinical documentation length for approved vs. denied cases, and payer-specific coverage policy flags updated quarterly from published LCD/NCD data. The gradient boosting classifier (XGBoost or LightGBM) is calibrated to output well-scaled probabilities rather than raw scores so that a predicted denial risk of 0.78 is meaningfully interpretable for your team. HIPAA SS164.312 technical safeguards -- audit controls, data integrity, transmission security -- 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.
Patient readmission risk scoring
Risk models that score each patient's 30-day readmission probability at discharge using clinical features (diagnosis, comorbidities, prior admissions, lab values), operational features (discharge destination, follow-up appointment timing), and social determinants where documented. High-risk patients enter an intensified post-discharge protocol. Allocates your care management resources to the patients who most need them rather than applying uniform follow-up to all discharges. Reduces readmission rates and the associated penalties in value-based care arrangements.
Data is pulled at discharge from your EHR via FHIR R4 Observation, Condition, Encounter, and MedicationRequest resources, or via direct HL7 v2 ORU and ADT message parsing where FHIR is not available. Clinical note text at discharge is processed with Med7 (for entity recognition of medications, dosages, and diagnoses) or AWS Comprehend Medical for ICD-10 and RxNorm entity extraction, providing NLP-derived features alongside structured EHR fields. The model architecture uses logistic regression as a transparent baseline (auditable by clinicians and compliance teams) with gradient boosting as the performance layer -- most deployments use the logistic regression output for clinician-facing explanation ("the three factors contributing most to this patient's risk are...") and the gradient boosting score for triage prioritisation. Model features are selected to exclude protected characteristics under 45 CFR Part 164 and CMS non-discrimination requirements. Risk score output includes the top contributing factors per patient, presented in the EHR workflow or a discharge summary dashboard, not a black-box score that clinicians are expected to act on without rationale. Risk stratification thresholds (low, medium, high) are calibrated to your post-discharge care management capacity, not set to generic cut-points.
Diagnostic image analysis support
Computer vision models that analyse medical images and surface findings for clinician review. Applicable to radiology (chest X-ray anomaly detection, CT finding flags), dermatology (lesion classification support), pathology (slide analysis support), and ophthalmology (retinal image screening). These are decision support tools: the model flags what it detects and the clinician makes the clinical decision. Designed and deployed within a workflow where the AI output is reviewed by a qualified clinician before any clinical action is taken.
Revenue cycle optimisation
Claims pattern analysis models that identify the coding patterns, documentation gaps, and payer-specific submission issues driving your denial rate. Works on your historical claims data: which codes are denied most frequently by which payers, which documentation elements correlate with clean claim submission, and which claim types have the longest time-to-payment. Output is actionable: specific coding guidance, documentation checklists, and payer-specific submission rules. Reduces clean claim rate improvement time compared to manual audit processes.
No-show prediction and care gap identification
No-show prediction models that score each scheduled appointment by cancellation or no-show probability using appointment type, patient history, scheduling lead time, and prior no-show patterns. High-risk appointments trigger proactive confirmation outreach or double-booking logic. Care gap identification models that scan your patient panel for overdue preventive and chronic disease management interventions and produce a prioritised outreach list for your care management team. Both use data already in your EHR and scheduling system.
Which clinical or operational problem are you trying to solve with AI?
Documentation burden, prior auth denials, readmissions, or revenue cycle: tell us the specific problem and we will assess which AI system addresses it and what your data supports.
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.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope AI for Healthcare Organisations in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
