AI for Healthcare Organisations

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.

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.

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.

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.

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 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.