10decodersPrior authorization reviews that answer themselves.
A regional managed care organization reviewed every patient intake form by hand against lengthy medical necessity policies. 10decoders deployed AI4Health — the platform reads each form, answers every policy criterion with a confidence score, models risk on both the approval and denial path, and drafts the patient and provider letters before the reviewer signs off.
Clinical judgement, not policy lookups.
Prior authorization review is a document problem wearing a clinical costume. Reviewers open an intake form, hold the diagnosis and treatment plan in their head, then walk a long policy document line by line to decide whether each medical necessity criterion is met. Nothing scores the criteria, nothing measures certainty, and nothing captures why a decision was reached — so the same case can be read two ways by two reviewers, and there is no structured record to defend either reading later.
10decoders built AI4Health to move that work off the reviewer's desk. Upload a patient intake form and the platform reads the full document, answers every medical necessity policy question with Yes, No or NA, and attaches a confidence score to each answer. It surfaces a recommended decision alongside a visual AND/OR decision tree taken from the original policy, models the downstream risk of both approving and denying, and auto-drafts the decision rationale, the patient letter and the provider response. The reviewer keeps the final call — and submits immediately or defers to just before the compliance deadline.
From manual policy reading to scored, traceable decisions.
The challenge
- Every intake form cross-referenced by hand against medical necessity policy documents
- No criteria scoring, no confidence measurement, no consistent escalation threshold
- The policy's AND/OR logic lived in the reviewer's head, not in the workflow
- Patient and provider letters drafted from scratch, producing inconsistent clinical language
- No structured audit trail to defend a decision in a payer dispute or regulatory audit
Our approach
- NLP reads the full intake document and answers each criterion Yes, No or NA
- A confidence score on every answer, with automatic escalation below threshold
- A visual AND/OR decision tree derived from the original policy document
- Risk modelling and cost projection on both the approval and denial path
- Auto-drafted rationale, patient letter and provider response — editable before submission
Built for the full prior auth workflow.
AI sits at the criteria layer, where the manual effort actually is. Reviewers keep the clinical decision — and gain the evidence behind it.
Medical necessity review
Reads every patient intake form and answers all medical necessity policy questions — Yes, No or NA — without a reviewer opening the policy document.
AND/OR decision tree
A visual tree derived directly from the original policy makes the logic path explicit — showing exactly which conditions carried the recommendation.
Dual-path risk modelling
Future risk is scored High, Medium or Low on both the approval and the denial path, with expected cost projections attached to each outcome.
Auto-drafted communications
Decision rationale, patient letter and provider response are drafted for every reviewed case — consistent clinical language, editable before submission.
Confidence & escalation
Every answer carries a confidence score. Anything below threshold is flagged for senior review rather than passed through silently.
Audit trail by design
Criteria answers, confidence scores, reviewer overrides, rationale and communications are all retained — searchable by case, reviewer, date and outcome.
Four capabilities. One platform.
AI4Health covers the workflow end to end — from intake form ingestion through to the letter that lands with the member.
Medical necessity review engine
Reads every patient intake form and answers all medical necessity policy questions with Yes, No or NA plus a confidence score — flagging low-confidence answers for escalation before a decision is made.
Decision tree & risk assessment
A visual AND/OR decision tree derived from the original policy documents, with future risk scored High, Medium or Low on both approval and denial paths and expected cost projections for each outcome.
Patient & provider communications
Drafts the decision rationale, the patient letter and the provider response for every reviewed case — consistent clinical language, editable before submission, with immediate or deferred processing to the compliance deadline.
Intake queue & EMR/EHR sync
A centralised intake queue with AI-verified eligibility and benefit status, compliance deadline tracking and urgency flags — synchronised with EMR and EHR systems, with a full audit trail on every decision and override.
From intake upload to a documented decision.
Four steps take a raw intake form through to a communicated, auditable outcome.
Upload intake form
Intake PDFs are uploaded individually or synced from EMR/EHR systems, and queued for processing on arrival.
AI checks every criterion
The platform reads the full document and answers each medical necessity question with a confidence score and an escalation flag.
Review decision & risk
Reviewers see the recommendation, the AND/OR policy tree, and risk plus cost projections for both approval and denial.
Submit with auto-letters
Approve, deny or request more information — letters draft automatically and submit now or defer to the compliance deadline.
What each team actually sees.
Role-scoped views and permissions — reviewers work the case detail, while compliance and clinical operations watch the queue and the trail.
Review cases with the evidence attached
- Every criterion answered automatically, with confidence scores and source traceability back to the intake document
- The recommendation shown alongside the policy decision tree and risk modelling on both paths — before the final call
- Approve, deny or request more information — with pre-drafted letters and one-click deferral to the deadline
The full queue and the full trail
- Intake status, AI recommendation, compliance deadline and urgency flags across the whole reviewer team in one dashboard
- Every answer, confidence score, override, rationale and communication retained and searchable for disputes and audits
- Letters generated consistently for every decision — no language drift, no missed communications
Defensible decisions, in your control.
What changed for each group — and the controls the platform is built on.
Judgement, not lookup
Reviewers stop cross-checking forms against policy documents. Criteria arrive answered and scored, and attention goes to the flagged answers that genuinely need clinical judgement.
A trail that holds up
Answers, confidence scores, overrides and rationale are logged on every case. Risk is modelled on both approval and denial, with cost projections supporting the coverage decision at case level.
HIPAA-aligned by design
Records are held in a HIPAA-compliant environment with access-controlled infrastructure and NDA-bound teams. Source code and IP ownership remain with the client throughout.
