For Health Plans & Risk-Bearing Provider Groups

Three cost-avoidance levers. One platform. Audit-tagged on every recommendation.

Referral appropriateness, operational care continuity, and decision defensibility — for health plans evaluating network cost and quality, and value-based provider groups standardizing clinical decisions. AI surfaces guideline-concordant and evidence-based recommendations clinicians act on at the point of care — each one logged with source, version, and timestamp.

01 — Referral appropriateness

40%+

Reduction in total specialty referrals

Operating data · vs. pre-production baseline

02 — Operational care continuity

Every

Open care item surfaced at the next encounter

By design · closed-loop commitment

03 — Decision defensibility

100%

Recommendations logged with source, version, timestamp

By design · immutable record

EHR integration

eClinicalWorks athenahealth NextGen Allscripts Greenway Veradigm Epic Cerner / Oracle Health Meditech

Drop-in integration for ambulatory EHRs.

Where the economics improve

Three operational levers, directly measurable.

PCP-manageable order routing

The Referral Review Engine reviews specialty referrals, imaging orders, DME, and ancillary services, identifies primary-care-manageable conditions, and routes them to PCP-LED management with cited clinical source authority. Reduces specialty referrals 40%, appropriately — with >88% clinician concordance. Hardcoded safety escalations ensure conditions genuinely requiring specialty input are never deferred. With optional preferred-network integration, the engine also reports out-of-network leakage rate alongside appropriate-referral rate — bundled metrics for plan dashboards. The engine also surfaces the guideline-recommended diagnostic workup and management pathway for each condition — so PCP-LED management means standardized, documented, evidence-based care, not just a deferred referral. For value-based primary care groups: the engine reduces time-per-referral decision from approximately 15 minutes to under two minutes, with guideline rationale attached — turning every referral into a documented, defensible clinical act.

Operational care continuity

The Care Management Platform handles TCM/CCM enrollment with cumulative-time tracking and provides EHR-documented follow-through for high-risk patients identified across the platform. Care Management notes are required to be listed at the next encounter — making closed-loop accountability a clinical commitment, not just a data flow.

Decision defensibility

Every recommendation across the platform is logged with cited clinical source authority, engine version, source authority version, and timestamp. Append-only audit trail via Postgres triggers; field-level diffs preserved. Exportable for plan audits and CMS data validation. The override workflow requires a documented reason that becomes part of the recommendation record.

Why plans and providers care

"Specialty referrals are the largest controllable spend after inpatient. Defensible inappropriate-referral prevention is what every plan is trying to underwrite — and what most platforms can't actually prove."

The economics · 30–40% of specialty referrals are PCP-manageable per industry reporting · $1,200 (all-in: visit + imaging + workup + leakage) per modeled avoidable specialty referral · referral rates and medical loss ratio are universally tracked across risk-bearing organizations

What we're not

Decision support — not autonomous decisioning.

There is a category of products that promise to remove the clinician from the loop. We are not that. The distinction matters at audit, at MA plan diligence, and most of all to the patient.

What we don't do

Autonomous AI decisioning

AI makes referral decisions that providers cannot override
Clinical judgment is replaced rather than supported
Safety decisions are AI-configurable, not hardcoded
Audit records are summary-level, not recommendation-level
Coverage and prior auth-style gatekeeping

What we are

Augmentative decision support

AI surfaces a recommendation; clinician decides
Override authority preserved on every non-safety decision
39 hardcoded safety escalations are non-overridable by anyone
Immutable per-recommendation audit record with cited source
Clinical workflow tool — not a payer gatekeeping system

What a pilot looks like

30 days. Live in days. Customizable. Compliant.

Pilot structure

01Day 1: Standalone platform available immediately. Referral teams can begin processing referrals through Alma's interface as requests come in — no integration required to start.
02Week 1: EHR integration complete for most ambulatory and major hospital-grade systems. Our team handles the technical lift end-to-end.
03Week 2: Joint clinical review. Alma physicians and your clinicians align on guideline application, specialty matrix calibration, and edge-case handling. Workflow adjustment list captured.
04Weeks 3–4: Workflow customization complete. Automation tuned to Client's preferred level — from full clinician review of every recommendation through to audit-tagged auto-routing — while maintaining compliance and audit standards required for health plans.

Robust enough for health plan deployment. Flexible enough to meet each Client where they are.

Success criteria

A≥30% reduction in total referrals, appropriately, at pilot sites
BOverride rate <15% — clinicians find recommendations defensible
CZero missed safety escalations in independent review
DProvider satisfaction (NPS-style) ≥ +20 after 30 days
EEngine uptime ≥99% across the pilot window

The financial case

Modeled cost avoided — decomposed by category.

Two tables. The first is the conservative specialist-only floor. The second is the full external-orders opportunity — specialist referrals, imaging, and ancillary orders, all reviewed by the engine via workup-verification gates.

Specialist referral baseline.

Specialist referrals only. 40% total-avoided rate × $1,200 (all-in: visit + imaging + workup + leakage) per episode.

Panel size

Referrals/month

Net avoided (40%)

Annual savings

5 PCPs (~10K patients)

200

$1.16M

10 PCPs (~20K patients)

400

160

$2.31M

25 PCPs (~50K patients)

1,000

400

$5.76M

50 PCPs (~100K patients)

2,000

800

$11.6M

100 PCPs (~200K patients)

4,000

1,600

$23.1M

40 specialty referrals/PCP/month (320 visits × 12.5% specialty referral rate) × 40% total-avoided rate × $1,200 (all-in) cost per episode = $19,200/PCP/month. Cost-per-episode anchor consistent with peer-reviewed eConsult cost-savings literature: Anderson et al., AJMC 2018 ($655 cost reduction per avoided face-to-face cardiology consult); Anderson et al., Health Affairs 2019 ($82/patient/month specialty episode reduction).

Total external orders — specialist + imaging + ancillary.

All three categories reviewed by the engine via workup-verification gates. Category-specific cost anchors.

Panel size

Orders/month

Avoided (40%)

Annual savings

5 PCPs (~10K patients)

350

140

$1.43M

10 PCPs (~20K patients)

700

280

$2.86M

25 PCPs (~50K patients)

1,750

700

$7.14M

50 PCPs (~100K patients)

3,500

1,400

$14.3M

100 PCPs (~200K patients)

7,000

2,800

$28.6M

Decomposed at 40% total-avoided rate (operating data; methodology in development): specialist refs (40 × 40% × $1,200 all-in) = $19,200/PCP/month + imaging (20 × 40% × $500 direct cost only, client-supplied) = $4,000/PCP/month + ancillary (10 × 40% × $150) = $600/PCP/month = $23,800/PCP/month combined. Imaging and ancillary cost anchors via Medicare PFS reference.

Note on what this model excludes

This model excludes the internal operational savings the engine generates — coordinator time recovered from chasing specialist notes, eliminated rework on incomplete referrals, and the HEDIS gap-closure opportunities recovered when the patient stays in primary care rather than being routed to a specialist. These are real and material; we exclude them to keep the financial case anchored to externally observable cost categories. Internal-savings modeling is available under NDA.

Anchored evidence base: Anderson et al., American Journal of Managed Care, 2018 (PMID 29350511). Anderson et al., Health Affairs, 2019. Forrest, Reasons for Outpatient Referrals from Generalists to Specialists (~30% of referrals possibly appropriate or inappropriate). Albini et al. (37% of referral forms classified inappropriate). Kaul et al., AJMC, 2016 (52% of US PCPs reported making unnecessary referrals on patient request).

Payer-ready data

Auditable, guideline-concordant referral data — for CMS, MA plans, and quality reviews.

The same engine outputs that drive operational analytics also produce the documentation payers and reviewers ask for. Three clients, one recommendation record per referral.

Medicare Advantage

STAR ratings and HEDIS measures.

— PCP-first management supports HEDIS measures
— Referral appropriateness for STAR ratings
— Reduced specialist utilization → lower MLR
— Audit trail demonstrates VBC delivery

MSSP & ACO

Auditable referral patterns.

— Referral patterns — now auditable
— Guideline adherence per encounter
— Shared savings via reduced per-episode cost
— Quality metrics tied to recommendation trail

Quality reviews

CMS audit and accreditation.

— Every recommendation timestamped with cited clinical source authority
— Audit tags per recommendation (CM, safety, best practice)
— Engine + source authority version tracked per record
— CSV export — ready for CMS audit or accreditation

Regulatory tailwind

Aligned with the way healthcare is paying.

The Referral Review engine supports the full spectrum of value-based care. That includes CMS Innovation Center priorities — the Advancing Chronic Care with Effective, Scalable Solutions (ACCESS) Model, the Long-term Enhanced ACO Design (LEAD) Model, and the Medicare Shared Savings Program — and extends to Medicaid managed care, commercial risk arrangements, and employer direct contracting. Any organization measured on quality, total cost, and patient outcomes can use Referral Review as part of its clinical and operational foundation.

The market is moving.

CMS is tying reimbursement to documented guideline adherence. Health plans are requiring referral appropriateness data for STAR ratings. Value-based contracts reward reduced specialist utilization with auditable documentation.

Primary care groups that can prove guideline-concordant referral management will win contracts. Those that cannot will lose them.

The question is not whether you will be asked for this data. It is whether you can produce it when asked.

See it on a sample of your referral data.

A 30-minute working session. We'll walk through the recommendation flow, the audit record, the override workflow, and the safety escalations — using a sample of referrals from your network.

Request a working session →