Clinical Intelligence & AI

Clinical Decision Support System Development, Population Health & Prior Authorization Automation

Rules-based and AI-driven CDSS embedded in EHR workflows. Population health management software that identifies risk before it becomes cost. Prior authorization automation that removes the bottleneck between clinical decision and payer approval.

CDSS That Lives Outside the Clinical Workflow

A clinical decision support system that requires a separate login delivers zero point-of-care value. Clinicians make treatment decisions without alerts because the CDSS was never integrated into the EHR workflow they actually use.

Population Data With No Layer to Act On It

Health systems holding years of EHR and claims data with no unified analytics layer, risk stratification happens in monthly reports, care managers identify high-risk patients by reviewing lists rather than receiving real-time alerts.

Prior Authorization Delays Disrupting Care

Manual prior auth processes, fax submissions, phone-based status checks, staff-intensive appeals, create days-long delays between clinical decision and payer approval, directly affecting patient outcomes in oncology, cardiology, and specialty pharmacy.

Analytics That Reach Leaders After Decisions Are Made

Dashboards refreshed weekly rather than in real time, requiring analyst queries to surface insights that clinicians and operators need at the moment of the decision, not in the next report.

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What we build

Clinical Intelligence & AI Services, What We Build

Five capability areas covering the clinical intelligence stack, from point-of-care decision support through population health, prior authorization automation, and analytics infrastructure.

Clinical Decision Support System Development

Rules-based and AI-driven CDSS delivered inside EHR workflows, not in a separate tool.

Rules-based CDSS: drug-drug interaction alerts, allergy checks, care gap reminders using Arden Syntax, GELLO, or CQL
CDS Hooks integration: real-time CDSS at Epic, Cerner, and Athenahealth order-select, patient-view, and appointment-book trigger points
AI-driven CDSS: sepsis prediction, readmission risk scoring, and diagnostic suggestion, with SHAP explainability outputs
FDA SaMD compliance: SaMD classification support, 510(k) documentation preparation, and clinical validation study design

Population Health Management Software

Unified patient registries combining EHR, claims, and SDOH data, with risk stratification, care gap identification, and outreach workflows.

Unified patient registry: EHR + claims + SDOH on a shared identifier, deduplicated, longitudinal, cross-facility
Risk stratification: HCC risk scores, ED utilization predictors, and chronic disease complexity scores, auto-assigned by tier
Care gap identification: automated patient lists for preventive services, chronic disease monitoring, and medication adherence
Value-based care reporting: HEDIS measure calculation, quality programme performance, and drill-down to individual care gaps

Prior Authorization Automation Software

Full Da Vinci prior authorization workflow, from order entry to payer decision, without manual staff involvement.

CRD (Coverage Requirements Discovery): payer requirements surfaced at EHR order entry before the order is placed
DTR (Documentation Templates and Rules): clinical documentation assembled automatically from EHR structured data
PAS (Prior Authorization Support): X12 278 request and response handling with payer-specific rule configuration
Denial workflow: structured appeal documentation, payer denial pattern analysis, and root cause reporting

Healthcare Analytics Dashboard Development

EHR, claims, and population data transformed into real-time clinical and operational visibility, built for the people who need to act on it.

Data pipelines: Epic Clarity/Caboodle, Athenahealth API, claims data, normalized into a query-optimized healthcare analytics platform
Clinical operations: OR utilization, ED throughput, bed management, LOS benchmarking, and readmission rate tracking
Financial analytics: charge lag, clean claim rate, denial rate by payer, days in AR, and collections performance
Population health dashboards: risk tier distribution, care gap prevalence, HEDIS performance, and value-based care P&L

Healthcare Data Warehouse Engineering

The governed data infrastructure that makes analytics, population health, and AI possible.

Data warehouse architecture: dimensional modelling for clinical and claims data, patient, encounter, diagnosis, procedure, lab fact tables
ETL pipelines: EHR, claims, LIMS, and device data ingestion with data quality validation and HIPAA-compliant PHI handling
Normalized datasets: LOINC-coded labs, SNOMED CT diagnoses, CPT procedures, RxNorm medications, validated against reference terminologies
HIPAA compliance: PHI access controls, row-level security, de-identification for research use cases, and data access audit trails

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Who we serve

Built for the Organizations Turning Data Into Decisions

Hospitals & Health Systems

Health systems running population-scale care management programmes needing CDSS in Epic or Athenahealth workflows.

EHR-embedded CDSS
Population health registries
Prior auth automation
HEDIS and quality reporting

See industry page

Payers & Insurers

Health plans automating prior authorization, building member risk stratification, and delivering real-time analytics to care management.

Da Vinci CRD/DTR/PAS
HCC risk adjustment
Star Rating performance
Claims intelligence dashboards

See industry page

HealthTech Startups

Companies building AI-driven CDSS tools, population health products, or analytics platforms on HIPAA-compliant infrastructure.

AI clinical decision support
Healthcare analytics platform
Prior auth automation
FDA SaMD compliance

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Frequently Asked Questions

What is the difference between a rules-based and AI-driven CDSS?

Rules-based CDSS executes deterministic logic, if conditions A and B are met, fire alert C. It is auditable, transparent, and preferred for drug safety, protocol adherence, and regulatory-facing functions. AI-driven CDSS uses ML models to generate probabilistic predictions, sepsis onset probability, readmission risk score, handling multi-variable clinical patterns rules cannot express. Most mature CDSS implementations use both: rules for safety-critical alerts, AI for prediction and prioritization.

How do you automate prior authorization with AI without creating compliance risk?

We implement automation at two levels. The first is documentation automation: NLP and structured data extraction assemble the clinical documentation needed for a prior auth request automatically from the EHR, removing staff burden without making clinical decisions. The second is decision support: AI models predict approval likelihood at order entry, surfacing the prediction as advisory to clinical staff, not as a system decision. All automation includes HIPAA-compliant data handling, audit trails, and clinical governance controls.

What must a healthcare analytics platform include to drive clinical decisions rather than reports?

Five essentials: real-time or near-real-time data refresh for operational decisions; workflow integration so insights surface in the EHR task list or care manager view, not a separate dashboard; actionability with a clear next step attached to every alert; patient-level drill-down alongside population-level trends; and data completeness combining EHR, claims, and SDOH, not EHR alone. Missing any of these produces reports that get reviewed and filed, not acted on.

How do you validate an AI clinical decision support model before deploying it clinically?

We apply a three-phase protocol. Phase 1, retrospective validation on held-out historical data: measuring sensitivity, specificity, PPV, and AUC across demographic subgroups. Phase 2, shadow mode: the model runs in production, predictions logged but not shown to staff, validated against real outcomes as they occur. Phase 3, supervised clinical deployment with feedback capture and continuous performance monitoring. This satisfies FDA SaMD validation requirements and provides the evidence that hospital governance committees require.

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Tell us your decision-support requirements, analytics data sources, and prior-authorization volume.

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