InScightful Schedule

InScightful Schedule – Generative Decision Support for Surgery Scheduling

Version: 1.0
Build: On-Premises / Air-Gapped
Last Updated: November 1, 2025

Overview

InScightful Schedule is a generative decision support system that produces provable, auditable recommendations for operating room scheduling, surgeon and anesthesiology pairing, block allocation, and day-of-surgery orchestration. It runs entirely on-premises, on standard CPU-based hospital infrastructure, and integrates seamlessly with Epic, Cerner, and other EHR ecosystems using FHIR and HL7 standards. No cloud dependency or data export is required.

The system is built on the InScightful theorem-proving engine, which transforms natural-language queries and scheduling data into verified inference chains. Each recommendation is generated through transparent logic, ensuring that every decision can be explained, audited, and trusted.

Core Capabilities

1. Generates optimized OR day plans and suggested schedules respecting all operational constraints.

2. Explains each recommendation with transparent mathematical proof chains.

3. Accepts natural-language queries about delays, utilization, staff assignments, and outcomes.

4. Detects and resolves conflicts in real time during cancellations, supply issues, or late starts.

5. Produces immutable, audit-ready logs for compliance and governance.

Key Benefits

• On-prem, fully air-gapped, compliant with SOC 2 and HITRUST frameworks.
• Deterministic, bounded inference costs—sub-second latency on CPUs.
• No GPU dependency, yet capable of GPU acceleration for scenario sweeps.
• Integrates cleanly with EHR data via FHIR and HL7 read-only connectors.
• Demonstrates measurable ROI by reducing overtime, turnover variance, and idle OR hours.

Quick Start

Step 1: Connect read-only EHR feeds (Schedule, Slot, Appointment, Practitioner, Location, and Device).
Step 2: Define operational constraints such as room hours, staffing, turnover templates, and block rules.
Step 3: Select optimization objectives—utilization, on-time starts, fairness, and overtime reduction.
Step 4: Run a dry-run plan against historical data for validation with stakeholders.
Step 5: Move from advisory-only mode to write-back mode after governance approval.

Architecture

The system architecture consists of:

1. Connector Layer – interfaces with EHR, staffing, and materials systems.

2. Semantic Engine – compiles tabular data into morphisms and probabilistic relations.

3. Optimizer – multi-objective solver that guarantees bounded execution cost.

4. NLQ Interpreter – translates natural-language queries into provable topologies.

5. Audit Core – records every transformation and proof artifact for validation.

6. UI/API Layer – provides dashboards, visualization, and REST/Events access.

Data Flow (Read-only default):
• Extract scheduling, block, staff, and equipment data.
• Compile it into a categorical graph with probabilistic constraints.
• Optimize according to weighted objectives.
• Generate schedule recommendations and natural-language explanations.
• Store proofs and audit trails for compliance.

Data Model

Entities include:

• Case – procedure, predicted duration, surgeon, anesthesia type.
• Block – owner, time window, OR, access policy.
• Room – capabilities, turnover template, and equipment.
• Staff – roles, credentials, availability, pairing history.
• Supply/Device – lot number, readiness, sterilization window.
• Events – delays, cancellations, PACU constraints.

Each recommendation is supported by a “proof chain” — a sequence of morphisms that map cases, resources, and constraints into measurable outcomes with probabilistic confidence metrics such as total variation distance and entropy deltas.

Optimization and Objectives

Primary objectives:
• Maximize utilization and throughput.
• Minimize overtime, idle time, and turnover variance.
• Enforce fairness across block owners and service lines.
• Improve on-time starts and reduce cancellations.

Constraints can be hard (room hours, credentialing, exclusive devices) or soft (preferences, fairness weights). Each constraint is logged with satisfaction level and penalty cost.

Inference operates via bounded circuits—compact probabilistic operators that execute predictably on CPUs, achieving deterministic performance even in peak operational conditions.

Natural Language Interface

Examples of supported queries:
• “Why is OR-3 likely to run late on Wednesday?”
• “Which anesthesiologist pairing yields the fewest delays for Dr. Gonzalez?”
• “Show a re-plan if Case 4 cancels at 10:15 AM.”

Each query returns a verified explanation, a ranked list of contributing factors, and a proof chain that validates the result. The language parser is grammar-compiled to fixed topologies, ensuring safety against prompt injection or unbounded generation.

Integration

• Read-Only Feeds: Schedules, Blocks, Staff Rosters, Materials, and Room Calendars.
• Optional Write-Back: Recommended changes to case times or room assignments.
• Identity: SSO with SAML or OIDC, fully role-based access (Scheduler, Surgeon, Anesthesia, Periop Manager, Analyst, Admin).

User Experience

Planner View:
• Timeline visualization of rooms, blocks, and turnover.
• What-if scenario comparison with live KPIs.
• Constraint and conflict explanations.

Rounds View:
• Real-time monitoring of cancellations, holds, or delays.
• Single-click re-planning with delta metrics and proof record.
• Notifications routed to appropriate staff roles.

Audit & Governance:
• Immutable, timestamped logs of every decision and change.
• Version-controlled configurations with approvals and signatures.
• Replayable scenarios for training and compliance verification.

Security and Compliance

• On-premises, no cloud dependency.
• Data encrypted in transit and at rest with TLS and AES standards.
• Secrets and credentials stored in isolated vaults.
• Read-only mode by default; least-privilege permissions.
• Audit evidence pack for SOC 2 and HITRUST validation.

Deployment

Hardware:
• One 40-core CPU server (128–256 GB RAM).
• Optional GPU for accelerated analysis.
• Secondary node for redundancy or high availability.

Deployment Options:
• Docker container or Kubernetes cluster.
• Offline binary agent for fully air-gapped networks.
• Configurable backups and update bundles signed for offline validation.

Public API (REST)

Authentication: Bearer token (OIDC client credentials).

POST /v1/plan – Generate a recommended schedule for a date range.
POST /v1/replan – Compute a new plan in response to an event (delay or cancellation).
POST /v1/nlq – Execute a natural-language query with proof output.
GET /v1/proof/{id} – Retrieve proof artifacts for audit.
GET /v1/health – Liveness and readiness check.

Sample Query:
POST /v1/nlq
{ "query": "Why is OR-3 likely to run late on Wednesday?" }

Response:
{ "answer": "Primary risk driver is turnover variance after Case 2 due to equipment delay.",
"factors": [{"name":"Turnover variance","weight":0.41},{"name":"Anesthesia pairing","weight":0.27}],
"proof_id":"PLAN-2025-11-12-A" }

Validation and Governance

Commissioning:
• Historical backtests over 6–12 weeks of data.
• SME review of recommendations.
• Policy and governance approval before production.

Ongoing Control:
• Two-person rule for configuration changes.
• Drift detection on distributions.
• Quarterly fairness and performance reviews.

KPIs and ROI

Primary Metrics:
• OR utilization, on-time starts, and case accuracy.
• Overtime reduction and turnover consistency.
• Block leakage and fairness by specialty.

ROI Highlights:
• Runs on existing CPU hardware—no new capital cost.
• Reduces overtime hours and cancellations.
• Increases surgical throughput and patient satisfaction.

Operations Playbook

• Review plan conflicts daily before 2 PM for the next operating day.
• Freeze plan windows per policy after approval.
• Re-plan only upon verified event triggers (cancellation, supply shortage, delay).
• Failover to last known stable data set during connector outages.

Frequently Asked Questions

Q: Do we need GPUs?
A: No. The system is optimized for CPU performance; GPUs are optional.

Q: Can it operate completely offline?
A: Yes. All models and configurations can run in an air-gapped environment.

Q: Does it modify the schedule automatically?
A: No. It defaults to advisory (read-only) mode. Write-backs require explicit authorization.

Glossary

Block Leakage – Unused or misallocated block time outside ownership.
Morphism – A typed transformation between data objects in the system’s categorical model.
Proof Chain – The sequence of morphisms and metrics that justify a recommendation.
Bounded Circuit – A finite computation graph that guarantees predictable runtime.

Contact

For pilots, security validation, or partnership discussions:

Ben Sprott
Founder & CEO | Cavenwell Industrial AI (InScightful)
ben@cavenwell.ai
www.cavenwell.ai

© 2025 Cavenwell Industrial AI Corp. All rights reserved. InScightful Schedule is a registered product of Cavenwell Industrial AI.