Capabilities

Healthcare access in rural and tribal America is a systems problem. We treat it as one.

We apply operations research, simulation science, and real-time systems engineering to build software that works in real life, not just on paper. We live next to the EHRs that are already deployed. We don't try to replace them.

Decision Support

Routing, dispatch, and resource placement for distributed workforces

Which responder, which route, which facility, where to place supply kits, how many providers a region needs. We use quantitative methods to turn these into answered questions in operational timeframes, with the coordinator always in the loop, not replaced by a black box.

Multi-objective. Stochastic parameters. Real-time re-evaluation as conditions change.

Simulation

Calibrated agent-based simulation for policy analysis

You can't test healthcare system changes with real patients. We build simulation environments with synthetic decision agents whose behavior is calibrated against real-world data. Run a thousand scenarios, compare outcomes, and quantify the impact of interventions before committing resources.

Monte Carlo methods. Bayesian parameter estimation. Healthcare desert modeling.

Real-Time Systems

Event-driven architecture for distributed coordination

Operational networks are distributed systems with unreliable connectivity, heterogeneous participants, and time-critical state changes. We build infrastructure designed for these realities -- not the clean-room version.

Fault-tolerant messaging. Consistency guarantees matched to domain requirements. Designed for degraded-mode operation.

Interoperability Without Replacement

Heterogeneous data fusion from operational sources

Every facility already has an EHR. Coordination is the layer those EHRs weren't built to carry: facility capabilities, provider availability across organizations, transport status, geographic coverage, road conditions. We build the interoperability layer between what exists and what's needed, using FHIR R4 where vendors support it and HL7v2 where they don't.

Standards-based where possible. Custom where necessary. Built for the data environments that actually exist, not the ones vendors promise.

Validation

Rigorous methodology for pre-deployment confidence

We don't ship unvalidated systems. Statistical testing against historical data, sensitivity analysis across parameter uncertainty, scenario stress-testing for edge cases, and human-in-the-loop evaluation with domain experts.

Ground truth comparison. Systematic uncertainty quantification. Peer-review mindset.

On-site Deployment

Complete coordination systems for communities starting from scratch

Many of the communities we serve have never had digital coordination tools. We deploy the full stack: a pre-configured on-prem appliance where that's the right answer, cloud where it isn't, plus the week-of-install implementation work that separates software that gets used from software that sits on a shelf.

Hardware specification. Connectivity planning. On-site implementation. Funded through the channels rural and tribal healthcare actually use.

The Coincident Suite

The capabilities above ship as an integrated product suite. One stack, bid-able as components, deployable together or piece by piece depending on what a program, facility, or prime contractor needs.

  • A clinical-data coordination layer that sits beside existing EHRs, normalizes what they emit, and carries the coordination state EHRs don't track.
  • An interfacility transport coordination application, already in production for a state trauma network.
  • A dispatch coordination application for specialized service networks (SANE, blood logistics, similar distributed-responder programs).
  • A simulation and scenario-modeling engine for policy what-ifs, counterfactual analysis, and gap studies at state or multi-state scope.
  • A coverage and routing analytics layer that answers "how long is the nearest X from here, under these conditions, at this hour?" for every cell in a state.
  • A public data API so other vendors, students, and research teams can build on the same data layer without integration friction.
  • An EHR interop bridge (FHIR R4 pull/push, HL7v2 via a small on-prem appliance) so the layer above can reach into any facility, including the critical-access clinics and tribal health centers running older systems.

Individual product identities will land on this site soon. Engagements today are suite-level.

How We Deploy

Five deployment paths. Most programs use more than one: cloud for the state coordinator, appliance for the critical-access clinic, API for the analytics team.

Hosted

For state programs and health systems ready to trust the cloud.

Coincident-hosted, single sign-on with MFA, we run the infrastructure and the on-call.

Cloud FHIR

For health systems with modern EHRs.

Direct FHIR R4 pull against Epic, Cerner/Oracle Health, Meditech. No new software in the facility.

Self-serve API

For third-party developers and prime contractors integrating us into their stack.

Public data API with scoped keys and rate limiting. Everything we expose internally, exposed outward.

On-prem Appliance

For rural clinics, critical-access hospitals, and tribal health centers.

A small pre-configured box. Listens for HL7v2 from the facility's existing EHR, ships normalized FHIR to the coordination layer over HTTPS. We bring it. We install it.

Professional Services

For anyone who needs us in the room.

Implementation, integration, and on-site coordinator training. Funded the way rural health dollars actually move: capital plus people, not SaaS line items.

Research Foundation

Coincident Systems emerged from the Department of Industrial & Management Systems Engineering at Montana State University, in collaboration with the BioReD Hub.

Our research focus is modeling human decision-making in resource-constrained healthcare systems. We apply operations research methods, agent-based simulation, and statistical learning to problems where the stakes justify the rigor.

Questions about our approach?

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