Healthcare · AI Decision Intelligence
PatientFlow AI is an emerging healthcare intelligence platform designed to connect clinical, operational, patient, and research data to help healthcare organizations improve patient flow, support decision making, identify emerging patterns, and accelerate healthcare research.
Designed around interoperability rather than a single EHR or EMR vendor, PatientFlow is being developed to work across diverse healthcare environments while maintaining human oversight, explainability, and responsible AI principles.
Interoperable
Designed for diverse EHR, EMR and healthcare data environments.
Evidence Based
Intelligence designed to connect insights with supporting evidence and data provenance.
Explainable
Human oversight and explainability are incorporated into the PatientFlow research model.
Research Driven
Developed through staged research, validation and healthcare partnerships.
The Origin of PatientFlow
For more than 30 years, Miraivant Advisory founder Andrew Bittan has explored how technology, automation, workflow design, and intelligent systems can help organizations move information faster and operate more efficiently.
PatientFlow brings that experience to one of the most consequential environments of all: healthcare.
"How can we increase the velocity of healthcare while reducing the inefficiencies that slow patients, clinicians, researchers, and critical information down?"
PatientFlow represents the next stage of that work, exploring how interoperable data, evidence based AI, automation, and research intelligence can work together to help healthcare move faster, with greater context and without removing the human judgment at its center.
PatientFlow isn't about making healthcare automated. It's about making healthcare move.
The PatientFlow research model is organized around four interconnected intelligence areas being developed through staged research and validation.
Designed to bring together information from EHR/EMR systems, laboratories, patient histories, operational systems, telemetry, and other authorized healthcare data sources to create a more complete information environment for decision support.
Designed to analyze clinical and operational signals to help identify bottlenecks, investigate workflow constraints, and help healthcare organizations explore opportunities to improve patient throughput and resource utilization.
Designed to apply evidence based intelligence methods to organize complex healthcare information into explainable insights for authorized clinicians, researchers, and healthcare organizations while maintaining human oversight.
Designed to analyze longitudinal and population level signals to investigate patterns that may indicate changing patient risk, operational pressure, or emerging healthcare trends.
From fragmented healthcare information to explainable, evidence backed intelligence.
01
PatientFlow is designed to connect information from multiple authorized healthcare systems and data sources using APIs, interoperability standards, and healthcare data exchange technologies.
02
Clinical, operational, patient, and research information can be structured into a common intelligence layer so information from otherwise disconnected systems can be evaluated together.
03
AI models are being developed to investigate relationships, patterns, evidence, and workflow conditions while maintaining data provenance and explainability.
04
PatientFlow is designed to surface evidence backed intelligence to authorized healthcare professionals, researchers, and operational teams while keeping humans responsible for consequential decisions.
PatientFlow Research
PatientFlow begins with a practical challenge: healthcare information is fragmented across systems, organizations, workflows, and research environments.
What becomes possible when healthcare organizations can responsibly connect those signals and use AI to identify patterns humans may not otherwise see?
Research AI approaches for identifying bottlenecks, workflow constraints, patient throughput challenges, and changing resource requirements.
Investigate how evidence backed AI can organize complex patient information and surface relevant clinical context while preserving professional medical judgment and human oversight.
Explore patterns across appropriately governed healthcare information that could provide insight into changing population health conditions.
Research whether aggregated and appropriately governed healthcare signals could contribute to earlier identification of unusual disease patterns and future epidemic or pandemic preparedness.
Explore how AI can connect clinical evidence, scientific literature, laboratory findings, and appropriately governed healthcare data to support researchers investigating medical and therapeutic hypotheses.
Collaborate with universities, academic medical centers, researchers, and healthcare organizations to scientifically evaluate PatientFlow concepts before consequential healthcare deployment.
Global Health • Evidence Without Agenda
Long Term Research ConceptDuring an emerging health threat, incomplete information creates an extraordinary challenge. Acting too early on weak signals can create unnecessary alarm. Waiting too long for certainty can allow a genuine threat to grow.
PatientFlow research explores a different approach: follow the evidence.
The long term vision is to investigate whether appropriately governed, aggregated healthcare signals can identify unusual patterns across institutions, regions, populations, laboratory findings, symptoms, clinical encounters, and scientific research.
PatientFlow would not determine whether a finding is politically convenient, publicly comfortable, or consistent with an existing narrative. The research vision is to build an intelligence system capable of surfacing statistically meaningful signals, preserving the evidence behind them, communicating uncertainty, identifying contradictory evidence, and enabling qualified researchers, clinicians, epidemiologists, and public health professionals to investigate.
"The goal is not to create alarm. The goal is to make evidence harder to ignore."
Investigate appropriately governed healthcare information for unusual patterns, relationships, or changes that may warrant further scientific or clinical investigation.
Preserve data provenance, supporting observations, confidence, contradictory signals, and uncertainty rather than presenting an unexplained AI conclusion.
Surface findings for qualified researchers, clinicians, epidemiologists, and public health professionals rather than allowing an AI system to independently declare an outbreak, diagnosis, or public health emergency.
PatientFlow should not tell healthcare what to believe. It should show healthcare what the evidence says, where that evidence came from, how strong it is, what contradicts it, and what deserves further investigation.
A staged approach from healthcare workflow intelligence toward broader healthcare research.
The roadmap below describes research directions, not currently deployed capabilities. Only the Now stage represents active development.
Now
Interoperability research, patient flow modeling, workflow intelligence, data normalization, explainability, and evidence based AI.
Next
Research partnerships, multimodal healthcare information, clinical decision support research, human validation, and healthcare system pilots.
Future
Population health pattern research, disease surveillance concepts, and healthcare system level intelligence.
Long Term Research
Research into connecting clinical evidence, longitudinal healthcare information, laboratory findings, and scientific literature to support medical and therapeutic research.
The PatientFlow Healthcare Intelligence Layer
Healthcare Data Sources
Healthcare Intelligence & Research Layer
Intelligence Outputs
Serving Stakeholders Across Healthcare
Research • Collaboration • Innovation
PatientFlow is entering its next stage of research and development. Miraivant Advisory is seeking healthcare organizations, academic medical centers, researchers, technology partners, and healthcare innovators interested in helping evaluate and validate the next generation of evidence based healthcare AI.
PatientFlow AI is an emerging research and decision support platform. Future clinical, population health, disease intelligence, and medical research capabilities described on this page represent areas of ongoing research and development. PatientFlow is not intended to replace professional medical judgment.