Clinician-Guided, Predictive Symptom Management for Cancer Care

The Next Generation of Cancer Symptom Management

More accurate and personalized symptom load predictions. Comprehensive, easy-to-tune supportive care recommendations. A more productive, efficient patient connection. Designed by leading supportive oncologists. Training on NCI-designated cancer center patient data.

An oncology clinician in her office. A patient at home during treatment.
EO Care 360° · For Clinicians

Better Predict, Prevent & Manage Symptom Spikes

Supportive care fails when symptoms aren't predicted and care becomes reactive.

EO Care 360° addresses both sides of the problem at once. The clinician gets a symptom forecast and plan to tune and manage. The patient gets a clear plan, provides timely feedback and is more intelligently triaged when needed.

Key Benefits

More Prediction Power

See What She Will Feel, Adapt Before She Feels It™

Supportive care today is reactive. The symptom arrives, then the call, then the fix. A better forecast moves the whole sequence earlier, to the days where a small change still works.

Day 1Day 7Day 14Day 21Where shetypically calls youPlan steps up here, before the peak she never feelsEO predicted symptom burdenWhat she actually feelsSYMPTOM BURDEN ** Illustrative shape, not patient data
  • We See It Coming

    The forecast runs on the regimen, her chart and her own answers, and it runs before the cycle does.

  • The Plan Moves First

    Support steps up on the ladder you already signed, on the day the model says it matters.

  • The Spike Never Lands

    No unplanned call, no held dose, no visit that existed only because nobody saw it coming.

Validated InputsClinical trials and guidelinesfrom ASCO, ESMO and NCCNClinical OntologyStructured knowledge ofdrugs, symptoms, interactionsPatient DataDe-identified records fromNCI-designated tier 1 centersYour PatientEMR chart, history, intakeand stated preferencesYour KnowledgeE.g., medication preferences,patient understandingPrediction &Guidance EngineSafety RulesFixed, clinician-signed limitsthat nothing may crossWHAT REACHES YOUSymptom forecastRanked planThe reasoningThe platform learns your preferences over time and you can query it whenever you like.Patent pending. All rights reserved.
How It Works · Technology

Explainable, Controllable & Intelligent

Bayesian models are used for recommendations and guidance. LLMs are only for writing, not deciding. Safety rules and clinician permissions/inputs override it all.

Sources of knowledge are the latest ASCO, ESMO and NCCN guidelines, thousands of de-identified NCI-designated tier 1 cancer center patient records, your patient's EMR data and your wisdom. And the platform learns your preferences over time.

How It Works · Experience Flow

Facilitating a More Effective Partnership

SarahPATIENT APPEO Care 360°PLATFORMDr. ReyesTEAM'S VIEW IN EHRChart inSarah's EHR, readautomaticallySarah's inputsAnswers topersonalized questionsinform her planForecast & planSymptom burdenpredicted, planproposedDr. ReyesapprovesSets the rules, tunes,signs offSarah lives theplanDaily plan, check-ins,feedbackPlan keeps upSmall changes happenon their own, with thewhyExceptions onlyAuto changes logged;anything else comesback to her123456
How It Works · Clinician Interface

More Decision Intelligence & Control

All delivered via your existing EHR interface with customizable workflows.

A personalized and more efficient start
01 · Pre-visit intakeA personalized & more efficient startThe model runs on the EHR first, then asks Sarah only what the chart cannot answer. Every patient-reported answer is tagged as hers.
You control what the model does on its own
02 · Auto-approval settingsYou control what the model does on its ownYou define the plan changes the model can make without you, by class, with an autonomy level set from her risk inputs. Prescriptions and controlled substances require your approval.
A complete symptom forecast for the next 30 days
03 · Predicted symptom loadA complete symptom forecast for the next 30 daysUp to 12 symptoms, ranked by chance × severity, with peak days, drivers and a confidence rating behind each one.
One plan, easy to edit and approve
04 · Treatment planOne plan, easy to edit & approveRecommended treatments covering all likely symptoms, scored as relief minus side-effect burden, with safety rules layered on top.
The evidence behind each recommendation
05 · EvidenceThe evidence behind each recommendationOpen any treatment to see expected relief, expected side effects and the graded evidence by symptom, with the guideline or study it rests on.
Why this and not that
06 · Ask the modelWhy this and not thatAsk anything, including questions about trade-offs derived from its own data: here, why cannabinoid therapy edged out a step-2 opioid for her pain.
Sign by item or all at once
07 · OrderingSign by item or all at onceApprove each order with its sig, route, quantity and pharmacy. Do it one at a time or all together.
Recommended patient monitoring
08 · Escalation riskRecommended patient monitoringBy symptom, the model recommends check-in cadence and outreach rules, for example whether an automated or human check-in is best. You edit as needed.
What the model changed, and why
09 · Plan updatesWhat the model changed, and whyA week of check-ins in one view: what improved, what to watch, and what the model did, based on the autonomy settings you defined, with reasoning.
How It Works · Patient Interface

An Easy, Empowered & Connected Experience

Pre-plan inputs: her current medications from the chart, confirmed before the plan is built
Pre-plan inputsShe connects to EMR, confirms meds and shares concerns and preferences for the platform and clinician to consider.
Stay on top: prevent the spike
Preventing & managing spikesDelivered by text after approval, organized around the day rather than by prescription.
A to-do opened: what to do, why it matters, why this one and what to expect
Clear instructions & explanationsWhat to do, when and what to expect, all delivered in context for the patient.
The complete toolkit, by symptom
The complete cabinet, by symptomEvery symptom management tool arranged by symptom and filterable by type, with ready carts for pharmacy and wellness items.
One report, one governed update
Updates fully communicatedShe reports constipation. Her plan is updated, per clinician permissions. She sees what changed and why.
An optional journey view: predicted symptom load and her history to date
An optional journey viewShe can opt in to see her predicted symptom load and a history of all she has experienced and done to date.
A clear route to a person
Personalized triageUrgent goes to 911 or care team phones. Non-urgent messages the team. Scenario examples are personalized for relevance and efficiency.
About Us · Born in Clinic

From One Treatment Modality to Many

EO began in supportive oncology doing one thing: scaled, evidence-based, clinically responsible cannabinoid care for patients at scale. Physicians at eight NCI-designated tier 1 cancer centers began referring patients to us.

The success we achieved (key results below) inspired our clinical partners to ask for a complete, multi-modal platform. We are currently training this platform further using anonymized patient data from a cancer center partner.

  • 54–75%of patients meaningfully improved on pain, sleep and anxiety, a 3-point or greater PROMIS change
  • 70%of the everyday activities patients chose for themselves got easier
  • $12,091estimated annual symptom-management cost saved per patient, from $23,169 to $11,078

Real-world results from EO's clinician-guided cannabinoid care in supportive oncology settings. Observational data from routine care, not a randomized trial. Cost impacts from Milliman-validated cost savings report, estimated per patient per year.

About Us · Team

Clinical Leadership

Every rule in the system, and the clinical policy inside it, is reviewed and signed by one of them before it runs.

Brooke Worster, MD

Brooke Worster, MD

Chief Medical Officer

A pioneering palliative care clinician and researcher, Dr. Worster is currently Director, Supportive Oncology at Jefferson Health in Philadelphia. She is a graduate of Temple University School of Medicine and completed her fellowship in palliative care and pain management at MGH Brigham and Dana Farber Cancer Center in Boston.

Jacob Strand, MD

Jacob Strand, MD

Clinical Advisor

Dr. Strand is currently Chair of the Enterprise Center for Palliative Medicine and Associate Chair, Division of Community Internal Medicine, Geriatrics and Palliative Care at the Mayo Clinic in Rochester, MN. His clinical work is centered on managing pain and enhancing quality of life for those with cancer and other serious illnesses.

Regina Benjamin, MD

Regina Benjamin, MD

Clinical Advisor

A primary care physician and public health leader, Dr. Benjamin served as the 18th U.S. Surgeon General, providing critical guidance on national health issues and championing preventive care. She is the founder and CEO of the Bayou Clinic and sits on multiple boards, including those of Kaiser Permanente, Ascension Health and Doximity.

About Us · Team

Founders

Sean Collins

Sean Collins

Chief Executive Officer

A longtime startup founder and investor in data-driven guidance platforms, Sean has focused for years on helping people and businesses make decisions in the context of uncertainty. He has deep expertise in digital and app-based startups, Bayesian and ML team formation, product commercialization and distribution.

Dave Batista

Dave Batista

Chief Experience Officer

A CX, design and marketing innovator, Dave co-founded, grew and sold two leading digital agencies (BEAM and Euro RSCG Circle). He has overseen product and experience design efforts for some of the largest and most innovative healthcare, financial services and consumer products brands in the world.

Kristen Parton

Kristen Parton

Head of Data Modeling

Kristen is an innovative quantitative and algorithmic modeler with experience across medical, biotech and financial services verticals. She is expert in Bayesian, AI and decision network model design, prototyping and performance evaluation.

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