Let’s see Tom in action
Scroll to see an overview that highlights how proactive care, intelligent workflows, and timely interventions make primary care more effective for everyone.
Scroll to see these capabilities in action.
1
Best Next Action™
2
Personalized in-between visit care
3
Medication adherence support & adjustment
4
Symptom assessment & resolution coordination
5
Behavioral coaching with wearable integration
Let’s go!
The brain of Tom
Tom brings together the data that lives far beyond the walls of a hospital—across +30 billion clinical data points and dozens of payer and EHR connections—to give care teams a fuller, real-time picture of each patient.
Tom Frontier Neural Net
Clinical Data Warehouse
Behavioral Health
Utilization & Cost
AvoidableEvents
Channelpreference
Drivers of Engagement
Unified 360°Patient View
Disease Risk & Exacerbation
FHIR DataRepository
Data Lake
Longitudinal Patient Record
Evidence-based Knowledge Corpus
Standard of care industry guidelines
BNA Intelligence
What does this mean for health systems, doctors, and patients?
1
Best Next Action™
For health systems, it means clearer decisions, stronger performance, and better outcomes at scale.
For doctors, that means less hunting and second-guessing.
For patients, it means care that is more coordinated, more timely, and more personal.
Let’s take look at a healthcare journey of a single patient.
Mrs. Linda Jones
61 year old middle school teacher
Condition(s):
- Type 2 Diabetes
- Obesity (BMI 37)
- Hypertension
- Chronic Kidney Disease (CKD Stage 3a)
PCP:
Dr. Williams
Dr. Williams
Primary Care Provider
Let’s jump in and start our journey with a routine visit with Linda’s doctor.
Clinical encounter & initial Tom AI agent engagement
Linda has a routine visit with Dr. Williams, who notes:
HbA1c is elevated at 7.6%
Blood pressure is slightly elevated at 142/86
Patient expresses fatigue and concern about weight
Dr. Williams and Linda agree on a plan and ambient listening scribe captures the information:
Start SGLT2 inhibitor (empagliflozin; trade name Jardiance) due to diabetes and CKD
Begin more consistent home glucose tracking
Set goals for weight loss and increased activity
Consider AI-enhanced health coaching platform
Let’s see how Tom starts providing continuous careto Linda, starting with a post-visit follow up call.
Tom prepares a post-visit call
Before contacting Linda, Tom focuses on Care Coordination needs, automatically gathers and synthesizes all relevant information from the shared assets:
Patient context summary
Pulls structured data from the EHR (diagnoses, medications, allergies, labs), social determinants data, and memory of previous Tom interactions.
E.g., “61-year-old teacher with T2DM, CKD 3a, on Invokana; motivated to lose weight but experienced prior affordability barrier.”
Tom generates an in-between visit plan of care
Reviews episodic items:
Fill new prescriptions
Reviews pending labs
Scheduled follow-up appointments
Longitudinal goals:
Walking ≥150 min/week
Weight loss target
Consistent home glucose tracking
Call objectives
Confirm fill and side effects
Reinforce exercise goal and discuss progress
Confirm eye exam scheduling
Capture new questions for Dr. Williams
2
Personalized in-between visit care
Let’s see how Tom communicatesthis back to the care team.
Care team summary
Tom sends a structured summary to the EHR inbox:
Meds:
Patient unable to fill empagliflozin due to insurance.
Requesting alternate medication.
Health behaviors:
Motivated to increase physical activity.
Health coaching program initiated.
Next, Tom continues to support Linda through her change in medication.
Medication adjustment
Dr. Williams switches Linda to an SGLT2 medication with lower copay (Invokana).
Tom calls to confirm fill and checks with Linda for adherence and concerns.
3
Medication adherence support & adjustment
Tom also integrates with wearableand remote monitoring data.
Tom symptom checking & care coordination
Two weeks later, Linda uses Tom to report burning with urination and increased frequency. She wonders if it's related to the dapagliflozin (Invokana).
Triage workflow:
Tom asks about symptoms and notes potential UTI
Flags medication side effect from Invokana as in the differential diagnosis
Sends structured alert to PCP care team
Recommends virtual or eVisit to evaluate
4
Symptom assessment & resolution coordination
Follow-up after treatment
PCP uses asynchronous eVisit to address the symptoms
PCP prescribes antibiotics for UTI and check urine culture
Tom checks on symptom resolutions after 4 days
Linda reports improvement and satisfaction
Tom offers behavioral coachingwith wearable integrations.
Integrations
Tom can integrate wearable and remote monitoring data—like Oura Ring—to add real-world health signals that help personalize outreach and support a more continuous relationship with primary care.
Electronic Health Records
Consumer Data
Laboratory
Wearables Data
Claims
Patient Reported Outcomes
Health Information Exchanges
Social Determinants of Health
Pharmacy
Conversational Data
Let’s see how Tom follows up with symptom checking and care coordination.
Tom behavioral coaching with wearable integration
Weekly follow-up
Linda has walked 3x/week, tracked via wearable device (Oura Ring)
Tom praises improvement in physical activity and offers encouragement
Tom as Linda’s health coach helps her set an achievable walking schedule and introduces healthy snacking ideas
Linda logs meals and activity and agrees to integrate passive wearable data
Tom sends an update to the EHR (without alert): “Improved adherence to exercise goal. Increased physical activity. Engaged with health coach.”
5
Behavioral coaching with wearable integration
Let’s see Tom in action
Scroll to see an overview that highlights how proactive care, intelligent workflows, and timely interventions make primary care more effective for everyone.
Scroll to see these capabilities in action.
1
Best Next Action™
2
Personalized in-between visit care
3
Medication adherence support & adjustment
4
Symptom assessment & resolution coordination
5
Behavioral coaching with wearable integration
Let’s go!
Unified 360°Patient View
Disease Risk & Exacerbation
FHIR DataRepository
Data Lake
Longitudinal Patient Record
Evidence-based Knowledge Corpus
Standard of care industry guidelines
Tom Frontier Neural Net
Clinical Data Warehouse
Behavioral Health
Utilization & Cost
AvoidableEvents
Channelpreference
Drivers of Engagement
BNA Intelligence
The brain of Tom
Tom brings together the data that lives far beyond the walls of a hospital—across +30 billion clinical data points and dozens of payer and EHR connections—to give care teams a fuller, real-time picture of each patient.
What does this mean for health systems, doctors, and patients?
1
Best Next Action
For health systems, it means clearer decisions, stronger performance, and better outcomes at scale.
For doctors, that means less hunting and second-guessing.
For patients, it means care that is more coordinated, more timely, and more personal.
Let’s take look at a healthcare journey of a single patient.
Mrs. Linda Jones
61 year old middle school teacher
Condition(s):
- Type 2 Diabetes
- Obesity (BMI 37)
- Hypertension
- Chronic Kidney Disease (CKD Stage 3a)
PCP:
Dr. Williams
Dr. Williams
Primary Care Provider
Let’s jump in and start our journeywith a routine visit with Linda’s doctor.
Clinical encounter & initial Tom AI agent engagement
Linda has a routine visit with Dr. Williams, who notes:
HbA1c is elevated at 7.6%
Blood pressure is slightly elevated at 142/86
Patient expresses fatigue and concern about weight
Dr. Williams and Linda agree on a plan and ambient listening scribe captures the information:
Start SGLT2 inhibitor (empagliflozin; trade name Jardiance) due to diabetes and CKD
Begin more consistent home glucose tracking
Set goals for weight loss and increased activity
Consider AI-enhanced health coaching platform
Let’s see how Tom starts providing continuous careto Linda, starting with a post-visit follow up call.
Tom prepares a post-visit call
Before contacting Linda, Tom focuses on Care Coordination needs, automatically gathers and synthesizes all relevant information from the shared assets:
Patient context summary
Pulls structured data from the EHR (diagnoses, medications, allergies, labs), social determinants data, and memory of previous Tom interactions.
E.g., “61-year-old teacher with T2DM, CKD 3a, on Invokana; motivated to lose weight but experienced prior affordability barrier.”
Tom generates an in-between visit plan of care
Reviews episodic items:
Fill new prescriptions
Reviews pending labs
Scheduled follow-up appointments
Longitudinal goals:
Walking ≥150 min/week
Weight loss target
Consistent home glucose tracking
Call objectives
Confirm fill and side effects
Reinforce exercise goal and discuss progress
Confirm eye exam scheduling
Capture new questions for Dr. Williams
2
Personalized in-between visit care
Let’s see how Tom communicatesthis back to the care team.
Care team summary
Tom sends a structured summary to the EHR inbox:
Meds:
Patient unable to fill empagliflozin due to insurance.
Requesting alternate medication.
Health behaviors:
Motivated to increase physical activity.
Health coaching program initiated.
Next, Tom continues to support Lindathrough her change in medication.
Medication adjustment
Dr. Williams switches Linda to an SGLT2 medication with lower copay (Invokana).
Tom calls to confirm fill and checks with Linda for adherence and concerns.
3
Medication adherence support & adjustment
Tom also integrates with wearableand remote monitoring data.
Tom symptom checking & care coordination
Two weeks later, Linda uses Tom to report burning with urination and increased frequency. She wonders if it's related to the dapagliflozin (Invokana).
Triage workflow:
Tom asks about symptoms and notes potential UTI
Flags medication side effect from Invokana as in the differential diagnosis
Sends structured alert to PCP care team
Recommends virtual or eVisit to evaluate
4
Symptom assessment & resolution coordination
Follow-up after treatment
PCP uses asynchronous eVisit to address the symptoms
PCP prescribes antibiotics for UTI and check urine culture
Tom checks on symptom resolutions after 4 days
Linda reports improvement and satisfaction
Tom offers behavioral coachingwith wearable integrations.
Integrations
Tom can integrate wearable and remote monitoring data—like Oura Ring—to add real-world health signals that help personalize outreach and support a more continuous relationship with primary care.
Electronic Health Records
Consumer Data
Laboratory
Wearables Data
Claims
Patient Reported Outcomes
Health Information Exchanges
Social Determinants of Health
Pharmacy
Conversational Data
Let’s see how Tom follows up withsymptom checking and care coordination.
Tom behavioral coaching with wearable integration
Weekly follow-up
Linda has walked 3x/week, tracked via wearable device (Oura Ring)
Tom praises improvement in physical activity and offers encouragement
Tom as Linda’s health coach helps her set an achievable walking schedule and introduces healthy snacking ideas
Linda logs meals and activity and agrees to integrate passive wearable data
Tom sends an update to the EHR (without alert): “Improved adherence to exercise goal. Increased physical activity. Engaged with health coach.”
5
Behavioral coaching with wearable integration
Let’s see Tom in action
Scroll to see an overview that highlights how proactive care, intelligent workflows, and timely interventions make primary care more effective for everyone.
Scroll to see these capabilities in action.
1
Best Next Action™
2
Personalized in-between visit care
3
Medication adherence support & adjustment
4
Symptom assessment & resolution coordination
5
Behavioral coaching with wearable integration
Let’s go!
Unified 360°Patient View
Disease Risk & Exacerbation
FHIR DataRepository
Data Lake
Longitudinal Patient Record
Evidence-based Knowledge Corpus
Standard of care industry guidelines
Tom Frontier Neural Net
Clinical Data Warehouse
Behavioral Health
Utilization & Cost
AvoidableEvents
Channelpreference
Drivers of Engagement
BNA Intelligence
The brain of Tom
Tom brings together the data that lives far beyond the walls of a hospital—across +30 billion clinical data points and dozens of payer and EHR connections—to give care teams a fuller, real-time picture of each patient.
What does this mean for health systems, doctors, and patients?
1
Best Next Action
For health systems, it means clearer decisions, stronger performance, and better outcomes at scale.
For doctors, that means less hunting and second-guessing.
For patients, it means care that is more coordinated, more timely, and more personal.
Let’s take look at a healthcare journey of a single patient.
Mrs. Linda Jones
61 year old middle school teacher
Condition(s):
- Type 2 Diabetes
- Obesity (BMI 37)
- Hypertension
- Chronic Kidney Disease (CKD Stage 3a)
PCP:
Dr. Williams
Dr. Williams
Primary Care Provider
Let’s jump in and start our journeywith a routine visit with Linda’s doctor.
Clinical encounter & initial Tom AI agent engagement
Linda has a routine visit with Dr. Williams, who notes:
HbA1c is elevated at 7.6%
Blood pressure is slightly elevated at 142/86
Patient expresses fatigue and concern about weight
Dr. Williams and Linda agree on a plan and ambient listening scribe captures the information:
Start SGLT2 inhibitor (empagliflozin; trade name Jardiance) due to diabetes and CKD
Begin more consistent home glucose tracking
Set goals for weight loss and increased activity
Consider AI-enhanced health coaching platform
Let’s see how Tom starts providing continuous careto Linda, starting with a post-visit follow up call.
Tom prepares a post-visit call
Before contacting Linda, Tom focuses on Care Coordination needs, automatically gathers and synthesizes all relevant information from the shared assets:
Patient context summary
Pulls structured data from the EHR (diagnoses, medications, allergies, labs), social determinants data, and memory of previous Tom interactions.
E.g., “61-year-old teacher with T2DM, CKD 3a, on Invokana; motivated to lose weight but experienced prior affordability barrier.”
Tom generates an in-between visit plan of care
Reviews episodic items:
Fill new prescriptions
Reviews pending labs
Scheduled follow-up appointments
Longitudinal goals:
Walking ≥150 min/week
Weight loss target
Consistent home glucose tracking
Call objectives
Confirm fill and side effects
Reinforce exercise goal and discuss progress
Confirm eye exam scheduling
Capture new questions for Dr. Williams
2
Personalized in-between visit care
Let’s see how Tom communicatesthis back to the care team.
Care team summary
Tom sends a structured summary to the EHR inbox:
Meds:
Patient unable to fill empagliflozin due to insurance.
Requesting alternate medication.
Health behaviors:
Motivated to increase physical activity.
Health coaching program initiated.
Next, Tom continues to support Lindathrough her change in medication.
Medication adjustment
Dr. Williams switches Linda to an SGLT2 medication with lower copay (Invokana).
Tom calls to confirm fill and checks with Linda for adherence and concerns.
3
Medication adherence support & adjustment
Tom also integrates with wearableand remote monitoring data.
Tom symptom checking & care coordination
Two weeks later, Linda uses Tom to report burning with urination and increased frequency. She wonders if it's related to the dapagliflozin (Invokana).
Triage workflow:
Tom asks about symptoms and notes potential UTI
Flags medication side effect from Invokana as in the differential diagnosis
Sends structured alert to PCP care team
Recommends virtual or eVisit to evaluate
4
Symptom assessment & resolution coordination
Follow-up after treatment
PCP uses asynchronous eVisit to address the symptoms
PCP prescribes antibiotics for UTI and check urine culture
Tom checks on symptom resolutions after 4 days
Linda reports improvement and satisfaction
Tom offers behavioral coachingwith wearable integrations.
Integrations
Tom can integrate wearable and remote monitoring data—like Oura Ring—to add real-world health signals that help personalize outreach and support a more continuous relationship with primary care.
Electronic Health Records
Consumer Data
Laboratory
Wearables Data
Claims
Patient Reported Outcomes
Health Information Exchanges
Social Determinants of Health
Pharmacy
Conversational Data
Let’s see how Tom follows up withsymptom checking and care coordination.
Tom behavioral coaching with wearable integration
Weekly follow-up
Linda has walked 3x/week, tracked via wearable device (Oura Ring)
Tom praises improvement in physical activity and offers encouragement
Tom as Linda’s health coach helps her set an achievable walking schedule and introduces healthy snacking ideas
Linda logs meals and activity and agrees to integrate passive wearable data
Tom sends an update to the EHR (without alert): “Improved adherence to exercise goal. Increased physical activity. Engaged with health coach.”
5
Behavioral coaching with wearable integration
Let’s see Tom in action
Scroll to see an overview that highlights how proactive care, intelligent workflows, and timely interventions make primary care more effective for everyone.
Scroll to see these capabilities in action.
1
Best Next Action™
2
Personalized in-between visit care
3
Medication adherence support & adjustment
4
Symptom assessment & resolution coordination
5
Behavioral coaching with wearable integration
Let’s go!
Unified 360°Patient View
Disease Risk & Exacerbation
FHIR DataRepository
Data Lake
Longitudinal Patient Record
Evidence-based Knowledge Corpus
Standard of care industry guidelines
Tom Frontier Neural Net
Clinical Data Warehouse
Behavioral Health
Utilization & Cost
AvoidableEvents
Channelpreference
Drivers of Engagement
BNA Intelligence
The brain of Tom
Tom brings together the data that lives far beyond the walls of a hospital—across +30 billion clinical data points and dozens of payer and EHR connections—to give care teams a fuller, real-time picture of each patient.
What does this mean for health systems, doctors, and patients?
1
Best Next Action
For health systems, it means clearer decisions, stronger performance, and better outcomes at scale.
For doctors, that means less hunting and second-guessing.
For patients, it means care that is more coordinated, more timely, and more personal.
Let’s take look at a healthcare journey of a single patient.
Mrs. Linda Jones
61 year old middle school teacher
Condition(s):
- Type 2 Diabetes
- Obesity (BMI 37)
- Hypertension
- Chronic Kidney Disease (CKD Stage 3a)
PCP:
Dr. Williams
Dr. Williams
Primary Care Provider
Let’s jump in and start our journeywith a routine visit with Linda’s doctor.
Clinical encounter & initial Tom AI agent engagement
Linda has a routine visit with Dr. Williams, who notes:
HbA1c is elevated at 7.6%
Blood pressure is slightly elevated at 142/86
Patient expresses fatigue and concern about weight
Dr. Williams and Linda agree on a plan and ambient listening scribe captures the information:
Start SGLT2 inhibitor (empagliflozin; trade name Jardiance) due to diabetes and CKD
Begin more consistent home glucose tracking
Set goals for weight loss and increased activity
Consider AI-enhanced health coaching platform
Let’s see how Tom starts providing continuous careto Linda, starting with a post-visit follow up call.
Tom prepares a post-visit call
Before contacting Linda, Tom focuses on Care Coordination needs, automatically gathers and synthesizes all relevant information from the shared assets:
Patient context summary
Pulls structured data from the EHR (diagnoses, medications, allergies, labs), social determinants data, and memory of previous Tom interactions.
E.g., “61-year-old teacher with T2DM, CKD 3a, on Invokana; motivated to lose weight but experienced prior affordability barrier.”
Tom generates an in-between visit plan of care
Reviews episodic items:
Fill new prescriptions
Reviews pending labs
Scheduled follow-up appointments
Longitudinal goals:
Walking ≥150 min/week
Weight loss target
Consistent home glucose tracking
Call objectives
Confirm fill and side effects
Reinforce exercise goal and discuss progress
Confirm eye exam scheduling
Capture new questions for Dr. Williams
2
Personalized in-between visit care
Let’s see how Tom communicatesthis back to the care team.
Care team summary
Tom sends a structured summary to the EHR inbox:
Meds:
Patient unable to fill empagliflozin due to insurance.
Requesting alternate medication.
Health behaviors:
Motivated to increase physical activity.
Health coaching program initiated.
Next, Tom continues to support Lindathrough her change in medication.
Medication adjustment
Dr. Williams switches Linda to an SGLT2 medication with lower copay (Invokana).
Tom calls to confirm fill and checks with Linda for adherence and concerns.
3
Medication adherence support & adjustment
Tom also integrates with wearableand remote monitoring data.
Tom symptom checking & care coordination
Two weeks later, Linda uses Tom to report burning with urination and increased frequency. She wonders if it's related to the dapagliflozin (Invokana).
Triage workflow:
Tom asks about symptoms and notes potential UTI
Flags medication side effect from Invokana as in the differential diagnosis
Sends structured alert to PCP care team
Recommends virtual or eVisit to evaluate
4
Symptom assessment & resolution coordination
Follow-up after treatment
PCP uses asynchronous eVisit to address the symptoms
PCP prescribes antibiotics for UTI and check urine culture
Tom checks on symptom resolutions after 4 days
Linda reports improvement and satisfaction
Tom offers behavioral coachingwith wearable integrations.
Integrations
Tom can integrate wearable and remote monitoring data—like Oura Ring—to add real-world health signals that help personalize outreach and support a more continuous relationship with primary care.
Electronic Health Records
Consumer Data
Laboratory
Wearables Data
Claims
Patient Reported Outcomes
Health Information Exchanges
Social Determinants of Health
Pharmacy
Conversational Data
Let’s see how Tom follows up withsymptom checking and care coordination.
Tom behavioral coaching with wearable integration
Weekly follow-up
Linda has walked 3x/week, tracked via wearable device (Oura Ring)
Tom praises improvement in physical activity and offers encouragement
Tom as Linda’s health coach helps her set an achievable walking schedule and introduces healthy snacking ideas
Linda logs meals and activity and agrees to integrate passive wearable data
Tom sends an update to the EHR (without alert): “Improved adherence to exercise goal. Increased physical activity. Engaged with health coach.”
5
Behavioral coaching with wearable integration