Boombostic Health

Healthcare is past the point of being impressed by AI demos.

The next phase is harder: proving value.

In this episode of Boombostic Health, Bradley Bostic talks with Mike Hollis, co-founder of Optura AI, about what healthcare leaders need to do now: move beyond pilots, cut through AI noise, and focus on the use cases that can actually deliver measurable outcomes.

Mike shares why so many organizations are stuck between excitement and execution, how leaders can evaluate AI maturity, and why the future of healthcare AI may be defined less by token usage — and more by trust, transparency, and accountability.

This conversation gets into the real work behind AI adoption: aligning teams, measuring ROI, building confidence, and choosing where AI can create both business value and societal impact.

Especially in areas like rare disease and underserved conditions, Mike makes the case that AI's biggest opportunity may not be where the hype is loudest.

Watch this episode if you're asking:

How do we know which AI initiatives are actually worth pursuing?
Why are so many healthcare organizations stuck in pilot mode?
What does ROI really look like in healthcare AI?
How do trust, transparency, and behavior change affect adoption?
Where can AI create meaningful impact beyond operational efficiency?

Featured moments:

00:00 — Why Optura AI was built
02:43 — AI's economic and societal opportunity in healthcare
03:13 — Why organizations get stuck in AI decision paralysis
05:47 — The metrics that help prove value
06:56 — From token economics to trust economics
10:18 — Why transparency and behavioral science matter
12:23 — The lesson healthcare leaders cannot ignore: ROI matters

Guest: Mike Hollis, Co-Founder, Optura AI
Host: Bradley Bostic
Podcast: Boombostic Health

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Healthcare does not just have a care delivery problem. It has a payment problem.

In this episode of Boombostic Health, Bradley Bostic sits down with Nitesh Shroff, CEO and co-founder of Arintra, live from ViVE, to discuss how AI is being used to tackle one of healthcare's most painful and overlooked challenges: revenue cycle management.

Arintra's origin story started with a shocking $19,000 hospital bill caused by a coding error. That experience exposed how fragile, confusing, and expensive healthcare billing can be for patients and providers — and led Nitesh and his co-founder to build an AI-powered approach designed to make healthcare payments more accurate, transparent, and efficient.

Nitesh explains how Arintra uses explainable AI, clinical and financial agents, and human-in-the-loop learning to automate medical coding, prevent denials, support prior authorization, and help health systems get paid accurately and on time. The company's technology works directly inside EHR workflows, including Epic and athenahealth, helping reduce friction for providers while improving trust and adoption.

This conversation goes beyond automation hype. It explores why revenue cycle has become mission-critical for health systems, why denials keep increasing, why the supply of skilled coders cannot keep pace, and why explainability may be the key to making AI work safely in healthcare.

In this episode:
00:00 — Live from ViVE with Nitesh Shroff of Arintra
01:36 — The $19,000 hospital bill that sparked Arintra's mission
04:22 — How explainable AI agents understand clinical and financial context
06:58 — Applying safety-first AI lessons from self-driving cars to healthcare
07:57 — Why documentation, coding, and payment must all align
09:04 — How customer pain points guide Arintra's roadmap
10:07 — The ROI opportunity for health systems
11:23 — Why revenue cycle complexity and denials are accelerating
12:38 — What better billing accuracy means for patients
14:58 — The future of prior authorization, compliance, and payment transparency

Healthcare revenue cycle is often treated as a back-office function. But as Nitesh and Bradley discuss, it is the financial lifeblood of healthcare — and one of the places where AI may create immediate, measurable impact.

Watch the full conversation to hear how Arintra is helping health systems reduce complexity, improve accuracy, and bring more trust into one of healthcare's most broken systems.

Subscribe to Boombostic Health for more conversations with the builders, operators, and leaders transforming the future of healthcare.

AI is everywhere in healthcare right now.

But Nancy Wright of GE HealthCare makes a sharper point: AI will only be as useful as the infrastructure underneath it.

In this episode of Boombostic Health, recorded live at ViVE 2026 in Los Angeles, Bradley Bostic sits down with Nancy Wright, VP of Digital Platforms at GE HealthCare, for a practical conversation about interoperability, cybersecurity, connected devices, workforce shortages, and the operational future of healthcare.

Nancy explains why interoperability is no longer just about exchanging clinical data. Health systems now need a more connected view across operations, finance, clinical workflows, biomedical engineering, supply chain, and cybersecurity.

The challenge is not replacing every system. It is creating a smarter data layer that allows existing platforms to keep working while giving leaders better visibility, stronger security, and more actionable intelligence.

The conversation also explores why healthcare cybersecurity must become proactive, why connected devices are expanding the risk surface, and how agentic AI could help reduce the burden on clinical and technical teams already stretched thin.

This is a conversation about what healthcare needs before AI can truly scale: connected infrastructure, secure data, and operational intelligence.

In This Episode

Bradley and Nancy discuss:

• Why healthcare's AI future depends on the data layer beneath it
• What interoperability really means inside modern health systems
• Why disconnected operational, clinical, and cybersecurity data creates risk
• How connected devices and IoT endpoints are changing healthcare cybersecurity
• Why health systems need proactive security partners, not reactive fixes
• How agentic AI could support biomedical, clinical, and operational teams
• Why partnership will matter as healthcare innovation accelerates

Key Moments

00:00 — Welcome from ViVE 2026 with Nancy Wright of GE HealthCare
01:08 — How AI is changing operations, labor, and interoperability
03:20 — Why interoperability needs a clearer definition
05:28 — Building a smarter layer across existing health system platforms
06:31 — Cybersecurity, bad actors, and healthcare's growing risk surface
08:48 — Connected devices, IoT endpoints, and proactive risk planning
12:09 — Why ViVE creates momentum for healthcare partnerships
14:15 — Agentic AI, workforce shortages, and the future of healthcare operations

Why It Matters

Healthcare does not need more disconnected technology.

It needs infrastructure that helps leaders see across the enterprise, secure connected assets, support overloaded teams, and turn fragmented data into action.

That is the foundation required for AI to become more than hype.

The $15 Billion Problem Hiding Inside Hospital Workflows

Everyone wants to talk about AI in healthcare.
Greg Miller wants to talk about the work no one sees.

In this live conversation from ViVE, Greg Miller, VP at Carta Healthcare, joins the Boombostic Health Podcast to expose one of healthcare's most expensive hidden problems: the billions spent manually abstracting clinical data for registries, quality reporting, accreditation, CMS measures, and benchmarking.

This is not basic data entry. It is highly skilled clinical work. Nurses and clinical experts are digging through EHRs, interpreting complex patient records, and making judgment calls that directly impact how hospitals measure quality and performance. Greg calls it "swivel chair interoperability," and it is costing U.S. healthcare an estimated $10–$15 billion every year.

But the answer is not just "throw AI at it."

Greg makes the case for hybrid intelligence: AI powerful enough to accelerate the work, with clinician oversight strong enough to earn trust. Because in healthcare, accuracy is not optional, workflow matters, and the market is moving past AI hype toward solutions that prove ROI fast.

The real question is no longer whether healthcare will use AI.

It is whether AI can actually solve the messy, expensive, high-stakes problems buried inside the system.

Timestamps

00:00 — Why clinical data abstraction is healthcare's hidden cost center
02:16 — Why registry work still requires clinical judgment
04:37 — The trust gap: why AI needs clinician oversight
05:38 — How Carta Healthcare repositioned around "hybrid intelligence"
10:01 — Why the AI hype cycle is giving way to ROI
11:54 — Why healthcare technology fails when it sits outside the workflow

Best Moments

Greg breaks down why some registry questions cannot be answered by simply searching the EHR. They require clinical inference, context, and expertise.

He also explains why healthcare buyers are getting sharper. A year ago, AI was the shiny object. Now, health systems want practical implementation, workflow integration, and measurable financial impact.

Key Takeaway

AI will not win in healthcare because it sounds impressive.

It will win when it removes burden, protects accuracy, fits the workflow, and earns clinician trust.

That is the promise of hybrid intelligence.

Featured Guest

Greg Miller
VP, Carta Healthcare

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How Duke Is Building AI That Actually Works in Healthcare | Matt Cox, Duke Health

Healthcare does not need more AI hype.

It needs AI that can survive clinical reality: compliance, governance, patient safety, workflow, physician adoption, and the brutal operational pressure every health system is facing.

In this episode of Boombostic Health, Bradley Bostic sits down live at ViVE 2026 with Matt Cox, Executive Director for Digital Value Creation at Duke Health, to unpack how Duke is moving from AI experimentation to real-world clinical impact.

Matt shares how Duke is building its own AI capabilities, including the Scout LLM platform, to safely support use cases across the health system — from clinical research and chart review to virtual nursing, ambient documentation, operational efficiency, and clinician burnout.

The standout example: Duke used Scout to review 200 pancreatic cancer patient charts in less than two hours — work that would typically take a PA or nurse practitioner one to two weeks.

That is not theoretical AI.

That is capacity creation.

That is clinicians working at the top of their license.

That is the future healthcare actually needs.

In this episode

Bradley and Matt discuss:

• Why Duke chose to build internal AI infrastructure instead of relying only on outside tools

• How Duke evaluates AI through governance, compliance, safety, bias, and clinical decision support

• The role of Duke Institute for Health Innovation and EHRX in exposing health data safely

• How Scout is helping accelerate chart review, clinical research, and protocol compliance

• Why AI's biggest opportunity may be giving time back to clinicians

• Duke's approach to investing in startups that solve real internal health system problems

• The growing importance of virtual nursing, ambient AI, and operating-room intelligence

• Why human factors may determine whether healthcare AI succeeds or fails

• What Matt is watching across the healthcare innovation landscape after ViVE

Key Takeaways

1. The winning health systems will not just buy AI. They will govern it.
Duke is not treating AI as a shiny object. It is building processes to test whether tools are safe, compliant, clinically appropriate, and operationally useful before scaling them.

2. AI only matters if it removes real friction.
The Scout example shows the real promise: not replacing clinicians, but removing time-consuming administrative and review work so highly trained people can focus on patients, research, and care delivery.

3. Innovation starts with the problem, not the pitch deck.
Duke's venture approach begins with internal pain points — nurse burnout, clinician burnout, access, capacity, and workflow — then looks for companies that can help solve them.

Chapters

00:00 — Live from ViVE 2026 with Matt Cox of Duke Health
01:22 — Matt's role leading digital value creation at Duke
04:05 — How Duke built the foundation for healthcare AI through EHRX
05:04 — Why Duke created its own LLM capability
05:52 — Governing AI through safety, compliance, and clinical decision support
07:21 — Inside Scout, Duke's AI platform
08:18 — Reviewing 200 pancreatic cancer charts in under two hours
10:11 — Using AI to free clinicians and expand capacity
11:52 — Duke's shift from reactive innovation to targeted venture investing
12:44 — Why nurse burnout and virtual nursing are top priorities
14:07 — Ambient AI and the fastest adoption Matt has seen in healthcare
15:44 — How Duke prioritizes investment and implementation opportunities
17:42 — What Matt is learning at ViVE: human factors, new roles, and the future of work

Featured Guest

Matt Cox
Executive Director, Digital Value Creation
Duke Health

Mentioned in this episode

• Duke Health

• Duke Institute for Health Innovation

• EHRX

• Scout LLM Platform

• Ambient AI

• Virtual Nursing

• Algorithm-Based Clinical Decision Support

• ViVE 2026

About Boombostic Health

Boombostic Health puts a spotlight on the people, technologies, and ideas moving healthcare forward. Hosted by Bradley Bostic, each conversation explores the practical innovations reshaping care delivery, diagnostics, data, AI, and the future of healthcare.

Subscribe for more conversations with the leaders building what comes next in healthcare.

Why Healthcare AI Needs Better Data Before Bigger Promises | Michael Meucci, CEO of Arcadia

Episode Description:
AI is moving fast in healthcare, but the real question is not who has the newest tool. It is whether the data behind it can be trusted.

In this episode of Boombostic Health, Marcus Gordon sits down with Michael Meucci, CEO of Arcadia, to discuss what it will actually take for AI to improve care delivery, reduce friction, and create measurable impact across the healthcare system.

Michael shares why healthcare organizations need complete, timely, clinically relevant data to make AI useful and safe. He explains how fragmented information can create risk instead of intelligence, why rural healthcare may become one of the most important innovation opportunities in the country, and how leaders need to rethink workflows instead of simply layering AI on top of broken processes.

The conversation also explores the growing role of the healthcare consumer, the pressure facing hospitals and health plans, and why the next 24 months may reshape how care is delivered, measured, and experienced.

Key Takeaways:

1. AI in healthcare is only as strong as the data behind it.
   Michael explains why trusted, complete, timely, and relevant data is essential for AI to support better decisions and avoid creating more noise.

2. Rural healthcare may become a major innovation frontier.
   With new national attention and investment, rural hospitals and critical access facilities have an opportunity to adopt modern care models, virtual-first approaches, and advanced technology in ways that could reshape access.

3. Leaders need to redesign workflows, not just adopt tools.
   Michael challenges healthcare leaders to rethink long-standing assumptions around EHRs, operating models, automation, and how teams use technology to manage patients, panels, and performance.

Topics Covered:
Healthcare AI beyond the hype
Why trusted data matters in clinical decision-making
The risk of incomplete or outdated data
How AI can support providers, patients, health plans, and health systems
The role of longitudinal patient data
Rural healthcare transformation
Health equity and access to care
Leadership in an AI-driven operating environment
Why workflow redesign matters
The future role of the healthcare consumer

Timestamps:
00:00 Introduction: Michael Meucci, CEO of Arcadia
00:16 What it means to lead a healthcare data and AI company today
01:18 Why healthcare needs AI to work amid cyber threats and margin pressure
02:48 Trust, efficacy, and data integrity in healthcare AI
03:28 Why AI models need timely, complete, and transparent data
05:00 The opportunity to expand access in rural communities
05:35 Rural health transformation and the future of critical access hospitals
08:05 Leadership, workflow redesign, and changing how teams think about AI
10:27 Rethinking tools like Salesforce, EHRs, and workflow systems
12:56 What is most exciting about the next 24 months in healthcare
13:29 Rural care, data liberation, and the rise of the healthcare consumer
14:57 Closing thoughts

Guest:
Michael Meucci, CEO of Arcadia

About Boombostic Health:
Boombostic Health features conversations with the leaders, innovators, and builders shaping the future of healthcare. Hosted by Bradley Bostic, the podcast explores the ideas, technologies, and operating models transforming care delivery, diagnostics, data, AI, and patient outcomes.

Unlocking the Future of Healthcare: Diagnostics, Data, and Value-Based Innovation

Healthcare transformation will not happen through technology alone. It will happen when diagnostics, data, clinical workflows, and incentives finally work together.

In this episode of Boombostic Health, Bradley Bostic sits down with Gary Albers and Steve Serota for a practical conversation on how diagnostics can move healthcare from reactive treatment to proactive, value-based care.

The discussion explores why laboratory medicine is often one of the most underutilized levers in healthcare, despite its ability to influence earlier intervention, better medication decisions, lower costs, and more personalized care. Gary and Steve share how health systems, payers, providers, and innovators can use diagnostic intelligence to close gaps in care, improve trust, and create measurable outcomes.

From pharmacogenomics and primary care workflow redesign to AI, biometric monitoring, and payer partnerships, this episode goes beyond theory and looks at what it actually takes to make value-based care work in the real world.

In this episode:

• How diagnostics can drive earlier, smarter, and more cost-effective care
• Why laboratory medicine needs a larger role in value-based healthcare strategy
• Gary Albers on building high-value outcomes through better diagnostic collaboration
• Steve Serota on turning lab data into actionable clinical intelligence
• Why fee-for-service adoption is slow and what is accelerating the move toward value-based models
• How pharmacogenomics and medication optimization can reduce avoidable costs
• The importance of primary care workflows in making diagnostics usable at scale
• How AI and continuous measurement can support more personalized interventions
• Why trust, privacy, and patient engagement matter in a more connected healthcare ecosystem
• The future of healthcare through biometrics, personalized medicine, and integrated care models

Key Takeaways:

• Diagnostics are not just tests. They are decision-making tools that can help identify risk earlier, guide treatment, and reduce waste across the healthcare system.
• Value-based care requires more than new payment models. It depends on better data, stronger workflows, aligned incentives, and measurable clinical impact.
• The future of healthcare will be more proactive, personalized, and data-driven, but adoption will depend on trust, usability, and proving value in real-world care settings.

Timestamps:

00:00 Introduction: Why diagnostics matter in healthcare transformation

01:01 Collaboration as a driver of value-based care

01:34 Gary Albers on high-value diagnostics and laboratory medicine

04:02 Steve Serota on lab data, patient outcomes, and clinical intelligence

07:19 Why value-based care adoption has been slow

08:36 Policy, incentives, and the strategic role of clinical data

10:52 Precision medicine, medication optimization, and cost savings

12:11 Measuring the impact of interventions in real time

14:05 Reimbursement models and the economics of diagnostics

15:01 Funding innovation and upfront investment in value-based care

16:12 Why primary care workflow is critical to diagnostic adoption

19:10 AI, data, and proactive healthcare decisions

22:38 Building trust with more complete patient data

27:43 Biometric monitoring, patient responsibility, and privacy

38:11 Tech giants, health plans, and the future of cost control

44:25 How diagnostics can deliver actionable, affordable outcomes

46:24 Final thoughts: Building the future through collaboration

About Boombostic Health:

Boombostic Health explores the people, ideas, and innovations reshaping healthcare. Hosted by Bradley Bostic, the show brings together leaders across diagnostics, data, AI, value-based care, and healthcare transformation to discuss what is working, what needs to change, and where the industry is headed next.

Unlocking the Value of Real-World Data in Healthcare

This episode dives into the critical role of real-world evidence and data in transforming healthcare, emphasizing how data privacy, de-identification, and innovative tokenization unlock large-scale insights with minimal risk. Presented by industry veterans, it explores methods to responsibly utilize data for research, clinical trials, and patient care improvements, while addressing common misconceptions and trust issues.

In this episode:

• The importance of real-world data and evidence for healthcare advancement
• How de-identification and tokenization protect patient privacy while enabling large-scale analysis
• The benefits of open source tokenization models versus proprietary solutions
• Practical steps to start using de-identified data for research and clinical decision-making
• The emerging role of AI, wearables, and consumer data in personalized healthcare
• The evolving landscape of data monetization, partnerships, and purpose-driven analytics
• Future of comprehensive data access, trust-building, and patient ownership considerations
• How AI accelerates insights while ensuring data quality and security

Timestamps:

00:00 - Introduction: Healthcare innovation in Indianapolis & the focus on real-world evidence
00:34 - Why healthcare data privacy regulations were designed for protection, not suppression
01:03 - Industry veterans John and Julie on safe data usage and innovation
01:56 - The 18-year journey into real-world data and evidence with HC1
02:38 - How de-identification preserves privacy, scales data, and enables AI-driven insights
03:21 - The role of data in addressing diagnostic gaps and patient journeys
03:53 - The necessity of large-scale, unbiased data for research and healthcare delivery
04:50 - Explaining tokenization simply and why it matters
05:06 - The challenge of integrating data from multiple sources without bias
06:17 - How consumer wearables add depth to patient understanding
07:10 - Therapy development, clinical trials, and the power of de-identified data
07:42 - The significance of bias reduction in healthcare data analytics
08:36 - Path to monetization: Purpose-driven data use versus the race to the bottom
09:00 - The importance of aligning with organizations sharing your mission
09:48 - Clinical trials and real-world evidence improving enrollment and outcomes
10:57 - Strategies for building trust and ensuring patient security in data sharing
11:25 - Practical steps for initiating de-identification & tokenization
12:17 - Privacy-preserving record linkage & open source tokenization solutions
13:20 - The significance of rigorous de-identification processes and certification
14:13 - How tokenization connects disparate datasets without compromising identity
15:11 - Open source solutions versus commercial fee-based tokenization providers
16:45 - The importance of responsible data sharing and avoiding exploitative marketplaces
17:41 - Enhancing clinical trials with real-world evidence and reducing risks
20:13 - The impact of regulatory changes and partnerships in trials
21:06 - Enabling precision medicine through aggregated, de-identified data
22:22 - The role of CROs and third-party organizations in trial success
23:20 - Using advanced AI for device tracking, supply chain, and supply chain data
24:18 - Challenges and opportunities with physician notes and unstructured data
25:41 - The ongoing need for AI refinement and risk management in de-identification
27:02 - Addressing the potential consequences of data breaches and errors
28:41 - The technical feasibility and limitations of perfect de-identification
29:46 - Handling physician notes, abbreviations, and unstructured data responsibly
31:07 - The future of diagnostics, genomics, and embedded AI in healthcare standardization
32:07 - How tokenized, integrated data empowers providers and payers
33:13 - The importance of clean, trusted data for AI accuracy
34:24 - Personalized, real-time insights improving patient care and provider decision-making
36:47 - The untapped potential of lab and rare disease data for proactive care
38:49 - The challenge of small data scale in specialty labs and opportunities for collaboration
40:37 - Using de-identified lab data to predict disease progression and improve outcomes
43:13 - Integrating consumer wearable and biometric data into healthcare insights
44:14 - The power of personal health data for early detection and prevention
45:24 - Expanding access to EHR data and overcoming legislative barriers
47:11 - Building a culture of data ownership and creating trust with patients
48:32 - The potential influence of patient incentives, transparency, and societal changes
50:09 - Closing thoughts: Trust, purpose, and technology shaping the future of healthcare data

Cancer care has made extraordinary clinical progress, but too many patients still fall through the cracks once they leave the clinic.

In this episode of Boombostic Health, Bradley Bostic sits down with Dr. Justin Bekelman, oncologist, co-founder, and CEO of Daymark Health, to discuss how value-based oncology can create a better care model for patients, providers, and health plans. After more than two decades at Penn Medicine, Dr. Bekelman left to build Daymark Health, a company that supports patients receiving active cancer treatment through in-home and virtual care. Daymark partners with health plans and works alongside each patient's oncology team to address clinical, behavioral, and social needs when and where they arise. 

The conversation explores why fee-for-service often fails to support the full cancer journey, why oncology is especially suited for value-based care, and why better care requires more than coordination.

It requires the ability to act. As Dr. Bekelman explains, "Cancer doesn't stop at five o'clock." 

Timestamps

00:00 Welcome and personal connection to cancer prevention

03:01 Dr. Bekelman's background and why patients still fall through the cracks

05:30 What value-based care means in oncology

07:28 Why supportive cancer care is under-reimbursed in fee-for-service

09:17 How Daymark Health partners with health plans

10:32 Supporting patients at home, virtually, and at no additional cost

12:44 Episodic capitation, shared savings, and oncology economics

15:18 Why cancer care is well suited for value-based models

17:24 Daymark as the "connective tissue" around the oncologist

20:34 Why coordination alone is not enough

23:01 Daymark's work with Blue Cross Blue Shield of Rhode Island

24:08 Steven's story and why cancer does not stop at 5 PM

27:37 Patient experience, home visits, and measurable results

30:19 Why this model could become the standard for cancer care

In this episode:

• Why cancer patients need support beyond the clinic
• How value-based care is being applied to oncology 
• Why Daymark works through health plan partnerships
• The difference between care coordination and clinical action
• How in-home care can help prevent avoidable complications
• Why behavioral health and social support matter in cancer treatment
• How better patient support can reduce total cost of care
• Why oncology may be one of the next major frontiers for value-based care

Diagnostics are no longer just about confirming what is wrong. They are becoming one of the most powerful tools for predicting what comes next.

In this episode of Boombostic Health, Bradley Bostic leads a timely conversation with healthcare and diagnostics leaders on how real-world patient data, genomics, AI, and embedded research models are changing the way healthcare systems identify risk, close care gaps, and deliver more personalized care.

The discussion moves beyond the promise of innovation and into the practical realities of adoption. How do we turn routine care into continuous learning? How do we prove the value of advanced diagnostics to payers? How do we expand access without creating new forms of bias? And how do we help patients and providers trust the data, the science, and the recommendations that follow?

From home-based testing and consumer-driven care to reimbursement, incidental findings, data diversity, and health economics, this episode explores what it will take to move diagnostics from the edge of healthcare innovation into the center of everyday clinical decision-making.

What You'll Learn

• How diagnostics are shifting from episodic testing to continuous healthcare intelligence
• Why real-world data is essential to predicting outcomes and improving care pathways
• How genomics and AI can support more personalized, proactive care
• What it takes to build the business case for diagnostic innovation
• Why reimbursement models must evolve to keep pace with scientific progress
• How embedded research can make routine care more evidence-driven
• Why patient trust, education, and transparency are critical to adoption
• How data diversity can help reduce bias and improve care for underserved populations

Why This Conversation Matters

Healthcare does not have a data shortage. It has an actionability problem.

The future of diagnostics will depend on the ability to connect data to decisions, research to routine care, and innovation to measurable impact. This conversation makes the case for diagnostics as a strategic foundation for better care, smarter economics, and more equitable outcomes.

Key Moments

00:00 — Why diagnostics are becoming central to healthcare innovation
02:38 — Embedding research into routine care
04:17 — Using data to predict health outcomes
06:10 — Making the case for payer coverage and cost savings
09:58 — Bringing diagnostic models into clinical workflows
11:14 — The rise of home-based and consumer-driven testing
14:23 — Managing incidental findings responsibly
17:34 — Connecting diagnostics to health economics
24:07 — Addressing disparities, access, and data bias
27:33 — Moving from research to routine clinical care
32:39 — Empowering patients through health technology

Featured Topics

Real-world evidence
Personalized medicine
Genomics and AI
Diagnostic innovation
At-home testing
Reimbursement and payer coverage
Health economics
Data diversity and bias
Patient engagement
Clinical workflow integration

Resources

hc1 
uMed 
Simple HealthKit

Closing Thought

The next era of healthcare will not be defined by more testing alone. It will be defined by whether diagnostics can help turn real-world data into earlier action, better decisions, and measurable improvements in care.