This is the second blog in what I’ll admit is the hardest segment of healthcare for me—pretty much anything involving chemistry. But this time, we’re all in on genetics.
In our first blog, we highlighted the remarkable 143-page report by the Ada Lovelace Institute titled “Predicting the future of health: The ethical considerations and societal impact of health AI models” (the Report). In our summary, we drew attention to the tremendous potential value of genetics and clinical data in consumer health. If you read nothing else—read the Summary of the Summary and our commentary below it.
In this final part, we’ll discuss the Report’s 10 recommendations and explore what’s possible today, particularly in helping Consumers—our sole focus at HealthScoreAI™. First, here are the actual recommendations without any editorial from us:
10 Key Recommendations
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Mandatory Prospective Impact Assessments
Require developers to conduct thorough assessments of potential harms and benefits before deployment, with special attention to impacts on marginalized groups. -
Transparent Documentation Requirements
Establish standards for documenting model development, validation processes, limitations, and intended use cases to allow meaningful scrutiny. -
Representative Data Standards
Develop guidelines to ensure training data adequately represents diverse populations, with mechanisms to identify and address gaps. -
Ongoing Monitoring Frameworks
Implement systems for continuous evaluation of model performance and impact after deployment, with clear procedures for addressing identified issues. -
Healthcare Professional Training Programs
Create comprehensive education initiatives to help clinicians understand, critically evaluate, and appropriately use health prediction AI. -
Patient Engagement Mechanisms
Establish structured approaches to involve patients in the design, implementation, and governance of health prediction systems. -
Regulatory Clarity
Define clear regulatory pathways tailored to health prediction AI that address its unique characteristics and risks. -
Explainability Requirements
Create context-specific standards for model interpretability, based on use case, risk level, and stakeholder needs. -
Data Governance Frameworks
Implement robust protocols for data access, consent, and privacy protection specific to health prediction applications. -
Health Equity Audits
Require regular assessments of how prediction models impact health disparities, with mandatory action to address any negative effects.
Implementation Case Studies
The Report includes several case studies that illustrate both successes and challenges:
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Multi-institutional Sepsis Prediction System:
A collaborative effort that successfully reduced mortality through earlier interventions—highlighting the importance of careful implementation and clinical workflow integration. -
Genomic-Enhanced Cancer Recurrence Prediction:
A model that combines histopathology images with genomic data to predict recurrence risk. It improved accuracy but raised questions about testing guidelines. -
Community-Based Chronic Disease Management:
A primary care initiative using prediction models to stratify patient risk and allocate resources—resulting in improved outcomes through a patient-centered approach. -
Mental Health Crisis Prediction Failure:
A cautionary example where inadequate validation and poor stakeholder engagement led to resource misallocation and privacy concerns.
The Summary of the Summary
The Report concludes that health prediction AI could represent a major evolutionary leap in healthcare—especially when integrating longitudinal health data with genomic information. This convergence has the potential to transform how we predict, prevent, and personalize healthcare. The recommendations aim to ensure these technologies improve outcomes and create more equitable systems while respecting human rights and values.
How Could Genetic Data Inform the Consumer?
As we noted in Part I, we’ve been working to not only aggregate consumer longitudinal data from Electronic Health Records (EHRs), but also to explore how genetic data could help inform consumers.
I’ve been told by actual doctors and geneticists that genetics typically enter the picture only when there’s a serious problem. Most of the time, it’s not relevant in primary care or among most first-level specialists. Genetic testing is expensive and usually only approved by insurance in the most critical cases.
We explored the market and found several companies offering direct-to-consumer genetic testing. We randomly selected one based on their appealing website—not to promote them, but to provide a real-world example. Here’s what they offer:
Comprehensive Genetic Screening
Our comprehensive DNA testing bundles provide insights into your genetic predisposition for 15,000+ conditions.
Includes:
Whole Genome Sequencing in a US-based CLIA-certified, CAP-accredited lab
Insights into your genetic health
Lifestyle & dietary recommendations tailored to your genes
Personalized SequencingAI-generated reports and guidance
They also feature a graphic advertising 25 reports for the discounted price of $999.
This is way over my head—but experts I’ve spoken with agree that having both longitudinal clinical data and even some genetic data could be incredibly beneficial for the average patient—not just those facing life-threatening conditions. These reports often come in formats that could be integrated into a combined consumer health portal.
With that kind of data, we could build a highly focused AI platform trained exclusively on reliable sources like the U.S. National Library of Medicine, the National Center for Biotechnology Information, NIH, HHS, and other known entities. This platform could track and statistically predict possible health issues based on a consumer’s current condition. As the Ada Lovelace Report suggests, it might even help predict the future.
A Quick Look at the Genetics Workforce
Just out of curiosity, I asked AI how many Ph.D. geneticists are currently in the U.S. The response was a bit sprawling, but here are the highlights:
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Around 200 genetics departments in U.S. universities and medical schools, with an average of 20 faculty members each—totaling about 4,000.
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Industry leaders like Illumina, Genentech, and Moderna, along with hundreds of biotech firms, also employ geneticists.
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It also mentioned MD/PhDs, and those in government agencies like the NIH, CDC, and USDA.
After refining the question, I got an estimated range of 8,000–15,000. Other experts suggest a more practical working number is closer to 5,000. Either way, it’s not nearly enough for a country with an estimated 341,110,188 people (U.S. Census Bureau, 2024).
The Consumer’s Dilemma
Healthcare consumers today have more access to their own health data than ever before—but the data is scattered across many providers. The average American will see as many as 30 doctors over a lifetime, across a dozen or more different EHR systems, making it nearly impossible to see the full picture.
But what if consumers could aggregate all their medical records, use a healthcare-only AI platform, and optionally add part of their genetic profile? We could take the most transformative technology ever invented—Artificial Intelligence—and finally use it to help consumers improve their health.
It would empower individuals to communicate more effectively with providers and stand up to insurance companies—not leaving their doctors to fight alone. This could be the beginning of a true Consumer Renaissance in Healthcare.
About HealthScoreAI ™
Healthcare is at a tipping point, and HealthScoreAI (HSAI) is positioning to revolutionize the industry by giving Consumers control over their health data and unlocking its immense value. U.S. healthcare annual spending has exceeded $5 trillion with little improvement in outcomes. Despite advances, technology has failed to reduce costs or improve care. Meanwhile, 3,000 exabytes of Consumer health data remain trapped in fragmented USA system of 500 EHRs, leaving Consumers and doctors without a complete picture of care.
HealthScoreAI seeks to provide a unique solution, acting as a data surrogate for Consumers and offering an unbiased holistic view of their health. With over 850 million medical claims denied annually in the U.S., HSAI intends on giving Consumers practical tools to respond to denial of care by insurers. We aim to bridge the gaps in healthcare access and outcomes. By monetizing de-identified data, HealthScoreAI seeks to share revenue with Consumers, potentially creating a new $100 billion market value opportunity. With near-universal EHR adoption in the USA, and advances in technology, now is the perfect time to capitalize on the data available, practical use of AI and the empowering of Consumers, in particular the 13,000 tech savvy baby boomers turning 65 every single day and entering the Medicare system for the first time. Our team, with deep healthcare and tech expertise, holds U.S. patents and a proven track record of scaling companies and leading them to IPO.
Noel J. Guillama-Alvarez
https://www.linkedin.com/in/nguillama/
+1-561-904-9477, Ext 355
https://get.sequencing.com/shop-all-bundles/
https://www.ncbi.nlm.nih.gov/gene
https://www.adalovelaceinstitute.org/Report/predicting-the-future-of-health/
https://www.themarginalian.org/2014/12/10/ada-lovelace-walter-isaacson-innovators/