At 35, Sarah suffered a heart attack that a €30 genetic test could have prevented. Her story isn’t unusual, it’s a mirror of how health systems worldwide confuse “standard care” with equity.
Sarah, a 34-year-old teacher, had visited her GP complaining of fatigue and chest tightness during exercise. Her cholesterol was significantly elevated at 8.2 mmol/L. Following standard guidelines, her doctor prescribed a statin and scheduled follow-up in six months. “Probably just lifestyle-related; over-weight” the notes read.
Eighteen months later, after persistently high cholesterol despite medication compliance, Sarah suffered her heart attack. Only then did anyone discover she carried a pathogenic mutation causing familial hypercholesterolemia, a condition affecting 1 in 250 people that genetic testing could have identified for €30.
Sarah’s heart attack cost €15,000 to treat, a 500-fold difference from the test that could have prevented it.
There’s a pervasive belief in healthcare that precision medicine represents luxury care: expensive, elitist interventions that increase health disparities. I believe this perception is wrong and dangerously backwards. I’m convinced that precision diagnostics, the basis of true precision medicine, are the only approach that’s economically sustainable, truly equitable, and capable of delivering healthcare that works for everyone.
The Equity Paradox
Sarah’s story illustrates the counterintuitive truth about precision diagnostics: it’s not luxury healthcare that increases disparities, it’s an efficiency tool that reduces them.
Without genetic information – the “standard care” – the average patient with depression tries multiple antidepressants before finding effective treatment. Each failed three-month trial costs more than whole genome sequencing while patients suffer. Population-based prostate cancer screening produces false-positive rates approaching 50%, meaning nearly half of men undergoing invasive biopsies don’t have cancer. Meanwhile, 80% of familial hypercholesterolemia cases remain undiagnosed, leading to preventable cardiovascular disease like Sarah’s heart attack.
This isn’t equitable care! It’s systematic inefficiency disguised as fairness.
Mobile and Global
Around the world, community-based precision tools are rewriting what equity looks like.
Geisinger Health System’s MyCode precision medicine program has enrolled over 90,000 participants across rural Pennsylvania communities, demonstrating that genetic testing can be successfully implemented in community health systems, not just elite academic medical centers. Separately, Christ Hospital Health Network, also in the USA, embedded pharmacogenomics testing across 30 community primary care clinics, discovering that 50% of patients had actionable genetic variations requiring medication changes.
The pattern extends globally with true molecular precision medicine. South Africa and Bangladesh deployed GeneXpert technology conducting millions of tuberculosis tests that simultaneously detect TB and drug resistance mutations in 2 hours versus weeks for traditional methods. South Africa’s 314 machines across 207 sites achieved 95% participation rates, while Bangladesh used filter paper collection methods to extend molecular diagnostics to remote areas.
In India, smartphone-based cameras with AI interpretation screened nearly 60,000 individuals for diabetic retinopathy, costing $1,320 per quality-adjusted life year saved while serving populations who would never access traditional eye care.
When precision medicine moves to where people live, it serves everyone better.
The Biomarker Revolution
Point-of-care precision diagnostics are already revolutionizing everyday care by bringing critical testing where medical needs actually happen.
Point-of-care troponin testing provides heart attack results in 10-20 minutes versus hours for traditional laboratory testing. New Zealand’s Midlands region implemented this across twelve rural practices, allowing 62% of chest pain patients to be safely managed locally, with zero cardiac events among those kept in rural care. Traditional testing often requires transfers that many cannot afford, creating a geography-based survival divide.
New kidney injury biomarkers detect damage within 24 hours versus days for traditional markers. This matters most for high-risk populations: Indigenous Australians experience kidney failure at 20 times the rate of other Australians, while rural Indians often cannot afford travel costs for repeat laboratory visits. Early precision detection means intervention before irreversible damage occurs, right in their communities.
Precision diagnostics make advanced healthcare more accessible, not less.
Digital Scaling Revolution
These tools follow the same economic principles that made smartphones ubiquitous. Unlike physical procedures requiring proportional scaling, digital diagnostics have minimal marginal costs once developed.
Genetic sequencing costs have dropped by a factor of 50,000 since the Human Genome Project. AI diagnostic algorithms become more accurate and less expensive with larger datasets.
The current high costs largely reflect limited scale and regulatory barriers, not inherent technological limitations.
The Evidence Double Standard
We demand gold-standard trials from new diagnostics, but accept decades-old pathways that fail millions daily without equivalent evidence. The Framingham Risk Score systematically underestimates cardiovascular risk in South Asian and African populations. Current UK NHS pathways miss 60-70% of familial hypercholesterolemia patients.
If we held established approaches to the same standards we demand from precision diagnostics, much of our “routine care” would require urgent revision.
What Next?
Whether precision medicine increases health disparities or reduces them depends on choices we make now. Policymakers must eliminate regulatory barriers that artificially inflate costs while demanding no equivalent evidence from existing approaches. Health systems can (must!) pilot precision programs in community settings, not just academic centers. We should measure success by population health outcomes and cost-effectiveness – measure health instead of disease.
Beyond Sarah’s €15,000 treatment cost, consider her lost work capacity, ongoing rehabilitation, and the psychological toll on her family. The true cost ripples through decades of diminished life potential that early detection could have prevented.
We can either cling to the inefficiencies of 20th-century medicine focused on managing disease, or build an equitable future where every Sarah is diagnosed before catastrophe. Precision diagnostics aren’t luxury medicine, they’re justice. The tools exist and the economics can work. What remains is the choice of whether we keep rationing inefficiency, or finally deliver equity through precision.
A reflection arising from thinking about Precision Diagnostics for my forthcoming book: https://lnkd.in/dN9xPFXx
Author’s Note: I use AI extensively in my research and for polishing final drafts, as English is not my native language. The opinions expressed here are entirely my own, and any errors – conceptual or factual – are my responsibility.
References/Sources
Familial hypercholesterolemia (FH): prevalence, underdiagnosis, and prevention
- CDC. About Familial Hypercholesterolemia (prevalence ~1 in 250). https://www.cdc.gov/heart-disease-family-history/about/about-familial-hypercholesterolemia.html
- CDC Genomics Blog. How Common Is FH? (Jan 25, 2021). https://blogs.cdc.gov/genomics/2021/01/25/how-common-is-fh/
- CDC Genomics Blog. Familial Hypercholesterolemia is Common and Undertreated in the United States (Mar 26, 2018). https://blogs.cdc.gov/genomics/2018/03/26/familial-hypercholesterolemia/
- Nordestgaard BG et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population. Eur Heart J. 2013;34(45):3478–3490. https://academic.oup.com/eurheartj/article/34/45/3478/435928
- NHS England London. Familial Hypercholesterolemia (FH) services and diagnosis rates. https://www.england.nhs.uk/london/london-clinical-networks/our-networks/cardiac/familial-hypercholesterolaemia/
- NHS Genomics Education Programme. Familial hypercholesterolaemia transformation project (25% detection goal). https://www.genomicseducation.hee.nhs.uk/about-us/supporting-the-national-transformation-projects/transformation-project-familial-hypercholesterolaemia/
- Lee C et al. New Case Detection by Cascade Testing in FH: Systematic Review. Circ Genom Precis Med. 2019. https://pubmed.ncbi.nlm.nih.gov/31638829/
- Kerr M et al. Cost effectiveness of cascade testing for FH. Heart. 2017. https://heart.bmj.com/content/103/14/1023
- Sturm AC et al. FH screening in the United States: A call to action. J Am Coll Cardiol. 2018. https://www.jacc.org/doi/10.1016/j.jacc.2018.03.521
Cholesterol levels: clinical thresholds
- NHS. High cholesterol — diagnosis and thresholds. https://www.nhs.uk/conditions/high-cholesterol/
- Mayo Clinic. High cholesterol — numbers explained. https://www.mayoclinic.org/diseases-conditions/high-blood-cholesterol/in-depth/cholesterol-levels/art-20048245
- Cleveland Clinic. Cholesterol numbers: what they mean. https://my.clevelandclinic.org/health/articles/11920-cholesterol-numbers-what-do-they-mean
Costs: MI (heart attack) vs. genetic testing
- Mihaylova B et al. Cost of acute coronary syndromes and MI in Europe. Pharmacoeconomics. https://pubmed.ncbi.nlm.nih.gov/21401305/
- Szucs TD et al. Costs of acute MI in Germany. Eur J Health Econ. https://pubmed.ncbi.nlm.nih.gov/15365898/
- Khera AV et al. Genetic testing for FH and cascade screening: costs & yield. J Am Coll Cardiol. https://www.jacc.org/doi/10.1016/j.jacc.2016.03.520
- Heart UK. Cascade testing and cost for family members. https://www.heartuk.org.uk/health-and-high-cholesterol/familial-hypercholesterolaemia
- NICE CG71. FH: identification & management (incl. genetic testing pathways). https://www.nice.org.uk/guidance/cg71
“Standard care” inefficiencies: depression & PSA screening
- Rush AJ et al. STARD trial outcomes in depression (multiple trials common). Am J Psychiatry. https://ajp.psychiatryonline.org/doi/full/10.1176/ajp.2006.163.11.1905
- Cipriani A et al. Comparative efficacy and acceptability of 21 antidepressants. Lancet. 2018. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32802-7/fulltext
- NCI Fact Sheet. Prostate-Specific Antigen (PSA) Test — false positives common. https://www.cancer.gov/types/prostate/psa-fact-sheet
- USPSTF. Prostate Cancer Screening Recommendation Statement (false positives / overdiagnosis). https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/prostate-cancer-screening
- Martínez-Benjamín JJ et al. False-positive PSA rate 46.8%. Actas Urol Esp (Engl Ed). 2022. https://pubmed.ncbi.nlm.nih.gov/34871414/
Underdiagnosis: FH
- NHS England. FH Implementation Guide — case finding and cascade testing. https://assets.publishing.service.gov.uk/media/5b646bbced915d37793abcda/familial_hypercholesterolaemia_implementation_guide.pdf
- UK Health Security Agency. Improving the diagnosis and treatment of Familial hypercholesterolaemia. https://ukhsa.blog.gov.uk/2018/08/14/improving-the-diagnosis-and-treatment-of-familial-hypercholesterolaemia/
- Akioyamen LE et al. Global FH prevalence and diagnosis gaps. Circulation. 2017. https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.116.026991
Community precision medicine programs
- Carey DJ et al. The Geisinger MyCode Community Health Initiative. Genet Med. 2016. https://www.nature.com/articles/gim201524
- Geisinger MyCode Community Health Initiative (350,000+ participants). https://www.geisinger.org/precision-health/mycode/frequently-asked-questions
- Geisinger. MyCode Community Health Initiative enrolls 350,000 participants (Sep 2024). https://www.geisinger.org/about-geisinger/news-and-media/news-releases/2024/09/03/14/14/mycode-community-health-initiative-enrolls-350000-participants
- Haga SB et al. Pharmacogenomics in primary care — implementation lessons. Pharmgenomics Pers Med. 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277063/
Global molecular diagnostics at scale (GeneXpert TB)
- da Silva MP et al. More Than a Decade of GeneXpert® MTB/RIF (Ultra) Testing in South Africa: 23M tests. PLoS One. 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10605857/
- Cox HS et al. Impact of Xpert MTB/RIF for TB diagnosis at scale. Lancet. 2014. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(14)70933-8/fulltext
- WHO. Xpert MTB/RIF diagnostic test — policy and implementation. https://www.who.int/activities/implementing-xpert-mtb-rif
AI/telemedicine diabetic retinopathy screening (India)
- Rachapelle S et al. Cost-utility of telemedicine for DR in India: $1,320/QALY. Ophthalmology. 2013. https://pubmed.ncbi.nlm.nih.gov/23211635/
- Rajalakshmi R et al. Smartphone-based DR screening with AI. Eye (Lond). 2021. https://www.nature.com/articles/s41433-021-01530-6
- Ting DSW et al. AI for DR screening: clinical performance. JAMA. 2017. https://jamanetwork.com/journals/jama/fullarticle/2653315
Point-of-care (POC) troponin & rural pathways
- NICE DG40. High-sensitivity troponin tests (turnaround time & pathways). https://www.nice.org.uk/guidance/dg40
- Carlton E et al. High-sensitivity troponin in primary care / ED: rapid rule-out. Heart. 2015. https://heart.bmj.com/content/101/16/1279
- Pickering JW et al. Rural implementation and safe discharge using hs-troponin pathways. Emerg Med J. 2016. https://emj.bmj.com/content/33/7/502
AKI biomarkers (earlier detection than creatinine)
- Kashani K et al. Discovery and validation of cell cycle arrest biomarkers (TIMP-2•IGFBP7) for AKI. Crit Care. 2013. https://ccforum.biomedcentral.com/articles/10.1186/cc12503
- Bihorac A et al. Clinical validation of TIMP-2•IGFBP7 (NephroCheck). J Am Soc Nephrol. 2014. https://jasn.asnjournals.org/content/25/10/2177
- Kellum JA et al. Kidney biomarkers vs creatinine lag. Nat Rev Nephrol. 2021. https://www.nature.com/articles/s41581-021-00445-0
Sequencing cost collapse & digital scaling
- NHGRI. The Cost of Sequencing a Human Genome (50,000× drop since HGP). https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost
- NHGRI. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program. https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data
- Goodwin S et al. Ten years of next-generation sequencing technologies. Nat Rev Genet. 2016. https://www.nature.com/articles/nrg.2016.49
Evidence bias in “standard” risk tools (Framingham)
- Tillin T et al. Framingham risk in UK South Asians: underestimation. Heart. 2014. https://heart.bmj.com/content/100/2/129
- D’Agostino RB Sr. et al. General cardiovascular risk profile for use in primary care: validation issues across ethnicities. Circulation. 2008. https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.107.699579
- Goff DC et al. ACC/AHA pooled cohort equations—calibration challenges. Circulation. 2014. https://www.ahajournals.org/doi/10.1161/CIR.0000000000000040
Large-scale precision medicine initiatives
- All of Us Research Program (NIH). https://allofus.nih.gov/
- Singapore National Precision Medicine Programme. https://www.npm.sg/
- Estonian Biobank (20% adult population). https://genomics.ut.ee/en/content/estonian-biobank
- Qatar Genome Programme. https://qatargenome.org.qa/
Additional population-level FH studies
- Akioyamen LE et al. Prevalence of Familial Hypercholesterolemia Among Ethnicities—Systematic Review and Meta-Analysis. Front Genet. 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8850281/
- Hu P et al. Prevalence of Familial Hypercholesterolemia Among the General Population and Patients With ASCVD. Circulation. 2020. https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.119.044795