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Data trust and the Data Trust Index
81 guides, published between November 2023 and September 2026. How is a health record scored before an AI system is allowed to act on it?
Guides
- Food as medicine program data quality: what nutrition intervention tracking needs
September 19, 2026
Food as medicine programs generate data across clinical encounters, community-based organizations, and grocery delivery platforms, but fewer than 20% of programs track outcomes with structured, linkable data. Without trust scoring on nutrition intervention records, the field cannot prove what works, for whom, or at what dose.
- SDOH screening program data quality: Z-code capture rates and what they mean
September 18, 2026
SDOH Z-code capture rates sit between 1% and 2.4% of claims nationally, despite screening programs running in thousands of facilities. The gap between screening a patient and producing a coded, trustable record is where most SDOH data programs fail. Understanding what Z-code capture rates actually measure, and what they miss, is the first step toward building social determinants intelligence that AI systems can act on.
- Care management outreach data trust: what population health targeting requires
September 18, 2026
Care management outreach fails when the targeting data is wrong. Roughly 30% of outreach attempts never reach the intended patient due to stale contact information, misattributed risk scores, and incomplete social determinant records. Population health targeting requires data trust infrastructure that most health plans and systems do not yet have.
- Discharge planning data quality: what care transition intelligence requires
September 18, 2026
Nearly one in five hospital discharges results in an adverse event within 30 days, and the root cause is rarely clinical. It is data: missing medication lists, stale provider directories, unverified post-acute capacity, and consent records that never followed the patient. Discharge planning data quality determines whether care transition intelligence works or fails silently.
- Utilization management data integrity: what concurrent review data needs
September 17, 2026
Concurrent review decisions depend on data that changes hour by hour, yet most utilization management systems treat clinical records as static inputs. When 30% of UM denials trace back to incomplete or stale data rather than clinical judgment, the integrity of the underlying record becomes the central problem. This post maps what concurrent review data actually requires across provenance, recency, and concordance.
- Hospital Compare data quality: what public reporting gets wrong about outcomes
September 11, 2026
Hospital Compare publishes quality data on over 4,000 hospitals, but the underlying data suffers from coding inconsistencies, risk adjustment gaps, and reporting lags that distort the outcomes consumers see. CMS public reporting data trust depends on layers of transformation that few users understand, and fewer question.
- ACO REACH data quality requirements and trust infrastructure needs
September 7, 2026
ACO REACH participants must report on 3 quality measures and satisfy CMS data submission requirements across claims, clinical, and beneficiary-level records. But meeting minimum reporting thresholds is not the same as having trustworthy data. Without a trust infrastructure that scores every record for provenance, recency, and completeness, REACH model participants are building risk-bearing contracts on data they cannot verify.
- CommonWell and Carequality: why health data networks have not solved trust
September 5, 2026
CommonWell Health Alliance and Carequality connect thousands of healthcare organizations, but connectivity is not trust. These networks solve the transport problem while leaving data quality, provenance, and consent governance entirely unaddressed, which means the health data flowing through them remains unscored and unverified.
- Epic vs Oracle Health: what the EHR duopoly means for health data trust
September 4, 2026
Epic and Oracle Health now control over 75% of U.S. hospital EHR installations, creating a duopoly where health data quality and trust standards are set by two vendors with fundamentally different architectures and business models. The concentration of clinical data in two proprietary systems does not guarantee that the data inside them is trustworthy, complete, or ready for AI.
- Temporal consistency in longitudinal health data: what data trust requires across time
September 1, 2026
A patient's health record collected over 10 years is only as trustworthy as the consistency of its measurements across time. Temporal consistency failures in longitudinal health data silently corrupt AI models, producing predictions built on contradictions that no cross-sectional audit can detect. Scoring data trust across time requires explicit evaluation of measurement drift, gap patterns, and chronological coherence.
- Health data completeness scoring: what missing fields cost AI model performance
August 31, 2026
A missing BMI field drops sepsis prediction AUC by up to 0.09. A missing race field can shift readmission risk scores by 12%. Health data completeness scoring quantifies these losses before they reach a model, turning invisible gaps into measurable costs that determine whether clinical AI performs or fails.
- Radiogenomics data trust: linking imaging phenotypes to genomic alterations
August 30, 2026
Radiogenomics links imaging phenotypes to genomic alterations, but the data pipeline connecting DICOM files to sequencing results has no standard trust framework. Without scored provenance, temporal alignment, and consent governance across both modalities, radiogenomics AI models inherit compounded errors from two of the most complex data types in medicine.
- Interpreter services data quality: what language concordance means for health AI
August 26, 2026
Over 25 million people in the United States have limited English proficiency, and their health records carry systematic data quality problems that most AI systems never detect. Language concordance between patient and clinician directly shapes the accuracy, completeness, and clinical relevance of every data point recorded during a healthcare encounter. Without scoring for this concordance dimension, health AI models trained on LEP patient data inherit silent distortions that compound across populations.
- Cost-effectiveness analysis data trust: what health economics models need
August 23, 2026
Cost-effectiveness analysis models in healthcare depend on data inputs that most organizations never verify. QALY calculations, transition probabilities, and utility weights all carry embedded assumptions about data quality, yet fewer than 15% of published CEA models report data provenance. Without a trust layer beneath the inputs, the outputs are economic fiction.
- Health technology assessment data quality: what ICER models require
August 22, 2026
ICER cost-effectiveness models depend on clinical, economic, and quality-of-life data that most organizations cannot verify for accuracy, completeness, or provenance. Without structured data trust requirements, health technology assessments inherit every upstream data quality failure. The gap between what ICER models require and what manufacturers actually submit is where billion-dollar coverage decisions go wrong.
- Predictive analytics in healthcare: why model accuracy starts with data trust scores
August 21, 2026
Predictive analytics in healthcare fails not because of bad algorithms but because of unscored training data. A model trained on records with unknown provenance, stale timestamps, and broken consent chains produces predictions that no clinician should trust. Data trust scores, applied before training begins, are the missing prerequisite for model accuracy.
- Waveform data trust: ECG and continuous monitoring data integrity for AI
August 19, 2026
ECG waveform data degrades at every step between the electrode and the AI model. Noise, sampling inconsistencies, metadata gaps, and broken provenance chains make continuous cardiac monitoring data one of the most difficult signal types to trust for clinical AI. Without structured integrity scoring, AI models trained on ECG waveforms inherit errors that no algorithm can recover from.
- Sexual orientation and gender identity data collection trust in healthcare
August 18, 2026
Only 19% of federally qualified health centers consistently collect sexual orientation and gender identity data from patients. The gap is not a technology problem. It is a trust problem, and it cascades into every AI model trained on incomplete demographic fields.
- Disability status data quality: the hidden demographic missing from most health AI
August 17, 2026
Roughly 1.3 billion people worldwide live with a disability, yet most health AI training datasets lack any standardized disability status field. This absence does not just create a gap in demographic coverage. It produces models that systematically misallocate risk, mispredict outcomes, and reinforce the very health disparities disabled populations already face.
- Health data broker accountability: what the FTC approach means for AI vendors
August 16, 2026
The FTC has settled with multiple health data brokers for unlawful sale of sensitive location and health data, and its enforcement model is shifting from reactive penalties to structural prohibitions. For AI vendors building on brokered health data, these actions redefine what accountability looks like, and what data trust infrastructure is now required to avoid regulatory exposure.
- The truth score ceiling: why Platinum-grade data cannot come from unverified sources
August 14, 2026
Unverified health data hits a hard ceiling in the Data Trust Index. No amount of recency, breadth, or quality improvement can push a record past Gold grade if its provenance chain includes unverified sources. The DTI Platinum ceiling is not a bug. It is the entire point of trust-scored health data.
- Data retention policy and trust scores: how aging data loses integrity over time
August 14, 2026
Health data loses integrity the moment it is created. A blood pressure reading from 3 years ago, an address from a prior state, a medication list from before a formulary change: each carries a trust score that decays on a predictable curve. Data retention policies that ignore this decay feed stale records into AI models that make real clinical decisions.
- Data minimization in healthcare AI: why collecting everything is a trust liability
August 13, 2026
Healthcare AI systems that collect everything create larger breach surfaces, higher regulatory exposure, and deeper patient distrust. Data minimization is not a privacy nicety. It is a structural requirement for any AI system that expects to operate under regulatory scrutiny and retain patient confidence.
- Housing instability data quality: eviction records and health data integration trust
August 8, 2026
Eviction records exist in over 3,100 county court systems with no standard format, no shared identifiers, and no reliable method for linking to health data. When health systems and Medicaid programs try to integrate housing instability data with clinical records, the result is a trust problem that undermines every downstream AI model, risk score, and care coordination workflow.
- Address-level SDOH data trust: geocoding accuracy and the census tract mismatch problem
August 5, 2026
Geocoding errors misassign up to 10% of patient addresses to the wrong census tract, silently corrupting every SDOH variable linked to that location. When health AI models consume area-level deprivation indices without verifying the geocoding step, they inherit systematic bias that disproportionately affects rural and minority populations. Address-level SDOH data trust requires scoring the geocoding pipeline itself, not just the social determinant variables it produces.
- Social risk factor screening data trust: AHC-HRSN instrument quality requirements
August 5, 2026
The AHC-HRSN screening tool captures social risk factor data across five core domains, but most health systems collect it without any quality framework. Without scoring for provenance, recency, and concordance, SDOH screening data fails the moment it enters an AI model or a CMS reporting pipeline.
- Race and ethnicity data quality: what self-reported vs inferred data means for AI
August 4, 2026
Up to 30% of race and ethnicity fields in EHR data are either missing or inferred from surnames and geocoding rather than self-reported by patients. When AI models train on this data without distinguishing source method, they replicate and amplify the very disparities health equity programs aim to fix. Understanding the difference between self-reported and inferred demographic data is a prerequisite for any clinical AI deployment.
- Health literacy and data quality: how patient-entered data degrades over time
August 3, 2026
Patient-entered health data loses clinical reliability at a measurable rate. Within 90 days of collection, self-reported medication lists, symptom logs, and health histories show concordance drops of 20-50% against verified clinical records. Understanding this decay curve is essential before any AI model trains on patient-reported inputs.
- Patient-reported outcome measure (PROM) data trust: collection quality requirements
August 2, 2026
Patient-reported outcome measures generate some of the most clinically valuable data in healthcare, yet PROM collection quality failures render up to 40% of submissions unusable for regulatory or AI purposes. Meeting PROM data trust requirements means enforcing provenance, temporal precision, and validation standards at the point of collection, not after the fact.
- Natural language processing on EHR notes: data trust requirements before NLP
July 30, 2026
Up to 80% of clinical data sits in unstructured EHR notes, yet most NLP pipelines never verify the trustworthiness of that text before processing it. Without provenance, recency, and quality scoring on the source notes themselves, clinical NLP outputs inherit every upstream data problem and amplify it at scale.
- Multi-omic data integration trust: combining genomics, proteomics, and metabolomics
July 28, 2026
Multi-omic data integration combines genomics, proteomics, and metabolomics into a single analytical framework, but most integration pipelines ignore the trust problem underneath. When three distinct data types with different provenance chains, quality standards, and temporal profiles converge, the weakest layer determines the ceiling of every downstream AI model.
- Emergency department visit data trust: what unplanned care data requires before AI use
July 24, 2026
Emergency departments generate some of the most chaotic, fragmented, and time-pressured data in medicine. Before any AI model uses ED visit data for triage, prediction, or resource allocation, that data must meet specific trust thresholds across provenance, recency, completeness, and consent. Most ED data fails on at least three of these dimensions.
- Clinical decision support trust: what the evidence base for CDS alerts requires
July 23, 2026
Between 49% and 96% of clinical decision support alerts are overridden by physicians, and the primary reason is not alert fatigue. It is data trust. CDS systems built on unscored, unstandardized health data generate alerts that clinicians cannot verify, cannot contextualize, and cannot act on safely.
- Care gap identification data quality: how trust scoring improves population health AI
July 22, 2026
Population health AI models flag care gaps using data that is often incomplete, outdated, or coded inconsistently across systems. Trust scoring each record before it enters a care gap identification engine reduces false positives, eliminates phantom gaps, and produces outreach lists that clinicians actually trust. Without a trust layer, care gap data quality degrades every downstream decision from risk stratification to quality measure reporting.
- Sepsis prediction algorithm data requirements: what trust score a model needs
July 21, 2026
Sepsis prediction algorithms consume six to dozens of clinical variables, but most validation studies never measure whether the underlying data was trustworthy enough to train on. A sepsis model scoring an AUROC of 0.85 on clean vital signs can collapse to near-random performance when fed late, miscoded, or unstandardized EHR data. This post maps the specific data requirements for sepsis AI and defines the trust score thresholds each input stream needs before any model should act on it.
- Readmission prediction model bias: how training data trust affects clinical AI
July 20, 2026
Readmission prediction models inherit bias from training data that was never verified for accuracy, completeness, or demographic representativeness. When hospitals deploy AI built on miscoded diagnoses, delayed claims, and missing social determinant records, the model does not predict readmissions. It predicts the gaps in the data it was trained on.
- Patient-generated health data (PGHD) and the trust threshold for clinical use
July 19, 2026
Roughly 98% of patient-generated health data never enters a clinical workflow. The gap is not technology. It is trust. Without a measurable threshold for provenance, recency, and validation, consumer health data remains invisible to the systems that need it most.
- Pathology report data quality: structured vs unstructured biopsy data trust
July 19, 2026
Synoptic pathology reporting is 174 times more accurate than AI extraction from narrative text, yet most health systems still train models on unstructured biopsy data. The gap between structured and unstructured pathology data is not a formatting preference. It is a trust problem that determines whether AI outputs are clinically safe or silently wrong.
- Mesothelioma occupational exposure signals and late-stage detection data
May 11, 2026
Mesothelioma kills over 2,500 Americans annually, with a median latency period of 30 to 50 years between asbestos exposure and diagnosis. Over 80% of cases are diagnosed at stage III or IV because the occupational exposure signals that predict risk sit in fragmented, unscored datasets that no clinical system connects. Behavioral intelligence offers a way to close that gap before patients present with advanced disease.
- Why health system CTOs are wrong about what makes AI trustworthy
May 10, 2026
Most health system CTOs define AI trustworthiness by model accuracy, HIPAA compliance, and vendor certifications. None of those address the actual failure point: unscored, unverified training data flowing into production AI without provenance, consent validation, or recency checks. The trust problem is not in the model. It is in the data the model never should have seen.
- The $3.5 trillion cost of bad health data: what fragmentation actually costs the system
May 9, 2026
Bad health data costs the U.S. healthcare system an estimated $3.5 trillion annually through redundant testing, failed care coordination, billing errors, and AI models trained on unverified records. The problem is not a lack of data. It is a lack of trust in the data that already exists.
- Data poisoning attacks on health AI: how bad training data creates adversarial outputs
May 9, 2026
Data poisoning attacks against health AI systems exploit the one layer most organizations never audit: the training data itself. A single corrupted dataset can shift diagnostic thresholds, suppress treatment recommendations for specific populations, or introduce systematic misclassification that persists through every downstream model. Without trust scoring at the point of data ingestion, health AI is building clinical decisions on an unverified foundation.
- Digital health app data: the gap between consumer trust and clinical trust
May 8, 2026
Over 350,000 health apps exist in major app stores, yet fewer than 2% have any clinical validation. Consumers trust their health app data far more than clinicians do, and this gap creates a structural problem for every organization trying to use patient-generated data in care decisions or AI training.
- Synthetic data in healthcare AI: when fabricated training data creates real bias
May 7, 2026
Synthetic health data inherits, amplifies, and launders the biases present in real-world clinical datasets. When healthcare AI models train on fabricated records that were never scored for provenance or consent, the resulting bias becomes invisible to standard audits. The only defense is scoring data trust before any model trains on it.
- Why recency is the most underrated dimension in health AI data scoring
May 7, 2026
Recency carries 15% of the Data Trust Index score, yet most health AI pipelines treat timestamps as metadata rather than a trust signal. Stale data silently degrades model accuracy, clinical decision support, and regulatory defensibility. Health data recency scoring is the single fastest way to separate actionable intelligence from archived noise.
- DataSpine and the geography of health risk: how place shapes health data trust
May 5, 2026
A patient's ZIP code predicts life expectancy more reliably than their genetic code. Geographic health data carries enormous weight in risk models, population health, and SDOH analytics, but most of it lacks provenance, recency, or consent verification. DataSpine scores geographic SDOH data across all 8 trust dimensions before it enters any model.
- How the Data Reservoir turns scored data into queryable intelligence
May 3, 2026
Most health data platforms store records. Few make those records queryable by trust score, consent status, and provenance chain simultaneously. The Data Reservoir is the layer that converts DTI-scored health data into queryable intelligence, letting researchers, health systems, and pharma teams run queries that return not just answers but auditable confidence levels.
- The DTI score as a contract: what Platinum-grade data actually guarantees
May 2, 2026
The DTI score assigns every health data record a grade from Bronze to Platinum across 8 weighted dimensions. Platinum-grade data is not a marketing label. It is a contractual guarantee that a record meets verifiable thresholds for provenance, consent, recency, quality, concordance, validation, breadth, and stability before any AI model or regulatory submission touches it.
- The first mover advantage in health data trust: why 2026 is the year institutions decide
May 1, 2026
Health systems that build data trust infrastructure in 2026 will own the competitive position that late movers cannot replicate. The window is narrow: regulatory pressure, AI deployment timelines, and payer contract shifts are converging on a single year. Institutions that wait until 2027 will spend more to catch up than early movers spent to lead.
- SuperTruth Will Never Profit From Children. Sean's Friends Is How We Made That a Legal Fact.
May 1, 2026
Most companies build a philanthropic arm after the money is in the door. We built Sean's Friends on Day One, before the company was profitable, because the order matters. Giving is the tenet. Profit follows the tenet.
- Why Innovaccer, Datavant, and AWS Health Lake are not solving the data trust problem
April 30, 2026
Innovaccer, Datavant, and AWS HealthLake each solve a real infrastructure problem. None of them solve the data trust problem. They move, link, and store health data without ever answering the question that matters most: should this record be trusted?
- Infrastructure trust vs data trust: why most healthcare data platforms miss the point
April 29, 2026
Most healthcare data platforms solve for infrastructure trust: uptime, encryption, access controls. Almost none solve for data trust: whether the records themselves are accurate, consented, current, and traceable. This distinction explains why billions in health AI investment still produce unreliable outputs.
- Information blocking rules and data trust: what the ONC final rule means for AI
April 28, 2026
The ONC information blocking final rule forces health data to flow. But flowing data is not trustworthy data. Without a trust layer that scores provenance, consent, and quality before AI models consume health records, interoperability becomes a pipeline for unreliable inference.
- The wearable data trust problem: from consumer device to clinical intelligence
April 28, 2026
Over 150 million Americans wear a health-tracking device, but almost none of that data meets clinical-grade trust standards. The gap between consumer wearable output and clinically useful biomarker intelligence is not a technology problem. It is a data trust problem, and it maps directly to provenance, consent, and validation failures.
- Data quality vs data trust: what is the difference and why it matters for healthcare AI
April 27, 2026
Data quality measures whether a record is accurate and complete. Data trust measures whether that record should be used at all. Healthcare AI needs both, but the industry has invested almost exclusively in quality while ignoring trust, and that gap is where clinical AI fails.
- Substance use disorder data and the special status challenge for AI systems
April 27, 2026
Substance use disorder records carry federal protections under 42 CFR Part 2 that exceed standard HIPAA rules. Most AI systems training on health data have no mechanism to detect, segment, or honor these restrictions, which means SUD data either gets excluded entirely or mixed in without proper consent governance. Both outcomes damage model accuracy and patient trust.
- Why EHR data needs a trust score before any AI model trains on it
April 26, 2026
Over 90% of U.S. hospitals run on Epic or Cerner, yet no standard process exists to score the trustworthiness of EHR data before it enters an AI training pipeline. Without a trust score, models inherit every documentation gap, coding inconsistency, and consent violation baked into the source record. The Data Trust Index changes that by scoring every record 0-100 before any model touches it.
- SuperTruth publishes peer-reviewed research on the Data Trust Index at Zenodo — what the paper covers and why it matters for health AI governance
April 26, 2026
SuperTruth has published peer-reviewed research on the Data Trust Index (DTI) at Zenodo, making the formal methodology for scoring health data integrity publicly available. The paper defines the 8-dimension framework, weighted scoring model, and trust tier classification system that scores every health data record 0 to 100. This is the first open-access publication of a structured trust scoring methodology purpose-built for health AI governance.
- You can now score a health record live on supertruth.ai — what the DTI pipeline actually does and why it matters
April 26, 2026
SuperTruth now lets you score a health data record live on supertruth.ai. The DTI pipeline evaluates every record across 8 trust dimensions and returns a 0-100 score before any AI model touches it. This post explains what happens inside that pipeline, step by step, and why it changes how health data enters production.
- Rare disease registries and data trust requirements for research use
April 25, 2026
Fewer than 6% of the estimated 10,000 rare diseases have an FDA-approved therapy, and fragmented registry data is a primary reason. Rare disease registries collect critical patient information, but without structured data trust requirements, most of that information cannot reliably support research, regulatory submissions, or AI model training.
- Hospital system AI readiness: what data trust infrastructure you need before deployment
April 24, 2026
Most hospital systems rushing to deploy AI lack the data trust infrastructure that determines whether models succeed or fail in production. Readiness is not about compute power or vendor selection. It is about whether your data can be scored, governed, and trusted before a single algorithm touches a patient record.
- Health equity data: measuring what we do not see in traditional health systems
April 23, 2026
Roughly 80% of health outcomes are driven by factors outside the clinical setting, yet most health systems collect structured data on fewer than half of those factors. Health equity data measurement requires capturing what traditional EHRs were never designed to see: housing instability, food access, transportation barriers, and the trust dynamics that determine whether patients share information at all.
- Siloed health data: the infrastructure problem nobody has solved yet
April 23, 2026
The average patient's health data is scattered across 19 different systems, and no integration standard has fixed the problem. Siloed health data costs U.S. healthcare roughly $150 billion per year in redundant testing, care delays, and administrative waste. The issue is not a lack of interoperability standards; it is that the data itself has never been scored for trustworthiness before anyone tries to connect it.
- How temporal drift destroys AI model accuracy in healthcare
April 22, 2026
Healthcare AI models lose up to 20% of their predictive accuracy within 12 months of deployment because the clinical data they trained on no longer reflects current patient populations. Temporal drift in health data is not a theoretical risk; it is the primary mechanism through which AI systems silently fail in production. Scoring data for recency before it reaches a model is the only structural fix.
- Rural health data gaps and how synthetic data fills them without compromising trust
April 20, 2026
Roughly 46 million Americans live in rural areas where health data is sparse, outdated, or missing entirely. Synthetic data generation promises to fill those gaps, but only if the underlying inputs carry verifiable trust scores. Without scoring, synthetic rural health data inherits the same blind spots it was designed to eliminate.
- Community health organizations and SDOH data quality: the trust gap
April 19, 2026
Community health organizations collect more social determinants of health data than ever, yet most of it fails basic integrity checks. The trust gap between what gets recorded and what can actually inform clinical or policy decisions costs the U.S. healthcare system billions annually and leaves the most vulnerable populations invisible.
- The eight dimensions of health data trust: a practical guide
April 19, 2026
The Data Trust Index scores every health data record from 0 to 100 across eight weighted dimensions. This practical guide breaks down each dimension, explains why the weights are set the way they are, and shows how the DTI engine converts raw health data into a trust-scored asset ready for AI, regulatory submission, and clinical use.
- The Verbal Medical Record: Why Patients and Families Become Their Own Health Data Systems
April 17, 2026
When Bobby Hill's father had a stroke and was rushed to Atlantic City Hospital, none of his records were there. He had just spent months watching his family become verbal medical records for their loved ones. This is not exceptional. It is the default — and it costs lives.
- The Data Trust Index: SuperTruth Publishes the First Formal Framework for Health Data Integrity Scoring
April 16, 2026
The Data Trust Index is now a published, citable academic framework. Today SuperTruth released the first formal paper describing the DTI — the eight-dimensional scoring system that functions as the FICO score for health data. DOI: 10.5281/zenodo.19601616
- Lung cancer data trust: what behavioral signals tell us before clinical presentation
April 16, 2026
Lung cancer symptoms often appear months or years before a clinical diagnosis, but behavioral signals in health data can surface risk much earlier. SuperTruth's VIOLET platform scores and interprets these pre-diagnostic patterns using trusted, validated data to close the detection gap.
- Rural Health Data Is Stale by Design. SDOH Scoring Is the Fix.
March 18, 2026
60 million Americans live in rural areas. They are the most underserved population in health data systems. Not because there is no data about them. Because the data is old.
- Why AI Models Trained on Unscored Health Data Will Fail in Production
March 14, 2026
Health AI doesn't fail at training time. It fails in production, in front of clinicians, when a model makes a recommendation based on data that was stale, unsourced, or never actually authorized for AI use. These failure modes are predictable — and they all trace back to unscored training data.
- Why Primary Source Verification Is Not Enough: The Case for Trust-Scored Provider Data
March 12, 2026
Primary source verification answers one question: is this license currently active? It does not answer the question that matters for AI-driven health plans: how much should any downstream system trust this record, for this specific use case, right now?
- Why Veteran Health Records Are the Hardest Data Problem in Medicine
February 10, 2026
The VA health system serves 9 million enrolled veterans. Their records span VA systems, DoD military health, and civilian EHRs from community care. The problem is not a shortage of data. It is a shortage of trust metadata.
- SuperTruth at the Eudemonia Health Innovation Lab: What Data Trust Means for the Future of Health
October 8, 2025
The Eudemonia Health Innovation Lab brings together the people actually building the future of health — not the people talking about it. SuperTruth is there because data trust is the infrastructure that makes everything else in health innovation possible.
- SuperTruth, Fitt Insider, and the Health Intelligence Layer the Wellness Industry Is Missing
July 17, 2025
The health and wellness industry generates more behavioral data than almost any other sector. Activity, sleep, nutrition, recovery, stress — tracked by millions of consumers across thousands of platforms. None of it has a trust layer. That is about to change.
- Clinical Trials Have a Data Supply Problem. The Patients Are There. The Infrastructure Isn't.
April 10, 2025
The patients are there. For almost every study, somewhere in the health system, there are people who meet the inclusion criteria, whose data would support the research, who might benefit from participation. The challenge is not the supply. The challenge is the infrastructure to find it.
- SuperTruth and imaware: What 105,000 Diagnostic Records Taught Us About Cancer Data Trust
January 30, 2025
imaware had 105,000 diagnostic records and could not trust any of them. Here is what happened when SuperTruth applied the Data Trust Index to oncology diagnostic data — and what it revealed that had been invisible for years.
- Healthcare AI Doesn't Need to Go Deep. It Needs to Go Long.
November 19, 2024
More parameters. More data. Better benchmarks. The assumption is that depth — going further into a dataset — produces better clinical intelligence. That assumption is wrong for healthcare. Healthcare AI doesn't need to go deep. It needs to go long.
- The Fragmented Health Record: Why the Most Valuable Data in Healthcare Lives Nowhere
May 14, 2024
Every provider encounter creates a data point. Most of them disappear. The patient who shows up carrying a folder of printed records is not a curiosity. They are the connective tissue of a system that has no other mechanism for continuity.
- Why We Built SuperTruth: A Promise Made in a Hospital Room
November 15, 2023
There are two stories behind SuperTruth. They happened to two different people, in two different hospitals, years apart. Neither of us knew the other yet. But they are the same story.