17 Americans die every day waiting for a kidney transplant. More than 80% of willing donors never reach surgery — not because of medical disqualification, but because the evaluation system was designed to screen them, not support them. Project Donor-Flow deploys 8 AI technologies across 8 pipeline stages to close the gap that is costing lives.
The living donor crisis is not a shortage of willing donors. It is a failure of intelligence — the failure to deploy AI where it is now demonstrably capable of preventing preventable attrition at every stage of the pipeline.
As of March 2024, 103,000+ Americans await a kidney transplant. Pretransplant mortality is 5.0 deaths per 100 patient-years (2023). Among adults newly listed in 2018–2020, a combined 26% had died or been removed due to deterioration within three years of listing.
OPTN, 2024 · Lentine et al., 2025 (SRTR Fig. KI 22)Five-year graft survival is 90.0% for LDKT vs. 82.2% for deceased donor transplant (ages 18–34). A 2024 UK propensity-matched cohort of 10,915 transplants found LDKT associated with 6.03% lower five-year graft failure risk (95% CI 4.71–7.35%).
Lentine et al., 2025 · Buse et al., 2024 (British Journal of Surgery)The LDCPR (2,107 consecutive candidates, 10 programs) confirmed 15.2% of non-approvals were candidate withdrawal before any formal decision. An additional 10.3% were denied for psychosocial reasons the literature characterizes as addressable support needs — not inherent medical barriers.
Lentine et al., 2021 (AJKD 78:3) — LDCPRPreventable attrition (withdrawal 15.2% + psychosocial 10.3%) = 25.5% of non-approvals = 255,000 Defects Per Million Opportunities. The current pipeline operates at ≈2.15σ — more than 3 standard deviations below the minimum acceptable healthcare standard of 3σ.
CIF Analysis · Lentine et al., 2021A multicenter study of 19,287 recipients found Black recipients had 37% lower LDKT likelihood (aRR 0.63; 95% CI 0.59–0.67; p<0.001) independent of community vulnerability. Of all waitlisted Hispanic patients, only 5.2% received LDKT vs. 11.4% of non-Hispanic White patients.
Axelrod et al., 2021 (JAMA Surgery) · Waterman et al., 2022OPTN collects program-level data. SRTR publishes it. USRDS tracks ESRD prevalence. The LDCPR documents failure modes. The problem is not absent data — it is the absence of computational intelligence to act on that data at the speed, scale, and specificity required to prevent the attrition it predicts.
CIF Framework Analysis, 2025From pre-inquiry population identification to post-donation longitudinal monitoring — every stage deploys the specific AI technology most appropriate to its data type, regulatory environment, and interpretability requirements.
Identifies ZIP codes, faith institutions, FQHCs, and community touchpoints with the highest concentration of individuals whose ML-derived profiles predict elevated donation intent among underrepresented populations — before any individual self-identifies.
Classifies publicly available social media content to identify pre-inquiry donation intent, classify motivation archetype, and route potential donors toward individualized outreach before they contact any transplant center.
Guides potential donors through pre-screening completion, manages scheduling friction, surfaces educational resources at motivationally appropriate moments, and provides proactive support when disengagement signals are detected.
Extracts and synthesizes relevant clinical signals from medical records into structured summaries for evaluating physicians — augmenting clinical review rather than replacing clinical judgment.
A composite psychosocial-readiness metric identifying candidates at elevated risk of withdrawal before withdrawal occurs. Equity-adjusted to prevent encoding of historical disparate-denial patterns. Trained with mandatory human clinical review layer.
Tracks each candidate's pipeline progress against the National Donor Velocity Benchmark, alerting coordinators to barrier signals before informed withdrawal becomes uninformed dropout.
Aggregates the candidate's complete clinical and psychosocial documentation into a structured, portable summary that eliminates repeated-documentation burden — a primary driver of process-fatigue withdrawal.
ML models monitor post-donation renal outcomes against population-matched baselines, feeding outcome data into the Closed-Loop Refinement System that continuously retrains and rebalances the DRS.
Lean Six Sigma provides the engineering backbone — ensuring every AI deployment is organized around measurable outcomes, data-driven root cause analysis, and continuous improvement.
CIF analysis of OPTN state data, USRDS ESRD prevalence, and AKF Report Card legislative grades identifies five states requiring priority Project Donor-Flow deployment.
Obesity: 40.8% (highest nationally)
Diabetes: 16.9% (highest nationally)
Zero improvement 2021–2023
No anti-discrimination law
Obesity: 37.1% (2nd nationally)
No anti-discrimination law
No job protection
Critical decline trend
Obesity: 36.3%
UAB nationally significant
Zero state-level support
Modifiable with the right system
2nd most populous state
Hispanic LDKT: 5.2%
vs. White LDKT: 11.4%
Documented equity crisis
CIF Home · FL-16
2022 legislation passed
3rd most populous state
Marie's Lifeline Act target
Applying the projected conversion rate improvement to the current U.S. living donor base produces measurable, calculable national impact — conservative by design.
6,226 (2023 baseline) × 0.40 = 2,490 additional annual donors from improved conversion alone — before accounting for increased inquiry volume from AI-guided outreach at Stages 1–2.
Up from 6,226 in 2023 — surpassing the 2019 all-time peak of 6,856 and exceeding HRSA's 2024 total of 7,030 within the first two deployment years.
2,490 × (17 deaths/day ÷ 91,000 waitlisted) × 365 = 1,158 preventable deaths avoided per year. Conservative: constant inquiry volume, no Stage 1-2 uplift, no Year 2–3 compounding.
The organizations that dominate the national conversation about kidney disease receive substantial funding from the dialysis and pharmaceutical industries. CIF does not — by founding constraint, not circumstance.
That independence eliminates the structural conflicts of interest that prevent other organizations from naming these failures and proposing solutions of this scope.
"The intelligence gap is real. The evidence base is complete. The framework is specified. What remains is institutional will — and the urgency of 17 deaths every day."
CIF advances Project Donor-Flow and Marie's Lifeline Compensation Act simultaneously — the only organization doing so from a fully independent position. AI optimizes the pipeline; legislation expands the population that enters it. The two interventions compound each other.
Three-time kidney transplant recipient. Author of Unbroken: Rising Above Chronic Kidney Disease. Jeff's lived experience as a patient provides primary-source insight into the systemic failures that published data documents but cannot fully explain. The LDCPR's finding that 50.7% of withdrawals cited "undisclosed" reasons is not a data gap to CIF — it is a description of shame, financial embarrassment, and overwhelm that the system's architecture produced and that Project Donor-Flow's design eliminates.
Registered nurse in radiation oncology. Namesake of Marie's Lifeline Compensation Act — the national legislative framework for direct living donor compensation modeled on New York State's 2022 law. Marie's clinical expertise in care delivery anchors the psychosocial, care partner, and post-donation dimensions of this framework.