Enterprise AI Analysis
Skin tone and clinical dataset from a prospective trial on acute care patients
This seminal dataset, derived from a prospective trial on acute care patients, provides an unparalleled resource for developing AI solutions to address critical health disparities. By comprehensively linking multi-modal skin tone measurements with electronic health record (EHR) data, it offers a unique foundation to investigate and rectify inaccuracies in medical devices like pulse oximeters, ultimately enhancing patient care and promoting health equity across diverse populations.
Executive Impact at a Glance
The ENCODE study delivers a meticulously curated dataset designed to power next-generation AI in healthcare. It provides tangible metrics crucial for understanding and mitigating disparities in patient monitoring.
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Comprehensive Patient Cohort
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Skin Tone Data Points
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High-Resolution Images
OMOP CDM
Standardized Data Model
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The ENCODE study employed a rigorous multi-modal approach to capture comprehensive skin tone data, ensuring robust insights for AI development. Measurements were taken at 16 different body locations using a variety of devices, providing a rich, diverse dataset.
Enterprise Process Flow: Robust Multi-Modal Data Acquisition
EHR Data Extraction
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Skin Data Collection (Multi-Modal)
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Data De-identification
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OMOP CDM Transformation
This dataset offers an extensive array of 167 features per skin location, encompassing administered visual scales, colorimetric, spectrophotometric, and photographic data. Integrated with comprehensive EHR details, it forms a powerful resource for granular analysis.
| Device (Type) | Measurement Space | Key Measurements |
|---|---|---|
| Administered Visual Scales (Card) | Categorical Skin Tone Scales |
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| Delfin SkinColorCatch (Colorimeter) | CIE, Color Index |
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| Konica Minolta CM700d (Spectrophotometer) | CIE, Hunter L*a*b*, Munsell |
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| Processed from mobile devices | Spectrum, CIE |
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The primary objective of this dataset is to facilitate research into health disparities, particularly those related to pulse oximetry inaccuracies. By linking diverse skin tone data with clinical outcomes, it empowers AI development to create more equitable and accurate diagnostic tools.
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AI-Driven Health Equity: Patients with Linked EHR & Skin Tone Data
Impact on Pulse Oximetry Accuracy
Pulse oximetry, a vital tool, is known to have significant racial and ethnic discrepancies, often underestimating hypoxemia in individuals with darker skin tones. These inaccuracies can lead to delayed or inadequate interventions, exacerbating health inequities.
By providing granular, multi-modal skin tone data correlated with EHR information, this dataset enables the development of AI models capable of adjusting or re-calibrating pulse oximeters. This directly addresses the root cause of these disparities, leading to more accurate diagnoses and equitable care.
The 2,438 mobile phone images included are particularly valuable for training computer vision models, allowing for the creation of new tools that can assess skin tone objectively and integrate this information into clinical decision-making workflows. This proactive approach aims to improve patient outcomes and reduce preventable harm.
Advanced ROI Calculator for AI in Clinical Data Analysis
Estimate the potential operational efficiencies and cost savings your enterprise could achieve by leveraging AI for advanced clinical data analysis and disparity research, building on insights from the ENCODE dataset.
Estimated Annual Savings
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Total Hours Reclaimed Annually
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Your AI Implementation Roadmap
A strategic phased approach ensures successful integration of AI-driven clinical insights, from data preparation to continuous improvement.
Phase 1: Data Integration & Harmonization
Focus on integrating diverse clinical and skin tone datasets using the OMOP Common Data Model, ensuring a unified and accessible data foundation for AI. This critical step prepares your existing EHR and new data sources for advanced analytics.
Phase 2: Model Development & Validation
Develop and rigorously validate AI models for disparity detection, predictive analytics, and device calibration using the rich features provided by the ENCODE dataset. This involves iterative training, testing, and refinement to achieve high accuracy and fairness.
Phase 3: Clinical Pilot & Feedback
Implement AI solutions in a controlled clinical environment, such as an acute care unit, to gather real-world feedback. This pilot phase allows for practical evaluation, identification of user needs, and further refinement of the AI tools before wider deployment.
Phase 4: Scaled Deployment & Monitoring
Roll out validated AI tools across the enterprise, with continuous monitoring for performance, patient safety, and equitable outcomes. Establish robust feedback loops and governance structures to ensure ongoing optimization and adaptation.
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