You can't harvest much from AI in healthcare without duly prepared data, as poor input inevitably equates to poor output. It’s the classic “garbage in, garbage out” scenario. In every industry, AI-ready data is a compound concept that is representative of the intended use case and includes all patterns and potential irregularities.
In healthcare, it’s even more complicated with the sensitive nature of patient information and the stringent regulatory requirements in the equation. In this article, our AI and data teams will zoom in on the concept of AI-ready data assets in healthcare and list the essential steps to prepare them for AI use.
What is AI data readiness?
AI data readiness describes the state of data that allows for effective use within AI applications. Preparing data for AI includes more than making that data available. It’s more about aligning that data with the precise requirements of specific AI algorithms.
In healthcare, AI-ready medical data also refers to datasets and metadata that allow for dependable and ethical AI analysis within defined use cases and support sufficient post-model explainability.
What are the essential features of AI-ready data?
The specific state of AI data readiness depends on your destination. For example, AI teams require very different datasets to build an algorithm that predicts readmission risks versus building a gen AI solution to generate patient discharge summaries.
However, if we are to define the core, high-level attributes of AI-ready data in healthcare, these include:
- High-quality — data reflects the true state of patients/medical events and is complete and consistent.
- Relevant — data fits the specific use case and has the necessary variables and features for the model.
- Accessible and interoperable — data is stored in a structured format according to standardized medical terminologies and is accessible through APIs.
- Ethically sound and auditable — data is compliant with applicable regulations, is fetched and used with informed consent, and is provenance-tracked.
- Contextualized — data is accompanied by detailed metadata that backs up post-model explainability analysis.
- Properly labeled — data has labeled target attributes relevant to the specific task or problem the AI model is intended to address.
To be all this, AI-ready healthcare data requires a secure and well-governed data intelligence environment that includes more than just storage and data access capabilities.
What does an AI-ready data environment include?
Successful and ROI-positive AI adoption hinges on having the right data in the right format and under the right conditions. The following components of an AI-ready data environment support the entire data lifecycle, making sure AI-ready data can be easily combined to form knowledge maps that merge medical knowledge with patient data and reduce the cost of model training.
Robust data governance
Data governance is the overarching framework that outlines the processes, policies, standards, and responsibilities to make sure healthcare data is managed effectively, responsibly, and securely. Essentially, it's about making sure information is handled correctly, safely, and legally by the right people.
Data governance also defines what it takes for the organization’s data to meet the external standards mandated by industry associations, government agencies, and other stakeholders. With AI in the mix, data governance becomes more of a stretch for a healthcare organization as it extends beyond traditional data management to include bias mitigation, AI risk management, explainability tools, and other AI-specific components.
Data quality management
Data quality management ensures that a target dataset checks all the boxes, including accuracy, completeness, consistency, uniqueness, validity, timeliness, and fitness for purpose. On a practical level, this component requires a combination of data management tools, including specialized monitoring, profiling, cleansing, remediation, and other tools.
For AI implementation, comprehensive data quality management is not just a prerequisite. It’s a continuous effort focused on refining input data to optimize model training and minimize the risks of unreliable output.
Metadata management
An often overlooked piece of the puzzle, metadata management gives context and structure to the troves of healthcare data to make it more understandable, usable, and interoperable. Dubbed "data about data,” metadata management details provenance, including things like the origin of a patient's lab result, the format of a medical image, or the lineage of data, to bolster data quality, interoperability, and credibility.
Along with these benefits, metadata management is yet another AI implementation enabler that helps AI developers quickly uncover relevant data, improve AI model performance, and select the right data features for model training.
Data security and privacy strategy
In healthcare, where data is a minefield of sensitive personal information, a solid data security and privacy strategy is imperative for developing ethical AI systems. Without it, the positive transformation potential of AI is overshadowed by its inherent risks, exposing healthcare organizations to data breaches and compliance violations.
Besides traditional technical, administrative, and physical safeguards, an AI-ready data security strategy should include the following elements:
- Privacy-preserving AI techniques such as federated learning, differential privacy, and others.
- Data minimization and de-identification, including purpose-driven data collection, synthetic data generation, and others.
- Granular access controls dedicated to restricting access to AI models and data.
- Consent management mechanisms to make sure patients explicitly agree to the use of their data in AI models.
- Regular audits and vulnerability assessments of AI systems.
Data lineage tracking
Data lineage documents and tracks the flow of data from its origin to its destination, including where it stems from, how it transforms as it travels through different systems, and where it ends up. Such a detailed history log allows AI-powered solutions to understand the data context, necessary to churn out more reliable outputs and to generalize better to new data.
From a user’s perspective, data lineage allows for a deeper understanding of the AI’s decision-making process, making AI initiatives more transparent.
Why the state of AI data readiness is crucial in healthcare
Investing in AI data readiness isn’t about AI only. It’s an investment in the entire healthcare organization's strategic advantage. By tackling long-standing challenges such as data fragmentation, inconsistent data formats, and a lack of standardized medical terminologies, healthcare players will not only pave the way for hassle-free AI adoption but also become a step closer to becoming a more agile, cost-effective, and patient-centric organization.
Scalability and reusability of an AI solution
AI-ready data is a higher-value form of data put together into a unified knowledge base. Acting as a single source of truth, this knowledge base becomes a reusable asset for various AI applications, saving companies the cost and effort of recreating data pipelines and models for each individual use case.
Better AI performance
Healthcare, with its complex medical concepts and high stakes involved, has high performance standards for AI. Clean and standardized data gives AI models the necessary context and structure to draw accurate conclusions from data, whether it’s disease prediction or personalized treatment recommendations.
Regulatory compliance and ethical AI use
The state of AI readiness for healthcare data implies that an organization has a data governance framework in place that incorporates policies, processes, and controls dictated by regulatory requirements such as HIPAA and GDPR. Data lineage tracking, which is a part of the data governance strategy, also provides a clear audit trail for tracing data flow and spotting potential compliance violations.
AI-ready data comes from rigorous data curation and preprocessing, which mitigate potential biases in the data and make sure the AI model turns out fair and equitable.
Patient safety
In healthcare, AI models often have a direct impact on patients’ well-being. Good data translates into a positive impact, while poorly prepared data leads to inaccurate predictions and can potentially put patients’ well-being in danger. Well-prepared, contextually rich healthcare data also enables an AI model to interpret medical data correctly and provide reliable medical recommendations.
Operational efficiency and cost reduction
Every year, organizations lose an average of $12.9 million due to poor data quality. In healthcare, the cost of incomplete or incorrect data is even higher due to the ripple effect on admin operations, clinical decision-making, and payer-provider communication.
By doubling down on data readiness alone, even without immediate AI implementation, healthcare providers can eliminate data silos and reduce manual data entry that eats into operational efficiency. Clean and consistent data also drives cost savings across billing and claims processing, clinical decision-making, resource management, and other healthcare areas.
How to evaluate AI data readiness? Five core criteria
According to Gartner, 50% of artificial intelligence projects will be discontinued by 2026 if they don't have AI-ready data to back them up. With only 18% of healthcare organizations being prepared for AI implementation, the healthcare industry is likely to look at an even higher AI project failure rate. To mitigate the risk of AI flops, healthcare organizations must proactively assess their data readiness according to the following criteria.
Data availability
- How easily can AI teams access relevant data? Are there any barriers to be overcome?
- Does the data need additional preparation or transformation?
- To what extent is the data readily accessible, retrievable, processable, and documented to be effectively picked up by AI algorithms?
Data volume and diversity
- Is there enough data to meet the data requirements of a specific AI project?
- Is the data representative of real-world scenarios, and does it have the necessary features?
- Do you know how the data was generated and collected?
Data quality and integrity
- How accurate and complete is the data?
- Are there any established processes to validate its quality and ensure it’s error-free?
- Does the available metadata allow for sufficient context?
Data governance
- Is there a comprehensive data governance framework in place that details standards for data ownership, access, and usage?
- Does your organization have systematic processes for data quality monitoring and improvement?
- Are there explicit mechanisms for data collaboration and sharing?
Data ethics and responsibility
- Does the data comply with relevant regulations, and can it continuously do so?
- Are there general and AI-specific data protection and access safeguards?
- Has the organization obtained informed consent from patients to use their data?
- Has the organization adopted processes to ensure fairness and equity in AI outcomes?
- Are there mechanisms to explain AI decision-making and hold it accountable?
How to get your data ready for AI
Although you can’t make data AI-ready up front, you can create a lasting structure for upcoming AI initiatives by following a series of AI data management practices implemented with a phased approach.
Conduct a data audit
As an initial preparation step, organizations must run a thorough analysis of their current data infrastructure and size up their ability to meet the unique requirements of AI model development and deployment.
An AI-focused data audit is qualitatively different from a traditional data evaluation. At Orangesoft, we account for aspects crucial for AI's effectiveness and ethical considerations, such as missing data points, metadata analysis, feature engineering readiness, bias assessment, and other data characteristics critical for AI success.
Develop a data transformation strategy
Based on the insights obtained from a data audit and the present level of data maturity, organizations outline the specific steps needed to transform data from raw to AI-usable. To become easily accessible for AI solutions, data must go through a series of transformations that may encompass such activities as metadata documentation, cleansing, standardization, and others.
In healthcare, the data transformation strategy must also align with established data security and privacy practices, complemented with security protocols required by law.
🔎 In a recent project, our team handled AI data readiness assessment and data transformation for a healthcare provider who struggled with data fragmentation and inefficiencies in care coordination. Our AI-driven curation resulted in a 60% reduction in patient record retrieval and enabled the implementation of predictive analytics for patient care.
Establish or evolve a data governance and management framework
Moving beyond the data preparation phase, organizations lay out a governance framework to ensure AI readiness is sustainable for the long term and data is managed as a strategic asset. Unlike traditional data management, AI-focused data management is more dynamic thanks to adaptable data pipelines, shared data environments, and other practices that support the exploratory nature of AI development.
Unlike traditional data management, AI data management makes a point of documenting the data context, lineage, or potential uses to make it easier for AI teams to understand the data and identify relevant features.
Foster a strong data-centric culture
No matter how prepared, polished, and AI-activated the data is, it remains inert without a culture that encourages its active use. A data-centric culture makes sure that every team player in the organization understands the value of data and how it works to drive better care outcomes and operational efficiency.
On a practical level, data-centric culture manifests as data literacy and responsible data practices woven into the very fabric of the organization, including specialized training and education, transparent data communication, and more.
Implement continuous improvement and monitoring
AI-ready data is not a set-and-forget concept. To keep data AI-ready, organizations require processes that enable ongoing evaluation and refinement of data quality, governance, and infrastructure based on existing and upcoming AI use cases. This constant improvement loop will ensure the data remains aligned with evolving AI requirements and maintains its value over time.
Getting your data AI ready starts with Orangesoft
The value of AI in healthcare stands proportional to the quality of data it’s fed on. AI-ready data is much more than cleanliness, structure, or quality. It’s about developing a comprehensive ecosystem that prioritizes quality, relevance, security, and explainability of information. Reinforced by data governance, data quality management, contextual metadata handling, and solid security strategies, this ecosystem will guarantee the responsible and effective deployment of artificial intelligence in healthcare.
If your organization is struggling with laying a data-driven path to AI adoption, Orangesoft’s team can guide you through every step of the process, from initial data audits and a roadmap to the development and implementation of custom AI solutions.