Data Strategy
Data Acquisition
A meticulous approach should be taken in gathering raw data that adheres rigorously to industry standards and specific project requirements. Your strategic partner should be able to identify the right data source, toolchain requirements, data module, and server requirements.
Post that, the right network bandwidth would be required to correctly analyse data.
In the healthcare and pharma sector, acquiring data like Digital Health Records (EHR), pathology and radiology reports, patient vitals, and demographic details is crucial. It’s essential to ensure this data is comprehensive, accurate, and in a format conducive to advanced AI analysis and predictive modelling.
Data Exploration
During this stage, the primary goal is to gain a comprehensive understanding of the data while evaluating its quality and cleanliness. With the right statistical analysis and visualisation techniques, the strategic partner should be able to unearth valuable insights and discern patterns thus enabling you with data-driven decision-making.
The lack of comprehensive disease data and the underrepresentation of rare symptoms pose significant challenges in data exploration for specific illnesses. This gap often results in discrepancies when comparing source data with individual patient records, which can adversely affect the precision and efficiency of AI-enabled diagnostic processes.
Data Pre-Processing
Data quality is paramount. Through rigorous data pre-processing procedures (Data Cleaning, Data Integration, Data Packaging, Data Sampling), the strategic partner should ensure that the data is pristine, accurate, and primed for advanced analysis and modelling, enhancing the reliability of the AI-driven solutions for the next stage.
To further refine data quality, debiasing plays a critical role, for example, in contexts where different medical devices yield varying image clarity. It’s essential to normalise data from diverse sources, such as radiology images from CT and MRI scans, which may have different resolutions and contrast enhancements. Also, the clinical data may be from patients having varying clinical history and co-morbidities. Unless the clinical background of individual is taken into consideration, the analysis may lead to erroneous results. Thus, debiasing process ensures uniformity and comparability across datasets, which is crucial for accurate AI modelling.
Data Confidentiality
In healthcare and pharmaceutical sectors, data confidentiality is crucial for maintaining patient trust and safeguarding sensitive information. This encompasses protecting personal health details and proprietary pharmaceutical data. Maintaining confidentiality is not only a legal requirement but also a fundamental ethical practice, crucial for patient safety and the integrity of medical research and commercial pharmaceutical interests.
Furthermore, data anonymization is key for confidentiality in healthcare and pharma. It involves removing personal identifiers from patient and research data, ensuring privacy while enabling AI to extract valuable insights. This process is vital for complying with privacy laws and maintaining trust and integrity.
Watch out for
- Insufficient Data Quality and Quantity
AI models require high-quality, relevant data for training and validation. If the data used is incomplete, outdated, or biased, it can lead to inaccurate predictions and hinder the success of the AI deployment. Data Source and Data Collection mechanism must be planned in advance keeping the project timelines in mind.
- Data Drift
Data drift is the alteration of data distribution over time, affecting AI model accuracy. It happens when incoming data differs from training data, necessitating continuous monitoring and retraining to maintain AI system effectiveness.
- Compliance with Regulations
Adherence to legal frameworks like HIPAA or GDPR is essential. These laws dictate stringent standards for data protection and confidentiality in healthcare.
- Access Control and Security Protocols
Implement strict access controls and robust security measures. This includes encryption of data and ensuring only authorized personnel can access sensitive information, protecting against unauthorised use and potential cyber threats.
AI Strategy
Understanding Quantitative Objectives
In the initial phase, the emphasis lies on developing a profound comprehension of the distinct objectives within your company. This deep understanding ensures seamless alignment between AI solutions with the overarching business goals and creative vision. It’s imperative to define the acceptable range for precision and accuracy of the AI models from the outset, with the strategic partner evaluating their feasibility.
For instance, in healthcare and pharma, reducing false negatives to zero is a critical goal, even if it means tolerating a 10-15% rate of false positives. Such precision is vital for reliable diagnostics. All AI-generated results must be clinically correlated to ensure their relevance and accuracy. While a certain degree of leeway can be afforded to false positives, the absolute minimization of false negatives is imperative to prevent missed diagnoses and ensure patient safety.
Watch out for
- Lack of ROI Clarity
AI initiatives often require significant investments. If the expected return on investment (ROI) is unclear or not well-defined, it can be challenging to secure funding or implement the project at scale. A clear understanding of the expected ROI is to be agreed upon between the strategic partner and the customer before starting the project.
- Scope Creep
Expanding the scope of the AI project beyond the initial objectives can lead to increased costs, longer timelines, and a higher risk of failure. It’s essential to maintain a clear focus on the original goals.
Model Building, Testing and Validation, Selection
AI experts meticulously construct model frameworks using advanced statistical procedures (Classification, Clustering, Regression, Association Analysis). Model training is carried out for these frameworks using training data sets. Error analysis and model validation ensure AI model fine-tuning and the right model selection from various candidate models for desired performance and accuracy.
The model building phase must include validation with diverse clinical data, covering both known and new scenarios. It’s vital to test the AI models in different clinical environments and to conduct field trials with medical professionals. These steps ensure the model’s accuracy and relevance in real-world settings, while direct feedback from healthcare experts aids in refining and selecting the most effective model.
Watch out for
Lack of Scalability and Integration
AI solutions must integrate seamlessly with existing systems and processes. If an AI model or system cannot integrate and scale effectively with the broader business environment, it may not be suitable for production.
Model Drift
Model drift occurs when a previously well-performing AI model starts making less accurate predictions due to changes in data patterns or external factors. Addressing model drift involves constant monitoring, retraining, and adapting the model with new data, maintaining its relevance and performance in evolving environments.
Production Deployment
The selected model is deployed on the production environment. Continuous monitoring of the model’s functioning with the live data sets is required to ensure that it meets the defined objectives, use cases, and accuracy. To address this, the system must be equipped for constant monitoring and capable of evolving the model based on ongoing feedback. This adaptive approach is crucial for maintaining model effectiveness and accuracy in the dynamic healthcare environment.
Watch out for
- Inadequate Change Management
Implementing AI often requires changes in workflows and processes. If there’s insufficient change management in place to guide employees through these transitions, resistance and project failure can occur. There must be a gradual transition plan in place from existing to new methods of operation.
- Lack of Continuous Monitoring and Optimisation
Failure to establish mechanisms for continuous improvement can result in the degradation of results over time. Implementing the right MLOps system is essential to enhance AI system performance, ultimately helping to reduce the Total Cost of Ownership.
Author
Abhijit De
AI Technology Head, Tata Elxsi
Abhijit De brings over two decades of rich IT industry experience, with 18 of those years dedicated to Tata Elxsi. His expertise spans delivery management, client relations, operations, and R&D. With international experience, he drives revenue growth and operational efficiency, while ensuring quality and client satisfaction.