Useful steps to consider when you want to implement AI for Pharmaceutical Logistics
1. Initial Idea: Identifying Opportunities for AI Implementation
Objective: Identify areas within pharmaceutical manufacturing and logistics operations where AI can bring significant benefits, such as enhancing efficiency, optimizing supply chain management, improving predictive maintenance, and ensuring regulatory compliance. The below steps can be followed:
Gap Analysis: Conduct a thorough assessment of current processes, challenges, and opportunities within the organization's manufacturing and logistics operations. Set up your Data strategy in which areas AI can improve your organisation like for example:
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Stakeholder Consultation: Engage key stakeholders, including manufacturing- & logistics managers, supply chain experts, regulatory compliance officers, end-users (doctors/nurses) and IT professionals, to understand their pain points and potential areas for AI intervention. Ensure you have the vision and objectives and defences clear on the following topics:
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Market Research: Explore off the shelve AI applications and solutions tailored made to the pharmaceutical manufacturers and related industry. Consider case studies, whitepapers, and industry reports from respectable peer review studies and sources.
Alternatively you can build an AI yourself – however this is a whole complete new project, due to your organisation needs to have the (AI) experts inhouse already.
Prioritization: Prioritize AI use cases based on their potential impact, feasibility, and alignment with organizational goals and regulatory requirements.
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2. Selecting the Right AI Solution: Ensuring Effectiveness and Compliance
Objective: Choose AI solutions that meet the specific needs of the organization while ensuring regulatory compliance and mitigating biases and you can follow the following steps:
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Requirements Definition: Clearly define the functional requirements, technical specifications, and regulatory constraints for the desired AI solution. Simply put, define your medical context in which AI will aid you. Using a patient journey map[1] on your service/product will help you identify where and in which stages AI could help (Research, testing trials, manufacturing, diagnostic, quality control, administrative tasks, analytics and so on)
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Vendor Evaluation: Evaluate AI vendors based on their experience in the pharmaceutical industry, track record of successful implementations, compliance with relevant regulations (e.g., FDA, EMA), and approach to bias mitigation.
Be aware that regulators will demand you have your risk mitigation and profiles in order no matter what solution you choose. The “AI maturity” can be seen in these forms and amount of risk and impact varies:
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Proof of Concept (PoC): Conduct a PoC with selected vendors to assess the feasibility and performance of their AI solutions in real-world scenarios. Ensure that PoC includes rigorous testing for bias and fairness and ensure compliance and mitigate your risks.
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Compliance Review: Collaborate with legal and regulatory experts to ensure that the selected AI solution complies with data privacy regulations (e.g., GDPR), industry standards (e.g., Good Distribution Practice), and ethical guidelines (e.g., IEEE AI Ethics Initiative).
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3. Testing and Implementation: Deploying AI Solutions Safely and Effectively
?Objective: Deploy AI solutions in a controlled manner, ensuring minimal disruption to operations while maximizing benefits, with the following steps
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Data Preparation: Cleanse and preprocess data to ensure accuracy, completeness, and relevance for AI model training. Use techniques such as data anonymization and de-identification to protect sensitive information. Remember the AI is only as smart as good as the data your train/feed it, if you put: “garbage in your output will be garbage as well”
Model Development: Develop AI models using appropriate algorithms (e.g., machine learning, deep learning) and techniques (e.g., supervised learning, reinforcement learning) based on the nature of the problem and available data.
Testing and Validation: Conduct thorough testing and validation of AI models using diverse datasets and realistic scenarios. Evaluate model performance metrics such as accuracy, precision, recall, and F1-score.
Pilot Deployment: Roll out the AI solution in a pilot environment or limited production setting to assess its performance, scalability, and user acceptance. Collect feedback from end-users and stakeholders for iterative improvement. Make sure your evaluate and do correction, before the AI model goes full scale.
Full-scale Deployment: Gradually scale up the deployment of AI solution across the organization's logistics and airfreight operations, ensuring proper training, documentation, and support for end-users.
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4. Follow-up, Monitoring, and Control Mechanism: Continuous Improvement and Risk Management
Objective: Establish mechanisms for ongoing monitoring, evaluation, and refinement of deployed AI solutions to ensure effectiveness, compliance, and fairness by the following steps:
Performance Monitoring: Implement monitoring tools and dashboards to track the performance of AI models in real-time, including key metrics such as accuracy, latency, and resource utilization.
Feedback Loop: Solicit feedback from end-users, domain experts, and stakeholders to identify issues, challenges, and opportunities for improvement. Incorporate feedback into iterative model updates and refinements. Additionally ensure mock recalls or simulation deviations to test if the AI is still doing what it suppose to do.
Bias Detection and Mitigation: Continuously monitor AI models for biases and unfair outcomes, leveraging techniques such as fairness-aware algorithms, bias detection frameworks, and diverse training data. Also to ensure the AI links relevant criteria and avoid the trouble with early models, where Wolfs were misidentified[2].
Compliance Audits: Conduct periodic audits and reviews to ensure that deployed AI solutions remain compliant with regulatory requirements, industry standards, and ethical guidelines. Create with AI as aid towards a culture of always doing it well, also when no one is looking. Because patient safety must be of utmost importance at all times.
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Training and Education: Provide ongoing training and education to employees about AI technologies, best practices, and ethical considerations. Foster a culture of transparency, accountability, and responsible AI usage within the organization.
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Conclusion:
Go ahead and use my plan as a comprehensive framework for a start of AI implementation. I've covered some critical key stages starting from idea to deployment and monitoring. Emphasizing on the importance of bias mitigation, and continuous improvement, which are crucial considerations in highly regulated industries like the pharmaceutical manufacturing, logistics and healthcare providers and unfortunately easilly overlooked and forgotten.
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Look forward from hearing from you all and in case my expertise can be of help, feel free to reach out.
Great advice! Planning is key for a successful AI implementation. ??