The AI Training

Healthcare Industry Path

The appearance of artificial intelligence (AI) as a tool for better health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health. Health care professionals and organizations must be prepared with practical knowledge to change and evolve to adopt AI.  This learning paths is devoted to on teaching health care professionals the intricacies of building an AI strategy, data mining, design an effective AI model, create an AI culture, governance practices , evaluations of AI applications, and evaluate how to apply Natural Language Processing (NLP) in your organization. These practical knowledges will be essential for healthcare professionals to help them positioning themselves in the marketplace.

Learning Path

The Learning Path has five (5) modules which enables you and your organization to create an AI holistic strategy to drive long-lasting business impact.

Module 1: AI Strategy

Module 1: AI Strategy
Module 1: AI Strategy

The healthcare industry is evolving rapidly with large volumes of data and increasing challenges in cost and patient outcomes. Early adopters of Artificial Intelligence (AI) in the healthcare space are gaining the benefits in terms of patient care and adding to their bottom line results, and everyone is taking notice. These companies are using AI for several scenarios including managing claims, detecting fraud, improving clinical workflows, and predicting hospital acquired infections. This module is devoted about strategic approach, and challenges of implementing an AI Strategy.

You will learn:

  • Evaluate a strategic approach to an AI-enabled organization.
  • Describe Industry 4.0 key components and functionality with AI that will impact health industry.
  • Evaluate go-to-market strategies using AI SWOT (strengths, weaknesses, opportunities and threats) model analysis.
  • Discover how AI can support your organization to improve efficiencies, cut costs, provide customer insights, and generate new product ideas.
  • Identify the roadblocks and steps to take for using artificial intelligence.

Module 2: AI Technology

Module 2: AI Technology
Module 2: AI Technology

Health care professionals and organizations must build their capacity and capabilities to understand and appropriately adopt AI technology. In this module, you'll gain practical knowledge that will enable you to apply AI in healthcare including, data mining and the readiness of algorithms for clinical practice, AI design life cycle, The Institute of Electrical and Electronics Engineers (IEEE )P7003 Algorithm Bias Consideration standard , Health Level Seven (HL7) Standard, machine learning (ML),  supervised & unsupervised learning, reinforcement learning, and explainable AI (XI).

You will learn:

  • Evaluate the technical processes and best practices for developing and validating AI models, including choices related to data, variables, model complexity, learning approach, set up, and the selection of metrics for model performance.
  • Explain the key issues and best practices for deploying AI models in clinical settings, including the software development process, the integration of models in health care settings, and approaches for model maintenance and surveillance over time.
  • Identify problems healthcare providers face that machine learning can solve.
  • Analyze how AI affects patient care safety, quality, and research.
  • Recognize what AI system needs to be explained and approaches for ensuring understanding by all members of the health care team.
  • Evaluate appropriate approaches for involving consumers and clinicians in AI/machine learning prioritization, development, and integration, and the potential impact of AI/machine learning algorithms on the patient-provider relationship.
  • Explain data quality, access, and sharing, as well as the use of both structured and unstructured data and the integration of non-clinical data is critical to developing effective AI tools.
  • Evaluate the process of deploying AI in clinical settings.
  • Describe how using AI to Improve Electronic Health Records {HER).
  • Describe stages of an EHR system development.
  • Explain the benefits of  the Integrating EHR/EMR with telemedicine features, remote patient monitoring, medical practice. management and other healthcare providers.

Module 3: AI Culture

Module 3: AI Culture
Module 3: AI Culture

There is a rapidly emerging need to ensure that all health care professionals have the capabilities required to navigate the complex world of AI within the health care setting. A successful AI strategy must consider cultural issues as well as business issues. Becoming an AI-ready organization requires a fundamental transformation in how you do things, how employees relate to each other, what skills they have, and what processes and principles guide your behaviors.

You will learn:

  • Explain new quintuple aim (humanity-centered design framework) to tie specific actions for change to improved outcomes in healthcare environments.
  • Describe some of the unintended consequences of AI in health care work processes, culture, equity, patient provider relationships, and workforce composition and skills, and offers approaches for mitigating the risks.
  • Recognize the challenges of an AI-enabled organization.
  • Recognize how ADKAR framework can help you lead organizational change.
  • Demonstrate how to apply the AI Maturity Model Assessment.
  • Describe the characteristics that foster an AI-ready culture.

Module 4: AI Governance

Module 4: AI Governance
Module 4: AI Governance

AI is enabling healthcare professionals to analyze health data quickly and precisely, and leading to better detection, treatment, and prevention of a multitude of physical and mental health issues. However, AI’s ability to interpret data relies on processes that are not transparent, making it difficult to verify and trust outputs from AI systems, it’s imperative that organizations have governing practices in place to ensure that it’s used responsibly. Responsible use of AI starts with organizations establishing their own guiding principles, then choosing and operationalizing a system of governance. We recognize that each organization have their own position on responsible AI, but we’re hopeful that our view will serve as a helpful starting point as others embark on their own AI journey.

You will learn:

  • Recognize best practices for development and deployment of AI systems in healthcare.
  • Identify the appropriate regulatory mechanism for AI/machine learning and approaches for evaluating algorithms and their impact.
  • Evaluate the requirements imposed on AI systems designed for health care applications legal and policy issues related to privacy and patient data.
  • Describe the governance principles ties into the broader organizational and clinical priorities.
  • Evaluate ethical framework to design digital mental health products.
  • Describe responsible AI global police framework.
  • Explain barrier to data sharing in public health.
  • Explain the  principles and practical considerations for integrating AI into clinical workflows.
  • Evaluate the technical, cognitive, social, and political factors in play and incentives impacting integration of AI into health care workflows.

Module 5: AI Applications

Module 5: AI Applications
Module 5: AI Applications

The emergence of AI  as a tool for better health care offers first-time opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health.  AI simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. Whether it's used to find new associations between genetic codes or to drive surgery-assisting robots, AI is reinventing and reinvigorating, modern healthcare through machines that can predict, comprehend, learn and act. This module explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare applications.

You will learn:

  • Evaluate how to apply Natural Language Processing (NLP) in your organization.
  • Evaluate AI solutions to aid in health care administration processes.
  • Identify common challenges and pitfalls in developing machine learning applications for healthcare.
  • Categorize AI applications by the primary task and main stakeholder.
  • Explain healthcare applicability of internet of things (IOT).
  • Describe Internet of things (IoT) integration with Electronic Health Record.
  • Evaluate how AI applications have been used to impact healthcare.
  • Evaluate the best practices of AI applications to promote fair and equitable healthcare solutions.
  • Identify problems healthcare providers face that machine learning can solve.
  • Describe the use AI to Improve Electronic Health Records (HER).

Expert Instruction

Instructor-led program delivered by Abel Pena-Fernandez an AI advisor, IEEE Senior Member.

Length of Program

Twenty (20) hours total per Industry path - 2 - 3 hours per week


$2448.00 per Industry Path per person - Five (5) Learning Path Modules

Contact Us for details and schedule.

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