Center for Cognitive Bias, Heuristics and Human Behavior

This six week online continuing education course identifies, explains, analyzes, evaluates and applies tools to manage cognitive bias, heuristics and unethical behaviors associated with Generative (GenAI) and Machine Learning (MLAI) artificial intelligence in healthcare, business, finance, engineering, policy making, manufacturing, marketing, research, communications and criminal justice. Registered students learn to recognize, eliminate, or control the prominent existing unconscious cognitive bias temptations that isolate GenAI and MLA data from true science-based judgment in decision making and flaw the implementation of decisions made.

Massive quantities of data, along with powerful analytic tools, seek to improve the information available for effective decision-making. However, even with copious data available, sophisticated tools frequently fail to eliminate uncertainty and inaccuracy in data interpretation and application. Private, public and government markets are increasingly volatile to the variations of artificial intelligence, which often lack nuance and complex contextual understanding of real-world situations, especially those involving human emotions and ethical considerations. Competition changes rapidly, customers migrate, new markets appear, cultures and policies evolve, modern technologies replace old ones, and application and outcome expectations shift or disappoint. Insights from raw data, no matter the amount or source, are not only fraught with compromise but often are flawed with a wide range of unrecognized cognitive biases. The intuition and judgement of competent executives, healthcare providers, sales managers, research scientists, financial administrators, educators and market analysts often find themselves trapped into viewing copious data through frames of reference limited by cognitive bias and ethical licensing. Decision makers unquestionably require practical tools presented in this course to combine the most informative understanding of applied cognitive bias recognition and control when interpreting and applying informative descriptive and predictive analytics involved in utilizing, applying, integrating, and interpreting GenAI and MLAI. From the foundational theories of generative adversarial network learning to transformative applications in healthcare, marketing and education, this course offers a comprehensive exploration of AI's multifaceted vulnerabilities, regardless of its source, to multiple cognitive biases and unethical applications. The goal of this course is to encourage individual self-reflection on the dynamic and rapidly evolving landscape of AI, providing students with both foundational knowledge and forward-looking insights in applications with best practice remedies against confounding cognitive bias and unethical applications.

As technology advances, it is crucial to accurately manage outcomes and evidence-based best practices against ethical dilemmas, privacy concerns, failed outcomes and unwanted societal implications. The course thoughtfully engages students with these issues through case studies, real-life examples, theoretical and practical insights, group discussions, innovative instructor podcast multimedia material and timely applicative instructor material.

Roles benefiting from CBDMSAI 6000 Artificial Intelligence Course:

• Senior executives, management professionals and scientific researchers who seek to deepen their understanding of the impact of human behavior and cognitive bias on decision making in their areas of expertise.
• Data scientists and engineers that extract insights from data to support decision-making using statistical and analytical methods.
• Internal/external consultants who identify opportunities for AI use in an organization.
• Legal experts who provide predictive insights into case and jury outcomes for client counseling.
• Product designers who automate tasks such as design optimization, simulation and testing to improve product quality and reduce development time and costs.
• AI ethicists and sociologists who are involved with ethical challenges, ensuring accountability and shaping AI futures.
• Policymakers and regulators who develop informed guidelines for GenAI and MLAI usage.
• Instructors and students exploring GenAI and MLAI bias/ethics.
• Information technology experts compelled to apply the latest advancements in cognitive bias remedies.
• Business leaders seeking to implement GenAI and MLAI responsibly.
• Healthcare providers dedicated to bringing the best outcomes to their patients.

This six week online continuing education certification course identifies, explains, analyzes, evaluates and manages cognitive bias/heuristic behaviors in decision making associated with healthcare administration, healthcare policy development, healthcare delivery and access, biomedical ethics, domestic and global public health, healthcare communications, pharmaceutical research, advertising and sales, healthcare service management and supply chain distribution, home health care services, telemedicine utilization, OTC medicine marketing and distribution, Generative (GenAI) and Machine Learning (MLAI) artificial intelligence healthcare applications, and healthcare scientific translational research.

The course guides the student through scientific evidence in the fields of experimental psychology, neuroscience, neurophysiology, behavioral economics, business analytics and psychosociology in achieving recognition, understanding and control of the multiple cognitive biases that impact decision-making strategies in healthcare. The student will learn to identify situations in which unrecognized cognitive bias, heuristics and ethical challenges arise in not only patient, client, community-provider delivery interactions but also in health-related supporting processes of recruiting, selecting, hiring, promotion and team management. The student will also learn strategies to identify how cognitive biases lead to faulty conclusions and decisions that negatively impact patient outcomes and organizational goals, including diminished quality of care, misdiagnosis and inadequate treatment plans. By the course’s completion, students will be able to identify and discuss the influence of bias on clinical algorithms employed for patient care, safety, and risk management. The course will also present multiple strategies that identify common harmful healthcare cognitive biases. This course also provides effective cognitive bias shared learning management tools to gain stakeholder support of healthcare business marketing, service delivery, third party and fee for service reimbursement and quality improvement plans throughout their implementation and ongoing lifecycle. Specific course outcomes include effective cognitive bias management in innovative healthcare policy development, translational healthcare research and product application, establishment of implementation requirements for quality improvement (QI), minimized resistance to (QI) acceptance, pharmaceutical utilization critical thinking, impact of GenAI and MLAI on predictive analytics, personalized medicine, robotics, telemedicine and patient access. The course thoughtfully engages students with these issues through case studies, real-life examples, theoretical and practical insights, group discussions, innovative instructor podcast multimedia material and timely, applicable instructor material.

Roles benefiting from CBDMHLTH 6008 Healthcare Delivery Course:

• Physicians, nurses, nurse practitioners, physician assistants, pharmacists, dentists, chiropractors, physical/occupational therapists, Allied Health professionals for the diagnosis, assessment, and treatment involving patient education, prevention and care.
• Healthcare/pharmaceutical executives and researchers involved with developing strategies for drug development, production and distribution.
• Healthcare administration professionals for human resource oversight and quality improvement.
• Healthcare advocates are involved with patient care.
• Epidemiologists and public health professionals for population care.
• Healthcare laboratory technicians for patient and research support
• Healthcare legal experts for ethical and legal compliance and protecting patient rights.
• Healthcare policy professionals for analysis, development, implementation and negotiation of new and existing policies.
• Pharmaceutical representatives for presenting product scientific and clinical information to healthcare professionals.
• Healthcare analysts for identifying data patterns and trends.
• Healthcare communication specialists for educators, podcasters, journalists and mass media professionals
• Insurance and third-party payer professionals are involved with being intermediaries to ensure appropriate payment and reimbursement for healthcare services.