AI-Assisted Assessment for Trainee Health and Social Care Workers

Scenario title

AI-Assisted Assessment for Trainee Health and Social Care Workers

Target audience

Health and Social Care Lecturer

Problem to solve – Learning Situation

Trainee health and social care workers often face challenges in assessing and managing complex patient cases effectively. They need to develop critical thinking skills, apply theoretical knowledge to practical situations, and make informed decisions in a timely manner.

Overview of scenario

In this scenario, trainee health and social care workers will engage in an AI-assisted assessment to evaluate their competency in handling real-life patient cases. The AI system will provide simulated patient data and support the trainees in analyzing the information, formulating appropriate care plans, and making evidence-based decisions.

Competencies covered from DigCompEdu

• Information and Data Literacy
• Digital Content Creation
• Problem-Solving
• Critical Thinking
• Decision-Making
• Collaboration

Curriculum Construct(s)

Remembering, Understanding and Applying information.

According to Revised Bloom’s Taxonomy (Anderson and Krathwohl, 2001) https://www.researchgate.net/publication/264675976_Transitioning_from_Teaching_Lean_Tools_To_Teaching_Lean_Transformation/figures?lo=1

Scenario description

Trainees are given access to an AI-assisted assessment platform designed specifically for health and social care. The platform contains simulated patient case studies with a range of medical conditions and care needs. Trainees will interact with the AI system to review patient histories, interpret diagnostic reports, analyze symptoms, and make decisions regarding appropriate care and interventions.

Scenario Objectives

• Apply theoretical knowledge and critical thinking skills to real-life patient cases.
• Analyze patient data to identify key issues and formulate appropriate care plans.
• Make evidence-based decisions in a simulated healthcare environment.
• Develop collaboration skills by seeking input from peers and healthcare professionals.
• Enhance digital competencies through the utilization of AI-assisted assessment technology.

Activity Plan

 Introduction (5 minutes):

• The trainer introduces the AI-assisted assessment scenario, its objectives, and the importance of applying theoretical knowledge in practical healthcare situations.

• Platform Familiarization (10 minutes):
  • Trainees are guided through a demonstration of the AI-assisted assessment platform, exploring its features, and understanding how to interact with the AI system.
• Case Study Analysis (30 minutes):
  • Trainees are assigned individual or small group case studies.
  • They review patient profiles, medical histories, diagnostic reports, and other relevant information provided by the AI system.
  • Trainees analyze the data, identify key issues, and formulate appropriate care plans based on their assessment.
• Decision-Making and Collaboration (15 minutes):
  • Trainees engage in discussion forums or collaborative platforms within the AI-assisted assessment system to share their analysis, discuss potential interventions, and seek input from peers and healthcare professionals.
  • They work collaboratively to evaluate different perspectives and refine their care plans accordingly.
• Evaluation and Reflection (10 minutes):
  • Trainees submit their finalized care plans and provide justifications for their decisions.
  • The trainer and/or AI system provide feedback on the trainees’ analysis, decision-making process, and the effectiveness of their care plans.
  • Trainees reflect on their experience, identifying strengths and areas for improvement.

Our notes from practice

Trainee health and social care worker, Sarah, is presented with a simulated patient case study involving an elderly individual with multiple chronic conditions. Using an AI-assisted assessment platform, Sarah reviews the patient’s medical history, laboratory reports, and notes from previous healthcare encounters. She analyzes the data, identifies potential risks, and formulates a comprehensive care plan that addresses the patient’s physical, psychological, and social needs. Sarah engages with other trainees in the online collaboration space to discuss alternative approaches and receive feedback on her proposed interventions. Through the AI system’s feedback and reflection, Sarah gains insights into her decision-making process and learns from her peers’ perspectives, ultimately enhancing her competency in managing complex patient cases.

Note: This scenario promotes a blended learning approach, combining AI-assisted assessment with instructor-led guidance and peer collaboration. The AI system serves as a supportive tool to facilitate learning and skill development, but it does not replace the importance of human interaction and feedback in the trainees’ learning process.