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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Related Experiment Video

Updated: Sep 12, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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Scalable Coaching and Appreciation Feedback for Optimal Learning and Decision-Making (SCAFFOLD).

Zach Landis-Lewis1, Peter Boisvert1, Farid Seifi1

  • 1Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

Scalable Coaching and Appreciation Feedback For Optimal Learning and Decision-making (SCAFFOLD) improves healthcare quality improvement by increasing provider engagement with feedback. This system generates motivational coaching and appreciation messages, enhancing feedback effectiveness.

Keywords:
Audit and feedbackclinical performance improvementmotivation

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Area of Science:

  • Healthcare Quality Improvement
  • Medical Informatics
  • Health Professions Education

Background:

  • Clinical performance feedback is crucial for quality improvement but often suffers from low healthcare professional engagement.
  • Variable evidence exists regarding the effectiveness of traditional feedback methods.
  • Lack of provider engagement is a significant barrier to optimizing feedback impact.

Purpose of the Study:

  • To introduce Scalable Coaching and Appreciation Feedback For Optimal Learning and Decision-making (SCAFFOLD), a precision feedback system.
  • To enhance provider engagement and feedback effectiveness through improved performance feedback reports and dashboards.
  • To assess the feasibility and function of SCAFFOLD in a large-scale quality improvement initiative.

Main Methods:

  • Development of the SCAFFOLD system to generate messages with high motivational potential.
  • Implementation of SCAFFOLD within a national-scale perioperative quality improvement consortium over six months.
  • Analysis of message generation and types (coaching vs. appreciation) for anesthesia providers.

Main Results:

  • SCAFFOLD generated 21,776 messages for 4,550 anesthesia providers.
  • 94% of messages were coaching-focused (20,391), while 6% were appreciation messages (1,385).
  • The system demonstrated feasibility for large-scale message generation.

Conclusions:

  • SCAFFOLD offers a feasible approach for generating tailored coaching and appreciation feedback messages.
  • The system enhances feedback emails, reports, and dashboards to boost provider engagement.
  • Precision feedback systems like SCAFFOLD show promise for improving the effectiveness of quality improvement strategies.