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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Related Experiment Video

Updated: Dec 7, 2025

Movement Retraining using Real-time Feedback of Performance
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Published on: January 17, 2013

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Improving measurement feedback systems for measurement-based care.

A Paige Peterson1, Corey Fagan1

  • 1Department of Psychology, University of Washington, Seattle, USA.

Psychotherapy Research : Journal of the Society for Psychotherapy Research
|September 25, 2020
PubMed
Summary
This summary is machine-generated.

Measurement feedback systems (MFS) impact clinical decisions differently based on patient progress. Graphs were most influential, with clinicians preferring to use all MFS features for better patient care.

Keywords:
clinical decision supportfeedbackmeasurement-based caremeasurement-feedback systempatient-reported outcome measureroutine outcome monitoring

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Area of Science:

  • Health Information Technology
  • Clinical Decision Support
  • Measurement-Based Care

Background:

  • Measurement feedback systems (MFS) are key to measurement-based care.
  • The specific impact of individual MFS features on clinicians is not well understood.

Purpose of the Study:

  • To evaluate how different MFS features influence clinicians' patient progress assessments.
  • To determine the effect of MFS features on treatment decisions across various clinical scenarios.

Main Methods:

  • 299 clinicians were randomly assigned to view 6 combinations of MFS features (graph, trajectory line, alert, treatment suggestions).
  • Clinicians assessed 3 clinical vignettes representing patient deterioration, no progress, and remission.
  • Data were collected via post-vignette and concluding questionnaires.

Main Results:

  • MFS features significantly altered clinicians' assessment accuracy, treatment change likelihood, and choices.
  • The most impactful feature varied by clinical scenario.
  • Graphs had a greater influence than other features, and most clinicians preferred using all features.

Conclusions:

  • Individual MFS features exert differential effects on clinician assessments and decisions.
  • The clinical status of the patient moderates the impact of MFS features.