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Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
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The forgetting health system.

Enrico Coiera1

  • 1Centre for Health Informatics, Australian Institute of Health Innovation Macquarie University Sydney Australia.

Learning Health Systems
|June 28, 2019
PubMed
Summary
This summary is machine-generated.

Learning health systems (LHS) must both remember quality processes and forget obsolete practices. Inspired by biology, LHS can actively decommission outdated elements to reduce complexity and inertia.

Keywords:
apoptosisclinical inertiacomplexitydecommissioningstandards

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Area of Science:

  • Healthcare Systems Engineering
  • Health Services Research
  • Learning Health Systems

Background:

  • Forgetting impacts learning by hindering retention of lessons and preventing adoption of new practices.
  • Learning health systems (LHS) require mechanisms to retain quality-improving processes and discard outdated practices.

Purpose of the Study:

  • To explore how learning health systems can strategically "forget" or decommission obsolete practices.
  • To leverage digital infrastructure and biological models for adaptive health service delivery.

Main Methods:

  • Analysis of the dual role of forgetting in learning.
  • Drawing parallels from biological models of programmed cell death for service decommissioning.
  • Identifying new data types for health service improvement.

Main Results:

  • New data forms are needed, including process metrics, human-level workflow data, and multivariate process patterns.
  • Retiring inefficient practices is crucial for reducing system complexity and inertia.
  • Biological models offer insights for self-detecting and internally orchestrated decommissioning of health service elements.

Conclusions:

  • Learning health systems should integrate digital infrastructure for data aggregation and adaptive mechanisms.
  • Adopting biological principles can enable LHS to "actively forget" obsolete practices, fostering continuous improvement.
  • Future LHS design should incorporate self-decommissioning capabilities inspired by programmed cell death.