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Updated: Jun 6, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Complexity in practice: understanding primary care as a complex adaptive system.

Beverley Ellis¹

¹School of Public Health and Clinical Sciences, University of Central Lancashire, Preston, UK. BSEllis@uclan.ac.uk

Informatics in Primary Care

|November 17, 2010

Summary

This summary is machine-generated.

Effective quality improvement in primary care organizations (PCOs) relies on both social interactions within multidisciplinary networks and robust information technology (IT) systems. This research presents a socio-technical model for enhancing clinical governance.

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Last Updated: Jun 6, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Area of Science:

Healthcare Management
Health Services Research
Quality Improvement Science

Background:

Empirical research on quality improvement (QI) in primary care.
Case studies on implementing clinical governance in two primary care organizations (PCOs).

Purpose of the Study:

Describe a novel socio-technical model for effective quality improvement.
Enhance understanding of clinical governance implementation.

Main Methods:

Literature review, surveys, in-depth interviews, and participant observation.
Purposively sampled case studies within a social constructionist perspective.
Contextualized clinical governance origins and informatics-enabled collaborative models.

Main Results:

Quality improvement in PCOs is driven by social interactions in multidisciplinary networks.
Information technology systems make aspects of care quality explicit.
Complex relationships between individuals and groups facilitate QI.

Conclusions:

Clinical governance improvement necessitates both social interactions and informatics.
Results exemplify emergent properties of complex adaptive systems.
Socio-technical lessons learned should guide future management strategies.