Strategic planning in medical education: enhancing the learning environment for students in clinical settings.
BACKGROUND
The 1999 Cambridge Conference was held in Northern Queensland, Australia, on the theme of clinical teaching and learning. It provided an opportunity for groups of academic medical educators to consider some of the challenges posed by recent changes to health care delivery and medical education across a number of countries.


OBJECTIVE
This paper describes the issues raised by the practical challenges posed by the current environment and how they might be addressed in ways that could promote more effective learning in clinical settings.


METHODS
A SWOT analysis is a tool that can help in forward planning by identifying the strengths, weaknesses, opportunities and threats presented by any situation. Our SWOT analysis was used to generate a list of items, from which we chose those most feasible and most likely to promote positive change.


RESULTS
Twenty different issues were identified, with four of them chosen by consensus for further elaboration. The discussion gave rise to four main recommended strategies: ensuring that clinical teachers thoroughly understand the purpose and process of learning in clinical settings; equipping learners with 'survival skills'; making the best use of learning resources within different clinical environments and making judicious use of information technology to enhance learning efficiency.


CONCLUSIONS
The four strategies were selected not only because of their inherent importance, but also because of their feasibility. Modest changes can motivate students to feel part of a clinical team and a 'community of practice' and enhance their capacity for self-regulated practice.
Gordon
J
J
Department of Educational Development and Evaluation, University of Sydney, Australia.
Hazlett
C
C
Ten Cate
O
O
Mann
K
K
Kilminster
S
S
Prince
K
K
O'Driscoll
E
E
Snell
L
L
Newble
D
D
D002982:Clinical Clerkship / Q000379:methods*; D006801:Humans; D007858:Learning; D008492:Medical Informatics Computing; D017145:Models, Educational