Quality Improvement in Healthcare
The Swedish healthcare system, although being one of the more efficient
care systems in the world with good medical outcomes at a moderate cost,
faces tremendous future challenges. An ageing population with more
patients suffering from multiple diseases together with accelerating medicotechnical developments is putting increasing pressure on the system. The quality and safety of the system has also been called into question.
Improvement science, where quality improvement theories and practices are
continuously being translated to a healthcare context, has emerged as one
possible solution to these challenges. However, there is need for a further
theoretical and practical development of the field.
The purpose of the thesis is to explore quality improvement initiatives in
healthcare systems, suggesting alternative ways of improving quality and
efficiency in healthcare organizations. The empirical material draws on
leveraging events during two long-term improvement initiatives in the
healthcare system of Skaraborg in the Western region of Sweden. The
author, working as a development director at the Skaraborg hospital group
(SkaS), played a major role in both cases as an inside action researcher. The
first case addresses a decade of development efforts that sought to improve
care for elderly people in West Skaraborg. The second case explores how
quality management ideas at SkaS were used to improve quality, efficiency
and safety in hospital care from 2006 to 2008.
The results of the research draw special attention to the importance of
moving beyond the established static, linear step-for-step models for quality
improvement, instead embracing a more open and processual view on
improvement. The thesis proposes that practices and theories from the
action research (AR) field in this respect are useful complements to the
emerging field of improvement science. AR practices entail an approach that
enhances joint learning and reflection in iterative action-reflection cycles.
Further, drawing from the vast repertoire of AR practices, cognitive,
structural, networking, and procedural learning mechanisms are vital
ingredients for quality improvement in complex healthcare systems.
Learning mechanisms connect all parts of the system but they also support
individual and organizational learning and action through new vocabularies,
frameworks and concepts, procedures and tools.