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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Lloyd, Amy Joseph-Williams, Natalie Edwards, Adrian Rix, Andrew Elwyn, Glyn |
| Abstract | Background Implementing shared decision making into routine practice is proving difficult, despite considerable interest from policy-makers, and is far more complex than merely making decision support interventions available to patients. Few have reported successful implementation beyond research studies. MAking Good Decisions In Collaboration (MAGIC) is a multi-faceted implementation program, commissioned by The Health Foundation (UK), to examine how best to put shared decision making into routine practice. In this paper, we investigate healthcare professionals’ perspectives on implementing shared decision making during the MAGIC program, to examine the work required to implement shared decision making and to inform future efforts. Methods The MAGIC program approached implementation of shared decision making by initiating a range of interventions including: providing workshops; facilitating development of brief decision support tools (Option Grids); initiating a patient activation campaign (‘Ask 3 Questions’); gathering feedback using Decision Quality Measures; providing clinical leads meetings, learning events, and feedback sessions; and obtaining executive board level support. At 9 and 15 months (May and November 2011), two rounds of semi-structured interviews were conducted with healthcare professionals in three secondary care teams to explore views on the impact of these interventions. Interview data were coded by two reviewers using a framework derived from the Normalization Process Theory. Results A total of 54 interviews were completed with 31 healthcare professionals. Partial implementation of shared decision making could be explained using the four components of the Normalization Process Theory: ‘coherence,’ ‘cognitive participation,’ ‘collective action,’ and ‘reflexive monitoring.’ Shared decision making was integrated into routine practice when clinical teams shared coherent views of role and purpose (‘coherence’). Shared decision making was facilitated when teams engaged in developing and delivering interventions (‘cognitive participation’), and when those interventions fit with existing skill sets and organizational priorities (‘collective action’) resulting in demonstrable improvements to practice (‘reflexive monitoring’). The implementation process uncovered diverse and conflicting attitudes toward shared decision making; ‘coherence’ was often missing. Conclusions The study showed that implementation of shared decision making is more complex than the delivery of patient decision support interventions to patients, a portrayal that often goes unquestioned. Normalizing shared decision making requires intensive work to ensure teams have a shared understanding of the purpose of involving patients in decisions, and undergo the attitudinal shifts that many health professionals feel are required when comprehension goes beyond initial interpretations. Divergent views on the value of engaging patients in decisions remain a significant barrier to implementation. |
| Related Links | https://implementationscience.biomedcentral.com/counter/pdf/10.1186/1748-5908-8-102.pdf |
| Ending Page | 9 |
| Page Count | 9 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17485908 |
| DOI | 10.1186/1748-5908-8-102 |
| Journal | Implementation Science |
| Issue Number | 1 |
| Volume Number | 8 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2013-09-05 |
| Access Restriction | Open |
| Subject Keyword | Health Services Research Public Health Health Informatics Health Policy Health Administration Health Promotion and Disease Prevention Shared decision making Implementation Patient-centered care Normalization Process Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Policy Public Health, Environmental and Occupational Health Health Informatics |
| Journal Impact Factor | 8.8/2023 |
| 5-Year Journal Impact Factor | 9.2/2023 |
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