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OP92 Improving the Effectiveness of Multidisciplinary Team Meetings for Patients with Chronic Diseases: Assessing the Predictors of Decision Implementation
  1. R Raine1,
  2. P Xanthopoulou1,
  3. I Wallace1,
  4. C Nic a’ Bháird1,
  5. J Barber2,
  6. A Clarke3,
  7. A Lanceley4,
  8. D Ardron5,
  9. M Harris6,
  10. J Blazeby7,
  11. E Ferlie8,
  12. S Gibbs9,
  13. M King10,
  14. G Livingston10,
  15. S Michie11,
  16. A Prentice12
  1. 1Department of Applied Health, University College London, London, UK
  2. 2Department of Statistical Science, University College London, London, UK
  3. 3Department of Plastic and Reconstructive Surgery, The Royal Free Hospital, London, UK
  4. 4Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
  5. 5Patient and Public Involvement Representative, National Cancer Research Institute, London, UK
  6. 6Patient and Public Involvement Representative, London, UK
  7. 7School of Social and Community Medicine, Bristol University, Bristol, UK
  8. 8Department of Management, King’s College London, London, UK
  9. 9National Heart and Lung Institute, Imperial College London, London, UK
  10. 10Department of Mental Health Sciences, University College London , London, UK
  11. 11Department of Clinical, Educational and Health Psychology, University College London , London, UK
  12. 12Royal College of Pathologists, London, UK

Abstract

Background Department of Health policy states that health care for chronic diseases should be delivered through multidisciplinary team meetings (MDMs). It is known that multidisciplinary teams should include certain features (for example commitment to leadership) to be effective. But additional factors need to be considered: e.g. the context in which MDMs operate, group decision processes, and patient related factors such as their preferences and socio-demographic characteristics. There is, therefore, a need for research on MDMs to identify factors that promote effective MDM decision-making in terms of decision implementation. We report on a study, unique in its size and scope, making its results applicable across the NHS.

Methods We undertook an observational study of 370 MDMs in 12 different teams (gynaecological, skin and haematological cancers, mental health, heart failure and memory clinics) in the North Thames area. Analyses used random effects logistic regression models, allowing for MDM clustering, to investigate the influence of MDM and patient related factors on decision implementation. Decision implementation and patient demographics were ascertained from medical records. The MDM characteristics examined were Team Climate Score, disease type, team skill-mix (Adjusted Teachman’s Index and number of professional categories represented), and whether defined co-morbidities and patient preferences were considered. The patient characteristics examined were age, gender and Index of Multiple Deprivation (IMD) score.

Results We will present descriptive analyses on 6053 discussions of 3184 patients. For example, discussions on 17% of patients led to no decision. Decision implementation ranged from 67% (Mental Health) to 79% (Memory Clinics). Reasons for non-implementation will be presented.

We will present the results of logistic regression on the patient and MDM related factors associated with decision implementation, including the extent to which patient related factors, for example their socio-economic circumstances, were associated with implementation. We will also describe the patient and MDM related factors associated with patient preferences being taken into account.

Conclusion We address recent calls for empirical research on MDM decision-making in routine practice to understand how and under what conditions MDMs produce effective decisions, as well as methods to effectively obtain and consider patient preferences. As the largest study of its kind in this area, and the first to examine and compare MDMs for different chronic diseases, this study enables identification of factors associated with good outcomes that are generalisable across healthcare.

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