Arduous implementation: Does the Normalisation Process Model explain why it's so difficult to embed decision support technologies for patients in routine clinical practice

Department of Primary Care and Public Health, School of Medicine, Cardiff University, Heath Park, CF14 4YS, UK.
Implementation Science (Impact Factor: 4.12). 01/2009; 3(1):57. DOI: 10.1186/1748-5908-3-57
Source: PubMed


Decision support technologies (DSTs, also known as decision aids) help patients and professionals take part in collaborative decision-making processes. Trials have shown favorable impacts on patient knowledge, satisfaction, decisional conflict and confidence. However, they have not become routinely embedded in health care settings. Few studies have approached this issue using a theoretical framework. We explained problems of implementing DSTs using the Normalization Process Model, a conceptual model that focuses attention on how complex interventions become routinely embedded in practice.
The Normalization Process Model was used as the basis of conceptual analysis of the outcomes of previous primary research and reviews. Using a virtual working environment we applied the model and its main concepts to examine: the 'workability' of DSTs in professional-patient interactions; how DSTs affect knowledge relations between their users; how DSTs impact on users' skills and performance; and the impact of DSTs on the allocation of organizational resources.
A conceptual analysis using the Normalization Process Model provided insight on implementation problems for DSTs in routine settings. Current research focuses mainly on the interactional workability of these technologies, but factors related to divisions of labor and health care, and the organizational contexts in which DSTs are used, are poorly described and understood.
The model successfully provided a framework for helping to identify factors that promote and inhibit the implementation of DSTs in healthcare and gave us insights into factors influencing the introduction of new technologies into contexts where negotiations are characterized by asymmetries of power and knowledge. Future research and development on the deployment of DSTs needs to take a more holistic approach and give emphasis to the structural conditions and social norms in which these technologies are enacted.

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    • "However there is a multitude of definitions and measures devoted to patient-centred care [28] [29], which tends to obscure its utility to guide practice. Alternatively, shared decision making (SDM) is an evidence-based approach that built on patientcentred care [30] [31] [32] but which emphasises implementation, as difficulty in adopting new ways of interacting with patients in routine care has been widely reported [33] [34]. Efforts to facilitate implementation are reflected in studies such as Elwyn et al. [35] in which a three-step delivery of SDM: choice, option and decision talk, is recommended. "
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    Patient Education and Counseling 07/2015; DOI:10.1016/j.pec.2015.07.005 · 2.20 Impact Factor
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    • "The Patient Protection and Affordable Care Act, signed into the law of the United States in March 2010, contains provisions aimed at encouraging the use of SDM, which represents an important entry point for SDM into public policy [6-8]. Despite these positive developments, the use of SDM remains low due to many barriers blocking its full implementation in clinical practice [9-11]. "
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    BMC Medical Informatics and Decision Making 08/2014; 14(1):71. DOI:10.1186/1472-6947-14-71 · 1.83 Impact Factor
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    • "Methods to improve health literacy and shared decision making [31] show some effectiveness, but are often difficult to implement in routine practice [39], where too much is often asked of patients [40]. Interventions designed to improve patient– provider communication [10] [23] are seen as essential to good patient-centered care, yet have not been routinely implemented into clinical care [41] [42]. 3.3. "
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    ABSTRACT: Objective Examine existing reviews of patient engagement methods to propose a model where the focus is on engaging patients in clinical workflows, and to assess the feasibility of advocated patient engagement methods. Methods A literature search of reviews of patient engagement methods was conducted. Included reviews were peer-reviewed, written in English, and focused on methods that targeted patients or patient-provider dyads. Methods were categorized to propose a conceptual model. The feasibility of methods was assessed using an adapted rating system. Results We observed that we could categorize patient engagement methods based on information provision, patient activation, and patient-provider collaboration. Methods could be divided by high and low feasibility, predicated on the extent of extra work required by the patient or clinical system. Methods that have good fit with existing workflows and that require proportional amounts of work by patients are likely to be the most feasible. Conclusions Implementation of patient engagement methods is likely to depend on finding a “sweet-spot” where demands required by patients generate improved knowledge and motivate active participation. Practice implications Attention should be given to those interventions and methods that advocate feasibility with patients, providers, and organizational workflows.
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