There is increasing interest in interventions that help patients become involved in decision-making about healthcare choices. One set of such interventions are known as 'decision aids', interventions that provide decision makers with information about the nature and probabilities of options and their attributes, assume that a deliberate choice is necessary, and often, though not always, provide methods to deliberate or clarify 'values' [1
]. These exist in a number of formats (paper-based, video, and web) and there are many ways in which they can be used in practice. They may be given to patients before consultations or made available for use during or after consultations with health professionals, either with the professional who is directly dealing with the patient or by asking the patient to receive guidance by another health professional. Therefore, there are a number of ways in which interactions using these interventions can take place that involve different settings and different professional groups. These interventions have a proliferating number of names including: 'patient decision aids' and 'decision support tools' among others. In this paper, we use the term decision support technologies (DSTs) to provide a generic description and to make the connection to the widely recognized interest in health technology assessment. In this context, O'Connor et al
] have defined DSTs as interventions:
'designed to help people make specific, deliberate choices among options (including the status quo) by providing information about the options and outcomes (e.g., benefits, harms) in sufficient detail that an individual could personally judge their value.'
These technologies may include:
'information on the clinical condition; outcome probabilities tailored to personal risk factors; an explicit values clarification exercise (e.g., a relevance chart, utility assessments of probable outcome states, a weigh scale); descriptions of others' experiences; and guidance in the steps of decision-making and communicating with others.' [1
There are now reports of large numbers of DSTs. A systematic review has been conducted [1
], an inventory of such interventions is available, and a system to assess their quality is also being developed [2
]. Although clinical trials seem to show that DSTs are useful in clinical practice, it is also clear that these technologies – and the shared decision-making approach which underpins their use – are not being widely adopted by health care professionals [3
]. Shared decision-making is used here to describe an approach of actively involving patients in making health care decisions. The approach assumes information provision and the existence of equipoise (legitimate viable options) [5
], so that patients, when informed may choose to be involved to the 'extent they prefer' [5
], recognizing that some people prefer others, such as health care professionals, to take decisions on their behalf.
Although numerous reviews have considered how best to implement clinical guidelines and other forms of evidence or evidence-based practice, few studies have examined the difficulty of introducing DSTs into routine practice in any depth. In those that have, a 'many barriers' argument has been an important explanation, such as the report by Holmes-Rovner et al
. of a study to determine the feasibility of DSTs in fee-for-service hospital systems including physicians' offices and in-patient facilities [6
]. Holmes-Rovner et al
. reported that the key obstacle was time pressure, although the authors also raise the possibility that this may not have been the only factor. They conclude that, to be successful, implementation processes would have to include system changes, such as the integration of DSTs into an informed consent process, or incentives such as payer negotiated requirements (where shared decision processes are assumed to be quality indicators), or reimbursement to professionals who make shared decision programs available to patients. Gravel and Légaré's systematic review revealed a taxonomy of barriers, including time constraints and lack of applicability to patient characteristics and to clinical situation [7
]. Such factors draw attention to individualized problems of employing DSTs, and it is increasingly recognized that the successful adoption of interventions depends on more complex interactions than one of overcoming barriers [8
We argue that a 'many barriers' explanation is insufficient and that a more holistic perspective is necessary. Existing theoretical models often focus on implementation and adoption of new technologies in terms of individual behavioral change [10
], or organizational diffusion [13
], rather than in terms of the work of using DSTs in practice. This is a core, but under-recognized, problem for DST researchers: the language of adoption and implementation of innovations dominates policy and practice debates about employing DSTs in clinical practice to the exclusion of considerations of their workability and integration for users. If we wish to understand why DSTs seem not to be operationalized by professionals, even when they are widely diffused and available, then it is their everyday embedding in clinical practice – rather than innovation and adoption by healthcare providers – that should be the focus of our attention. In this paper we have used a theoretical framework – the Normalization Process Model (NPM) [16
] – to explain factors [6
] that promote and inhibit the implementation of DSTs in routine practice settings.
The Normalization Process Model
The NPM developed by May and colleagues is a theoretical model that focuses attention on factors that have been empirically demonstrated to affect the implementation and integration of complex interventions in healthcare [28
]. See Table for definitions of its constructs and dimensions. It is intended to facilitate understanding from a process evaluation perspective, and has been used across a range of contexts [29
]. Normalization is defined as the routine embedding of a complex intervention in healthcare work [16
], and the NPM offers a robust structure for investigating the collective work that leads (or not) to this. The NPM is structured as follows.
Definitions of constructs and dimensions of the Normalization Process Model applied to Decision Support Technologies
Implementation processes are composed of chains of interactions in which a complex intervention (a new or modified way of thinking, acting upon, or organizing practice) is made coherent and enacted in a healthcare setting. Implementation processes are managed and 'owned' through behaviors that denote cognitive participation by healthcare professionals and other personnel, including patients.
A complex intervention is enacted through different kinds of interactional and material work. This work may be highly structured (enacting a research protocol, for example), or diffuse (in operationalizing a policy decision in a large organization). This work is located in the endogenous or immediate conditions of encounters between people using the intervention, and the exogenous conditions that structure these encounters.
In their immediate conditions of practice, people operationalize a complex intervention when they engage in co-operative interactions that are characterized by specific patterns of conduct (congruence), and expectations about their outcomes (disposal). The potential operationalization of a complex intervention is determined by its 'interactional workability'. People organize a complex intervention through shared knowledge and practice (accountability), and beliefs about its value and meaning (confidence) within organizational networks. The potential of a complex intervention to be embedded in a network is determined by its 'relational integration'.
In the exogenous conditions that structure encounters between participants in a complex intervention, work is distributed according to specific formal or informal roles (allocation), and evaluated by reference to shared beliefs about action (performance). The distribution of work connected with a complex intervention is determined by its potential for 'skill set workability' within a division of labor. People enact it by drawing on their capacity to assign the necessary intellectual property, personnel, and material resources (execution); and to seek to link it to its operational contexts by sustaining the allocation of these resources (realization). The capacity of people to participate in or with a complex intervention is determined by its potential for 'contextual integration' into the specific setting.
Patterns of collective action and their outcomes are continuously evaluated by participants in implementation processes, and the formality and intensity of this monitoring indicates the nature of cognitive participation and collective action. Formal patterns of monitoring (for example, clinical trials) focus attention on normative elements of implementation (measuring them against ideas about how things ought to be [34
]), rather than the conventions (how things are worked out in practice [35
]) of social relations and processes upon which informal patterns of monitoring are focused. The shift from formal to informal appraisal by participants is an important signal of the routine embedding of a complex intervention.
Set out in this way, the model offers a simplifying structure for understanding three things: the relationships between a complex intervention and the context in which it is implemented; the processes by which implementation proceeds, including interactions between people, technologies, and organizational structures, and the work that proceeds from these; and a process-oriented assessment of outcome that also considers the potential and actual workability and integration of a complex intervention as accomplishments of its users.