Why Do Evaluations of eHealth Programs Fail? An Alternative Set of Guiding Principles
Authors: Trisha Greenhalgh, Jill Russell
Journal: PLoS Medicine (2010)

Much has been written about why electronic health (eHealth) initiatives fail [1]–[4]. Less attention has been paid to why evaluations of such initiatives fail to deliver the insights expected of them. PLoS Medicine has published three papers offering a “robust” and “scientific” approach to eHealth evaluation [5]–[7]. One recommended systematically addressing each part of a “chain of reasoning”, at the centre of which was the program's goals [6]. Another proposed a quasi-experimental step-wedge design, in which late adopters of eHealth innovations serve as controls for early adopters [5]. Interestingly, the authors of the empirical study flagged by these authors as an exemplary illustration of the step-wedge design subsequently abandoned it in favour of a largely qualitative case study because they found it impossible to establish anything approaching a controlled experiment in the study's complex, dynamic, and heavily politicised context [8].

The approach to evaluation presented in the previous PLoS Medicine series rests on a set of assumptions that philosophers of science call “positivist” [9]: that there is an external reality that can be objectively measured; that phenomena such as “project goals”, “outcomes”, and “formative feedback” can be precisely and unambiguously defined; that facts and values are clearly distinguishable; and that generalisable statements about the relationship between input and output variables are possible.

Alternative approaches to eHealth evaluation are based on very different philosophical assumptions [9]. For example,


“interpretivist” approaches assume a socially constructed reality (i.e., people perceive issues in different ways and assign different values and significance to facts)—hence, reality is never objectively or unproblematically knowable—and that the identity and values of the researcher are inevitably implicated in the research process [10].


“critical” approaches assume that critical questioning can generate insights about power relationships and interests and that one purpose of evaluation is to ask such questions on behalf of less powerful and potentially vulnerable groups (such as patients) [11].




Beyond Questions of Science
Catwell and Sheikh argue that “health information systems should be evaluated with the same rigor as a new drug or treatment program, otherwise decisions about future deployments of ICT in the health sector may be determined by social, economic, and/or political circumstances, rather than by robust scientific evidence” ([6], page 1).

In contrast to this view of evaluation as scientific testing, scholars in critical-interpretivist traditions view evaluation as social practice—that is, as actively engaging with a social situation and considering how that situation is framed and enacted by participants [12]–[20]. A key quality criterion in such studies is reflexivity—consciously thinking about issues such as values, perspectives, relationships, and trust. These traditions reject the assumption that a rigorous evaluation can be exclusively scientific. Rather, they hold that as well as the scientific agenda of factors, variables, and causal relationships, the evaluation must also embrace the emotions, values, and conflicts associated with a program [19]. eHealth “interventions” may lie in the technical and scientific world, but eHealth dreams, visions, policies, and programs have personal, social, political, and ideological components, and therefore typically prove fuzzy, slippery, and unstable when we seek to define and control them [21].

Kushner observes that “The [positivist evaluation] model is elegant in its simplicity, appealing for its rationality, reasonable in asking little more than that people do what they say they will do, and efficient in its economical definition of what data count” ([18], page 16). But he goes on to list various shortcomings (summarised below), which were illustrated in our evaluation of a nationally stored electronic Summary Care Record (SCR) in England [21],[22]. The SCR was part of a larger National Programme for IT in the National Health Service [23], viewed by many stakeholders as monolithic, politically driven, and inflexible [4],[8].

The first problem with scientific evaluation, suggests Kushner, is that programs typically have multiple and contested goals; hence, no single set of goals can serve as a fixed referent for comparison. An early finding of our evaluation was that the SCR program had numerous goals (e.g., politicians were oriented to performance and efficiency targets, doctors saw the main goal as improving clinical quality in out-of-hours care, and civil liberties lobbyists perceived the program an attempt by the state to encroach on individual privacy) [21].

Second, outcomes are not stable; they erode and change over time and across contexts. In the SCR program, it was originally planned that patients would access their electronic record from home via linked software called HealthSpace, thereby becoming “empowered”. But HealthSpace was subsequently uncoupled from the SCR program because it was deemed “high risk” by civil servants [24].

Third, Kushner suggests, the causal link between process and outcome is typically interrupted by so many intervening variables as to make it unreliable. In the SCR evaluation, we documented 56 such variables—including training, permissions, physical space, technical interoperability, local policies and protocols, professional sanction, and point-of-care consent [21].

Fourth, key characteristics of program success may not be articulated in the vocabulary of outcomes and may not yield to measurement. One such dimension of the SCR program was the variable culture of e-governance across different organisations (e.g., the extent to which it was acceptable for staff to forget their passwords or leave machines “logged on” when going to lunch).

Finally, program learning that leads away from initial objectives threatens failure against outcome criteria. In the SCR program, an early finding was that predefined milestones (e.g., number of records created by a target date) were sometimes counterproductive since implementation teams were required to push forward in the absence of full clinical and patient engagement, which sometimes led to strong local resistance. We recommended that these milestones be made locally negotiable. But because critics of the program interpreted missed milestones as evidence of “failure”, policymakers took little heed of this advice.