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. 2018 Dec 21;6(12):e11468.
doi: 10.2196/11468.

Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Affiliations

Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Ibukun-Oluwa Omolade Abejirinde et al. JMIR Mhealth Uhealth. .

Abstract

Background: Digital innovations have shown promise for improving maternal health service delivery. However, low- and middle-income countries are still at the adoption-utilization stage. Evidence on mobile health has been described as a black box, with gaps in theoretical explanations that account for the ecosystem of health care and their effect on adoption mechanisms. Bliss4Midwives, a modular integrated diagnostic kit to support antenatal care service delivery, was piloted for 1 year in Northern Ghana. Although both users and beneficiaries valued Bliss4Midwives, results from the pilot showed wide variations in usage behavior and duration of use across project sites.

Objective: To strengthen the design and implementation of an improved prototype, the study objectives were two-fold: to identify causal factors underlying the variation in Bliss4Midwives usage behavior and understand how to overcome or leverage these in subsequent implementation cycles.

Methods: Using a multiple case study design, a realist evaluation of Bliss4Midwives was conducted. A total of 3 candidate program theories were developed and empirically tested in 6 health facilities grouped into low and moderate usage clusters. Quantitative and qualitative data were collected and analyzed using realist thinking to build configurations that link intervention, context, actors, and mechanisms to program outcomes, by employing inductive and deductive reasoning. Nonparametric t test was used to compare the perceived usefulness and perceived ease of use of Bliss4Midwives between usage clusters.

Results: We found no statistically significant differences between the 2 usage clusters. Low to moderate adoption of Bliss4Midwives was better explained by fear, enthusiasm, and high expectations for service delivery, especially in the absence of alternatives. Recognition from pregnant women, peers, supervisors, and the program itself was a crucial mechanism for device utilization. Other supportive mechanisms included ownership, empowerment, motivation, and adaptive responses to the device, such as realignment and negotiation. Champion users displayed high adoption-utilization behavior in contexts of participative or authoritative supervision, yet used the device inconsistently. Intervention-related (technical challenges, device rotation, lack of performance feedback, and refresher training), context-related (staff turnover, competing priorities, and workload), and individual factors (low technological self-efficacy, baseline knowledge, and internal motivation) suppressed utilization mechanisms.

Conclusions: This study shed light on optimal conditions necessary for Bliss4Midwives to thrive in a complex social and organizational setting. Beyond usability and viability studies, advocates of innovative technologies for maternal care need to consider how implementation strategies and contextual factors, such as existing collaborations and supervision styles, trigger mechanisms that influence program outcomes. In addition to informing scale-up of the Bliss4Midwives prototype, our results highlight the need for interventions that are guided by research methods that account for complexity.

Keywords: Ghana; antenatal care; clinical decision support; mHealth; program evaluation; systems analysis.

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Conflict of interest statement

Conflicts of Interest: NA and RAA work for organizations involved in the implementation of the B4M proof-of-concept and were actively involved in the project. Other authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Initial program theories. Features and characteristics of the intervention- (I); Contextual factors are denoted (C1) and (C2) for environmental and health system context respectively; Outcomes are denoted (O1) or (O2) representing adoption and utilization respectively; Mechanisms are identified (M1) or (M2) following the outcomes they are linked to, with related explanatory mechanisms further depicted (m1) or (m2); Actor or user characteristics are denoted (A); (Oa) represents additional outcomes. ANC: antenatal care; B4M: Bliss4Midwives.
Figure 2
Figure 2
Summary of findings. Ecosystem of ICAMO factors underlying the adoption (O1) and utilization (O2) of B4M within a complex context (concentric circles C1 and C2) and features of the B4M intervention (I). M1 and M2 are mechanisms related to outcomes O1 and O2 , mediated by user characteristics (A). Bullet points highlight other facilitating (+) or inhibitory (–) factors influencing usage behavior. ANC: antenatal care; B4M: Bliss4Midwives; ICT: information and communication technology.

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