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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Jul 3, 2018
Date Accepted: Oct 4, 2018
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

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

JMIR Mhealth Uhealth 2018;6(12):e11468

DOI: 10.2196/11468

PMID: 30578177

PMCID: 6320439

Unraveling the Black Box of Mobile Health: Realist Evaluation of a Digital Innovation for Antenatal Care

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 (B4M), 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 B4M, 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 B4M 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 B4M 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 B4M between usage clusters.

Results:

We found no statistically significant differences between the 2 usage clusters. Low to moderate adoption of B4M 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 B4M 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 B4M prototype, our results highlight the need for interventions that are guided by research methods that account for complexity.


 Citation

Please cite as:

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

JMIR Mhealth Uhealth 2018;6(12):e11468

DOI: 10.2196/11468

PMID: 30578177

PMCID: 6320439

Per the author's request the PDF is not available.