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. 2019 Mar 8;4(2):e001153.
doi: 10.1136/bmjgh-2018-001153. eCollection 2019.

How and why front-line health workers (did not) use a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana

Hannah Brown Amoakoh  1   2 , Kerstin Klipstein-Grobusch  2   3 , Evelyn Korkor Ansah  4 , Diederick E Grobbee  2 , Linda Yveoo  5 , Irene Agyepong  5
Affiliations

How and why front-line health workers (did not) use a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana

Hannah Brown Amoakoh et al. BMJ Glob Health. .

Abstract

Introduction: Despite increasing use of mHealth interventions, there remains limited documentation of 'how and why' they are used and therefore the explanatory mechanisms behind observed effects on beneficiary health outcomes. We explored 'how and why' an mHealth intervention to support clinical decision-making by front-line providers of maternal and neonatal healthcare services in a low-resource setting was used. The intervention consisted of phone calls (voice calls), text messaging (short messaging service (SMS)), internet access (data) and access to emergency obstetric and neonatal protocols via an Unstructured Supplementary Service Data (USSD). It was delivered through individual-use and shared facility mobile phones with unique Subscriber Identification Module (SIM) cards networked in a Closed User Group.

Methods: A single case study with multiple embedded subunits of analysis within the context of a cluster randomised controlled trial of the impact of the intervention on neonatal health outcomes in the Eastern Region of Ghana was performed. We quantitatively analysed SIM card activity data for patterns of voice calls, SMS, data and USSD. We conducted key informant interviews and focus group discussions with intervention users and manually analysed the data for themes.

Results: Overall, the phones were predominantly used for voice calls (64%), followed by data (28%), SMS (5%) and USSD (2%), respectively. Over time, use of all intervention components declined. Qualitative analysis showed that individual health worker factors (demographics, personal and work-related needs, perceived timeliness of intervention, tacit knowledge), organisational factors (resource availability, information flow, availability, phone ownership), technological factors (attrition of phones, network quality) and client perception of health worker intervention usage explain the pattern of intervention use observed.

Conclusion: How and why the mHealth intervention was used (or not) went beyond the technology itself and was influenced by individual and context-specific factors. These must be taken into account in designing similar interventions to optimise effectiveness.

Keywords: clinical decision-making; low-resource setting; mHealth; maternal health; neonatal health.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Trend in mobile call detail record use during the first eight months of intervention implementation. SMS, short messaging service; USSD, Unstructured Supplementary Service Data.
Figure 2
Figure 2
Mapping of pattern of Closed User Group (CUG) communication via phone calls and text messaging among clusters. The district colours indicate the frequency of CUG communication per cluster. The arrows show the direction of communication flow from one cluster to the other. The colour of the arrows indicate the frequency of inter-cluster communication with a given cluster. The pattern (frequency) of closed user group communication via phone calls and text messaging within and between the clusters as illustrated in figure 2 was significantly different (p value <0.001).
Figure 3
Figure 3
Factors explaining the observed pattern of mHealth intervention usage.
See this image and copyright information in PMC

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