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

BMJ Global Health, Volume 4, No. 2, Year 2019

Interpretation

Introduction Despite increasing use of mHealth interventions, there remains limited documentation of â € how and why’ they are used and therefore the explanatory mechanisms behind observed effects on beneficiary health outcomes. We explored â € 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. This study design was an exploratory and explanatory single case study with multiple embedded units of analysis. The case was defined as ‘how and why a mobile phone-based front-line health worker clinical decision-making support intervention was used (or not)’. Each embedded subunit of analysis was defined as ‘a district in which the intervention was deployed’. This case study was conducted within the broader context of a CRCT of the impact of the intervention on neonatal health outcomes in 16 districts in the Eastern Region of Ghana randomised into eight intervention and eight control districts (clusters). Each of the eight intervention districts was treated as an embedded subunit of analysis of the case study. The CRCT has been described in detail elsewhere.20 A cluster in the CRCT and in this study is a district. Ghana is divided into 10 regions, each of which is further divided into geographic local government administrative areas known as districts. We used mixed quantitative and qualitative methods of data collection in each of the eight districts. Data sources included routine Vodafone call log data, key informant interviews (KIIs) with FHWs and facility managers, and focus group discussions (FGDs) with FHWs. The call log data were routinely collected by Vodafone Ghana, the telecommunication company that provided technical support for the intervention throughout the CRCT. We analysed all mobile call detail record subtypes (mCDRs) as logged on the Vodafone archived database regarding utilisation of the project’s SIM cards for any purpose (phone calls, texting, accessing the USSD or use of data) during the first eight months of an 18-month intervention period. Data regarding CUG communication was included in this archived data. Prior to the data extraction, phone numbers assigned to the various users were collated such that each intervention user (FHW), the health facility as well as the cluster the user worked in was documented and coded in the Vodafone database. This ensured that SIM cards and phone numbers could be traced back to the cluster, health facility and FHW using the project phones. FGDs and KIIs aimed to provide explanatory insights into the patterns of use of the phones observed from analysing the call log data. We initially thought that perspectives and experiences of facility nurse managers might be different from those of front-line midwives. Since there were usually only one or two facility nurse managers to several front-line midwives, we planned to hold KIIs with the facility nurse managers and FGDs with the front-line midwives. The FGDs were to stimulate frank discussions of experiences and opinion about the intervention, while KIIs were used to obtain insight on how and why the intervention was used from a managerial view and shared-phone user’s experience. No theories regarding the observed pattern of use of the intervention were postulated prior to qualitative interviews. We conducted the qualitative interviews immediately after the CRCT closed to avoid introducing a confounding element into the intervention. We considered it important to reflect the three levels of healthcare delivery at district level in Ghana, that is, hospitals, HCs and CHPS compounds and zones and the differences between them. We therefore aimed to purposively select a facility from each of the three levels in each of the eight districts. Within each of the three levels in a given district, there was no clear indication of differences that required purposive selection. We therefore randomly selected one health facility from the several at each level within each of the eight districts to participate in KII, and two health facilities from each level to select respondents to participate in FGD. We sampled health facilities for KIIs and FGDs using a random sequence generator in Microsoft Excel30 and sampled health facilities for KIIs first. After health facility selection for KIIs, the head of the maternity unit and the holder of a shared-use phone in hospitals and HCs were purposively sampled to be interviewed. In the CHPS compound, only the head of the maternity unit was interviewed as typically each CHPS compound had only one-shared use phone allocated to them by the project team. Regarding FGDs, health facilities already selected for KIIs were excluded from the sample except where there were very few health facilities in a cluster. To ensure representation of health facilities from all levels of the healthcare system in FGDs, where there was one hospital in a cluster, the hospital was purposively selected to participate in FGDs. In instances where the same health facility was selected for both FGD and KII, the respondents for FGDs and KIIs were different. Following sampling of health facilities for FGDs, any FHW or midwife who had knowledge about the use of the project mobile phone was invited to participate in FGDs; the decision as to who exactly would attend the FGDs was made by the head of the health facility sampled. At least one FGD and two KIIs were scheduled to be conducted in each of the eight districts (clusters) at a location arranged by the district health management team. The arranged venues were usually the district health administration office or hospital conference rooms in the cluster. We collected all qualitative data from 9 April 2018 until 27 April 2018. Each FGD consisted of FHWs from the different health facilities sampled within the district. We aimed to keep conducting FGDs and KIIs until no new themes were emerging (saturation). We estimated that this would mean about 4–8 FGDs31 and 6–10 KIIs. We analysed the data from each interview immediately after it closed to inform whether to keep going or not. By the time we had completed data collection and analysis of one FGD in each of the eight districts, we realised we were finding the same themes in the analysis. We therefore stopped the FGDs. In the case of the KIIs, by the time we had completed nine KIIs in three districts we realised the themes were the same across the KIIs, across the districts and between the KIIs and the FGDs. We therefore stopped the KIIs and invited planned KII respondents in the remaining five districts to join their district FGD. Three of the investigators (HBA, LY and IAA) worked with one research assistant to collect the qualitative data. All FGDs and KIIs were conducted face-to-face and audiotaped to facilitate transcription of data collected with notes being taken by HBA as well. All KIIs and FGDs were conducted and transcribed in English. The KIIs lasted on average 28 min, while FGDs lasted averagely 1 hour 26 min. Qualitative data analysis was done on a rolling basis after each FGD or KII. All data analysis was initially done by each of the eight districts for themes, commonalities and contrasts. The data were then compared across the eight districts for commonalities and contrasts. We triangulated the quantitative findings from the Vodafone call log data analysis and the qualitative findings from the FGD and KII. Data were checked for errors and exported from Excel spreadsheets30 to Stata V.1332 for cleaning and analysis. The category and the number of staff in maternity homes and CHPS are similar. Both facility types usually have 1–2 midwives who run the health facility post assisted by 2–3 CHOs to provide antenatal, neonatal and conduct routine normal uncomplicated deliveries. Some CHPS may however not have a midwife at post; in such situations, deliveries are only conducted if a pregnant woman presents in second stage of labour with the head of the baby in the perineum. In the case of some maternity homes, trained traditional birth attendants who work under the supervision of a midwife may be present. Due to the similarities in organisational structure, personnel and health services provided by CHPS and maternity homes participating in this study, the call log data from these two facility types were combined for analysis. We further classified health facilities into two groups of remote and non-remote areas based on access. Remote facilities were either located >30 min walk, or >15 min motorbike ride from the main district township, and had poor road access (uneven and untarred roads overcrowded with weeds and shrubs) leading to them. Non-remote health facilities were either located within 30 min walk or 15 min motorbike ride from the main district township, and had good road access leading to them. mCDRs for all explanatory variables of interest (clusters, level and location of health facility, type-of-phone (individual-use or shared-use)) were analysed and expressed in numbers and percentages. Analyses of the mCDRs were performed for the combined 8-month data and also disaggregated into monthly intervals for each explanatory variable. χ2 tests were applied to these analyses to assess the significance of the observed pattern of intervention usage. The SIM cards that used these mCDRs were analysed and expressed as percentages. Descriptive analysis of the CUG communication within and across each category of explanatory variable was also performed and expressed as number of voice and short messaging service (SMS) mCDRs records and their percentages. χ2 and Fisher’s exact tests were applied to these analyses to assess the significance of the pattern of CUG communication. To further understand how the CUG communication was used in each cluster, we identified SIM cards that used the CUG and the health facilities they communicated with. No tests were applied to this in-depth analysis as there were several empty cells. The voice recordings were transcribed during and continued after data collection. Transcriptions were done verbatim by non-data collectors. Each transcription was cross-checked by two persons (data collectors, including HBA). Data were manually analysed by thoroughly reading each transcript to identify themes, commonalities and contrasts emerging from the data that shed insights into the patterns of use of the intervention observed from the Vodafone call log data and why and how these patterns occurred using an inductive approach. Three of the study investigators performed the data analysis. Consensus on emerging themes was reached if a minimum of two of the data analyst agreed on an emerging theme.

AI Digest

Study Justification:

– The study aimed to explore how and why an mHealth intervention to support clinical decision-making by front-line providers of maternal and neonatal healthcare services in Ghana was used.
– The study addressed the limited documentation on the “how and why” of mHealth interventions, which is important for understanding the mechanisms behind observed effects on beneficiary health outcomes.
– By understanding the factors influencing the use of the intervention, the study aimed to provide insights for designing similar interventions to optimize effectiveness.

Study Highlights:

– The phones in the intervention were predominantly used for voice calls, followed by data, SMS, and USSD.
– Over time, the use of all intervention components declined.
– Individual health worker factors, organizational factors, technological factors, and client perception of health worker intervention usage explained the pattern of intervention use observed.

Study Recommendations:

– The study recommends that future mHealth interventions should take into account individual and context-specific factors that influence the use of the intervention.
– Designers of similar interventions should consider factors such as demographics, personal and work-related needs of health workers, resource availability, information flow, phone ownership, attrition of phones, network quality, and client perception.
– The study highlights the importance of understanding the “how and why” of mHealth interventions to optimize their effectiveness.

Key Role Players:

– Front-line health workers
– Facility managers
– District health management teams
– Researchers and data analysts
– Telecommunication company (Vodafone Ghana)

Cost Items for Planning Recommendations:

– Mobile phones (individual-use and shared-use)
– Subscriber Identification Module (SIM) cards
– Technical support from the telecommunication company
– Training and capacity building for health workers
– Data collection and analysis
– Transcription and analysis of qualitative data
– Venue and logistics for key informant interviews and focus group discussions
– Travel and accommodation for researchers and data collectors

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is based on a single case study with multiple embedded subunits of analysis. The study design used mixed quantitative and qualitative methods of data collection, including SIM card activity data, key informant interviews, and focus group discussions. The data analysis involved both descriptive analysis and inductive thematic analysis. The evidence provides insights into the patterns of use of the mHealth intervention and the factors influencing its usage. To improve the strength of the evidence, future studies could consider expanding the sample size and using a more diverse range of data sources for triangulation.

Abstract

This study design was an exploratory and explanatory single case study with multiple embedded units of analysis. The case was defined as ‘how and why a mobile phone-based front-line health worker clinical decision-making support intervention was used (or not)’. Each embedded subunit of analysis was defined as ‘a district in which the intervention was deployed’. This case study was conducted within the broader context of a CRCT of the impact of the intervention on neonatal health outcomes in 16 districts in the Eastern Region of Ghana randomised into eight intervention and eight control districts (clusters). Each of the eight intervention districts was treated as an embedded subunit of analysis of the case study. The CRCT has been described in detail elsewhere.20 A cluster in the CRCT and in this study is a district. Ghana is divided into 10 regions, each of which is further divided into geographic local government administrative areas known as districts. We used mixed quantitative and qualitative methods of data collection in each of the eight districts. Data sources included routine Vodafone call log data, key informant interviews (KIIs) with FHWs and facility managers, and focus group discussions (FGDs) with FHWs. The call log data were routinely collected by Vodafone Ghana, the telecommunication company that provided technical support for the intervention throughout the CRCT. We analysed all mobile call detail record subtypes (mCDRs) as logged on the Vodafone archived database regarding utilisation of the project’s SIM cards for any purpose (phone calls, texting, accessing the USSD or use of data) during the first eight months of an 18-month intervention period. Data regarding CUG communication was included in this archived data. Prior to the data extraction, phone numbers assigned to the various users were collated such that each intervention user (FHW), the health facility as well as the cluster the user worked in was documented and coded in the Vodafone database. This ensured that SIM cards and phone numbers could be traced back to the cluster, health facility and FHW using the project phones. FGDs and KIIs aimed to provide explanatory insights into the patterns of use of the phones observed from analysing the call log data. We initially thought that perspectives and experiences of facility nurse managers might be different from those of front-line midwives. Since there were usually only one or two facility nurse managers to several front-line midwives, we planned to hold KIIs with the facility nurse managers and FGDs with the front-line midwives. The FGDs were to stimulate frank discussions of experiences and opinion about the intervention, while KIIs were used to obtain insight on how and why the intervention was used from a managerial view and shared-phone user’s experience. No theories regarding the observed pattern of use of the intervention were postulated prior to qualitative interviews. We conducted the qualitative interviews immediately after the CRCT closed to avoid introducing a confounding element into the intervention. We considered it important to reflect the three levels of healthcare delivery at district level in Ghana, that is, hospitals, HCs and CHPS compounds and zones and the differences between them. We therefore aimed to purposively select a facility from each of the three levels in each of the eight districts. Within each of the three levels in a given district, there was no clear indication of differences that required purposive selection. We therefore randomly selected one health facility from the several at each level within each of the eight districts to participate in KII, and two health facilities from each level to select respondents to participate in FGD. We sampled health facilities for KIIs and FGDs using a random sequence generator in Microsoft Excel30 and sampled health facilities for KIIs first. After health facility selection for KIIs, the head of the maternity unit and the holder of a shared-use phone in hospitals and HCs were purposively sampled to be interviewed. In the CHPS compound, only the head of the maternity unit was interviewed as typically each CHPS compound had only one-shared use phone allocated to them by the project team. Regarding FGDs, health facilities already selected for KIIs were excluded from the sample except where there were very few health facilities in a cluster. To ensure representation of health facilities from all levels of the healthcare system in FGDs, where there was one hospital in a cluster, the hospital was purposively selected to participate in FGDs. In instances where the same health facility was selected for both FGD and KII, the respondents for FGDs and KIIs were different. Following sampling of health facilities for FGDs, any FHW or midwife who had knowledge about the use of the project mobile phone was invited to participate in FGDs; the decision as to who exactly would attend the FGDs was made by the head of the health facility sampled. At least one FGD and two KIIs were scheduled to be conducted in each of the eight districts (clusters) at a location arranged by the district health management team. The arranged venues were usually the district health administration office or hospital conference rooms in the cluster. We collected all qualitative data from 9 April 2018 until 27 April 2018. Each FGD consisted of FHWs from the different health facilities sampled within the district. We aimed to keep conducting FGDs and KIIs until no new themes were emerging (saturation). We estimated that this would mean about 4–8 FGDs31 and 6–10 KIIs. We analysed the data from each interview immediately after it closed to inform whether to keep going or not. By the time we had completed data collection and analysis of one FGD in each of the eight districts, we realised we were finding the same themes in the analysis. We therefore stopped the FGDs. In the case of the KIIs, by the time we had completed nine KIIs in three districts we realised the themes were the same across the KIIs, across the districts and between the KIIs and the FGDs. We therefore stopped the KIIs and invited planned KII respondents in the remaining five districts to join their district FGD. Three of the investigators (HBA, LY and IAA) worked with one research assistant to collect the qualitative data. All FGDs and KIIs were conducted face-to-face and audiotaped to facilitate transcription of data collected with notes being taken by HBA as well. All KIIs and FGDs were conducted and transcribed in English. The KIIs lasted on average 28 min, while FGDs lasted averagely 1 hour 26 min. Qualitative data analysis was done on a rolling basis after each FGD or KII. All data analysis was initially done by each of the eight districts for themes, commonalities and contrasts. The data were then compared across the eight districts for commonalities and contrasts. We triangulated the quantitative findings from the Vodafone call log data analysis and the qualitative findings from the FGD and KII. Data were checked for errors and exported from Excel spreadsheets30 to Stata V.1332 for cleaning and analysis. The category and the number of staff in maternity homes and CHPS are similar. Both facility types usually have 1–2 midwives who run the health facility post assisted by 2–3 CHOs to provide antenatal, neonatal and conduct routine normal uncomplicated deliveries. Some CHPS may however not have a midwife at post; in such situations, deliveries are only conducted if a pregnant woman presents in second stage of labour with the head of the baby in the perineum. In the case of some maternity homes, trained traditional birth attendants who work under the supervision of a midwife may be present. Due to the similarities in organisational structure, personnel and health services provided by CHPS and maternity homes participating in this study, the call log data from these two facility types were combined for analysis. We further classified health facilities into two groups of remote and non-remote areas based on access. Remote facilities were either located >30 min walk, or >15 min motorbike ride from the main district township, and had poor road access (uneven and untarred roads overcrowded with weeds and shrubs) leading to them. Non-remote health facilities were either located within 30 min walk or 15 min motorbike ride from the main district township, and had good road access leading to them. mCDRs for all explanatory variables of interest (clusters, level and location of health facility, type-of-phone (individual-use or shared-use)) were analysed and expressed in numbers and percentages. Analyses of the mCDRs were performed for the combined 8-month data and also disaggregated into monthly intervals for each explanatory variable. χ2 tests were applied to these analyses to assess the significance of the observed pattern of intervention usage. The SIM cards that used these mCDRs were analysed and expressed as percentages. Descriptive analysis of the CUG communication within and across each category of explanatory variable was also performed and expressed as number of voice and short messaging service (SMS) mCDRs records and their percentages. χ2 and Fisher’s exact tests were applied to these analyses to assess the significance of the pattern of CUG communication. To further understand how the CUG communication was used in each cluster, we identified SIM cards that used the CUG and the health facilities they communicated with. No tests were applied to this in-depth analysis as there were several empty cells. The voice recordings were transcribed during and continued after data collection. Transcriptions were done verbatim by non-data collectors. Each transcription was cross-checked by two persons (data collectors, including HBA). Data were manually analysed by thoroughly reading each transcript to identify themes, commonalities and contrasts emerging from the data that shed insights into the patterns of use of the intervention observed from the Vodafone call log data and why and how these patterns occurred using an inductive approach. Three of the study investigators performed the data analysis. Consensus on emerging themes was reached if a minimum of two of the data analyst agreed on an emerging theme.

Materials

N/A

Innovations

Based on the provided description, the study explores the use of an mHealth intervention to support clinical decision-making by front-line providers of maternal and neonatal healthcare services in Ghana. The intervention consisted of phone calls, text messaging, internet access, and access to emergency obstetric and neonatal protocols. The study used a mixed quantitative and qualitative approach to analyze SIM card activity data, conduct key informant interviews, and hold focus group discussions with intervention users. The findings revealed that the phones were predominantly used for voice calls, followed by data, text messaging, and accessing emergency protocols. The study identified individual health worker factors, organizational factors, technological factors, and client perception as influencing the pattern of intervention use. The study emphasizes the importance of considering these factors when designing similar interventions to optimize effectiveness.

AI Innovations Description

The study described the use of a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana. The intervention included phone calls, text messaging, internet access, and access to emergency obstetric and neonatal protocols. The study aimed to explore how and why the intervention was used by front-line health workers in a low-resource setting.

The study used a single case study design with multiple embedded subunits of analysis. The case was defined as “how and why a mobile phone-based front-line health worker clinical decision-making support intervention was used (or not).” Each embedded subunit of analysis was defined as “a district in which the intervention was deployed.” The study was conducted within the context of a cluster randomized controlled trial (CRCT) of the intervention’s impact on neonatal health outcomes in the Eastern Region of Ghana.

Data collection involved mixed quantitative and qualitative methods. Quantitative analysis was performed on SIM card activity data, including patterns of voice calls, text messaging, data usage, and access to emergency protocols. Qualitative analysis involved key informant interviews and focus group discussions with intervention users. The data were analyzed for themes related to the patterns of intervention use and the factors influencing usage.

The study found that the phones were predominantly used for voice calls, followed by data usage, text messaging, and access to emergency protocols. Over time, the use of all intervention components declined. The qualitative analysis identified individual health worker factors, organizational factors, technological factors, and client perception as factors influencing the pattern of intervention use.

The study concluded that the use of the mHealth intervention went beyond the technology itself and was influenced by individual and context-specific factors. These factors should be considered when designing similar interventions to optimize effectiveness.

Overall, the study provides insights into the use of mHealth interventions to improve access to maternal health. The findings can inform the development of innovative solutions to address the challenges in accessing maternal healthcare in low-resource settings.

AI Innovations Methodology

Based on the provided study description, here are some potential recommendations for improving access to maternal health:

1. Strengthening mHealth intervention components: Based on the findings that voice calls were the most commonly used component, efforts can be made to enhance the functionality and usability of voice call features. This could include providing additional training and support to front-line health workers on how to effectively use voice calls for clinical decision-making support.

2. Increasing awareness and education: The study highlighted the importance of individual health worker factors and client perception in the use of the intervention. Therefore, it is crucial to invest in awareness campaigns and educational programs to ensure that both health workers and clients understand the benefits and purpose of the mHealth intervention. This can help increase acceptance and utilization.

3. Improving technological infrastructure: The study identified technological factors such as attrition of phones and network quality as barriers to intervention use. To address this, efforts should be made to improve the technological infrastructure, including providing reliable and durable mobile phones, ensuring network coverage in remote areas, and addressing any technical issues that may arise.

4. Tailoring interventions to context-specific needs: The study emphasized the influence of context-specific factors on intervention use. Therefore, it is important to consider the unique needs and challenges of each district or region when designing and implementing mHealth interventions. This could involve conducting needs assessments and engaging local stakeholders to ensure that the intervention is relevant and effective.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify specific indicators that reflect improved access to maternal health, such as the number of antenatal care visits, the percentage of skilled birth attendance, or the reduction in maternal mortality rates.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or region. This could involve conducting surveys, reviewing existing health records, or analyzing available data sources.

3. Develop a simulation model: Create a mathematical or computational model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, health worker capacity, technological infrastructure, and contextual variables.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations on the selected indicators. This could involve adjusting variables related to the recommendations, such as the percentage of health workers using voice calls or the improvement in network quality.

5. Analyze results: Analyze the simulation results to determine the projected changes in the selected indicators. This could include comparing the baseline data with the simulated data to quantify the potential improvements in access to maternal health.

6. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data, if available. Refine the model based on feedback and additional data to improve its accuracy and reliability.

7. Communicate findings: Present the simulation findings in a clear and concise manner, highlighting the potential impact of the recommendations on improving access to maternal health. This information can be used to inform decision-making and prioritize interventions for implementation.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. Therefore, it is recommended to adapt the methodology to suit the needs and resources of the particular study or project.

Author

Dima Health

Statistics:

Citations: 11

Identifiers:

DOI: 10.1136/bmjgh-2018-001153

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Technology and Innovations, Workforce

Study Countries:

Ghana

Study Design:

Case Study, Cross Sectional Study, Exploratory Study, Grounded Theory, randomised-control-trial

Study Approach:

Mixed-methods, Qualitative, Quantitative, Systematic Review