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Background: Lay health workers (LHWs) are critical in linking communities and primary healthcare (PHC) facilities. Effective communication between facilities and LHWs is key to this role. We implemented a mobile health (mHealth) system to improve communication and continuity of care for chronically ill clients. The system focused on requests from facility staff to LHWs to follow up clients and LHW referrals of people who needed care at a facility. We implemented the system in two rural and semi-rural sub-districts in South Africa. Objective: To assess the feasibility of the mHealth system in improving continuity of care for clients in PHC in South Africa. Method: We implemented the intervention in 15 PHC facilities. The clerks issued recalls to LHWs using a tablet computer. LHWs used smartphones to receive these requests, communicate with clerks and refer people to a facility. We undertook a mixed-methods evaluation to assess the feasibility of the mHealth system. We analysed recall and referral data using descriptive statistics. We used thematic content analysis to analyse qualitative data from semi-structured interviews with facility staff and a researcher fieldwork journal. Results: Across the sub-districts, 2,204 clients were recalled and 628 (28%) of these recalls were successful. LHWs made 1,085 referrals of which 485 (45%) were successful. The main client group referred and recalled were children under 5 years. Qualitative data showed the impacts of facility conditions and interpersonal relationships on the mHealth system. Conclusion: Using mHealth for recalls and referrals is probably feasible and can improve communication between LHWs and facility staff. However, the low success rates highlight the need to assess facility capacity beforehand and to integrate mHealth with existing health information systems. mHealth may improve communication between LHWs and facility staff, but its success depends on the health system capacity to incorporate these interventions.
To assess the feasibility of a mHealth system to improve the continuity of care for clients in a PHC programme in South Africa. This was a mixed-methods evaluation study, implemented over 10 months, from June 2015 to March 2016. We used both quantitative and qualitative methods of data collection and analyses to assess the feasibility of the multi-component mHealth system [27,28]. This required a research team experienced in both approaches [29]. We used an embedded design in which the primary focus was on the quantitative dataset. We then used the qualitative data to understand and explain the quantitative findings [30]. We planned to implement the mHealth system in a rural setting, as we thought that these settings would benefit most from the improved communication offered through a mobile health system. Following consultations, the Western Cape Department of Health suggested that the study be conducted in two rural sub-districts in the Eden Health district (Flowchart 1), one of the seven health districts in the Western Cape Province of South Africa. These two study sub-districts were selected because each already had a well-functioning team of LHWs. The Eden district has a total estimated population size of 613,124 [31]. The two study sub-districts represented approximately 21% of the total district population and were substantially different in size (the population of Sub-district 1 estimated at 26,064 and Sub-district 2 at 101,298) [32,33]. Most residents’ first language is Afrikaans (73%), followed by isiXhosa (25%) and English (2%) [34]. In 2016, 40.5% of the population in the district lived below the poverty line (USD 320 per month) [31]. The Eden district performs slightly worse on key health indicators, compared to the Provincial average. For example in 2015, 16% of newborns were underweight in the district, compared to the 14.5% average in the Western Cape, and the maternal mortality rate was higher in Eden (69.9/100 000 live births) when compared to the Provincial rate (58.3/100 000) [31]. PHC facilities in the Eden sub-districts provide basic healthcare services, including treatment for TB, HIV/AIDS and non-communicable diseases, and maternal and child health services. The hospital in Sub-district 1 that participated in the study provides maternity services, basic surgery, and emergency services. Clients who needed to consult a doctor had to book an appointment in advance as doctors visited the respective facilities only on certain days of the month. As noted earlier, each of the selected two study sub-districts had a well-functioning team of LHWs (Flowchart 1), and most of these LHWs lived in the communities in they worked. In each of these districts, the Provincial Department of Health had contracted a non-governmental organisation (NGO) to recruit, manage and pay LHWs to provide community-based PHC services on behalf of the Department of Health. This form of contracting out is common in the Western Cape Province [35]. The LHWs received non-professional training on various topics related to the services they provide, which include supporting clients whom the health facility assigns to them as well as health promotion activities. The LHWs worked 4.5 h a week and earned between 99 and 122 US$ per month, depending on their level of training. At the time of the study, the NGO in Sub-district 1 had two LHW teams (29 LHWs in total), and the NGO in Sub-district 2 had four teams (64 LHWs in total). Each team was supported by a supervisor, who was a retired nurse. Demographic data for the LHWs and supervisors within each sub-district is detailed in Table 1. Settings in which the study was implemented Supervisor and LHW demographics No demographic data were collected about the clerks. The three key role players involved in the implementation of the mHealth system were the LHWs, their supervisors, and mHealth clerks, henceforth referred to as clerks. Each facility manager appointed one staff member, in most cases from the administrative staff, to act as clerk. Managing the mHealth system was in addition to the clerks’ other duties. LHWs were given smartphones for the project, and clerks and nurses tablets, to manage the system. The main feature of the system was to enable LHWs to record their routine client visits, that is monitoring how they were doing on treatment, do pill counts, and conducting general health assessments in clients’ households, on project-funded smartphones. The system enabled real-time access for supervisors to these reports. The system also enabled two-way communication between LHWs and clerks. LHWs, supervisors and clerks received 2 days of training on how to use the system, offered by a for-profit mHealth service provider (Mobenzi), who developed the system. Thereafter, the implementation staff had 2 weeks to practice using the system before the system went live. Though there were a paper-based recall and referral system in use before the intervention, it was not standardised. The mHealth system could be considered a completely new system to the participating facilities. The mHealth recall and referral process were as follows (Figure 1): Firstly, the healthcare professionals at the facility instructed the clerk to ask an LHW to locate and advise a client to return to the facility (hereto referred to as recalls, Figure 2). The clerks issued these requests through the tablet, and these were received by the LHW on their smartphones while working in the community. Real-time communication ensued between clerks and LHWs when they discussed recall progress using the system. The supervisors’ role in the recall process was added in month 5 of the implementation, after this was requested by them. The late addition was due to pre-project consultations suggesting that this functionality was not necessary. mHealth recall and referral system Example of the recall format of correspondence between the LHW and clerkfacility Secondly, when LHWs identified a person, who could have been an existing client or someone else in the community with a health problem, for example, headaches or wounds requiring care, they would advise that person to seek care at the facility (hereto referred to as referrals, Figure 3). LHWs sent a notification of these instances to the clerk’s tablet, using their smartphones. The clerks closed recalls and referrals, respectively, as successful, when the person arrived at the facility, or unsuccessful when the person failed to attend at the facility. Example of the format for a referral sent by an LHW to the health facility The digital health interventions included in the mHealth system evaluated in this study targeted healthcare providers and can be classified as follows, using the World Health Organization’s classification of digital health interventions [36]: The main question we wanted to answer using quantitative data was whether the mHealth system allowed facility staff and LHWs to, respectively, recall existing clients and refer community members with health concerns to healthcare at the health facilities. All recall and referral data via the mHealth system in the two study sub-districts were stored on a Mobenzi server and exported to Excel by the research team. The data included a date and time stamp, sender and recipient, geographic location, and content of the messages between LHW and clerk. As reasons for recalls and referrals were recorded without predetermined categories, the research team manually coded the recall and referral reasons, categorising them according to most frequent reasons – the codes developed are shown in Table A1. As we collected service indicators that were not in use in the study sites prior to this study, baseline data were not available. The qualitative component of the study aimed to provide an understanding of the implementation processes and how the participants perceived the mHealth system. These data were intended to help us contextualise the quantitative findings through incorporating participant perspectives. We used two methods of qualitative data collection: semi-structured individual and group interviews, conducted at the end of the project with all of the participating LHWs, supervisors, clerks, and facility managers; and a fieldwork journal kept during the implementation of the study. WO collected the data. The interview questions included how LHWs, supervisors, and clerks felt about using the mHealth system; whether this system changed their practices; their views regarding barriers and facilitators to implementation; and how the mHealth system compared to the paper-based system. We invited the facility managers to join the interviews with the clerks, as it was important for us to ascertain their perceptions, experiences, and recommendations, too. We include the findings from seven clerk/manager interviews, as these cadres were key to the implementation of the mHealth system within the facility, and had a good overall view of the implementation processes. In total, 12 of the 15 clerks, and three of the eight facility managers participated in the interviews. In some instances, facility managers were responsible for two facilities, and the four participating mobile facilities were managed by some of the ‘fixed facility’ managers. The remaining three facilities and clerks were not available at the time that WO conducted these interviews. The staff of some facilities were interviewed together as this was the most convenient approach for gathering data from staff in remote, neighbouring facilities. The fieldwork journal detailed the researcher’s reflections and observations during the fieldwork visits. For instance, visiting the LHWs in the most remote areas highlighted the challenges of regular contact with, and reporting to, supervisors based in the main towns in the respective sub-districts. Recalls were categorised as successful if the clerk recorded that the client attended the facility as requested. Failed recalls included the following categories: (i) clients who failed to attend the facility as requested; (ii) unclosed recalls, i.e. where either the clerk or LHW did not respond to the other’s latest correspondence; (iii) LHWs who did not view the recall; and (iv) clerk errors, for example, when the clerk sent the request to the wrong LHW. There were no time limits on keeping recalls open. Referrals were categorised as successful if a person attended the facility for the reason he/she was referred by the LHW. Referrals expired within 14 days of being issued and were then categorised as failed referrals. We collected data from June 2015 until February 2016, the second last month of the study, to ensure that recalls and referrals issued in February could be acted upon by the end of the study in March 2016. We used Excel and R statistical software (https://www.r-project.org/) for the descriptive statistics, calculating facility- and sub-district level averages of recall and referral numbers and success rates, and to present participant demographics. In order to understand and contextualise our results, we applied a qualitative content analysis approach to the interview data from clerks and facility managers [37]. We used Atlas.ti version 8.1 (https://atlasti.com/product/v8-windows/) to conduct this content analysis. As we were primarily interested in explaining the recall and referral outcomes, we analysed the interview data deductively at the manifest level [37]. We used both the apriori themes from the interview guides, including, e.g. barriers and facilitators to implementation, while allowing additional themes to emerge from the data to explain our quantitative results. SA read and reread the transcripts to familiarise herself with the data and generated condensed meaning units from the data. These meaning units were then further reduced to codes, from which categories were generated that related directly to the quantitative data, see Table 2 for an example of the analysis. The analysis was checked by WO, and differences resolved by discussion. WO referred to the fieldwork journal as the interviews were being analysed, looking for content that could illuminate and further explain the views that participants shared during the interviews. The qualitative and quantitative data are reported in parallel in the results below. Example of qualitative analysis Categories, sub-categories and example codes and quotes (sub-category in bold where codes and quotes are related). Prior to conducting this study, ethical approval was obtained from the South African Medical Research Council (EC016-11/2014). The approval included the participant information sheet and informed consent form signed by all participants. All interviewees provided signed informed consent, and the interviews took place in a private space in their respective facilities, at a time that was convenient for them. The interviews were conducted in the language preferred by participants, which was predominantly Afrikaans. Interviews were audio-recorded, transcribed and translated into English. Before the study commenced, we provided the LHWs with an information flyer in plain language and asked them to use this when describing the study to their clients. LHWs were instructed during the training to only use the mHealth system after clients were briefed and had given consent to participate.
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