Factors that influence delay
Factors influencing delay in maternal healthcare according to the TDM model are summarized in Fig. 4. They are divided into three categories: socio-economic/cultural factors, accessibility of care, and quality of care. In addition, all factors influencing care are separated into those on the demand side (the patient) and those on the supply side (the healthcare provider). Following paragraphs further explain challenges identified in: Demand side through literature research, demand side through field research, supply side through literature research, and supply side through field research (i.e. semi-structured interviews and focus group discussion), respectively.
In previous research in the area, financial dependence of women, lack of education, and traditional beliefs were stated as the main of causes of delay in seeking care (first delay) on the demand side [45, 46]. Another key factor influencing the first delay identified in the literature was social stigma [10]. In [8, 47] pregnant women declared that distance to facilities, geographical situation and their economic status makes access to emergency care impossible at nighttime, hence, affecting the time it takes to identify and reach a healthcare facility (second delay). Finally, negative previous experiences came up as an influencing factor for the first and third delays [48]. As a result of these factors, among other problems, only 20% of women in the area go for ANC during the first semester. ANC visits are important to early detection and prevention of pregnancy related complications.
Some of these results were confirmed during the field research. On one hand, during the focus group discussion and in the semi-structured interviews with nurses, different stakeholders explicitly explained how lack of education led to maternal deaths. Furthermore, CHNs and stakeholders, confirmed poor road infrastructure, geographical situation, and the communities’ economic status as factors affecting the second delay during interviews and focus group. The lack of affordable transportation was a new factor that CHNs and mothers in the focus group identified during the field research. On the other hand, social stigma and negative previous experiences as factor influencing the quality of care (third delay) and first delay did not come up frequently during the interviews, but some stakeholders freely talked about it during the focus group.
On the supply side, the literature identifies social stigma, staff attitude towards patients, and competitiveness between facilities as factors influencing the first delay. Lack of equipment and human resourcers, organizational loopholes, and poor road infrastructure were identified as factors affecting the second delay, particularly in emergency cases [47, 48]. Finally, the literature reported that TBAs are a preferred choice of care due to their social recognition [10]—however, their lack of training affects the third delay.
On the field research, lack of equipment and human resources, organizational loopholes, poor road infrastructure and TBAs as prefered choice of care were confirmed during interviews and focus group discussions. A factor that was found only in the interviews was a lack of knowledge of patients previous complications, and how it affects the first delay. Finally, factors that were identified only in the interviews and affect the first and third delays were: inconsistency in communication, a poor referral system, and lack of access to current knowledge.
Ehealth solutions to reduce delay
Figure 5 summarizes how eHealth solutions can help reduce delays in receiving MHC. The solutions are further developed in the following subsections.
Delay 1: Decision to seek care
As shown in Fig. 5, the literature reported two types of eHealth solutions with the potential to reduce the first delay. The first type is spreading health information using mobile phones, mainly through SMS or voice messages [24, 32]. Other alternatives use hotlines and call centers [28]. The aim of these solutions is to bridge the gap between communities, health facilities, and information services. Due to low mobile phone penetration in rural areas [49], some solutions rely on the use of the mobile phones of community volunteers. Previous studies showed the potential impact of these solutions to inform decisions to seek care [22, 50, 51]. In addition [32], showed that this intervention increased skilled birth attendance, but it did not find evidence of increased knowledge among recipients.
The second type of solution aimed at providing mobile financial services to pregnant women. It allows them to save and access financial services for MHC. Available studies outline the potential of fusing financial mobile services with healthcare [31]. However, the impact of these solutions has not been evaluated yet.
Delay 2: Identifying and reaching a facility
There is no general agreement about the best way to apply eHealth to reduce the second delay. Typical initiatives connect health workers in isolated areas with emergency systems through phone calls and SMS [34, 51]. Communication systems are effective at reducing maternal deaths and increasing skilled birth attendance. However, in order for these to be effective, availability of transport and good road infrastructure are also required [29].
Delay 3: Receiving adequate care
Three types of solutions for reducing the third delay have been identified: solutions that improve clinical practices, those that offer care at a distance, and health management systems. Electronic medical records (EMRs) and decision support systems are typical ways to facilitate improved clinical practice. EMRs systems have the potential to reduce medical errors as well as improving referrals and coordination between facilities. These systems are scaling up in developing countries. Decision support systems, including checklists and questionnaires, are effective at improving clinical and patient outcomes [33, 37]. Although EMRs and decision support systems are used in different facilities, their use is rare in remote areas [27, 39].
The second type of solution, eHealth to facilitate care at a distance, relates to telemedicine like monitoring and communication systems between clinicians [38]. These systems offer services that provide care of similar quality to conventional care, but cost less [52]. Also known as telehealth systems, they allow the remote analysis of patient information by well-trained medical staff. Typical outcomes are improved diagnostic accuracy, reduced waiting times, and improved referral management [35, 36]. Remote monitoring systems have shown great potential for the management of chronic diseases [40]. Therefore, they can be applied in monitoring high risk pregnancies [26].
Outcomes in patient care from the use of health management systems, such as hospital management software, have not been measured. They have the potential to improve efficiency and reduce cost by improving logistics and the allocation of needed resources [53]. Furthermore, data about healthcare in remote areas can be gathered through these systems, increasing knowledge and facilitating research to improve MHC [38].
Challenges to eHealth implementation
In this section, the different challenges to eHealth implementation are explained, based on the 5C model proposed by Dr. Peter Drury in [44]. Accordingly, they are divided into the five different components: context, content, connectivity, capacity, and community.
Context
Ehealth solutions must be adapted to the poverty context. Semi-structured interviews showed how health workers perform their jobs with very limited resources; they need to make the most of what they have. Therefore, the first step to providing solutions that have a positive impact on patient outcomes is understanding the needs of health workers. The ICT infrastructure assessment showed that remote areas face infrastructure problems such as poor or non-existent roads, limited access to electricity, and lack of telephone lines. In addition, high temperatures, humidity, and dust are prevalent in these communities. Regarding funding, the adoption of the SDGs by the Ghanaian Government leaves few financing opportunities for eHealth projects. All of these factors need to be considered before designing an eHealth solution.
Community
Rural communities in Kpando are characterized by low education levels and strong cultural beliefs. During the interviews, it was identified how these characteristics highly affect the opinion and attitude towards maternal health services. Pregnancy is not seen as a risk, and insufficient information is available on safe practices during pregnancy. Moreover, both field and literature showed how the decision to attend healthcare is influenced by family and previous experience. Thus community characteristics have to be considered in the design and implementation process.
Previous research demonstrated that the decision about what facility to attend is influenced by health workers and facility reputation [10]. The reputation is based on previous experiences of friends or family and direct knowledge of the staff. In the geographical area of this study, TBAs and traditional medicine are common. The community highly respects and trusts the practice of TBAs. They are easy to reach, and their services are affordable [8]. During the focus group discussions, TBAs explained how GHS had stopped providing them with training. In these discussions, other stakeholders (mothers and fathers) agreed that providing training to TBAs could be the key to improving MHC in islands and remote communities.
Previous research showed that women who receive MHC are generally satisfied with the care [46]. However, literature and field research showed how stigma toward teenage pregnancies and single women negatively influences the attitude of health workers toward their patients. This affects the information patients share with health workers. For instance, health workers provided examples of how patients might hide information about previous complications and sexually transmitted infections (STIs).
Capacity
The 5C model refers to capacity as technical capacity (i.e. infrastructure available), and manpower capacity, meaning not only the amount of human resources available but their skills (both technical and medical) [44]. Around 80% of the interviewed health workers had basic IT skills and own smartphones. However, training will be needed for the effective deployment of an eHealth solution. Unfortunately, staff rotation between facilities is common (as was observed during the research period), which could be a challenge for the effectiveness of this training.
The ICT infrastructure assessment showed that infrastructure is limited. Medical and IT equipment is often insufficient in CHPS zones. Technical support is not available, even in urban areas. Health workers pointed out broken equipment that could not be fixed due to the lack of technical support. When health workers were asked about community outreach, they all pointed to a lack of healthcare personnel. Therefore, outreach programs are conditioned to the number of patients that attend the facility at a given time. Two health workers even gave real examples of how the lack of doctors had recently led to maternal deaths.
Connectivity
Lack of wired networks is the main challenge for connecting rural and urban areas. In rural areas, network coverage and mobile phone penetration provide new connectivity opportunities. However, the coverage in rural areas is very unstable and varies between operators. Network coverage was measured in the selected health facilities and communities. The results show that 2G and 3G are available in 86% of the facilities; however, 3G is available 20% of the time, while 2G is available 76% of the time—and 4% of the time no connection is available. Facilities in urban areas have good mobile connections to allow video and web communications, but in rural areas the capacity is limited to web browsing. Nevertheless, web browsing typically requires more bandwidth than most eHealth applications. Thus, the bandwidth of rural areas should often be enough. Blind spots, where even SMS and phone calls are difficult, are an additional problem. Nevertheless, the ‘blind spot’ limitation occurs frequently in hospital workplaces worldwide.
Content
A key factor identified during the interviews is how different facilities follow different protocols. Sometimes the care provided is not evidence-based; it is common to base diagnosis only on the answers to standard questions, without physical examination. Furthermore, the official referral guideline systems are not always well applied.
Health workers identified lack of structure and unavailable data about pregnancy complications as negatively influencing the care provided. The relevant information is necessary for appropriate diagnostics. However, in isolated communities, low attendance at the facilities and poor record-keeping results in relevant information being seldom recorded.
Solution outline
This subsection outlines an eHealth solution that might optimize efficacy and feasibility based on the findings this work. Additionally, it might address the some of the main needs in MHC for patients and health workers in the situation considered in this work. Moreover, it is designed to fit under the current work practices of Kpando and areas with similar characteristics. A schematic is presented in Fig. 6. This solution, falling within the field of mHealth, is an application for Android mobile phones or tablets handled by front line health workers (CHNs in CHPS zones). It is cloud-based and uses the mobile communication networks available in the facilities.
The solution should be focused on providing a pregnancy monitoring system and bridging the distance between facilities and communities. It has the potential to make a substantial impact in the third delay and solve direct challenges in content and context as defined by the 5C model. To fulfill this potential, decision support systems and data gathering services need to be included in the application; they would help develop evidence-based birth plans to increase skilled maternal health attendance. The solution should be adapted to fit into the workflow of healthcare facilities in order to be used in facilities and outreach programs. Equipping CHNs with mobile phones or tablets and basic medical equipment to monitor pregnancies would be a requirement.
The application would allow facilities to share information, improving patient tracking and collaboration between professionals. It would provide health administrators with access to data summaries, which could improve reporting and logistics systems — addressing the content and capacity of the 5C model. The system should be designed to work offline, asynchronously sending the information to the cloud server whenever a connection is available. Cloud services are accessible in Ghana and allow remote maintenance of the network, a feasible solution to reduce connectivity challenges.