Health Worker mHealth Utilization: A Systematic Review : CIN: Computers, Informatics, Nursing

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Health Worker mHealth Utilization

A Systematic Review

White, Alice MS; Thomas, Deborah S.K. PhD; Ezeanochie, Nnamdi MD; Bull, Sheana PhD

Author Information
CIN: Computers, Informatics, Nursing 34(5):p 206-213, May 2016. | DOI: 10.1097/CIN.0000000000000231


The use of cell phones worldwide has expanded rapidly over the past decade in both developed and developing countries. By the end of 2013, there were 6.8 billion mobile-cellular subscriptions globally.1 Close to 100% of the population was covered by a mobile signal, a drastic increase from 20% coverage in 2003.1 Ownership of mobile phones is increasing worldwide, even in poor-resource settings.2 The universality of cell phones provides an opportunity for their use in broad and scale up of technology-based health interventions, particularly in developing and resource-poor areas.

Mobile platforms, such as phones and tablets, have tremendous potential to affect healthcare delivery and health outcomes. A proliferation of innovations that integrate the use of mobile and wireless devices to improve health outcomes, healthcare services, and health research into care delivery, often called “mHealth,” has occurred concomitantly with the growth of cell phone usage.3

Researchers have implemented mHealth applications in a range of settings and multitude of health targets4 for facilitation of care delivery, medical records charting, patient and health worker education, disease prevention, and patient self-management. These tools can improve surveillance, clinical care, prevention, and self-management. Furthermore, they have the potential to expand population-level public health impact through wider dissemination and scale-up for widespread use.5 Successful mHealth interventions intensify their effects when they are guided by behavioral and social science theory to help in the design, implementation, and analysis of effects.6

Although mHealth has previously focused on prevention and self-management for behavioral change at the individual level, attention has recently broadened toward targeting the healthcare worker as a possible sustainable intervention model. For this review, the authors considered healthcare workers in developing countries who are foundational to the success of delivery systems. Health workers in developing countries have a range of education, experience, and status within the healthcare system. Positions include informal community health workers (CHWs), such as community leaders, who may not have any formal education; paid CHWs with formal education and training who provide care to community members in rural and urban settings; and paid clinic-based health workers who are primarily located at health facilities. This range of health workers is integral to providing healthcare in rural settings, where infrastructure obstacles, such as transportation, prevent consistent healthcare. The success of programs that target this diverse group providing care is dependent on resources, training and education, and supervision.7 Evidence shows that mHealth improves communication, decreases transportation time, decreases program costs, improves data quality, and increases access to resources.7 Integrating mHealth solutions for all types of health workers may have the potential to increase efficiency and quality of care delivery, resulting in more positive effects on patient and population health.

While multiple reviews of mHealth in these settings have recently been published, this review is unique in several ways. Hall et al8 include an assessment of mHealth interventions that target individuals to improve their health behaviors and outcomes. Here, the focus is on health workers and builds on the recent work of Källander et al,5 who conducted a systematic review of mobile health solutions for CHWs in diverse settings. This work expands on previous reviews in two ways. First, the methods employed for selecting and including articles is comprehensive rather than exemplary. Second, the findings focus on advantages and disadvantages of each type of mHealth solution as evidenced across a diverse number of studies. While Braun et al7 reviewed mHealth solutions and included strategies for health education more broadly beyond care delivery and included the use of social media to promote health more generally in their review, this review is more focused, emphasizing how mHealth can improve health worker professional experiences.


A systematic literature review was conducted of mHealth interventions targeting health workers in low-resource settings published between March 2009 and May 2015. Inclusion criteria for the review included studies focused on the use of mobile technology by a health worker in a low- or middle-income country. Articles without a technological intervention targeted at health workers were excluded. Telemedicine, remote diagnostic tools, and tools specific to education in medical school were also excluded. The PubMed database was used to systematically search a combination of Medical Subject Headings (MeSH), listed in Table 1. This article focused on PubMed because it indexes articles from more than 70 countries, making it particularly appealing to synthesize research from global settings.9 Terms were categorized by technology user, technology device, use of technology, and health outcome. Terms within each category were linked with “OR” statements and terms between each category were linked with “AND” statements. For the full search entry, see PubMed Database Search Entry, May 2015 (Supplemental Digital Content 1,, which lists specific terms and operators. Searches were limited to English articles studying humans. Articles were then screened by title and abstract. The full text of all remaining articles was read. While reading each full article, reviewers tracked the primary user, country, disease or condition, study design, theory, and technology use. Reviewers documented the objectives and primary findings for each article in an effort to facilitate a synthesis of findings across studies.

Table 1:
MeSH Terms Use in PubMed Database Search

A total of 1017 potentially relevant articles were identified through the PubMed database. Of these, 662 articles were excluded based on the title. Subsequently, 303 articles were excluded because the abstract did not meet the criteria. The full text of 52 articles were reviewed. Of these, 21 articles were excluded because they did not focus on utilization of technology by a health worker in care delivery. Thirty-one articles were included in the final review. A κ score of 0.90 was calculated based on the results of a secondary reviewer.


Reviewers categorized objectives and primary findings according to intervention targets at different levels of healthcare delivery. Ultimately, review findings were summarized by and organized into four major groups: (1) data collection during patient visits, (2) health worker and patient communication, (3) communication between health workers doing outreach in the community and those located at clinics or hospitals, and (4) population surveillance. The articles are summarized according to these groupings in Table 2.

Table 2:
Summary of Systematically Reviewed Health Worker mHealth Articles

Six of the 31 articles were grouped into more than one area (see Table 2). Specifically, 14 articles were related to health data collected at a patient visit to facilitate patient care (group 1). For example, electronic medical records would fall in this category. Seven articles were identified as communication between a health worker and patient (group 2). For instance, health workers would text patients to remind them to take medication. Twelve articles were allocated to communication between health workers (group 3), such as field health workers accessing electronic decision-making aids or contacting a hospital-based physician for decision support. Finally, six articles were assigned to group 4, data collection for surveillance or research-based purposes. For example, community-based interviewers collected sociodemographic data in household surveys. One study employed a crossover design, one study employed cross-sectional surveys, eight were cluster-randomized trials, three were mixed-methods surveys to assess acceptability and ease of use, and the remainder (18) were program evaluations (without control groups).

The most common primary user of the technology was a CHW (14 studies). Other users included clinicians (two studies), pharmacists (one), midwives or birth attendants (five), community interviewers (one), village elders (one), peer mentors (one), field worker (one), caregiver (one), mobile healthcare worker (one), clinic and community health assistant (one), rural health workers (one), and laboratorians (one). The technology used was short message service or text messaging (12 studies), combination text messaging and voice (2), short message service mobile researcher (2), electronic medical record (2), or smartphone/smartphone application/or personal data assistant (13). Most studies were in Africa, including Ethiopia (two), Ghana (two), Kenya (five), Malawi (two), Nigeria (one), Rwanda (one), South Africa (five), Tanzania (three), Uganda (three), and Zambia (one). Other studies were conducted in Bangladesh (one), China (one), Colombia (one), India (one), Indonesia (one), and Peru (one). Health outcomes studied included AIDS/HIV (5), prevention of mother-to-child transmission (PMTCT) (3), maternal and child health (10), malaria (4), tuberculosis (1), cardiovascular disease, and multiple outcomes or general health (7).

Summary of Findings by Group

The following is a summary of the findings across studies by each of the four groups.

Group 1: Health Data Collected at a Patient Visit to Facilitate Patient Care

Fourteen articles had a goal of improving health data collection at a patient visit to facilitate patient care, of which one was a cluster randomized control trial,10 one was acceptability survey,11 and 12 were program evaluations.12–23 Several consistent themes emerged from these articles, including a high degree of acceptability with a paradoxical low degree of use, documentation of improvements in data quality with mHealth approaches, and identification of barriers to mHealth related to preexisting systemic data management problems.

While several studies documented a high level of interest and acceptability among health workers,12,15,17,20–22 they also documented low actual use and challenges in use, particularly without incentives other than improved work efficiency (eg, monetary incentives or personal phone-use incentive and no penalty for not using the technology).13,17,22,23 As such, there was a high demand and need for training with the mHealth technology, as well as training to reinforce skills and health worker responsibilities.13,16 A study on newborn weights found an increase from 40% to 100% accurate birth weights (recorded within 1 week of birth) because of the efficiency of a mHealth intervention compared with pen-and-paper systems.15

Many studies focused on mHealth use at the interface between the healthcare worker and the patient identified underlying issues with the healthcare worker system that were not unique to the mHealth intervention. These included perceived stress from heavy work and patient caseloads, the belief that patients should have greater autonomy regarding their health, and resentment that health workers would not be compensated for additional work generated from using a phone. Patient time increased with the mHealth interventions primarily because questions could not be skipped and visits were more thorough.13,22 While these outcomes may not be directly related to the mHealth intervention, but rather a symptom of the broader healthcare system, the reviewed studies acknowledged the importance of considering these factors during an intervention, as they may be assuaged or aggravated by the intervention. For example, stress from heavy work and patient caseloads could be increased in the short-term as workers must be trained on how to use the technology. In turn, the efficiency of the technology may result in an increase in patient load, which was generally viewed as a success to the program overall, but resulted in stress to the individual worker.

Group 2: Facilitating Communication Between Health Workers and Patients

Seven articles studied communication between a health worker and patients, with the emphasis on improving health worker efficiency by saving travel time and gaining work time.10,11,21,24–27 Texts focused on increasing access to skilled attendants at birth,25 patient medication adherence,21,24 appointment reminders,21,25 and tracking patients.10,11 There was greater improvement in urban areas as compared to rural areas in health outcomes for patients after a text message reminder intervention,25 but this was not the case in a program directed at pharmacists to help their patients increase adherence through text.24 Fuel savings and travel time-savings were substantial for both health worker and patient,21,26 and it became easier to enroll patients into programs.21

Group 3: Facilitating Communication Between Health Workers

Twelve articles studied communication between health workers.11,21–23,27–34 Communication by mobile phone was highly acceptable to health workers.29–31 Communication, mostly via text messages and phone calls, improved patient outcomes and health worker efficiency with increased protocol compliance, decreased error rates, and decreased time and expense spent contacting supervisors.28,31,34 Communication between health worker and supervisor happened more frequently and efficiently when health workers did not have to travel to the clinic or institution31 and when they had access to systems that linked patient data, such as an electronic medical record system.14 In addition to improving patient health outcomes, text message reminders facilitated an adherence to protocols, which had not been previously followed.28,34 Another found that traditional birth attendants increased their skills and confidence using mobile phones to access information via mobile phone on managing birth complications.27

Group 4: Data Collection for Surveillance or Research

Six articles studied data collection for surveillance or research-based purposes. These articles were concerned primarily with differences between pen-and-paper collection and personal data assistant or smartphone collection in areas where interviewers collect information in low-resource settings.35–40 These studies found that mobile phone systems improved pen-and-paper systems because they were easier to transport,20,37,38 had significantly fewer data entry errors,37,38,40 were more cost efficient,37,38 and could detect data falsification or troubleshooting survey problems.37,39 Overall, these studies found that mobile phone use, particularly smartphones, resulted in significantly more efficient and reliable data collection than traditional pen-and-paper methods.

Advantages and Disadvantages

Advantages cut across all four of the groups reviewed, including acceptability, usability, health and program outcomes, technical infrastructure, data quality, and cost. Specific examples with each of the four groups reviewed are outlined in Table 3. Health worker acceptability, or the acceptance of using technology to facilitate their work, was generally very high in qualitative surveys.13,19,20,29 In studies comparing pen-and-paper data collection with mobile device collection, researchers consistently observed improvements in data quality.15,19,37,40 Some improvements in health outcomes were observed,11,25 and many increased program enrollment due to better organization and workflow.15,21,32 While initial startup costs were high, phone replacement was low, and most studies reported minimal ongoing maintenance costs.14,15,31,38–40

Table 3:
Advantages and Disadvantages to Using Technology to aid Remote and Rural Workers

Most studies also reported disadvantages to applying technology, many of which were related to existing infrastructure or healthcare challenges, including Internet access, availability of electricity, theft and security, health worker education level, and absence of local skills in programming and technological operation.11,35 While acceptability was high, actual use was low when the existing alternative was still available.13 There were technical issues related to infrastructure, including Internet access and electricity.11,13,19,37 As mentioned above, maintenance costs were minimal and programs usually resulted in cost savings, even when initial investment was high.15,31,38–40 One article found no improvement in medication adherence after the intervention.24

Although not mentioned explicitly as a disadvantage, an important criticism noted from the review is the very limited attention to theory in design, implementation, or analysis of mHealth for health workers, either from behavioral and social science or computer science. Only two of the 31 reviewed articles explicitly mention the use of theory to guide their work.27,30 Having a theoretical perspective in mHealth has been identified as critical to enhance program effects, albeit for interventions targeting individual behavior change and health outcomes rather than health worker.6 In systems design, a growing attention to theory in the design of user interfaces has been shown as important to increase acceptability and usability of programs.41


This article presents a synthesis of the findings from 31 peer-reviewed studies related to the use of mobile technology by health workers in resource-limited settings. The review identified four main groups where mHealth innovations have been used for health delivery improvement, including data collection during care delivery, health worker and patient communication, communication between health workers and the care delivery system, and health surveillance activities.

Overall, the findings demonstrate a substantial benefit to healthcare workers, their patients, and care delivery systems when mobile technology tools, such as smartphones and tablets, are used. Acceptability of these tools for care delivery is high, and evidence shows that the use of mHealth tools can improve communication between health workers and their patients, health workers and clinic staff, as well as between health workers and their supervisors. Use of mHealth tools by health workers is associated with improved compliance with treatment protocols among patients and improved health outcomes. mHealth tools are used successfully in surveillance efforts to improve quality and efficiency of data collection.

The articles reviewed also identified some important limitations to the use of mHealth tools for healthcare delivery in resource-poor settings. Although there is high acceptability of tools, there is not universal and continued use. This suggests that incentives are needed to facilitate adoption and use that are targeted at various components of the healthcare system. For example, incentives can be aimed at the health worker through training or monetary compensation. Additionally, policies that obligate use can be established at the systems level. However, before policies that require use of mHealth tools can be realistically established, a careful assessment is likely needed to ensure organizational readiness to train users and offer technical support for devices and data management.

The variability in success across urban and rural settings, suggesting greater benefit in health outcomes among urban compared to rural populations, is an additional limitation to mHealth tools. Although it is not completely clear why this variation may exist, one explanation could be that urban populations may have greater access to and utilization of technological tools. This suggests that careful attention is needed to the availability, distribution, and reasons for cell phone usage across populations served by health workers to ensure using mobile devices, particularly for communication between health workers and patients, is appropriate.

While this review is limited inasmuch as the focus is from a limited time frame, does not include industry reports and publications that are not peer reviewed, and may reflect a positivity bias related to those articles accepted for peer-reviewed journals, it still offers important insights that can be useful to healthcare providers, administrators of care delivery systems, and researchers in mHealth. Because it is becoming increasingly more acceptable and common to integrate smartphones and tablets into primary care delivery in resource-poor settings, systematically understanding the successes and shortcomings is relevant for ensuring best practices become applied.

The information presented in this synthesis reveals numerous advantages for using technology as an integral part of healthcare delivery and suggests that widespread acceptance of these tools may contribute to overall improvements in quality and outcomes. However, more research is needed to understand whether and how the use of phones translates into improvements in health outcomes for patients and improvements in population health for communities.

This systematic review suggests a path for mHealth integration into healthcare delivery, developing appropriate technology and administrative infrastructure to support such initiatives. As implementation increases, a critical consideration of costs associated with technology infrastructure will be required to evaluate whether investment in this infrastructure is warranted. It may be that the existing more “low-tech” approaches to data collection are sufficient. However, if decision makers determine that infrastructural investment in technology for healthcare delivery is appropriate, then attention to multiple areas to maximize this investment is needed. Several careful considerations are necessary, including equipment choices (computers, servers, phones, and tablets), sufficient staff who can program and maintain such equipment, development of protocols and training programs for healthcare workers to effectively use technology, development of policies and incentives to motivate use, and attention to regular process evaluations to ensure efficiency and quality in data collection and communication.


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Community health worker; Developing countries; Mobile phone; mHealth

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