The use of clinical decision-support tools on mobile devices may help primary healthcare providers, including frontline healthcare workers in developing countries, to improve the quality of services provided. The provision of appropriate, evidence-based, quality healthcare services is a concern of global policy makers.
Description of the condition
To improve the quality and safety of preventative and clinical healthcare services remains a challenge. There is widespread recognition that the quality of healthcare services varies widely, and is often suboptimal across healthcare systems (Moja 2014). In primary healthcare, despite the availability of knowledge, and specific diagnostic, treatment, and management protocols, there is often a discrepancy between the knowledge and the application. This know-do gap in the quality of healthcare has been widely cited as a key barrier to improving healthcare outcomes (Blank 2013; Mohanan 2015). A range of systemic factors contribute to the deficiencies and challenges in delivering high-quality evidence-based healthcare services.
Globally, the World Health Organization (WHO) projects a shortfall of 12.9 million healthcare providers by 2035 (WHO 2014). Having the right type of provider, at the right time, at the right place, continues to be a challenge worldwide. Clustering of health personnel in capital cities and other urban areas, and out-migration from low- and middle-income countries (LMICs) to high-income countries (HICs), further diminishes the number of healthcare providers available in rural areas (Dussault 2006). Especially in LMICs, the lack of trained primary healthcare providers has prompted policy makers to explore a shift of key tasks from higher to lower cadres of health workers (Baker 2007; Lehman 2008; WHO 2007). While the transition of vital primary healthcare services to a lower cadre of workers is feasible, it requires ongoing training support to ensure that service quality and safety standards are maintained (Rednick 2014; WHO 2007).
In other clinical settings, even when an adequate number of providers with the right training are available, the quality of care is variable. Busy healthcare providers may struggle to stay abreast of current evidence and apply it consistently. They may also lack information on alternate risk-reduction approaches, and not be sure which treatments work best (Kocher 2010). Time-constraints may result in the omission of essential information for counselling the patient, and long work hours may result in increased errors (Bright 2012).
Standardized protocols, which provide critical information at the point-of-care, support decision-making and guide healthcare providers through the process of diagnosis and management. They can introduce efficiencies into the system, optimize the time with the client, and improve the overall quality of services (Bright 2012; Mickan 2014).
Description of the intervention
Several challenges encountered in clinical practice could benefit from the use of clinical decision-support systems. A clinical decision-support system (CDSS) is 'any electronic system designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patient-specific assessments or recommendations that are then presented to clinicians for consideration' (Kwamoto 2005). The increasing ubiquity and affordability of smartphones and tablets has made it possible for clinical decision-support tools to become available to healthcare providers on handheld devices at the point-of-care. Mobile decision-support tools can potentially address some of the challenges faced by many healthcare systems regarding adequate training of healthcare providers, shifting clinical tasks from clinicians to frontline health workers with limited formal training, and improving the quality of preventative, diagnostic, and treatment care across a range of health issues.
Over the last decade, personal digital assistants (PDAs), and other wireless mobile devices, such as smartphones and tablets are increasingly being used at the point-of-care to obtain evidence and guidance on clinical conditions, do necessary calculations for drugs, and access other medical information. They even support more advanced clinical decision-support systems linked to a comprehensive patient database (Divall 2013). Where before, in most low-income settings, only basic features, such as voice calls (or interactive voice response (IVR)) and short message service (SMS) were commonplace, the pace of growth in mobile technology increasingly allows for a range of functionality — low-cost access to the internet, high quality cameras for still and video footage, applications stored on-device, pre-loaded audio or video clips and images, global positioning service (GPS), and the potential to connect additional sensors and devices — that allows for the development of very sophisticated point-of-care decision-support systems, even in low resource settings.
Mobile CDSS may vary in the range of functionality and applications to improve diagnoses, facilitate evidence-based screening, counselling, and treatment, and improve workflow efficiencies
Broadly, CDSS may serve the following functions:
guide the healthcare provider through process algorithms using if...then... rules, based on evidence-based clinical protocols;
provide the healthcare provider with a checklist, based on clinical protocols;
provide step-by-step guidance to screen clients by health status or risk status, possibly using models based on machine learning, where mathematical functions might be used to classify patients into risk groups.
CDSS may be integrated with electronic health records or stand alone. For the purpose of this review, we will focus only on mobile decision-support tools that are not integrated with an electronic health record or management system, or are integrated with such a system but can be used independent of it.
How the intervention might work
Approaches to using mobile clinical decision-support tools vary substantially across countries and contexts, in part, depending on the availability of technological and healthcare infrastructure, and the costs of mobile devices and data packages. In addition to the costs and infrastructure, the level of sophistication of a CDSS would also depend on the complexity of the disease, the purpose of the CDSS (for example, screening alone, or screening integrated with risk assessment and counselling), and the capacity of the healthcare providers to adopt such systems.
At the most basic level, a decision-support system might comprise of a process algorithm, and transfer a paper-based protocol into a digital format. For example, a series of digital If...then... logic-guided questions may be used to assess appropriate contraceptive choices for a client, based on demographic information and preferences. By inputting client data in a systematic way, a decision-support tool might be used to identify and prioritize clients into risk groups. Additional point-of care support mechanisms, such as automated algorithmic instructions that prompt healthcare workers to follow certain guidelines, and provide tailored counselling messages and recommendations, might be added to such a system. For example, in addition to the assessment for an appropriate contraceptive, the decision-support tool may provide a number of recommendations, and prompt the healthcare worker to discuss risks for each contraceptive method, and care and follow-up for the contraceptive method chosen by the client. At each step, the provider might be required to check off a counselling item that has been discussed, before the system moves to the next set of questions and recommendations. In theory, such a system would promote comprehensive evidence-based counselling, and improve the overall quality of care provided by the healthcare worker.
In high-income countries, CDSS is typically used in clinical settings by trained healthcare providers or medical students. In LMICs, decision-support tools are used for both in-clinic and community-based outreach visits, by a range of healthcare providers, including clinicians, midwives, and lay healthcare workers.
Why it is important to do this review
The use of decision-support tools for clinical decision-making has been well-established, and is supported by some emerging evidence. A systematic review of 148 randomized, controlled trials on the effectiveness of CDSS, reported that the use of CDSS was associated with significant improvements in a range of morbidity outcomes, healthcare process measures related to performing preventive services, diagnostic testing, and improved adherence to treatment regimens (Bright 2012). However, over the last decade, clinical decision-support tools have transitioned from being operated on stationary computer systems to wireless mobile devices, which provide unique opportunities for point-of-care assessments, diagnoses, and management. Furthermore, most healthcare systems in LMICs, especially in rural areas, do not have the required infrastructure for computerized CDSS. However, the use of these tools on wireless digital devices, make them accessible to healthcare providers in LMICs, which was not possible previously. Despite the substantial investments and global interest in using mobile digital devices to support clinical decision-making, specific evidence on the effectiveness of such interventions on clinical and public health practice is limited.
Two systematic reviews assessed whether the use of handheld computers, primarily personal digital assistants (PDAs), improved access to information and supported point-of-care clinical decision making (Divall 2013; Mickan 2014). Compared to paper resources, the studies suggested that using handheld computers improved access to information, adherence to clinical guidelines, and appropriate diagnostic decision making. One study reported improvements in data collection quality (Divall 2013). This review will build on the existing studies, to include the use of other mobile devices, such as smartphones and tablets — the most current forms of handheld digital devices, especially in low- and middle-income countries. One review assessed the feasibility of, and barriers to, using digital point-of-care decision-support tools by healthcare providers in Africa (Adepoju 2017). Based on largely descriptive and observational studies, conducted in seven sub-Saharan African countries, the review concluded that healthcare providers found mobile decision-support tools useful; however, they expressed concerns about altered workflows and increased workloads. The review identified technical and infrastructural support, and adequate training, as key barriers to adopting clinical decision-support tools in the sub-Saharan African context.
Digital, mobile, wireless technologies provide an innovative and accessible platform to accelerate health services and improve quality of care for some of the most hard-to-reach populations. Given the recent emergence of such technologies for health, there is considerable demand from ministries of health, donors, and decision makers for evidence-based guidance to invest in such technologies. In response to this global need, the WHO is developing guidelines to inform investments on digital health approaches. This review constitutes one of six reviews on the effectiveness of digital health interventions that will be used directly to inform these WHO Guidelines.