Factors influencing adoption of EMR systems in developing country setting

Adoption of EMR is a major challenge not only in developing countries, but also in some developed countries. For example, only 9% of hospitals in the USA had adopted electronic medical record keeping by 2008. In India, adoption is relatively higher, with 60% of hospitals using electronic medical record keeping in surgery room (Kalogriopoulos et al., 2008). However, African countries lag far behind in terms of availability and utilization of EMR (Akanbi et al., 2011). Adoption challenges are more prevalent in poor country contexts, especially in Africa. According to Kalogriopoulos et al (2008), before developing any EMR for roll-out, it is important that three key factors are taken into consideration. First is availability of resources – these include electricity, finances, and software and hardware infrastructure. Second is the population – for a small population targeted to be served, “flat-file” databases or stand-alone network systems can be considered. If the EMR is targeting to serve a big population size, consideration should be given to coded databases and networked systems (e.g. WAN or LAN). In terms of location, if EMR is to be set in a remote area, consider the different contexts surrounding the area. For example, the EMR should be in position to overcome challenges related to unavailability of internet, electricity, and phone services among others. Accordingly, some of the key factors that facilitated EMR adoption for some African countries that have made some successful attempts to adopt EMR are related to; availability of personal computers and access to internet, (Akanbi et al., 2011).
Through available literature, we identify a wide array of barriers to adoption of EMR in developing countries, ranging from high-level issues of policy nature to infrastructural, financial, and human resource capacity issues among others. Consequently, key barriers to adoption of EMRs in Africa are; poor network infrastructure, high procurement and maintenance costs, high cost of commercial software packages, lack of comfort amongst health workers in regard to using EMR, and inadequate competent manpower and skills to develop the required infrastructure (Akanbi et al., 2011; Kalogriopoulos et al., 2008). Despite these challenges, the emergence of open software (e.g. OpenMRS) provides immense opportunity for African countries to gain access to EMR.
Other barriers to EMR system adoption in Africa include; lack of capital resources; attitude towards EMR being a complex system to use; limited computer knowledge among health workers; inadequate funding or resources; underutilization of ICT in developing or African countries; and inadequate training of healthcare personnel (Komakech, 2013; Sood et al., 2018). Another strand of literature identifies major pitfalls in implementing EMR systems in African context. These are; lack of user training; poor initial design which limits capabilities and expansion potential; complex systems that are difficult to use; over-dependence on one individual champion; lack of involvement of local staff in EMR system design and testing; lack of perceived benefits for users who collect data; lack of back-up systems; poor system security (viruses and spyware); lack of or poor data back-up mechanisms, and lack of technical support (Sood et al., 2018).
According to literature specific to Ugandan context, barriers to adoption of EMR in the country are: inadequate technical personnel to implement computer-based patient records (limited knowledge and necessary skills); clinicians are hesitant to use computer systems while attending to patients – they are resistant to change and prefer to write on paper files, while others lack knowledge to use the systems; inadequate funding for EMR system establishment (as well as high cost of EMR software and equipment); lack of involvement of clinicians and hospital administrators while developing the systems – which in most cases makes systems not customized to meet user needs; and difficulty (including costs) associated with conversion of older paper-based records into EMR systems (i.e. entry of backlogs into EMRs) – the process is time consuming, yet there is limited personnel (Banshanga & Ejiri, 2016; WHO, 2006).



No comments yet, be the first!


Your email address will not be published.