Health data analytics involves practice–based research strategies by which experts and specialists retrieve, explore and explicate accessible qualitative and quantitative data from available records. Such records can include the electronic health records, which is very fundamental because of the accessibility of comprehensive and colossal volume of electronic health records (Simi & Sankara, 2017).
Although seemingly unacknowledged, the rate at which health data analytic practices has improved to what it is in Nigeria today is impressive. Data analysis solutions, such as artificial Intelligence (AI), databases, statistics, and visualization, amongst other unlimited applications can be seen today in various health and medical specializations. These technologies highlight the importance of health data across various dimensions of health, while introducing new ways to deal with data analytic, it can be observed that there is still room for growth.
A lot of factors such as costs and management of data collection and analysis technologies, purpose, manpower, security situations and need, serve as indicators for reasons and level of health data collection and analysis in Nigeria. These factors play an important role in decision making, budget allocation, impact, needs assessment and personnel health management amongst others.
To ease the problem of poor data analysis, technological companies today have created needed analytics for development and implementations of healthcare reforms. Among such companies is ehealth4everyone, ranked as a leading innovative data analytics software developer that significantly improves data collection, extrapolation and storage with products such as Carekojo, Datakojo, Dhistance, Smatacare and HealthThink. These softwares are deployed and utilized by individuals, hospitals and healthcare facilities both internationally and nationally.
Irrespective of these efforts, research still indicates that the problem of usage and data availability for analytics still exists in today’s case scenarios with the Nigerian health care sector who despite its unique position in Africa, is still greatly undeserved in the healthcare sphere (Osain, 2011).
The question therefore remains, “why and how is this challenge still existing?” And, “moving forward, what can be done about this?”.
Studies have been conducted to answer the questions stated, with different conclusions and recommendations being drawn up. An example can be the study by Idoga, Toycan & Nadir (2018), where it was identified that while various reforms have been put forward by the Nigerian government to address the wide ranging issues in the healthcare system, they are yet to be implemented at the state and local government area levels. This study went further to highlight one of the reasons behind this to be healthcare consumer’s inability to accept the technology, especially in developing countries due to reasons, such as the data security and the improper utilization of available information and communication technologies (ICTs) in healthcare.
Recommendations being pivoted on the deployment of data analytics linkages especially big data analytics in the Nigerian healthcare care facilities for data storage, volume, velocity, variety, veracity and value, while assisting with data collation, creation, retrieval, management, and analysis in decision making (Oluigbo, Nwokonkwo, Ezeh & Ndukwe., 2017).
Plans to consider when mapping out the future of data analytics in the healthcare system may therefore include the identification of factors influencing the acceptance and utilization of developed health analytics systems by health care consumers and medical professionals in Nigeria which according to Idoga et al (2018), were identified and characterized into five cardinal areas:
- Performance expectancy associated with perceived usefulness of the platform for healthcare consumers and remote accessibility.
- Effort expectancy, associated with ease of use for the healthcare consumer.
- Facilitating conditions, associated with the availability of required IT infrastructures, technological know-how and technical support.
- Data security, associated with privacy and trust of healthcare consumers.
- Information sharing, associated with social influence, value of service and the needs of the platform.
Implementable recommendations may be directed towards
- Systems updating; laying emphasis on patient privacy observation, improved accountability both on figures/ ratios, personnel and in-patient attendance.
- Active surveillance recognition input for data collection,documentation and report generation amongst others.
- Personnel or manpower training in data analysis and methods implemented in both qualitative and quantitative methods need to be acquired by medical and health professionals.
- Promoting usage of these platforms, amongst users and facilities.
- Employment or recruitment of efficient manpower and conducting of training.
Our user-friendly products have features which address these issues stated above, and are available for mobile download on google play store and are also accessible on their various websites .
Datakojo https://www.datakojo.com/
Carekojo https://carekojo.com/
Healththink https://healththink.org/
Smartacare https://ehealth4everyone.com/smartacare/
Dhistance https://dhistance.com/
External sources:
Oluigbo, I. V., Nwokonkwo, O. C., Ezeh, V. N., & Ndukwe, N. G. (2017,
December 31). Revolutionizing the Healthcare Industry in Nigeria: The Role of
Internet of Things and Big Data Analytics. International Journal of Scientific
Research in Computer Science and Engineering, 5(6), 1-12. Research gate.
Retrieved August 16, 2021, from
lutionizing_the_Healthcare_Industry_in_Nigeria_The_Role_of_Internet_of_Thin
s_and_Big_Data_Analytics/links/5a7e1b1a0f7e9be137c4d664/Revolutionizing-th
Osain, W. M. (2011, 10). Journal of Pharmacy & Bioallied Sciences, 3(4). 10.4103/0975-7406.90100
Idoga, P. E., Toycan M., Nadiri H., & Çelebi E. (2018). Factors Affecting the Successful Adoption of e-Health Cloud Based Health System From Healthcare Osain, W. M. (2011, 10). Journal of Pharmacy & Bioallied Sciences, 3(4). 10.4103/0975-7406.90100.
Simi, S. M., & Sankara, N. K. (2017, October 19). Data Analytics in Medical Data: A Review. International Conference on Circuits Power and Computing Technologies (ICCPCT), 2017, 1- 4, doi: 10.1109/ICCPCT.2017.8074337.
