Revolutionizing the Healthcare Industry in Nigeria: The Role of Internet of Things and Big Data Analytics

: The impact of Internet of Things (IoT) and big data cannot be overemphasized. The aim of this research is to propose a research model for predicting the role of data analytics and Internet of Things in revolutionizing the healthcare industry in Nigeria, and thereafter assess the impact of proposed IoT and big data analytics model to the healthcare sector of Nigerian economy. This study used a quantitative research approach and an analytical field survey, considering five federal medical centers across the country, as well as a number of IT experts and academician. In proposing a model for healthcare industry revolution through big data analytics and Internet of Things, six drivers was identified – Remote Sensing Technology, Cloud Computing, Big Data Center, Data Visualization, Standard User Interface and Display


INTRODUCTION
An excerpt from the wok of [1] reads "If we had computers with knowledge about everything -using data they gathered without any help from us; we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best. Computers need to be empowered with a means of gathering information, so they can see, hear and smell the world for themselves, in all its random glory". This is the future we believe in -The Internet of Things! In this current dispensation with evolving technology, changes are rapid and the modus operandi is constantly disrupted. Internet of Things (IoT) and big data analytics are such disruptive technologies; these technologies have the potential to change the way healthcare is delivered.
1. Volume: At present the data created is in terabytes. In no distant future, we are likely to generate data in excesses of petabytes. The social media, financial institution, medical institution, government, Sensors, Logs produces data in terabytes every day and this amount of data is difficult to be analyzed using traditional analytic techniques. Volume basically refers to the size of data. 2. Velocity: At present, the rate of change of data occurs at a very high speed and so rapid through archived and legacy data collections or through streamed data from multiple resources sensors, traditional file records, cellular technology, social media and connected networks. Velocity refers to the speed of data. 3. Variety: it refers to the structure of data, whether structured, semi structured, unstructured. At present data comes in different forms including data-streams, text, and multimedia format. Unstructured data is difficult to analyze with traditional tools and techniques. It is therefore difficult to analyze data which is constantly in motion using the traditional techniques. 4. Veracity: Veracity of data includes data consistency (or certainty) and data trustworthiness. Data can be unreliable due to incompleteness, ambiguities, deception and uncertainty resulting from data inconsistency. 5. Value: It is defined by the added-value that the collected data can bring. It refers to the value that the data adds to creating or contributing to existing knowledge. It is of the opinion that valuable information somewhere within the big data.
Data variability refers to data changes during processing and lifecycle. Increasing variety and variability also increases the attractiveness of data and the potentiality in providing unexpected, hidden and valuable information.

Internet of Things in the Healthcare Sector
The healthcare industry have experience radical changes over the years. Today, we now have more medical devices and technologies that are increasingly integrated and connected togetherwearable health monitoring devices are prime examples.  The IoT devices fall into four main categories  (technologies); consumer products for health monitoring, wearable external medical devices, internally embedded medical devices, and stationary, but networked, medical devices [10]. These technologies can help improve outcomes and quality of life, improve access to care, empower patients, and reduce healthcare costs [15]. Internet of Things promises to manage the personalization of care services and can maintain a digital identity for every person.
Wireless Sensor Network (WSN) is an important enabling technology of IoT. It connects a number of sensor and actuator nodes into a network through wireless communication. This integrates the network into a higher level system through a network gateway [16]. Connecting WSNs to the Internet is possible in three main approaches. The first proposed approach consists of connecting both independent WSN and the Internet through a single gateway shown in Figure 3. This approach is currently adopted by most of the WSNs accessing the Internet, and presents the highest abstraction between networks. The second approach shown in Figure 4 forms a hybrid network which shows an increasing integration degree. It is composed of both, a considered network structure which remains independent and a few dual sensor nodes who can access the internet. In the third approach in Figure 5, multiple sensor nodes can join the Internet in one hop (router) [17]. Other enabling technologies of Internet of Things includes: Radio Frequency Identification: Radio Frequency Identification (RFID) is a system that transmits the identity of an object or person wirelessly using radio waves in the form of a serial number. RFID technology provides a reliable, secure, inexpensive, efficient and accurate means of solving identification issues of objects or services.
Electronic Product Code: Electronic Product Code (EPC) comes in either 64 bits or 98 bits; and it is recorded electronically on an RFID tag, which serves as a massive upgrade over the barcode system. EPC can store information about the type of EPC, product's serial number, specification, and manufacturer information.
Wireless Fidelity: Wireless Fidelity (Wi-Fi) is a broadband technology that allows computers and other devices to communicate via wireless signals. Wi-Fi provides an efficient and high speed wireless network connectivity across a local area (homes, offices, airport, and schools) or metropolitan area (towns and rural communities). The integration of Wi-Fi into notebooks, handhelds and Consumer Electronics (CE) devices has accelerated the adoption of Wi-Fi to the point where it is nearly a default in these devices [18]. Wi-Fi technology support any of the IEEE 802.11 together with dual-band, 802.11a, 802.11b, 802.11g and 802.11n.
Bluetooth: Bluetooth wireless technology is an inexpensive, short-range radio technology that eliminates the need for proprietary cabling between network devices and other peripherals. Bluetooth has an effective range of 10 -100 meters, and it communicates at less than 1 Mbps. Bluetooth uses specification of IEEE 802.15.1 standard.
Actuators: An actuator converts energy into motion, which means actuators drive motions into mechanical systems. It takes hydraulic fluid, electric current or some other source of power. Actuators can create a linear motion, rotary motion or oscillatory motion. Three types of actuators includes electrical actuators (ac and dc motors, stepper motors), hydraulic actuators (uses hydraulic fluid to actuate motion), and pneumatic Actuators (uses compressed air to actuate motion).

Big Data Analytics in the Healthcare Sector
The effective utilization of data is becoming very competitive since data is perceived to create value for businesses. However, the demand for greater insight and knowledge from information so as to make smarter, real time and informed decision has promoted the

III. CONCEPTUAL FRAMEWORK
To develop an effective information systems or business plan, the organization must have a clear understanding of both its long and short term information requirements. Strategic or factors analysis approach argues that an organization's system requirements are determined by a small number of success factors. If these goals can be attained, success of the firm or organization is assured [22].
The study of [13] proposed a comprehensive data analytics framework for smart healthcare devices. The study designed a Cyber Physical System (CPS) of collaborating computing elements controlling physical entities. CPS as proposed in the study is a network of interacting elements. In implementing CPS using Big Data, the study proposed three enablers; Sensor Technology, Big Data Center, and Cloud Computing. Sensor technologies increased smartphone penetration and reduced sensor costs enabling more users to carry sensors for long periods of time. Cloud computing allows computing services to be provided over a network.
In reviewing and understanding the applications of IoT in personalized healthcare to achieve excellent healthcare at affordable costs, [17] posited that Wireless Sensor technology is an important enabling technology of IoT. It connects a number of sensor and actuator nodes into a network through wireless communication. In addition, the study Healthcare efficiency is improved by enabling display devices to deliver a great deal of information with the help of graphics user interfaces (GUIs). T he wo r k o f [ 2 ] also agreed that sensor technologies can transform healthcare through internet of things. Analytics and data visualization is becoming critical to the complete patient record in healthcare industry. Combining patient genomic data with clinical data helps cancer treatment ( [12]; [14]).
The research of [15] posits that as wireless network gateways in the Internet of Things continues to mature, more and more exciting new IoT-driven healthcare applications and systems will emerge. Wireless network gateways are information hubs that collect sensor data, analyze it and then communicate it to the cloud via wide area network (WAN) technologies. According to the reviewed literatures, these six drivers are the underlying dominant technologies that propels the growth, adoption and widespread diffusion of IoT and big data analytics in an industry or organization. These drivers were used in developing the model for predicting the influence of Internet of Things and Big data analytics to the healthcare sector of Nigeria.

IV. MATERIALS AND METHODS
Research design is a framework or blueprint for conducting marketing research project. It provides details of the necessary procedures for obtaining the information needed to structure and to solve marketing research problems. The researcher chose an empirical research approach and a quantitative field survey, which is aimed at conducting a thorough study of big data analytics and Internet of Things (IoT), determine its drivers from study of relevant literatures and observation of different techniques, and finally adopting a suitable analytical method on the drivers; first to help formulate a model for predicting the influence of big data analytics and IoT for healthcare industry revolution of Nigeria, and validating the role of Big data analytics and IoT by way of determining its benefit(s) to the healthcare industry in Nigeria. Five healthcare facilities were considered for use in this research; One hundred (100) IoT and big data analytics experts in the recently concluded 2017 NITEC conference were also reached for their input. The staff in these healthcare facilities and the field experts constitutes the population size for this study. A suitable sample size was calculated from the total population using the Yamann Taro formula for finite population. From the total population size of 22950 and a significant level of 0.05, the calculated sample is 293; corresponding to the number of respondents approached for the research. Both primary and secondary data was used in this study. The secondary data was gotten from relevant and related literature while the primary data was derived from administering a multiple choice structured questionnaire on the subject matter. The questionnaire is based on Likert five-point ordinal scale (ranging from "Strongly Agree to Strongly Disagree") and they were administered to the selected sample. For this research, multiple regression analysis and multivariate analysis of variance (MANOVA) were the two methods of data analysis found ideal for use in this research to help achieve our aim.

V. RESULT PRESENTATION AND INTERPRETATION
The respondents were asked their area of specialization. This is to enable the researcher make a better and informed recommendation. From Table 1, majority of the respondents are those that should make use of these IoT devices and big data analytics firsthand. Respondents specialized in laboratory and diagnostics, administrative services, pharmacy and IT experts polled highest representing 48, 42, 37 and 35 respondents respectively. The least response was from the academicians (17 representing 6.8%). The coefficient of correlation (R) shows the relative relationship between the combined predictors and the dependent variable.
The result on Table 2 shows that the independent variables combined were in a relationship with the dependent variable of 0.881 which is a very strong relationship. The R Square value tells us how much of the variance in the dependent variable is explained by the predictors in the model. It shows in details how much the independent predictors explain the dependent variable. What this means is that our model, using the six drivers Wireless Network Gateway, Cloud Computing, Data Visualization, Remote Sensing Technology, Big Data Center, Standard User Interface and Display explains about 76.3% of the variance in Healthcare Industry Revolution in Nigeria. This percentage variance explanation is very high (way above 50%). It shows that much of the independent variables (Wireless Network Gateway, Cloud Computing, Data Visualization, Remote Sensing Technology, Big Data Center, Standard User Interface and Display) explain the dependent variable -Healthcare Industry Revolution. Our model (using the predictors) is thus a good fit in determining the dependent variable. The adjusted R squared (R squared less error) provides a better estimate; providing a predictive explanation of 75% of the variance in the dependent variable.   The coefficient summary of Table 4 evaluates the relative influence of each of the independent predictors in the model. What we want to know is the influence of each of the drivers in predicting the outcome (healthcare industry revolution). Result of Table 4 shows that all six drivers makes very strong significant and positive influence on healthcare industry revolution in Nigeria, with remote sensing technology (0.421 -42.1%) having the strongest influence followed by Cloud computing technology (0.374 -37.4%).
Considering the results from Table 2 to Table 4, the six drivers showed strong relative influence on healthcare industry revolution. This means that the model of IoT and big data analytics using the six drivers can thus be readily ensure the growth and revolution of the healthcare industry in Nigeria. The researcher aimed at analyzing the impact of big data analytics and Internet of Things in the healthcare industry in Nigeria based on these key areas identified.  Research and Development) have effect on the significant MANOVA result. The objective is to statistically determine if the proposed model using the big data and Internet of Things drivers have significant impact on the five key areas, and by extension having impact on the healthcare industry in Nigeria. Having considered all the statistical significance from the MANOVA result of Table 6 on the impact of the model using the six drivers on healthcare industry in Nigeria, we can conclude that the predicted model of Figure 7 using the six drivers will have positive significant impact on healthcare industry in Nigeria.

Revolutionized
The research also found out that adopting and implementing Big data analytics and Internet of Things technologies in the healthcare sector of Nigeria will not only ensure Remote Health Monitoring of patient, Collaborative Disease Management, Predictive Maintenance of Medical Equipment, change in Decision Making Power as well as Fostering Research and Development; potential benefits such as Telemedicine (Virtual consulting), Genomic analytics, Epidemic outbreak prediction and control, Fraud detection and prevention, and Effective clinical care and support can also be derived.

VI. CONCLUSION AND RECOMMENDATIONS
Based on results from the analysis and review of related literatures, the researcher draws the following conclusion; It is paramount to state emphatically that there is need for radical growth and redevelopment in the healthcare industry of Nigeria. Owing to this need, the result from this study lay emphasis on the fact that big data analytics and Internet of Thingsusing the six identified drivershave an important role to play in revolutionizing the healthcare industry in Nigeria, since all the drivers relates and influences this development and revolution by well over 75% as shown in Table 2.
In addition, the results from the study using the six predictor drivers agrees with some related literatures reviewed in the course of this research work. This result can thus be adopted by other researchers and further explored in details especially as related to the subject matter.
An industrious and revolutionized healthcare sector in Nigeria is to the benefits of every Nigerian. Applying the latest technological trends to healthcare operations and management will only help alleviate and/or eradicate the problems encountered in this system. Based on the above premise, we conclude that big data techniques as well as Internet of Things technologies are significant and influences healthcare industry development in Nigeria. What this means is that healthcare industries should pay very good attention to these technologies in other to ensure proper adoption and diffusion.
The researchers therefore recommends that top management and relevant stakeholders should provide their maximum support and contribution in realizing the potentials of these two technologies. In addition, these healthcare industries should provide adequate training for their staff to keep them abreast with the latest technology and other ICT techniques. Government agencies and relevant private sector should be keen on the current trends of global technological evolution, to enable them promulgate policies that encourage best practices for the growth of recent ICT technologies in the healthcare sector. Finally, the healthcare sector should harness the various benefits of these recent technologies; including for remote patient monitoring, fraud detection and prevention, epidemic outbreak prediction and control, as well as for telemedicine.