1.1 Introduction
Big Data is a the generic term for data sets of structured and unstructured data that are extremely large and complex so that the traditional software, algorithm, and data repositories are inadequate to collect, process, analyze, and store the information (Asante-Korang & Jacobs, 2016; Kyoungyoung Jee & Gang Hoon Kim, 2013; Khoury & Ioannidis, 2014; Tan, Gao, & Koch, 2015), has become an intensively studied area in recent years. With the development of the Internet, the mobile Internet, the Internet of things, social media, biology, finance, and digital medicine, the volume of data has increased dramatically. Big Data not only describes the large size of data as its name suggests but also implies rapid data processing ability and novel technology and approaches for handling the data (Krumholz, 2014). The concept of Big Data is popular in a variety of domains. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership.
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
In the 21st century, Big Data went through a series of evolutionary steps, and software in suitable environment has been developed. With the growth of information exchanges, Big Data has been expanded to a certain scale, not only in its size but also in data technology. In terms of its five main characteristics, volume, variety, velocity, variability, and veracity, state- of-the-art techniques, technologies, and equipment are required to deal with Big Data in correlation analysis, clustering analysis, modeling, prediction, and hypothesis verification. Thus, advanced hardware and software are required for data acquisition, extraction, processing, analysis, and storage. Currently, infrastructure for Big Data includes servers, storage systems, cloud service, and networking equipment. Software for Big Data includes parallel and distributed file systems, retrieval software, and data-mining software (Anderson & Chang, 2015).
In ESUT Teaching Hospital, the hospital provide qualitative Health care services but maintains that they do not just heal mere physical illness which attacks the human body, but a much deeper and holistic healing of the entire human person. These service areas include all the wards (medical and surgical for male and female, pediatrics, chest unit and the maternity section as a whole. Other departments are out patient department (OPD), laboratory department, pharmacy department, central sterling and supply department (C.S.S.D), X-RAY department community medicine and the mobile clinic, and theatre department. The roles of these departments are complementary and depict what they call team-work in patient management, the patient always beings at the center.
Big Data in medicine and clinics includes various types and large amounts of data generated from hospitals, such as clinical data, and medical imaging. It is often closely associated with doctors and patients. In other words, Big Data in medicine is generated from historical clinical activities (Tsumoto, Hirano, & Iwata, 2013) and has significant effects on the medical industry. For instance, it can assist in planning treatment paths for patients, processing clinical decision support (CDS), and improving health care technology and systems (Kyoungyoung Jee & Gang Hoon Kim, 2013).
In the medical domain, Big Data comes from hospital information resources, surgeons’ work, activities of anesthesia, physical examinations, radiography, magnetic resonance imaging (MRI), computer tomography (CT), information of patients, pharmacy, treatment, medical imaging, and imaging report (Tan et al., 2015; Wang & Alexander, 2013). These clinical activities generate a large number of records including identification information of patients, diagnosis, medicine scheme, notes from physicians, and sensor data (Tan et al., 2015; Wang & Alexander, 2013).
Health care systems are organizations established to meet the health needs of target populations. Their exact configuration varies from country to country. In some countries and jurisdictions, health care planning is distributed among market participants, whereas in others planning is made more centrally among governments or other coordinating bodies. In all cases, according to the World Health Organization (WHO), a well-functioning health care system requires a robust financing mechanism; a well-trained and adequately-paid workforce; reliable information on which to base decisions and policies; and well maintained facilities and logistics to deliver quality medicines and technologies. In a seminar report of an exploration by an expert committee, the institute of medical literature review did not reveal any substantive evidence of the strengths of paper records.
The challenges encountered that led to the execution of the research work is that, care providers waste precious time searching and browsing for unscheduled patient record to collect all information pertinent to the actual situation, Difficulty in accessing certain patient file location, the manual system of post natal appointment scheduling of patient is usually stressful and Inaccuracies in patient’s medical record keeping due to human errors.
It is against the background that the developments of this software to enable health workers schedule appointment meet-ups with post natal patients, so the software users should not give false appointment information that is out of the software deeds. This research work is based on providing adequate information about medical records. At the end of this project, the research work, the software will be able to provide precise and concise patient records from the database.
1.3 Statement of Problems
Investigation revealed the challenges encountered that led to the execution of the research work include the following;
- Poor storage of medical records as a result of system low memory usage.
- Care providers waste precious time searching and browsing for unscheduled medical patient record to collect all information pertinent to the actual situation,
- Difficulty in accessing certain medical patient file location,
- The manual system of medical information system has no proper accountability on the distribution of healthcare facilities, and
- Inaccuracies in post natal patient’s medical record keeping due to human errors.
1.4 Aim and Objectives of the Study
The aim of the study is to design and implement a Big Data Application to Health Care using ESUT Teaching Hospital as a case study. In achieving this aim, the following specific objectives were set out as follows to develop an application software that will:
- Enable the care providers search and browse medical patient record without delay.
- Store big data of patient medical file record in a database that will enable care providers to be able to locate patient files in the health facility.
- Reduce the time spent by staff filling out forms, freeing resources for more critical tasks.
- Ensure data integrity and provide a database for future statistical and management reporting.
1.5 Significance of Study
This system when completed and implemented it will help provide adequate information about patient’s medical record and facilitates quick medical appointment scheduling. This project will be beneficial to individuals such as: pharmacist and doctors, nurses, care providers by providing accurate and complete information about post natal patient’s medical record thereby saving patient time during post natal medical checkup.
The result of this design will aid healthcare organizations enhance team work, collaboration and knowledge sharing among the employees through an integrated communication system. It will significantly reduce paperwork involved with submitting data, documents, reports etc. Besides, the study will serve as reference material for subsequent researcher in the field or related topics.
1.6 Scope of Study
The study focuses on the design and implementation of Big Data Application to Health Care. The study covers only the Medical Information System duties in ESUT Teaching Hospital.
1.7 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Establishment Policies: Establishment policies posed a serious limitation as most staffs are not ready to release information needed for this project work. There were lots of information needed from the staffs of this institution to enhance the study which took them time to release or they did not release at all for security purposes, hence the scope was reduced.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).
1.8 Definition of Terms
Medication: A medication is a substance that is taken in to or placed on the body that does one of the following things: most medications are used to cure disease or condition. (For example, antibiotics are given to cure an infection. Medications are also given to treat a medical condition).
Prescription: A prescription is a health-care program implemented by a physician or other qualified health care practitioner in the form of instructions that govern the plan of care for an individual patient.
