Significance of Big Data in Promoting Healthcare Organisations
TITLE: How can Big Data improve the ability of medical practitioners to provide the best treatment?
Big data has been regarded as a significant part of ICT, which has been changing the operations of healthcare facilities to provide the best treatment for the patients seeking medical help. This research proposal primarily focuses on the role and significance of big data in promoting healthcare organisations and the way it guides and provides recommendations to medical practitioners to improve the medical facilities within the healthcare organisations. The major achievements and drawbacks have been identified with regards to the research project, along with the identification of the potential limitations and ethical concerns related to carrying out the research. Furthermore, a positivism philosophy, along with a deductive research approach and a descriptive research design for conducting the research, has been established.
1.0 Research Background and Significance
1.1 Research Background
It has been seen that the use of ICT and emerging technology has helped the industry of different sectors. One of the emerging use of technology in health care is use of Big data. Significant use of big data has helped the industry to improve the service and modify the service on the basis of the requirements of the patients and consumers. In this context, some of application area of Big Data can be mentioned.
1.1.1 Analysis chosen
The analysis chosen for the research regarding application of the big data in the health care system is the semantic analysis. The reason for choosing this type of analysis is that it will help to make the contextual relevance between the gathered information from the literature review. Apart from that the depth of the research can be achieved through this analysis. Some of the information those can be relevant in the research are regarding the different perspective of the supplication of big data in health care industry.
1.1.2 Use of Big Data for the modification of the health care service
The main function of the big data is to collect the data or information from the consumers and the patients. In this case, the collection of the data and information can be achieved through the conduction of surveys (Gleason, 2015). The collected information is sorted through big data tools. After the sorting of the information is can be understood which part of the system in health care is needed to be modified.
1.1.3 Use of ICT in big data management
It has been seen that the use of ICT can be indulged in the proper management of applying big data in health care management. There are different applications of ICT that are helpful for the proper implementation of the big data in the system. It is needed to be noticed, the big data is an emerging application. However, in order to implement this application, there is a need for ICT. In this context, the use of cloud computing can be mentioned (Ma et al. 2017). The application of big data in health care needs the proper management of a large amount of data and information. In this context, it is impossible for the health care organizations to manage the storage of that information in the physical storage and the server system. In that case, the large amount of the information generated from the application of the big data can be stored in the cloud storage. This will mitigate the overhead cost for the maintenance of the physical storage, apart from that the data and the information can be managed in a proper way.
1.1.4 Possible challenges in the use of Big Data
It has been seen there are certain challenges that can be faced while the implementation of the big data in the health care management system. The use of Big Data in the organization has helped the health care industry to generate e-prescription and e-health care system. This has made the living of the patients easier. However, the main challenge in this system is the security issues. While the implementation of the system, if there is any security issue developed in the system, there is a possibility that the outcome will not be proper. As the health care sector is a sensitive sector, the accuracy of the outcome is important.
Apart from that, it has been seen as the big data is an emerging technology, in most of the cases, the application software needed for the big data system is not compatible with the existing hardware at health care organizations. Apart from that, the implemented system can face issues regarding the lack of optimization.
For some of health care organizations, it has become big challenge to implement the big data in the health care system as the implementation cost is high and needs more capital investment. Quality along with Accessibility and the cost are known as “Iron Triangle” for the health care organizations. All the health care organizations need to achieve this triangle in order to provide better services to the patients (Kruse et al. 2016). However, it will become a challenge for some of the organizations to maintain this triangle with implementing the big data in the health care system.
1.1.5 Mitigation of the issues in the application of big data
It has been seen that issues that are raised for the application of the big data can be mitigated through the proper steps. It can be said that most of these steps are needed to be taken by health care organizations who are implementing the big data system in the organization. Apart from that the mitigation of the security issues can be done through the implementation of the proper protocol. In this case, the maintenance of the implemented system can be done through the technical experts recruited by the organizations.
Apart from that, the users are needed to be careful about the rules and regulations of using the big data system in the organization. The users, in this case, are the managing the health care organization and the patients using the system. Patients are the end users in this case. Both these users are needed to be aware of maintaining the security rules. In this context, an example can be given (Heinrich et al. 2016). Patient using the big data-driven system in health care can be assigned to a portal. Through that portal, he or she can manage the overall system. However, there is a possibility that the patient may not log out from the portal after using it. In this case, the other user using the system can use the portal and can see the personal medical information of that respective patient, which is not desirable.
1.1.6 Significance of the analysis
There are certain questions that can be raised form the discussion of the application of big data in the health care system. Before the implementation of the bog data in the health care system, there is a need for the evaluation of the cost for the implementation of the system (Joyia et al. 2017). Apart from cost, health care organizations should recruit technical experts who are capable of handling big data.
The aim of the organizations in the health care sectors are not only maintained the implementation of the big data in a proper way. There are different applications of big data that can be used and implemented in health care organizations. However, the suitable application of the big data in health care organizations is dependent upon type of services respective health care organization is providing. The suitable use of big data can be done through suitable application of the big data.
1.2 Significance of research
Handling, analysing and managing data in any industry which is rapidly growing and developing poses as a major challenge for organisations operating in the particular industry segment. Big data provides a massive scope of opportunities for handling and effectively managing the vast load of data gathered by the healthcare organisations (Baro et al. 2015). Big data has been chiefly implemented in the development of healthcare analytics, which has been able to change the way of operations in medical facilities. It not only helps medical practitioners by providing guidance for avoiding preventable diseases but also aids in the prediction of mass epidemic outbreaks and appropriate measure to address the situations (Luo et al. 2016). In addition to that, improvement of the quality of life and administration of medical facilities as well as the cost cut down are thoroughly analysed with big data.
The importance of big data can be attributed to the massively growing costs of healthcare in every major economy. According to the reports of the ONS, which is commonly known as the Office for National Statistics, the cost of medical expenses of UK in 2016, contributed to about 9.8% of the GDP, amounting to around £191.7 billion (Ons.gov 2018). Moreover, it has been established that the costs of long-term healthcare expenditure attributed to about £35.5 billion for the year 2016 in the UK (Ons.gov 2018). Figure 1 illustrates a table depicted the expenditure in medical and healthcare facilities according to the government records, along with the overall contribution to the GDP and the percentage of total expenditure growth, which has been found to be an average of about 4% (Ons.gov 2018).
Figure 1: Cost of medical expenses between 2013 and 2016
(Source: Ons.gov 2018)
Figure 2 represents the growth rates in the functions of medical healthcare facilities, which contribute to the major segment of the economy (Ons.gov 2018). It may be stated in this context that the Patient Portal Interaction System enhances patient engagement, along with the development of e-prescriptions and more (Belle et al. 2015). Moreover, the security of the data stored as well as the confidentiality is ensured through the use of big data systems in healthcare organisations (Sivarajah et al. 2017). Medical imaging, as well as predictive analysis, are integral parts of the big data usage in the healthcare facilities, which may prevent unnecessary ER visits by predicting the potential issues to be faced by the patients and suggesting the potential and the most efficient and beneficial course of action (Wang et al. 2018).
Figure 2: Growth rates in functions of medical facilities in the UK in 2016
(Source: Ons.gov 2018)
As identified through various literature on the subject, the use of a systematic framework and approach may shape the macro-model discussion, using the implementation of big data in healthcare (Chen et al. 2017). Additionally, Figure 3 depicts the multiple uses of big data in the healthcare industry. For instance, the maintenance of EHR or Electronic Health Records has proven to be a big help when it becomes a challenge to manage the physical documents in medical facilities (Hilbert 2016). In addition to that, real-time alerting through the CDS software or the Clinical Decision Support software suggests prescription changes or warnings to the medical practitioners, which may aid them in rendering better services to the patients (Dimitrov 2016). Regardless, a few challenges have been evident in the use of big data as well, despite the major breakthrough achieved by this particular segment of ICT in the field of medicine and healthcare (Kruse et al. 2016). For instance, gathering the data for the predictive analysis may prove to be a challenge for the big data systems.
Figure 3: Uses of Big data in healthcare
(Source: Catalyst 2018)
2.0 Research Aims: Objectives and Questions
2.1 Research Aims
The primary aim of this research is to identify the role and the significance of big data for the improvement of the ability of medical practitioners, as well as the systems to improve the healthcare facilities rendered to the public. The research hypotheses, thus formulated are:
H0= There is no verified or established a relationship between the role of big data on the improvement in medical and healthcare facilities
H1= There is evidence demonstrating a strong link between the use of big data to the improvement of healthcare facilities and the abilities of medical practitioners for providing better facilities for the patients
2.2 Research objectives
Certain themes have been identified and established for proceeding with the research on big data. The objectives on which this research is to be primarily based on have been outlined as follows:
- To identify the use of big data in healthcare facilities for the effective handling and management of data
- To establish and evaluate the impact that big data has on the productivity of a healthcare organisation
- To investigate the major challenges and drawbacks in the implementation of big data in healthcare organisations for the improvement of the medical facilities
- To explore and assess the major achievements of the implementation of big data in the healthcare organisations for effective data management and improvement of the medical abilities of the practitioners
- To study the impact big data has on influencing the abilities and role of the medical practitioners in healthcare facilities
2.3 Research questions
The research questions, thus formulated based on the overall identified research objectives, have been established as follows:
- What is the use of big data in healthcare facilities for the efficient handling and management of data?
- How does big data influence the productivity of a healthcare organisation?
- What are the significant challenges and drawbacks in the implementation of big data in healthcare organisations for the improvement of the medical facilities?
- What are the significant achievements of the implementation of big data in the healthcare organisations for effective data management and improvement of the medical abilities of the practitioners?
- What impact does big data have on influencing the abilities and role of the medical practitioners in healthcare facilities?
3.0 Research Methodology
3.1 Research philosophy: Positivism
The research onion depicted in Figure 4 highlights the key aspects or domains to be taken into consideration for proceeding with a research project. A mixed methodology is to be implemented for conducting this research as it involves the collection and analysis of both secondary as well as primary data (Melnikovas 2018). It may be mentioned in this regards that positivism research philosophy is being considered by the researcher for proceeding with the research project, as a part of the proposed epistemology. This philosophy primarily holds true with respect to the gathering of information, which is chiefly dependent on observations validated by credible evidence and data (Mayer 2015). This evidence provided the basis for the development of the hypothesis for conducting the research. Therefore, positivism has been chosen as the philosophy to be implemented for the research project. In addition to that, epistemology and positivism consider the aspect of observable social reality is taken into account as opposed to the implementation of realism, which lacks the scientific approach for data collection and analysis (Melnikovas 2018).
Figure 4: Research onion
(Source: Saunders et al. 2015)
3.2 Research strategy: Action research
Action research can be regarded as an effective method of reflecting the implications for transforming and evolving the existing process in healthcare related to big data (Bulmer 2017). This research aims at identifying the potential challenges in the implementation of big data and thereby provides recommendations for the rectification of the same. It may be stated in this regards that this is one of the major reasons for the consideration of action research as an effective research strategy. Regardless, it is to be noted that case study and other variants of research strategy have not been considered as it requires the application of a controlled environment for the observational case study (Bulmer 2017).
3.3 Research design: Descriptive
Descriptive research design has been considered in this regards as it chiefly focuses on the key domains of naturalistic observation, as may be supported through the proposed survey for the collection of primary quantitative data (Melnikovas 2018). In addition to that, it is to be taken into account that the implementation of a correlation design has not been undertaken considering the aspects of a case-control study, which, as formerly mentioned is not applicable in this regards. Furthermore, the use of semi-experimental, as well as experimental designs, have not been considered as it is not likely to result in the desired outcomes for studying the net impact of big data in healthcare facilities in a number of ways (Nardi 2018).
3.3.1 Research approach: Deductive
A deductive approach has been taken into consideration for this particular research project as it aids in the development of theories for drawing conclusions out of the research outcomes from the primary and secondary data (Tetnowski 2015). In addition to that, it is primarily concerned with the verification or falsification of the hypothesis previously formulated for proceeding with the research project (Hughes & Sharrock 2016). It also provides a justification and means of finding answers to the identified research questions. However, an inductive approach has not been implemented in this case as it works in a manner contrary to that of a deductive approach. It deals with the formation of hypothesis or theories based on the outcomes of the research, instead of developing a hypothesis and proceeding with the research to prove or disprove the theories (Hughes & Sharrock 2016).
3.3.2 Instrumentation and tools
Various softwares are required for the calculation of statistical data and for the statistical representation of the quantitative data gathered. In addition to that, the questionnaire is tools, which are to be developed in par with the research requirements (Hughes and Sharrock, 2016). The questionnaire for the survey would primarily comprise close-ended questions to generate quantitative data, while the questionnaire for the survey would consist of open-ended questions (Eriksson & Kovalainen 2015). Open-ended questions provide an opportunity for identifying public opinion on the subject of the research.
3.4 Data type and techniques for data collection
3.4.1 Quantitative data collection
Quantitative data is to be collected from primary sources by means of a survey. It may be stated that the primary data collection procedure would involve a survey with the participants being the staff at major hospitals and medical facilities of London. A questionnaire is to be developed for carrying out the survey, thereby consisting of close-ended questions to generate a numerical data, which would later be converted to statistical data, along with the statistical representation of the same (Jebb et al. 2017). The aforementioned procedure is to be regarded as a part of the primary data analysis.
3.4.2 Qualitative data collection
It is to be noted in this regards that the qualitative data to be gathered for this research may be categorised as primary as well as secondary data collection methods. It may be stated in this regards that the primary data for qualitative research is to be gathered through interviews with the Head of major medical or healthcare organisations in the UK. This would enable the researcher to gather circumstantial evidence regarding the role and implementation of big data into healthcare facilities (Jebb et al. 2017). Furthermore, the secondary sources for data collection would depend on the formerly identified themes for this research project, namely the “Challenges faced in the management of Big Data in Healthcare” and the “Management of Big Data in Healthcare with respect to ICT”. Published literary works, such as articles, books, as well as journals are recognised as reliable sources of information for the research (Jackson 2015). Additionally, it is to be noted that research papers, scholarly articles are also to be taken into consideration, along with online published reports by government organisations.
3.4.3 Sampling and sample size
Random sampling is done for the respondents of the survey. About 50 individuals, comprising the staff and nurses at the major healthcare organisations of UK would constitute the sample size for the survey for this research project. The organisations primarily taken into account are namely, The Royal London Hospital, St. Thomas’ Hospital and The Queen’s Medical Centre. In addition to that, the sample size for the interview would include the directors of the aforementioned healthcare organisations. Thus, the sample size for the interview is 3, constituted by the directors of The Royal London Hospital, St. Thomas’ Hospital and The Queen’s Medical Centre. A contingency plan has been devised for the interview, in case appointments with the Directors of the facilities cannot be obtained, interviews shall be conducted with the Administrative head or the manager of certain departments at the hospitals.
3.5 Ethical considerations
It may be stated in this regards that the assurance of the quality along with the integrity of this particular research project regarding the key functions of big data in medical or healthcare facilities require thorough verification as well as validation of the sources implemented for the entirety of the research. Moreover, the sample size is to be selected upon receiving informed consent from the participants or respondents. Furthermore, it is imperative that the anonymity and confidentiality of the respondents of the survey is maintained, in accordance to the GDPR or General Data Protection Regulation in the UK Data Protection Act of 2018. In addition to that, it is to be emphasised that voluntary participation from the participants of the survey and interview has been obtained (Teherani et al. 2015). Furthermore, avoiding topics related to discrimination of religion, race, gender, demographics and ethnicity is to be maintained. Hence, the impartial nature of the research may be established in the process of carrying out research on big data. Lastly, compliance with the Human Rights Act of 1998 is to be noted, avoiding offence to the respondents of the research (Kansagra et al. 2016).
3.6 Limitations of the research
One of the major drawbacks or shortcomings predicted for this research project is ensuring the reliability or validity of the data (Brannen 2017). It is to be taken into account obtaining appointments for organising and carrying out interviews with the selected sample size for the qualitative analysis from the primary source, may be regarded as a challenge. In addition to that, the budget presented in Table 1 may be of concern to the researcher. Compliance with the allocated budget may be difficult due to potential miscellaneous additions to the budget. However, cost cut downs may effectively reduce the budget as well. Furthermore, carrying out the overall research within the estimated time frame of 4 months or 16 weeks may also be a challenge for the researcher, owing to certain unforeseen events, such as delay in receiving appointments with participants in the research and more.
4.0 Timeline, Facilities, Budget
The timeline for the research has been predicted to be approximately 4 months or 16 weeks for the initiation and completion of the research including the final research project submission.
|Tasks||Week 1-2||Week 3-4 ||Week 5-6||Week 7-8||Week 9-10||Week 11-12||Week 13-14 ||Week 15-16|
|Topic selection and approval of the proposal|
|Cost breakdown planning and time schedule|
|Secondary data collection and literature review|
|Identification of the research methodology|
|Obtaining appointments for interview|
|Sample size selection for survey|
|Primary data collection|
|Secondary data collection|
|Primary data analysis (Qualitative)|
|Primary data analysis (Quantitative)|
|Secondary data analysis|
|Conclusion and Recommendations |
|Final submission |
Figure 5: Gantt chart or timeframe for research
(Source: Author’s creation)
The facilities provided for this research include access to the library for data collection. Furthermore, aids for obtaining appointments for the interviews as a part of the qualitative data collection process are provided to the researcher. Additionally, budget allocation is maintained for proceeding with the research smoothly.
The budget breakdown for the project related to the significance of big data has been estimated to be approximately £ 1,500, as represented in Table 1 below:
|Resources for expenditure||Expenses or cost |
|1. Stationery ||£ 400|
|2. Travel or commute||£ 400|
|3. Library fee||£ 200|
|4. Gathering data from secondary sources, subscription to online articles and blogs||£ 500|
Table 1: Budget or Cost breakdown for research
(Source: Author’s creation)
The major inclusions in the budget involve the purchase of stationery, travelling expenses, library fees for gathering data regarding the healthcare industrial consequences of the role of big data, from secondary sources such as published journals, articles as well as scholarly research papers and books. In addition to that, online sources such as government websites, published blogs and articles have been found to be important for research. Therefore, subscription and purchase of the online research papers require a fee, which is included in the cost breakdown structure for the research on big data.