1 Data collection and storage
Before we perform any experiment or research, it’s important for us to collect the data to be used in the experiment or the research. In our case, we are interested in collecting the data which can help us to understand how artificial intelligence is affecting the field of healthcare. For our research to be successful, we must identify the best sources of data, collect the most appropriate data, record it, store it appropriately, and use it as required to generate the desired results of our research.
1.1 Data sources
The main aim of our paper is to study how artificial intelligence is affecting healthcare and so the main data sources to be used will be major hospitals, clinics, and other healthcare centers in our societies (Beam et al., 2018). From these medical centers, we’ll be able to see how artificial intelligence is applied and how it is affecting the performance of the medical centers.
1.2 Data collection
After identifying the most appropriate data sources, the required data is collected and recorded in the table shown below. The table shows the sources of the data, the type of the data, the format of the data, the fee incurred, among other specifications of the data and the requirements for the data collection.
Table 1: Data collection table
Data source name
Source organization (major hospitals, clinics, and the other healthcare organizations)
Data description
Data file format
Charge fee
Target data source
Data 1
Major hospitals
Application of artificial intelligence in major hospitals
txt
Free
Yes
Data 2
Clinics
Application of artificial intelligence in clinics
txt
Free
Yes
Data 3
Other healthcare centers
Application of artificial intelligence in other healthcare centers
txt
Free
Yes
1.3 Data storage
After collecting all the required data, another table is created to store the raw data collected from the data sources. Storage of data is very important as it makes sure the data is safe and can be used in the future when required (Lu et al., 2015). The data storage table is shown below:
Table 2: Data storage table
Data source name
Date of collection
Saved file location
Saved file name
Saved file format
Number of records
Survey from major hospitals
22/4/2018
//raw data/
Survey.txt1
txt
50
Survey from clinics
25/4/208
//raw data/
Survey.txt2
txt
80
Survey from other healthcare centers
29/4/2018
//raw data/
Survey.txt3
txt
150
2 Design and implementation
After the collection and the storage of the data, the data needs to undergo data pre-processing and feature selection or the dimension reduction before it can be analyzed and implemented as required to obtain the desired results of the research.
Data pre-processing is any form of processing done on the raw data to prepare it to be fit to be used in an experiment or research. Data pre-processing is a common practice in the data mining process where it is done to transform the data into a format which will be easily and effectively used by the users (Ramírez-Gallego et al., 2017, pp.39-57). Like in data mining, in our case data preprocessing is done to transform the raw data into a more favorable data format which will be easily understood and analyzed to obtain the desired results of our research.
We have many processes involved in data pre-processing where some of the major processes include data cleaning, data integration, data transformation, data reduction, data discretization, among other processes (García, Luengo, and Herrera, 2016). Data pre-processing can be represented diagrammatically by the figure shown below:
Figure 1: Data pre-processingData cleaning or data cleansing is the process of sorting or detecting and removing corrupt and inaccurate records of data from the collected data set to make sure you’ll remain with only the accurate and the necessary data which will be useful in the analysis (Cody, 2017).
Data integration is the process of combining the data from different sources to obtain one set of data which will be valuable and relevant to be used in the analysis. In our case, the data from the major hospitals, the clinics, and other healthcare centers is combined to obtain one data set which will be analyzed easily to understand how artificial intelligence affects the field of healthcare (Cudré-Mauroux, 2017, pp.5-6).
Data transformation is the process of converting all the integrated data into the format which is required during the analysis of the data (Heer, Hellerstein, and Kandel, 2015).
Data reduction is the process of transforming data into a correct, simpler, and well-organized and well-ordered data which can be manipulated or analyzed with much ease to obtain the desired results (Rehman et al., 2016, pp.917-928).
Data discretization is the technique of converting large and complex data sets into smaller, finite, and simpler data sets which can be easily understood and analyzed with much ease to obtain the desired results (Ramírez?Gallego et al., 2016, pp.5-21).
In our case, the collected raw data about the effects of artificial intelligence on the healthcare field undergo the whole process of data pre-processing to get the most suitable data which will be used in the analysis.
2.2 Feature selection or dimension reduction
After data pre-processing, features selection or dimension reduction is done to select the most appropriate features and do a further reduction to remove all the unnecessary data to make sure we’ll be left with only the data to be used in the analysis (Hira and Gillies, 2015). A new table shown below is prepared to record the data after feature selection and dimension reduction.
Table 3: Feature selection and data reduction table
Date
Data source name
Purpose of pre-processing
Method of pre-processing
Original data records
Results data records
New data file name
2/5/2018
Data 1
Avoid duplicity
Data reduction(cleaning)
50
35
Survey.txt11
2/5/2018
Data 2
Feature selection
Data integration
80
64
Survey.txt22
2/5/2018
Data 3
Filter the data
Data reduction
150
127
Survey.txt33
2.3 Experiment designing
This section explains how the research was conducted to obtain the desired results of how artificial intelligence is affecting healthcare.
To carry out our research successfully, we used hybrid methodology or the mixed research methodology which made our research easy since we collected and analyzed both numerical and non-numerical forms of data (Creswell and Clark, 2017). We used interviews and questionnaires as the main methods of data collection to obtain our desired data from the major hospitals, clinics, and the other healthcare centers in our societies (Flick, 2017). A simplified table showing some of the main questionnaire questions used in data collection is shown below:
Table 4: A table of questionnaire questions
Question 1
What’s the name of your organization?
Question 2
Do you use artificial intelligence in your medical operations?
Question 3
If yes, please give some of the major operations where you use artificial intelligence in your organization
Question 4
What are the major benefits of artificial intelligence in your organization?
Question 5
What are the major challenges facing artificial intelligence in your organization?
Question 6
In your own views, has artificial intelligence helped to improve the quality of services offered in your organization and do you support the use of artificial intelligence in your organization or it should be ended?
After visiting various major hospitals, clinics, and other healthcare centers where we interviewed various healthcare personnel and gave various questionnaire forms with some questions about the effects of artificial intelligence on their performance, we obtained the following simplified results:
Table 5: A table showing the organizations used in data collection
Data source name
Total number of organizations used in data collection
Number of organizations using artificial intelligence in their operations
Number of organizations not using artificial intelligence in their operations
Major hospitals
50
46
4
Clinics
80
67
13
Other healthcare centers
150
112
38
Table 6: A table showing the organizations which supported the use of artificial intelligence in their operations
Data source name
Total number of organizations using artificial intelligence
Number of organizations supporting the use of artificial intelligence in their operations
Number of organizations who don’t support the use of artificial intelligence in their operations
Major hospitals
46
45
1
Clinics
67
65
2
Other healthcare organizations
112
108
4
2.4 Implementation
After obtaining all the required data and doing all the required modifications, the implementation stage is undertaken.
After collecting, modifying, and recording of the required data, the data is usually analyzed using the appropriate software and tools. In our case, the main software to be used in the analysis of data are Ms. Word and Ms. Excel which will be used to analyze the obtained data and tabulate them in tables and charts which will help to enhance their visualization and their understanding to the other people who may be interested in the results of the research (Ward, Grinstein, and Keim, 2015).
After doing some analysis, a table can be drawn to represent the results of the number of organizations using artificial intelligence in percentages.
Table 7: A table showing the numbers and the percentages of the organizations using artificial intelligence in their operations
Data source name
Total number of organizations used in data collection
Number and percentages of organizations using artificial intelligence in their operations
Number and percentages of organizations not using artificial intelligence in their operations
Major hospitals
50
46 (92%)
4 (8%)
Clinics
80
67 (83.75%)
13 (16.25%)
Other healthcare centers
150
112 (74.67%)
38 (25.33%)
The data shown in the table above can be represented by the pie charts shown below:
Figure 2: A pie chart showing the percentage of hospitals using and those not using artificial intelligence in their operationsFigure 3: A pie chart showing the percentage of clinics using and those not using artificial intelligence in their operationsFigure 4: A pie chart showing the percentage of the other healthcare centers using and those not using artificial intelligence in their operationsThe bar graph shown below can be used to represent the data sources (the organizations used in the data collection process)
Figure 5: A bar graph showing the numbers of the data sources
The number and the percentages of the organizations supporting and those not supporting artificial intelligence in their operations are shown in the table below:
Table 8: A table showing the numbers and the percentages of the organizations supporting the use of artificial intelligence in their operations
Data source name
Total number of organizations using artificial intelligence
Number of organizations supporting the use of artificial intelligence in their operations
Number of organizations who don’t support the use of artificial intelligence in their operations
Major hospitals
46
45 (97.83%)
1 (2.17%)
Clinics
67
65 (97.01%)
2 (2.99%)
Other healthcare organizations
112
108 (96.43%)
4 (3.57%)
From the table above, we can get the pie charts shown below:
Figure 6: A pie chart showing the percentage of hospitals supporting and those not supporting artificial intelligence in their operations
Figure 7: A pie chart showing the percentage of clinics supporting and those not supporting artificial intelligence in their operationsFigure 8: A pie chart showing the percentage of the other healthcare centers supporting and those not supporting artificial intelligence in their operationsThe bar graph shown below can be used to represent the healthcare organizations using artificial intelligence in their operationsFigure 9: A bar graph showing the organizations which support artificial intelligence in their operations3 Result analysis
This section will analyze the estimated results and the actual results obtained in the whole research process.
3.1 Results estimation
Before carrying out any research, it’s good to have a rough idea of the results you expect to get in the research. The rough idea helps the researchers to make some estimations of the expected results of the research. In our case, we expected that we have many major hospitals, clinics, and other healthcare centers using artificial intelligence in their operations and we also expected that artificial intelligence has helped to improve the performance of the healthcare organizations and so most of the healthcare organizations support artificial intelligence in their operations (Iyengar, Kundu, and Pallis, 2018, pp.29-31).
We also expected to find that there are some challenges facing the application of artificial intelligence in the field of healthcare and the healthcare organizations are undertaking some measures to help in addressing some of the major challenges affecting the use of artificial intelligence in their operations (Price and Nicholson, 2017). The expectations we have explained can be seen as some of the major estimations of the results which we made before carrying out the main research.
3.2 Results summary
From the analysis done above, we can make the following summary of the results:
92% of the major hospitals use artificial intelligence in their operations.
83.75% of the clinics use artificial intelligence in their operations.
74.67% of the other healthcare centers use artificial intelligence in their operations.
From the same data analysis, we can also say that of all the organizations which use artificial intelligence in their operations:
97.83% of the major hospitals support the use of artificial intelligence in their operations.
97.01% of the clinics support the use of artificial intelligence in their operations.
96.43% of the other healthcare centers support the use of artificial intelligence in their operations.
The summary of the results given above clearly shows that artificial intelligence has affected the healthcare sector positively, and that’s the main reason it’s supported by a very large percentage of the medical organizations using it (Hamet and Tremblay, 2017, pp.36-40). Therefore, we can end our research by saying that medical organizations should embrace the use of artificial intelligence in their operations as it has very many benefits. Those medical organizations which have not yet incorporated the use of artificial intelligence in their operations should strive to do so as fast as possible for them to enjoy the many benefits. Lastly, we can say that the use of artificial intelligence in the medical organizations has some few challenges and the organization using it should look for some appropriate measures to address these challenges for them to enjoy the many benefits with few or no challenges.
4 Outline of the research and result analysis
References
Beam, A.L., Kompa, B., Fried, I., Palmer, N.P., Shi, X., Cai, T. and Kohane, I.S., 2018. Clinical Concept Embeddings Learned from Massive Sources of Medical Data. arXiv preprint arXiv:1804.01486.
Cody, R., 2017. Cody’s data cleaning techniques using SAS. SAS Institute.
Creswell, J.W. and Clark, V.L.P., 2017. Designing and conducting mixed methods research. California: Sage publications.
Cudré-Mauroux, P., 2017, June. Big Data Integration. In Telecommunications (ConTEL), 2017 14th International Conference on (pp. 5-6). IEEE.
Flick, U. 2017. The Sage Handbook of Qualitative Data Collection. California: SAGE.
García, S., Luengo, J. and Herrera, F., 2016. Data preprocessing in data mining. New York: Springer.
Hamet, P. and Tremblay, J., 2017. Artificial intelligence in medicine. Metabolism-Clinical and Experimental, 69, pp.36-40.
Heer, J., Hellerstein, J.M., and Kandel, S., 2015. Predictive Interaction for Data Transformation. In CIDR.
Hira, Z.M., and Gillies, D.F., 2015. A review of feature selection and feature extraction methods applied to microarray data. Advances in bioinformatics, 2015.
Iyengar, A., Kundu, A. and Pallis, G., 2018. Healthcare Informatics and Privacy. IEEE Internet Computing, 22(2), pp.29-31.
Lu, G., Ho, L., Danilak, R., Mullendore, R.N., Jones, J. and Tomlin, A.J., Western Digital Technologies Inc and Skyera LLC, 2015. Data reliability schemes for data storage systems. U.S. Patent 9,021,339.
Price, I.I. and Nicholson, W., 2017. Artificial Intelligence in Health Care: Applications and Legal Implications.
Ramírez?Gallego, S., García, S., Mouriño?Talín, H., Martínez?Rego, D., Bolón?Canedo, V., Alonso?Betanzos, A., Benítez, J.M. and Herrera, F., 2016. Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(1), pp.5-21.
Ramírez-Gallego, S., Krawczyk, B., García, S., Wo?niak, M. and Herrera, F., 2017. A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing, 239, pp.39-57.
Rehman, M.H., Chang, V., Batool, A. and Wah, T.Y., 2016. Big data reduction framework for value creation in sustainable enterprises. International Journal of Information Management, 36(6), pp.917-928.
Ward, M.O., Grinstein, G. and Keim, D., 2015. Interactive data visualization: foundations, techniques, and applications. AK Peters/CRC Press.
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