Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective
Abstract
:1. Introduction
2. General Empirical Approach
2.1. Data Collection and Analysis
2.2. Quality and Ethics
3. Findings
3.1. Word Association Survey
3.1.1. Big Data: Content Analysis
3.1.2. Big Data: Similarity Analysis and Core/Periphery Analysis
3.1.3. Big Data: Interpretations of Social Representations
3.2. Semi-Structured Interviews
3.2.1. Big Data: Content Analysis
3.2.2. Correspondence Analysis: Defining Big Data
My lay understanding of big data is that it starts with collection of data, for example, temperature, blood pressure, and so on, from a single patient. Then you end up collecting such data from many patients… Eventually, you accumulate information to help you answer questions like, ‘What are the variations in body temperature for certain diseases?’ Big data puts together the bigger picture of what is happening…—Interviewee #13
It’s all about being able to collect data about service delivery… Big data analytics then looks at the data that has been collected over time and uses the same data to… improve health services.—Interviewee #16
Traditionally data was structured. Data was structured until social media came, and we discovered that through social media everyone is a generator of information or data… So when we talk about big data it’s about all these uncorrelated data which is everywhere, being generated by different people, being generated by devices, in an unstructured way.—Interviewee #7
When you talk about big data you are talking about large amounts of data that have been collected over time and stored somewhere. And where is this data coming from? It’s coming from transactions either due to human interaction or due to items that have been programmed.—Interviewee #9
3.2.3. Correspondence Analysis: Benefits of Big Data Technologies
3.2.4. Correspondence Analysis: Challenges of Big Data Technologies
One of the challenges that we face when it comes to scaling up the use of these technologies is the issue of budget constraints. A majority of projects rely on donor funding, and therefore, these initiatives are not sustainable once the donor is unable to continue.—Interviewee #14
The main challenge is the lack of resources. But it is also about resource allocation, lack of prioritisation, and limited understanding about the benefits of these technologies. There are other things considered more basic such as drugs, so that when it comes to things such as the internet it is not considered as basic although they may play a role in making services better.—Interviewee #12
How many people are in technology and understand health informatics? Very few…and those few ones are stretched. County health records officers are trained to keep the records, not run analytics. Data may be exist… but unless they are trained on how to carry out data analytics, they can only wait for somebody else to do it.—Interviewee #8
So let’s say you visited the hospital and underwent some tests… results show you have heart problems and you are given a pacemaker to help your heart keep the rhythm. You perhaps only want your close family members know your health status. Let’s say that that pacemaker is connected to the internet as part of the IoT so that it can update your doctor on your status, how it (pacemaker) is working, and its battery and so on… if a bad guy happens to know that you have this pacemaker and it’s connected to the internet, he could have the device hacked and try to control your life. This can turn out to be a life-and-death issue that you don’t want to contemplate—it seems farfetched but possible.—Interviewee #7
3.2.5. Correspondence Analysis: Solutions to Big Data Challenges
Technology is still seen as a luxury… However, technology is still relevant. If the government allocated more resources in terms of internet and connectivity, that would be helpful. Other players too have responsibility. They can pick and strengthen areas that the government has not been able to address…—Interviewee #14
Traditionally government have existed for policy. So the government needs to provide the relevant policies and laws… Additionally, the government needs to also put in the right environment, for example, in terms of incentives so that the private sector to roll out such projects. The ripple effect would be massive gains for the populace.—Interviewee #12
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Government | Private Sector | Total | |
---|---|---|---|
IT | 42 (40%) | 29 (28%) | 71 (68%) |
Health | 21 (20%) | 13 (12%) | 34 (32%) |
IT | Health | ||
Female | 14 (13%) | 12 (11%) | 26 (25%) |
Male | 57 (54%) | 22 (21%) | 79 (75%) |
Total respondents | 105 |
Topic Theme | Sample Codes | Topic Theme | Sample Codes |
---|---|---|---|
T1 Size/Volume | massive, large data sets, high volume, huge, colossal, many variables, tonnes of data | T9 Security | security, privacy, hacking, encryption, loss, no privacy |
T2 Data | raw data, data, streaming data | T10 Unstructured | multimedia, unstructured data, social media, disorganisation |
T3 Technology | information technology, ICT, blockchain, Hadoop, infrastructure, servers | T11 Insight | information, strategic, knowledge, evidence, patterns, trends, preferences |
T4 Applications | Google, research, survey, policy, YouTube | T12 Opportunities | transformational, potential, value, gold rush, monetisation, research potential, business opportunities |
T5 Analytics | integration, analysis, aggregation, processing, data mining, download, consolidation, predictive analytics | T13 Time | time, long time, different times |
T14 Cost | expensive, money | ||
T6 Speed | velocity, data stream, data explosion, fast | T15 Cloud/Internet | cloud, online, internet, connectivity |
T7 Complexity | variety, complex, multiple sources, diversity | T16 Smart/Artificial Intelligence | smart logic, artificial intelligence, intelligence, learning, habits |
T8 Storage | databases, data repositories, warehouse, data lake, hard drive | T17 Fourth Industrial Revolution | 4th industrial revolution |
T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | |
---|---|---|---|---|---|---|---|---|
T1 | 1.000 | 0.354 | 0.049 | 0.047 | 0.125 | 0.108 | 0.095 | 0.083 |
T2 | 0.354 | 1.000 | 0.000 | 0.000 | 0.255 | 0.027 | 0.075 | 0.114 |
T3 | 0.049 | 0.000 | 1.000 | 0.111 | 0.022 | 0.000 | 0.050 | 0.000 |
T4 | 0.047 | 0.000 | 0.111 | 1.000 | 0.043 | 0.000 | 0.000 | 0.036 |
T5 | 0.125 | 0.255 | 0.022 | 0.043 | 1.000 | 0.000 | 0.064 | 0.167 |
T6 | 0.108 | 0.027 | 0.000 | 0.000 | 0.000 | 1.000 | 0.056 | 0.042 |
T7 | 0.095 | 0.075 | 0.050 | 0.000 | 0.064 | 0.056 | 1.000 | 0.071 |
T8 | 0.083 | 0.114 | 0.000 | 0.036 | 0.167 | 0.042 | 0.071 | 1.000 |
T9 | 0.023 | 0.000 | 0.000 | 0.105 | 0.091 | 0.000 | 0.048 | 0.217 |
T10 | 0.108 | 0.056 | 0.000 | 0.000 | 0.047 | 0.000 | 0.056 | 0.042 |
T11 | 0.173 | 0.137 | 0.091 | 0.056 | 0.204 | 0.063 | 0.083 | 0.023 |
T12 | 0.024 | 0.026 | 0.063 | 0.118 | 0.022 | 0.000 | 0.000 | 0.000 |
T13 | 0.056 | 0.061 | 0.000 | 0.000 | 0.050 | 0.000 | 0.143 | 0.000 |
T14 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.125 | 0.000 | 0.000 |
T15 | 0.000 | 0.025 | 0.118 | 0.050 | 0.067 | 0.133 | 0.048 | 0.037 |
T16 | 0.024 | 0.054 | 0.063 | 0.056 | 0.122 | 0.000 | 0.000 | 0.000 |
T17 | 0.000 | 0.000 | 0.000 | 0.000 | 0.026 | 0.000 | 0.000 | 0.000 |
Sum of similarity | 2.270 | 2.183 | 1.565 | 1.620 | 2.303 | 1.553 | 1.788 | 1.831 |
T9 | T10 | T11 | T12 | T13 | T14 | T15 | T16 | T17 | |
---|---|---|---|---|---|---|---|---|---|
T1 | 0.023 | 0.108 | 0.173 | 0.024 | 0.056 | 0.000 | 0.000 | 0.024 | 0.000 |
T2 | 0.000 | 0.056 | 0.137 | 0.026 | 0.061 | 0.000 | 0.025 | 0.054 | 0.000 |
T3 | 0.000 | 0.000 | 0.091 | 0.063 | 0.000 | 0.000 | 0.118 | 0.063 | 0.000 |
T4 | 0.105 | 0.000 | 0.056 | 0.118 | 0.000 | 0.000 | 0.050 | 0.056 | 0.000 |
T5 | 0.091 | 0.047 | 0.204 | 0.022 | 0.050 | 0.000 | 0.067 | 0.122 | 0.026 |
T6 | 0.000 | 0.000 | 0.063 | 0.000 | 0.000 | 0.125 | 0.133 | 0.000 | 0.000 |
T7 | 0.048 | 0.056 | 0.083 | 0.000 | 0.143 | 0.000 | 0.048 | 0.000 | 0.000 |
T8 | 0.217 | 0.042 | 0.023 | 0.000 | 0.000 | 0.000 | 0.037 | 0.000 | 0.000 |
T9 | 1.000 | 0.000 | 0.000 | 0.125 | 0.000 | 0.000 | 0.111 | 0.000 | 0.000 |
T10 | 0.000 | 1.000 | 0.000 | 0.071 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
T11 | 0.000 | 0.000 | 1.000 | 0.000 | 0.069 | 0.036 | 0.057 | 0.029 | 0.037 |
T12 | 0.125 | 0.071 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.067 | 0.000 |
T13 | 0.000 | 0.000 | 0.069 | 0.000 | 1.000 | 0.200 | 0.000 | 0.000 | 0.000 |
T14 | 0.000 | 0.000 | 0.036 | 0.000 | 0.200 | 1.000 | 0.091 | 0.000 | 0.000 |
T15 | 0.111 | 0.000 | 0.057 | 0.000 | 0.000 | 0.091 | 1.000 | 0.000 | 0.000 |
T16 | 0.000 | 0.000 | 0.029 | 0.067 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 |
T17 | 0.000 | 0.000 | 0.037 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
Sum of similarity | 1.721 | 1.379 | 2.057 | 1.516 | 1.578 | 1.452 | 1.736 | 1.415 | 1.063 |
Topic Theme | Sum of Similarity | Salience (Weighted Frequency) | Coreness | Core/Periphery | |
---|---|---|---|---|---|
T5 | Analytics | 2.30 | 16.41 | −0.412 | CORE |
T1 | Size | 2.27 | 14.02 | −0.422 | |
T2 | Data | 2.18 | 11.68 | −0.435 | |
T11 | Insight | 2.06 | 12.31 | −0.341 | |
T8 | Storage | 1.83 | 8.17 | −0.273 | PERIPHERY |
T7 | Complexity | 1.79 | 5.33 | −0.238 | |
T15 | Cloud/Internet | 1.74 | 4.42 | −0.172 | |
T9 | Security | 1.72 | 4.25 | −0.188 | |
T4 | Applications | 1.62 | 4.83 | −0.147 | |
T13 | Time | 1.58 | 0.96 | −0.164 | |
T3 | Technology | 1.57 | 3.92 | −0.140 | |
T6 | Speed | 1.55 | 2.50 | −0.155 | |
T12 | Opportunities | 1.52 | 3.92 | −0.112 | |
T14 | Cost | 1.45 | 0.67 | −0.090 | |
T16 | Smart/AI | 1.41 | 3.87 | −0.133 | |
T10 | Unstructured | 1.38 | 3.25 | −0.137 | |
T17 | 4th Industrial revolution | 1.06 | 0.25 | −0.026 |
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Maina, A.M.; Singh, U.G. Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective. Information 2022, 13, 441. https://doi.org/10.3390/info13090441
Maina AM, Singh UG. Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective. Information. 2022; 13(9):441. https://doi.org/10.3390/info13090441
Chicago/Turabian StyleMaina, Anthony M., and Upasana G. Singh. 2022. "Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective" Information 13, no. 9: 441. https://doi.org/10.3390/info13090441