Big-Data Technology in Finnish Healthcare: Barriers and Possible Ways Out
Taiwo, Adewale (2019)
Metropolia Ammattikorkeakoulu
2019
All rights reserved
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-201902222637
https://urn.fi/URN:NBN:fi:amk-201902222637
Tiivistelmä
Purpose:
The purpose of this study was to identify the barriers of big-data technology and innovations in healthcare, and the possible solutions to locally combat them
Aim:
The objective of this study is to investigate the barriers of big-data technology in Finnish healthcare as identified by stakeholders and also produce recommendations to address the identified factors.
Method:
Qualitative method was used to collect the data. Data were collected by using semi-structured interviews with open-ended questions together with the use of probing and closing questions. Also included is the unstructured participant observation diaries, and insightful emails. Deductive approach of content analysis method was used in analyzing the collected data. The participants were selected to have a good representation of all the stakeholders of Finnish healthcare. Twenty-three interviewee participated, the majority of whom are high position personnel and professionals.
Results:
The results of this study showed that many stakeholders in the industry believed that big-data analytics could be the solution to most of the current challenges of healthcare. However, the majority declined to compare the success rate of big-data in healthcare to other services, arguing that healthcare is unique and cannot be compared to other industries. As expected, the problem of data security and strictness of governments regulations on data was the most mentioned by all the participants (95% and 100% respectively). This supported some of the widely claimed barriers. However, the trust issue was not seen as a barrier, it is statistically significant (p< 0.05)
Conclusions:
This study established that the highly restrictive government regulatory policies of data privacy and security are one major challenge that is limiting the full potential of big-data analytics in Finland’s healthcare industry. The result also suggests for an automatic systemic anonymization of data that would make health data to be less attractive to cyber-attacks and become more accessible to the data scientists.
The purpose of this study was to identify the barriers of big-data technology and innovations in healthcare, and the possible solutions to locally combat them
Aim:
The objective of this study is to investigate the barriers of big-data technology in Finnish healthcare as identified by stakeholders and also produce recommendations to address the identified factors.
Method:
Qualitative method was used to collect the data. Data were collected by using semi-structured interviews with open-ended questions together with the use of probing and closing questions. Also included is the unstructured participant observation diaries, and insightful emails. Deductive approach of content analysis method was used in analyzing the collected data. The participants were selected to have a good representation of all the stakeholders of Finnish healthcare. Twenty-three interviewee participated, the majority of whom are high position personnel and professionals.
Results:
The results of this study showed that many stakeholders in the industry believed that big-data analytics could be the solution to most of the current challenges of healthcare. However, the majority declined to compare the success rate of big-data in healthcare to other services, arguing that healthcare is unique and cannot be compared to other industries. As expected, the problem of data security and strictness of governments regulations on data was the most mentioned by all the participants (95% and 100% respectively). This supported some of the widely claimed barriers. However, the trust issue was not seen as a barrier, it is statistically significant (p< 0.05)
Conclusions:
This study established that the highly restrictive government regulatory policies of data privacy and security are one major challenge that is limiting the full potential of big-data analytics in Finland’s healthcare industry. The result also suggests for an automatic systemic anonymization of data that would make health data to be less attractive to cyber-attacks and become more accessible to the data scientists.