According to Oracle, around the year 2005 people start noticing the amounts of data generated by Facebook and YouTube. Since then, the buzzword ‘big data’ had taken off. The on-going definition is still the one formulated by Gartner: “big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity”.
In many industries, this development has had an enormous impact. The healthcare industry was also promised benefits, however there is still no substantial value delivered by big data and its analytics (Dhindsa, Bhandari and Sonnadara, 2018). In this blog, the challenges of big data in the healthcare industry will be covered.
The big data is not a big as it seems
Even the largest publicly available data set of an intensive care unit, the Medical Information Mart for Intensive Care, has insufficient data according to Adibuzzaman et al. (2018). The authors explain that in medical research, clinical questions require such specific information from small cohorts in the data set, that they are mostly insufficient in providing statistical confidence.
Privacy
Although there could be willingness to analyze healthcare data, researchers are not always granted access to information easily because of the privacy sensitive nature of the data. Even if data is anonymized, “a query to find any patient who is of Indian origin and has some specific cancer diagnosis with a residential zip code 3-digit prefix ‘479’ may result in only one subject; thus exposing the identity of the individual” (Adibuzzaman et al 2018).
Acceptation
In other (mostly tech) industries, big data analytics is changing whole business models (BCG, 2019). However, due to the risk aversive nature of the healthcare industry, change is harder to achieve here. For example: the adoption of beta blockers to prevent hart failures took 25 years from the point that the research paper was published (Smith, 2013). Another factor is that many machine learning algorithms work as a ‘black box’, which further complicates acceptation as it is difficult to trace back the findings of big data analytics (Adibuzzaman et al., 2018).
The three issues mentioned above form a challenge for the healthcare industry in terms of its pursuit of big data analytics. Still, Adibuzzaman et al. (2018) conclude that if more institutions share complete, temporal and precise data, and if regulatory institutions find ways to overcome privacy and acceptation issues, then there will be a bright future for big data analytics in the healthcare industry.
References:
Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2018). Big data in healthcare – the promises, challenges and opportunities from a research perspective: A case study with a model database. AMIA … Annual Symposium proceedings. AMIA Symposium, 2017, 384–392.
BCG. (2019). [online] Available at: https://www.bcg.com/capabilities/big-data-advanced-analytics/transforming-business-models.aspx [Accessed 6 Oct. 2019].
Dhindsa, K., Bhandari, M. and Sonnadara, R. (2018). What’s holding up the big data revolution in healthcare?. BMJ, p.k5357.
Gartner IT Glossary. (2019). What Is Big Data? – Gartner IT Glossary – Big Data. [online] Available at: https://www.gartner.com/it-glossary/big-data [Accessed 6 Oct. 2019].
Oracle.com. (2019). What Is Big Data? | Oracle. [online] Available at: https://www.oracle.com/big-data/guide/what-is-big-data.html [Accessed 6 Oct. 2019].
Smith, M. (2013). Best care at lower cost. Washington, D.C.: National Academies Press.
Hi Sven, thank you for the interesting article. I think that, as you mentioned in the last part of your article, the aformentioned issues for the adoption of big data analytics in the healthcare industry will be solved in the future. Specifically, the role of governments and institutions will be fundamental in the adoption of such a technology in healthcare (Zillner S., Neururer S., 2016). Indeed, These two actors will be decisive for improving the people’s acceptance of big data by ensuring privacy and promoting the evolution of the healthcare sector. Pharmaceuticals’ institutions, clinical research and governments will increase the impact of data analytics’ solutons by integrating heterogeneours and distributed data sources ( Bresnick J., 2017). Government will help enterprises to implement big data both with suitable policies, laws and infrastructure aimed at helping healthcare’s companies in dealing with the privacy and responsabilities issues ( Wang et al., 2018).
Malaysia governemnt is a clear example of how creating a “background” infrastructure can help actors in the Healthcare Industry. In 2017, the Ministry of Health Malaysia has launched the Malaysian Health Data Warehouse (MyHDW). The MyHDW has been used in the last two years to collect patient’s data from private hospital, private clinics and public hospitals, university hospitals, National Registration Department, National Department of Statistics and other related agencies. Doing so the the MyHDW has became a one stop centre whose data are used by healthcare providers to make decisive decision on treatments ( Fatt QK., Radamas A., 2018).
The U.S. Department of Health & Human Services (HHS) has helped solving the problem related to data security and privacy by creating the HIPAA Security Rule which is a list of technical safeguards for organizations that store protected health information. Technical safeguards varies from transmission security to authentication protocols, going through controls over access, integrity, and auditing. The HHS has helped in creating a common and shared way of acting, which help companies in detecting failures in the security system, in creating modular data that can be used by different actors thanks to the shared ” security management process” (Bresnick J., 2017).
So, the future of an Healthcare system is not so far and governments and institutions are working on making it happen (NEJM Catalyst, 2018).
References
Bresnick, J. (2017). Top 10 Challenges of Big Data Analytics in Healthcare. [online] HealthITAnalytics. Available at: https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in-healthcare [Accessed 6 Oct. 2019].
Fatt QK, Ramadas A (2018) The Usefulness and Challenges of Big Data in Healthcare. J Healthc Commun Vol.3 No.2:21
NEJM Catalyst. (2018). Healthcare Big Data and the Promise of Value-Based Care. [online] Available at: https://catalyst-nejm-org.eur.idm.oclc.org/big-data-healthcare/ [Accessed 6 Oct. 2019].
Wang, L., Yang, M., Pathan, Z., Salam, S., Shahzad, K., & Zeng, J. (2018). Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique. Sustainability, 10(11), 3956.
Zillner S., Neururer S. (2016) Big Data in the Health Sector. In: Cavanillas J., Curry E., Wahlster W. (eds) New Horizons for a Data-Driven Economy. Springer, Cham