To: The Board Members
From:
Re: Healthcare Analytics
This memo intends to address the major talking points on healthcare analytics in addition to the benefits and challenges that hospitals face in implementing these data analytics. It has become increasingly important in recent years to analyze enormous amounts of health data. This necessity has led to the emergence of data mining and analytics to help in analyzing this data (Bates, Saria, Ohno-Machado, Shah & Escobar, 2014). Comprehensive data analytics could potentially transform the healthcare sector to greater heights as it has done with the other industries. Here are some of the major talking points of healthcare analytics:
Legislations and incentives that could promote the release and accessibility of data
There should be legislation in place to improve the ease of accessing public data regarding patients, medical advances, and health insurance. Some of the policies that have been put in place in recent years include the wide-ranging affordable care act which was enacted in March 2010, The Health Information Technology for Economic and Clinical Health Act, and the 2009 open government directive (Groves, Kayyali, Knott & Van Kuiken, 2013). The Centre for Medicare and Medicaid Services established the office of data analytics and information products to facilitate the acceleration of user sophistication and the exchange of information as a result helping to collaborate with the private sector.
The standardization of data and the ease of use
Every day more data is released and therefore the federal government has to ensure that all the appropriate stakeholders have access to this information in standard forms (Groves et al., 2013). The Health Data Initiative has also strived to make data easier to use by the developers by making sure that these data can be read by machines and that these data can be downloaded.
Source of Innovation in Healthcare
Data analytics and the release of big data could act as an inspiration to several companies to establish innovative healthcare applications. There is, therefore, the need to review business models from participants and company profiles (Groves et al., 2013). Such reviews could reveal the how big data revolution has developed new healthcare innovators.
Sustaining the Momentum
There are several strategies that can be used by the stakeholders who are committed to innovation to realize their goals and reap the rewards of big data. Some of these strategies include; investing in the capabilities of everyone who will be using the data and establishing a common ground for the governance and usability of data (Groves et al., 2013).
Benefits and Challenges of Data Analytics
The benefits of big data analytics include; cost reduction, improved decision making and new products and services. Big data analytics is very helpful in offering business intelligence that can be instrumental in reducing costs and improving the efficiency of operation. Additionally, we can use big data analytics to analyze past data and predict the future, therefore, improve the decision making the process of a business (Tan, Gao & Koch, 2015). Furthermore, we can use data analytics to analyze past data about products and feedbacks from customers to help introduce better products in the future.
The challenges of big data analytics are experienced in the segmentation of useful data from clusters. This is because the data that is present for analysis is normally made up of both organized and unorganized data that is normally very difficult to understand (Tan et al., 2015). Additionally, there is a shortage of talented personnel who have the skills in data analytics.
A software tool that can be used by hospitals for big data analytics is the eClinicalWorks which is an Electronic Medical Records software. This system contains information about the treatment history of a patient. It helps to track patient data over a long period of time from several healthcare providers (Sun & Reddy, 2013). The system is very instrumental in ensuring precise and efficient care for the patient.
References
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.
Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’ revolution in healthcare. McKinsey Quarterly, 2, 3.
Sun, J., & Reddy, C. K. (2013, August). Big data analytics for healthcare. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1525-1525). ACM.
Tan, S. L., Gao, G., & Koch, S. (2015). Big data and analytics in healthcare. Methods of Information in Medicine, 54(6), 546-547.
