Healthcare Analytics and PICO

Healthcare Analytics and PICO

Challenges Faced in the Implementation of Healthcare Analytics in the Healthcare Industry

The healthcare industry is rife with the reality of incorporating healthcare data into its operations to remain competitive. Healthcare analytics is the analysis of healthcare data to predict disease trends, outcomes and prevent the spread of these diseases (Dinov, 2016). It helps managers make better decisions by providing business intelligence that includes real-time information. In the healthcare industry, these analytics provide financial and administrative data and data to improve patient care. Clinical data and patient behavior and behavior can help aid in determining the effectiveness of the services offered. It serves an excellent service to any given organization that eventually trickles down to an increase in revenue. The healthcare insurance players have used data analytics to promote healthy lifestyles, which turn out to fewer insured claims at the end of the day.

However, the healthcare analytics industry has faced a myriad of challenges in its quest to aid in healthcare efficiency and maximize profits for the firms involved. Data collection is one such problem. The mining of healthcare data is challenging since there is no singular source from patient records, claims, and reimbursements from employers and government sites. Besides, monitoring traffic on social media to check what influences consumers’ health is complex with the rise of individual’s demand for privacy. Therefore, it is not easy to use analytics unless you have a considerable budget set aside for it.

Additionally, healthcare data is mostly fragmented as it comes from various sources. A variety of factors determines the health of individuals. These are the same factors that form the avenues of data collection.

Privacy is the cornerstone of the healthcare industry. Healthcare practitioners and industry players are sworn to an oath of secrecy. Security of patient’s data depends on adherence to the HIPAA act and the use of unbreakable electronic health records. Besides, with the dynamic regulatory requirements, storing vast data sets and complying with them has become difficult.

Healthcare data is usually stored for long as they are required to do so by law. Besides, this data is essential in researching clinical trials (Undavia& Patel, 2020). However, this means more costs. Some of the data end up being irrelevant. Besides, healthcare data is dynamic, and more often than not, there will be a need to update the stored data. The problem is that big data sets are very volatile. One has to be very careful when making changes to them as one may lose them all. Data handlers must clearly understand what particular data sets need updating and how to conduct all this without losing the data’s integrity. Industry players must ensure that they are not creating duplicate records as they update the information. This may present a challenge when the information is required for urgent decision making.

Healthcare data is meant to be shared. Providers do not operate in a vacuum. They have to share patient records with insurance providers and government agencies mandated to monitor disease trends. However, there are differences in how information is stored and implemented across various organizations. Data storage differ in their configurations and backends and are therefore hard to conveniently share. There is a gleam of hope since there are data exchange systems that have been created to aid.

Application of PICO

Problem-Several challenges were identified in the course of this research. The challenges range from data collection, privacy and security of patient data, redundancy of data, and the lack of systems to facilitate sharing this information. Analytics of healthcare data has become an enormous undertaking for the industry. Organizations now have to integrate analytics into their clinical and operational processes. Therefore, these problems must be sorted out to maximize the benefits of data insights.

Intervention-To handles these challenges, there have to be data handlers training on ways to handle and store data. This will reduce redundancy and loss of data. Besides, there needs to be a category of individuals who develop metadata, which will allow data scientists to utilize it for scientific studies. To prevent the infiltration of patient private data, organizations need to remind their staff of the principle of confidentiality frequently. Besides, they should limit the number of people who have access to this data.

Comparison group- There has been a recorded success in the use of data analytics in other sectors. For instance, it has helped reduce traffic by configuring systems that predict and coordinate traffic flow (Lee C. et al., 2017). By analyzing the events taking place around a given area, for example, by using registration data, it can predict and reduce traffic. Another example is in the e-commerce, where by monitoring traffic, the business is able to conduct target marketing and sales. In the entertainment industry, data analytics are responsible in the personalization of the news and services that are offered by organizations.

Outcomes- The use of data analytics is a new trend but of significant impact. Various organizations can utilize it to maximize their profits. The reduction of costs by utilizing data analytics has made this possible. It can be helpful in clinical trials and by insurance companies and government agencies to their usefulness. Data analytics have provided organizations with a competitive edge to enable them understand the behavior of their customers and become more robust in outperforming their competitors in the given industry that they are in.

References

Dinov, I. (2016). Volume and value of big healthcare data. Journal Of Medical Statistics And Informatics, 4(1), 3. https://doi.org/10.7243/2053-7662-4-3

Lee C. et al. (2017) Big Healthcare Data Analytics: Challenges and Applications. In: Khan S., Zomaya A., Abbas A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_2

Undavia, J., & Patel, A. (2020). Big Data Analytics in Healthcare. International Journal Of Big Data And Analytics In Healthcare, 5(1), 19-27. https://doi.org/10.4018/ijbdah.2020010102