Industry Challenges to Healthcare Analytics – Health System Example

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"Industry Challenges to Healthcare Analytics" is a wonderful example of a paper on the health system. Healthcare analytics is implemented in the healthcare industry with the objectives of improving the general quality of care provided to patients, reducing the costs of operations, and increasing revenues. However, just like any other technology, the implementation of analytics in healthcare organizations faces several challenges. One significant problem is privacy and security; healthcare analytics needs information about patients to analyze promoting health care and other healthcare procedures (Adibuzzaman, DeLaurentis, Hill, & Benneyworth, 2017). Low security implies that the application of healthcare analytics may lead to the breach of data and leak patient's sensitive information to unauthorized parties; the patients’ privacy will be abused.

Therefore, the operational efficiency and elimination of triggers for additional care will be adversely affected, leading to increased medical costs. Furthermore, accessing patients' healthcare information can be affected by vulnerabilities due to patients' secrecy concerns guarded by local and federal laws. Health care providers have a fear of breach of privacy and lawsuit, which hinders them from sharing patient health data. Improving the clinical outcomes using healthcare analytics will thus become a challenge and will even affect the revenue; with security and privacy issues resulting from healthcare analytics, then the taxes will decrease.

The universal healthcare value in terms of care will reduce, and in terms of costs will increase. The Value Life Cycle   Another challenge is the nature of healthcare data; the majority of healthcare data is in unstructured form since it is stored in EHR and makes it difficult for easier accessibility and utilization into the healthcare analytics. Some clinical data obtained in various medical systems within an organization always has a different purpose and not well integrated (White, 2014).

It leads to a higher likelihood of having data redundancy, low-quality data, and untrusted sources of data.   The value life cycle helps in the classification of possibilities and difficulties of healthcare organizations and has three stages: value discovery, realization, and optimization (Davenport, 2014). The value discovery stage is essential in distinguishing the areas in an organization that knowledge is important. In contrast, the value optimization phase enables a healthcare organization to concentrate on program expansion to enhance the contemporary investment benefit (Davenport, 2014).

The value realization stage sets the transformational procedures to assist in estimating the results, mobilize, and give care according to the knowledge of managing practices and benchmarks. The nature of the healthcare data dictates the ease of realizing the value from healthcare analytics at various levels of examination. Such a challenge will affect the health care industry's value life cycle by becoming a barrier to the successful implementation of healthcare analytics. Application of PICO The problem is the existence of several challenges - privacy and security concerns and the nature of healthcare data issues - due to the application of healthcare analytics in healthcare.

The challenges can be intervened through the use of new security measures such as scanning for vulnerabilities in healthcare systems to ensure patients’ information is secure and free from any data breaches. Unstructured data can be intervened through the use of better data storage tools such as databases. A comparison intervention to using better storage and improving security is improving the procedures and methods used in healthcare analytics.

After the application of better interventions, there will be a higher likelihood of solving the challenges and improving the quality of care rendered to the patients. Moreover, healthcare organizations will realize high healthcare value, better operational efficiency, and reduced medical costs, including improved clinical outcomes.


Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2017). 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, 384–392:

Davenport, T. (2014). Big Data at Work Dispelling the Myths, Uncovering the Opportunities. Boston: Boston Harvard Business Review Press 2014.

White, S. E. (2014). A review of big data in healthcare: challenges and opportunities. Open Access Bioinformatics, (6):13-18:

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