How Predictive Analytics Can Yield Superior Patient Care for Healthcare Providers

Have you ever had to make a trip to the emergency room only to find yourself in a crowded waiting area for a long period of time? Predictive analytics is a promising solution to alleviate patients' distasteful experiences with the quality of care they receive at healthcare facilities. Additionally, healthcare providers can use this new technology to reduce their operating costs and improve profitability. However, this opportunity is not without significant risks to both the patients and healthcare organizations.

One of the benefits that patients would immediately notice is the reduced wait time to be seen by a medical professional. Healthcare providers do this by integrating various data points in their algorithm models, including historical data, reportable diseases, seasonal sickness and weather patterns, calendar variables and public holidays in order to predict the number of patients who may need medical attention. As a result, hospitals can optimize their patient-to-staff ratio and therefore improve both the quantity and quality of care for their patients.

Another benefit of predictive analytics is the ability to cut down on revenue losses from patients who skip their appointments without giving advance notice. This would lead to a greater amount of available appointments for other patients seeking care and improve overall profitability. Additionally, healthcare providers can manage their supply chain operations more effectively using predictive analytics. Clinicians claim to spend 17% of their workweek managing problems related to inventory instead of providing patient care. Predictive analytics can be used to streamline inventory management and negotiate contracts with suppliers, which would enable medical professionals to spend more time caring for patients and improve the quality of treatment received by patients.

There are a few significant risks related to predictive analytics that both patients and healthcare providers should consider. One of the risks is privacy protection for patients' records. Hospitals and other healthcare facilities accumulate and store an extensive amount of personal information on cloud-based databases. As big data continues to be a target for hackers, it is more crucial than ever for healthcare organizations to safeguard their patients' information and address their risks and legal responsibilities.

One of the greatest risks is structuring an ethical and moral framework for developing algorithms related to predictive analytics. Maintaining such a framework is important because predictive analytics models can often yield a bias outcome. A majority of people place a high level of trust in algorithms without realizing that the algorithms are created by imperfect humans who hold natural prejudices and biases. One way for a healthcare organization to combat potential bias is to develop a collaborative framework with outside parties such as government agencies, regulatory bodies and other associations to help establish guidelines for what acceptable characteristics an algorithm should possess. This would ultimately help maintain a patient-centered perspective when it comes to implementing predictive analysis in the healthcare industry.

Overall, predictive analytics open up a realm of opportunities to improve patient outcomes; however, organizations need to evaluate the benefits in line with the risks to avoid potentially disastrous effects on the business.