With the healthcare sector beginning to leverage advanced technologies such as predictive analytics and AI, healthcare organizations, health care agencies, and primary health providers must be aware of its benefits and risks.
Importance of Predictive Analytics in Healthcare
To analyze the benefits of predictive analytics in healthcare, it is imperative for healthcare service providers to acknowledge the myriad ways through which they can benefit from this discipline. Having said that it’s crucial to note that predictive analytics in healthcare plays a crucial role in improving operational management including the overall improvement of business operations, personalization of medicine or drug therapies that assist and enhance the accuracy of diagnosis, and cohort treatment and epidemiology to assess potential risk factors for public health.
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Predictive Analytics in Healthcare: Pros and Cons
Benefits of Predictive Analytics in Healthcare
- Improving operational efficiency of business processes
- Accuracy of diagnosis and treatment through personalized medicine & drug therapies
- Increased insights to enhance cohort treatment
Improving Operational Efficiency of Business Processes
Predictive analytics in healthcare plays an important role in enhancing the efficiency of business processes by scrutinizing patient data sets to determine admission and readmission rates, while also helping businesses to monitor and analyze staff performance in real-time. Also, when compared to big data and predictive analytics which currently play an integral part in healthcare operations management, real-time reporting is relatively new but can provide timely insights to drive the value of services offered.
Accuracy of Diagnosis and Treatment through Personalized Medicine & Drug Therapies
Predictive analytics in healthcare plays a key role at the individual level by helping healthcare service providers to leverage prognostic analytics and big data to find cures for certain unfamiliar diseases. These insights can then be used by healthcare organizations to dynamically adjust their strategies in line with the discoveries and familiarize themselves with new conditions. Predictive analytics in healthcare is also paving the way for new possibilities by introducing powerful models for modeling mortality rates at an individual level.
Increased Insights to Enhance Cohort Treatment
Digitization of healthcare processes and the legislated performance reporting requirements of healthcare organizations have enabled businesses to easily access patient data sets to make crucial decisions. Predictive analytics in healthcare also includes large population studies using volumes of health system data including geographic, demographic, and medical condition information that can generate profiles of community and other cohort health patterns and inform health organisations to create early interventions that aim to reduce the financial and resource load on the public health system in the future.
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Predictive Analytics in Healthcare: What are the risks involved?
- Ongoing technological advancements and impact on decision-making
- Moral hazard and human intervention points with the machine
- Lack of regulation and algorithm bias
Technological Advancements and their Impact on Decision-Making
Technological advancements have brought about several changes across industries and the healthcare industry is no exception. Though these factors have changed business processes significantly, certain aspects of healthcare are still heavily dependent on traditional approaches of data management.
Moral Hazards and Human Intervention Points with the Machine
The accuracy of machine-generated results may be proven to be higher than that of human predictions. However, few ethicists believe that human touch is vital for progression and leveraging predictive analytics in healthcare decision-making is not acceptable. To successfully leverage predictive analytics in healthcare decision making one needs to reevaluate the importance of aligning business objectives with the ethical standards and intervention points for when the human decisions are far more critical than that of machine-generated results.
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Lack of Regulation and Algorithm Bias
Predictive analytics in healthcare or any other industry for that matter are based on advanced algorithms that are developed by ‘humans’- the ones who hold prejudices and biases. This paves the way for a possibility of bias or impartial representation of data sets. Moreover, extrapolative predictive analytics models require a sizable amount of data that represents the entire patient population as opposed to a mere fraction of them. The challenge here lies in ensuring an unbiassed representation of patient data sets.
The advantages associated with predictive analytics in healthcare overshadow the risks associated with them. Notably, predictive analytics in healthcare has benefitted millions of healthcare organizations, with patients able to enjoy an improved service delivery that anticipates challenges and addresses them proactively. Healthcare service providers would also benefit from machine-driven results, given how easy it is to analyze data and take necessary actions to improve the efficacy of services rendered.