Applications of Predictive Analytics in Healthcare

Over the past few years, the healthcare industry has been keen to embrace technology, such as augmented reality and predictive analytics, to revolutionize treatment and generate increasingly valuable insights for advanced patient care. Applications in healthcare are proving to be beneficial in various use cases, such as streamlining operational processes, personalized treatment, and tracking and predicting disease outbreaks.

In 2022, 72% of healthcare leaders surveyed worldwide believed that predictive analytics would positively impact patient health outcomes in clinical settings. ~ Statista

This article explores the benefits of predictive analytics in healthcare and its applications.

What is Predictive Analytics in Healthcare?

Predictive analytics uses multiple techniques, such as data mining, modeling, statistics, and AI, to analyze historical and real-time data to generate predictions about future events or actions that inform decision-making. In healthcare, it can enable healthcare workers to analyze patient data and identify optimal treatment plans that will work best for them.

The technology is already being used to deliver value in multiple healthcare settings, such as physician practices, to enhance clinical trials. Also, healthcare insurance companies use it for efficient health claim processes and to reduce operating costs. One of the most significant contributions in healthcare is personalized and accurate treatment.

Applications of Predictive Analytics in Healthcare

From reducing costs on appointment no-shows to speeding up tasks like discharge procedures and enhancing cybersecurity, predictive analytics have several applications in healthcare. Here is a list of applications in healthcare.

Readmission Prediction

Predictive analytics can assist healthcare providers in identifying patients at a high risk of being readmitted to a hospital. This allows them to target additional care and support to the individuals requiring it the most at the right time. Such tools leverage readily available Electronic Health Records (EHR) to accurately identify readmission risk for patients before hospital discharge.

A study published in JAMA Network Open discusses how researchers used predictive analytics to identify all-cause 30-day readmission risk for pediatric patients. The designed model analyzed approximately 29,988 patients with 48,019 hospitalizations to get results.

Advanced Cybersecurity

The healthcare industry faces several cybersecurity challenges, including malware attacks that can damage systems and compromise patient privacy, distributed denial-of-service (DDoS) attacks that impede the delivery of care, and medical data theft for financial gain, resulting in large-scale data breaches.

Predictive cybersecurity analytics come in two main types: vulnerability-based solutions that help discover gaps in healthcare systems and threat-focused platforms to discover potential threats.

Using AI-based predictive analytics solutions, the healthcare sector can block high-risk activity, monitor their data in real-time, and implement multi-factor authentication (MFA) to enhance cybersecurity. This can help to prevent data breaches, protect patient information and ensure the continuity of care.

Effective Clinical Trials

Clinical researchers have widely adopted predictive analytics for modeling clinical trials. It can improve clinical research using predictive modeling to predict clinical outcomes and make better treatment decisions, thus speeding up clinical trials and reducing costs. Also, predictive analytics help identifies drug response phenotypes, predict the development of diseases, and evaluate the efficacy of different treatments.

One of its recent use cases was when Johnson & Johnson used machine learning to identify suitable trial locations and accelerate the development of COVID vaccine by predicting COVID-19 surges so that the vaccine trials could start earlier.

Predicting Patient Engagement & Behavior

Predictive analytics enables healthcare organizations to understand patients’ needs better and personalize their treatment approach. This can help improve patient engagement and tailor care to each individual’s unique healthcare needs and preferences. By analyzing data, predictive analytics can predict which patients are likely to miss appointments and help administrators plan clinician schedules and allocate resources accordingly.

Furthermore, it can predict which interventions or healthcare messages are most effective for specific patients or groups. Healthcare organizations can identify patterns and trends that can help them to understand what type of care or communication is most likely to resonate with different patients.

Healthcare Marketing

Predictive analytics can play a crucial role in healthcare marketing. It can help organizations connect potential patients with the right doctor and facility. Moreover, It can assist healthcare organizations in gaining a deeper understanding of consumer behavior. This is done by analyzing the data of patients looking for healthcare information online.

This data can include search queries, website visits, and clicks. It can help identify patterns and signals that indicate what patients are looking for and what care they need. As a result, healthcare organizations can achieve more efficient use of their marketing budget and improve the effectiveness of their campaigns using personalization, resulting in higher ROI.

Human Intervention in Healthcare Predictive Analytics

In a data-driven healthcare environment, keeping the human element in mind is essential. The principle of human-centered design is the basis for creating healthcare technology and programs. They are easy to understand and use for patients and enable accurate decision-making.

Predictive analytics models are based on historical and real-time data and statistical algorithms. This can sometimes produce results that may be biased and not consistent with real-world medical knowledge or practice. Human healthcare professionals, such as doctors and nurses, are essential to validate the predictions made by the analytical models. Also, they can interpret the results in the context of a patient’s unique clinical situation.

Therefore, human intervention is critical for healthcare predictive analytics. Medical experts can cross-check and validate analytical models’ predictions and help ensure that they are accurate and clinically relevant.

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