The Top 8 Uses of Data Analytics in Healthcare

Imagine healthcare transformed, not by intuition, but by powerful insights hidden in mountains of data. That’s the promise of data analytics.

Healthcare providers today face many challenges, each impacting care, quality and operational efficiency, e.g., errors in billing processes, inefficient allocation of resources, and more. Yet those healthcare organizations that utilize predictive analytics have reported significant benefits, including reduced patient risk and lower overall costs.

Here are eight ways data analytics is reshaping the healthcare industry:

01. Predict and prevent: Healthcare providers can use data analytics to analyze patient data to identify individuals at high risk for heart failure or diabetes. This allows for early intervention and, ultimately, better outcomes. After all, when it comes to healthcare, prevention is better than cure.

For example, Mayo Clinic has used an AI model to identify patients at high risk of developing sepsis with 70% accuracy, leading to a 20% reduction in sepsis-related deaths.

In addition, predictive analytics has extended its benefits to home care by identifying at-risk patients who might require emergency transport within the next 30 days. This proactive approach helps prevent unnecessary hospital readmissions, reducing the costs associated with acute care and rehabilitation.

02. Personalized care: Data analytics enables health care providers to tailor treatments to patients’ unique needs and genetic makeup, moving away from “one-size-fits-all” medicine. For example, Flatiron Health analysed real-world cancer data to identify subgroups of patients likely to benefit from specific treatments, improving patient outcomes and increasing drug trial success rates by 15%.

03. Optimize hospital operations: Healthcare providers can analyze patient flow, resource utilization, and staffing levels to improve staffing efficiency and patient flow and benefit from reduced costs. For example, Cleveland Clinic used analytics to streamline patient flow, reducing wait times by 30% and significantly increasing patient satisfaction.

04. Develop better drugs: The analysis of clinical trial data can help identify promising new treatments faster and with greater accuracy. For example, AstraZeneca used AI to analyze drug compounds, reducing drug discovery time by 25% and costs by 10%.

05. Improve clinical decision-making: When life and death can be a matter of moments, quipping doctors with real-time data-driven insights to make more informed diagnoses and treatment plans becomes critical. Medical professionals want to make correct decisions when diagnosing or treating patients. The better quality real-time healthcare providers have, the better it is for the patient. For example, St. Jude Children’s Research Hospital implemented a clinical decision support system that reduced medication errors by 50%.

06. Combat fraud and waste: Healthcare providers can use data to analyze insurance claims data to identify and prevent fraudulent activity, saving precious healthcare dollars. For example, Humana used big data analytics to identify and prevent fraudulent insurance claims, saving the company $2.3 billion annually.

In addition, healthcare operations can minimize unscheduled workflow disruptions by predicting the maintenance needs of medical equipment, such as MRI scanners, before they arise. This predictive approach to maintenance ensures that equipment is available when needed and reduces the downtime that can impact patient care.

07. Reduction in Adverse Events: Predictive analytics has been used to detect early signs of patient deterioration in practical applications, such as in ICU settings. This allows for timely interventions, significantly reducing the likelihood of adverse events.

One hospital reported a 35% reduction in adverse events and a more than 86% reduction in cardiac arrests through the use of automated early warning scoring, which had a dramatic impact on those patients at risk of heart attack.

08. Empower patients: Giving patients access to their health data and analytics tools enables them to participate actively in their care. For example, MyChart® by Epic allows patients to access their medical records and lab results electronically, increasing patient engagement and improving medication adherence. The more data patients have, the better they can look after themselves, saving them harm and freeing finite healthcare resources for those in most need.

Conclusion:

These are just a glimpse into the exciting world of data analytics in healthcare. Understanding these possibilities can help you make informed decisions, improve patient care, and confidently navigate the future of healthcare.

Remember, data analytics is not a magic bullet, but its potential to transform healthcare and deliver better patient outcomes is undeniable. By leveraging its power, you can create a future where care is more personalized, preventive, and efficient, ultimately improving the lives of patients and your organization’s success. After all, isn’t that what we all want?

Check my previous blog at – Forging the Path Ahead: Insights from The AWS London Summit – Kieran Gilmurray