The Top 8 Uses of Data Analytics in Healthcare.

Introduction

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. Examples include errors in billing processes, inefficient allocation of resources, and more. Yet those healthcare organisations that utilise 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. Prediction and Prevention

Healthcare providers can use data analytics to analyse 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. Personalised Care

Data analytics enables healthcare providers to tailor treatments to each patient's 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. Optimising Hospital Operations

Healthcare providers can analyse patient flow, resource utilisation, 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. Developing 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%.

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05. Improving Clinical Decision-Making

When life and death can be a matter of moments, equipping 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 analyse insurance claims and 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 minimise 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. Reducing 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. Empowering 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 examples 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 health provision.

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 personalised, preventive, and efficient, ultimately improving the lives of patients and your organisation's success. After all, isn't that what we all want?

Kieran Gilmurray

Kieran is a technology, data analytics and automation expert, recently named as one of the Top 50 AI & Intelligent Automation Influencers in the world. With nearly 30 years experience leading digital transformation in organisations, he is also the author of the book “The A to Z of Organizational Digital Transformation.”

https://www.dhirubhai.net/in/kierangilmurray/

Sources:

• Using predictive analytics in health care | Deloitte Insights
• Transforming Emergency Room Efficiency with Striim's Real-Time Data Analytics - Striim
• Predictive analytics in healthcare: three real-world examples | Philips
• Enhancing Patient Care: The Role of Striim and GenAI in Modern Healthcare