Reducing costs and readmissions through data analytics in health care

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Leading data experts insist on leveraging big data for the health care industry. And clearly, they are not wrong.

Did you know that over 57% of organizations use data analytics in health care-related decisions? This is why the utilization of quality data that are free from errors must be undertaken across all platforms and workflows to improve workflow productivity and make informed choices.

But how does data analytics achieve this?

From predictive analytics to real-time monitoring, this article explores six proven ways physicians and other health care providers can use data to reduce costs while improving care quality.

Top ways for reducing costs and readmissions through data analytics

Below are six ways showing how data analytics in health care is working wonders.

1. Using predictive analytics for better health outcomes

Predictive analytics takes the guesswork out of planning for the future. With artificial intelligence and machine learning algorithms, it turns data from the past or present into reliable forecasts. The growing trust in this approach explains why the market is on track to hit $28.1 billion by 2026.

Speaking of the health industry, this technology helps health care providers in the following ways:

• Identifying patients at high risk of readmission
• Analyzing patterns in symptoms that get skipped by human observations
• Analyzing patient medical history data
• Planning the what-if scenarios in surgery and treatments
• Providing feedback by tracking patient vitals and other metrics

For example, hospitals use predictive tools to flag patients requiring follow-up care, allowing for personalized interventions that reduce costly readmissions. A well-known success story is New York University Grossman School of Medicine, which developed a large language model algorithm, NYUTron, to predict outcomes like readmissions and length of stays.

Using the tool, they successfully predicted 80% of readmissions and also saved $5 million in costs.

Thus, by proactively addressing risks, predictive analytics not only lowers costs but also enhances overall patient satisfaction.

2. Improving patient data with electronic health records

Electronic health records (EHRs) have made data sharing and coordination so much easier — and that too in real time. These records consolidate diagnostic, administrative and treatment information into a single platform, improving efficiency and accuracy.

With patient data getting digital, EHRs allow doctors to do the following:

• Access data in real time so they can take action before patient health worsens
• Communicate and collaborate within departments
• Reduce costs on duplicate tests and unnecessary visits
• Avoid the chance of errors from handwritten notes or lost files
• Simplify repetitive administrative tasks like scheduling appointments and billing

Spend less time on paperwork and more on patient care — an essential step in cost management

3. Use real-time monitoring for chronic disease management

Wearable technology and real-time monitoring have made managing chronic conditions more effective. Diseases like diabetes, high blood pressure, migraine and stroke can be managed on these devices.

One such recent and well-known example is the COVID-19 pandemic. In the second wave, when oxygen vitals were most important, and in times of emergency where a slight delay could cause life-and-death situations, wearables like pulse oximeters emerged as a lifesaver.

Patients can send the readings directly to their doctor. If the oxygen count goes low, the health care team can act swiftly, preventing costly hospitalizations.

Even today, people use oximeters, heart rate and fitness trackers to get health metrics easily.

4. Cost reduction through operational efficiency

After surveying the reasons for patient care delays, researchers found that the main reason that contributed the most was inefficient scheduling practices.

Today, countries like the U.S. spend almost 18% of their gross domestic product on health care. Despite such heavy investment, almost 25% of their health care spending has gone to waste due to administrative inefficiencies.

This is the reason improving operational efficiency has become vital for creating a more sustainable and effective health care system. Thus, by adopting data analytics in health care, one can do the following:

• Reduce patient wait times by analyzing schedule data.
• Analyze surgeon availability by tracking it on enterprise resource planning software to avoid delays in bookings.
• Address patient needs and tailor customized plans and services to improve patient health and overall satisfaction.
• Automate administrative tasks like billing and sending appointment reminders. This can free up staff time and allow them to focus on patient care.
• Improve resource allocation for staffing levels and equipment availability.
• Identify trends in patient no-shows, allowing clinics to take proactive measures, like sending reminders, to reduce these occurrences.

5. Manage hospital supply chain costs

The supply chain forms a considerable portion of the medical investment. All the essential medical supplies, drugs, medications and equipment directly impact patient care and operational budgets. So, if there are any disruptions, it can lead to heavy revenue losses, unnecessary delays in treatment and risks to patient health.

Health care institutions with systems powered by big data can optimize their supply chain costs by doing the following:

• Tracking supply chain levels: Analytics tools monitor stock levels continuously, ensuring supplies are always available. This reduces last-minute scrambling or shortages that can compromise care.
• Evaluating supplier performance: Data tools can be used to check delivery timelines, product quality and contract adherence by suppliers.
• Forecast demand accurately: Using historical purchase and sales data, you can estimate how much stock is needed, avoiding situations of overstocking.
• Automating supply chain processes: Everything can be achieved online, from requisition notes to purchase orders and invoices.

6. Detect and prevent fraud with advanced analytics

Data breaches and fraud incidents have become common in the health care sector. Hence, safeguarding patient data using analytics tools has become very important. Advanced analytics tools help health care organizations do the following:

• Spot unusual patterns: Tools can flag discrepancies by analyzing billing and claims data, such as duplicate charges or suspiciously high bills.
• Check for cyberattacks: Analytics monitor the system’s network activity to detect and prevent cyberattacks.

Conclusion

Health care providers use data analytics to make informed choices and improve care quality. By predicting patient outcomes, optimizing the allocation of resources and spotting fraud, it’s helping reduce costs, preserve valuable resources and enhance services.

This growth of data analytics promises a more effective health care system, one that’s accessible, affordable and truly beneficial to the common masses.

Stephan Hawke is a seasoned digital strategist and writer with more than a decade of experience in health care technology, software development and digital transformation. Based in New York, he has collaborated with leading organizations, providing insights into how emerging technologies like AI, IoMT and blockchain are reshaping the health care industry. His articles have explored topics ranging from telemedicine advancements to custom health care software solutions, offering actionable insights for industry leaders.