Leveraging Big Data Analytics to Reduce Healthcare Costs

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The healthcare sector is witnessing rapid technological advancements at present, many of which have caught momentum in the past decade alone. Big data analytics is one technology that is taking healthcare by storm.

Whether aiming to avoid risks of infection, improve the quality of care, advance research, or do anything in between; providers have access to piles of data today.

However, just having the data at hand won’t help if your organization doesn’t follow a sequential pattern to arrange, analyze, and translate it.

This is one of the many reasons data analytics is extremely vital in healthcare. By making use of analytical techniques, healthcare providers can not only make sense of the past, but also chart the future: one which gets desired outcomes in a cost-effective manner.

In this piece, we will be looking at a few ways data analytics aids in streamlining processes as well as reducing cost of both acquiring and delivering care. 

1) Improved Patient Interventions

The secret to reducing costs while maintaining the quality of care lies with identifying right high-risk and rising risk patients.

To effectively choose patients that have a higher probability of benefiting from interventions, health systems need data from across the continuum of care.

Care management tools that leverage analytics can help healthcare organizations identify the right patients and reduce costs in key ways.

With the help of data congregated from Electronic Medical Records (EMR), it has now become comparatively easy to pinpoint patients that are suffering from, or are at an increased risk of developing chronic conditions.

This is done by monitoring how frequently these patients visit their healthcare provider or the Emergency Department (ED), or on the basis of hospitalization records. 

Organizations can also customize algorithms to specifically target individual patients or patient groups that might be suffering through a common disease.

Healthcare providers can also leverage data analytics to formulate a strategy that helps them identify multiple admissions, or certain diagnoses data, to determine when patients may need palliative or hospice care.

The National Council on Aging estimates that approximately 95% of healthcare costs for older Americans can be attributed to chronic diseases. Therefore, data analytics can play a significant role in driving interventions, which ultimately leads to greater cost savings.

2) Elimination of Unnecessary Testing

The electronic health record (EHR) is an important tool for bolstering information exchange and communication across care teams in an established healthcare setting or hospital. 

When technologies such as machine learning and predictive analytics are applied to EHR data and input from remote patient monitoring devices, healthcare providers can access robust clinical decision support (CDS) that may help to discover human errors and prevent costly adverse events.

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Healthcare Finance News recently reported that St. Louis Children’s Hospital was able to bring down the number of $6,000 tests ordered for Dravet Syndrome, a rare form of epilepsy, by leveraging big data analysis.

These tests are often ordered because the standard of care for infants with seizures is to first check for chromosomal microarray, which is identified with the Dravet Syndrome test. 

However, Dr. Nephi Walton used data analytics to discover that 0% of Dravet Syndrome tests for this reason returned positive in the last 5 years. This finding caused the facility to alter its standard of care and reduce the number of costly tests it was running.

3) Boosting Patient Engagement

For patients with existing chronic diseases, it is more of a compulsion to engage in management strategies and stick to treatment plans in order to be able to maintain their health and keep care costs low. 

Self-management interventions powered by analytics can easily enable such patients to independently track their health and engage in their own care in an active way, further reducing healthcare-related costs on the patient’s part.

Organizations can also leverage patient data to develop predictive risk scores and design individualized treatment strategies.

We tend to identify quite a few people who have different chronic conditions or other issues that call for enhanced management. When we use predictive models to look at all the variables, it helps us prioritize those patients who are really going to be receptive to changing something in their lifestyle, such as nutrition or exercise,” Lillian Dittrick, Fellow of the Society of Actuaries told Health IT Analytics in a recent interview.

Patients with mental illnesses can also benefit from tailored treatment plans, and organizations can use existing data to design individualized care strategies.

4) Furnishing Smart Staffing Solutions

There’s one classic problem that every shift manager faces these days: how many people should be put on the staff at any given point in time for the smooth functioning of the organization?

If you put on more workers than required, you run the risk of having unwanted labor costs add up. On the other hand, with fewer workers, you can end up having unsatisfactory customer service outcomes – which can be fatal for patients in that particular sector.

Data analytics is successfully aiding managers in solving this problem, at least at a few hospitals in Paris.

A white paper published by Intel elaborates how 4 hospitals, that are part of the Assistance Publique-Hôpitaux de Paris, have been utilizing data from various resources to come up with hourly and daily predictions of how many patients are expected to be at each one of these hospitals.

One of the key data sets is 10 years’ worth of hospital admissions records, which data scientists crunched using “time series analysis” techniques.

These analyses allowed the researchers to see relevant patterns in admission rates. Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends.

Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving overall patient care and reducing the costs associated with it.

Closing Words

It can be easy for healthcare facilities to concentrate on sweeping changes for cost-saving measures. However, the most effective and easiest ways to save money are often ones that work around reducing resource wastage that adds up over time. 

These opportunities can often be difficult to detect on your own, but when you leverage modern technological innovations such as data analytics, it becomes easier to identify ways to save significant money by improving your workflows and processes.

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