Big Data Analytics In Healthcare: the Most Common Applications

Terenty Marinich
Digital Content Manager
July 5, 2019

We have started to explore big data connections with the healthcare domain. The first part of the series of blog posts was all about the benefits of big data and obstacles on the way of its wide implementation.

Now it is time to have a look at what applications in healthcare are already found by big data analytics.

Here are the selected examples of these big data applications in modern healthcare. Certainly, it is hard to cover all the application areas of big data but we’ve decided to select the most widespread and interesting examples that are making the biggest impact so far.

EHR

We should start with the Electronic Health Record (EHR) systems. This is by far the most widespread application of this big data. The patients hold their medical digital records, and the collected data contains info about medical histories, allergies, analysis, and tests results, etc.

Source: pixabay.com

It significantly reduces paperwork for doctors as all they have to do is modify a file.

EHR allows setting reminders (for doctor’s appointments or lab tests), track prescriptions and… systematizing data for future analysis.

EHR implementation is still a problem for some countries, though 94% of the US hospitals are among the adopters of these systems. Same goes for Europe. By 2020, the centralized European health record system should become a reality.

So how does it help exactly?

According to  big data healthcare report by McKinsey, “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”

Error Prevention Matters

Mistakes in healthcare are proven to be very costly. Firstly, there are prescription errors. As Network for Excellence in Health Innovation states, those cost around $21 billion per year in the USA and affect over 7 million patients. They lead to 7,000 annual deaths.

Big Data is leveraged for the analysis and is able to reduce mistakes and save lives. Those physicians who are dealing with the big flow of patients in a day could use some tool that helps to spot prescription errors before they occur. The example of a company that produces such software is MedAware.

It’s daunting to see some errors that occur and are mentioned on their website…

Source: medaware.com

It is undeniable that the tools that reduce the number of health- and even life-threatening doctors’ mistakes with medication prescription serve the noble purpose and it is hard to overvalue them.

Fraud Prevention Operations

Data breaches are more likely in healthcare than in any other industry and now more than ever. Personal data has immense value on the black market. Some organizations have started utilizing predicting analytics to identify changes in network traffic and likewise predict cyber attacks.

Source: pixabay.com

Big data is both a problem and a solution to this matter. The bigger the volume of data, the harder it is to secure, but also, machine learning and preventive analytics significantly decrease the chances for data to be stolen.

The Centers for Medicare and Medicaid Services saved $210.7 million on preventing fraudulent schemes by using predictive analytics.

Patients Admission Predictions

Effective staff management is the key to the money savings of the healthcare facilities. Parisian hospitals use big data and specially designed machine learning software for admission rates predictions.

Source: flickr.com

Here is how it works. Four hospitals (as parts of Assistance Publique-Hôpitaux de Paris (AP-HP) domain) were staffed according to the results of hospitals admission records. The gathered information from internal and external sources – including 10 years’ worth of hospital admissions records.

That way they were able to come up with the day and hour-level predictions of the number of patients expected to admit the hospitals at the defined hours and build the staffing strategy accordingly.

After the successful trial, the project was launched at forty-four facilities.

While it is hard to count the exact profit from the improvement of certain management procedures across multiple facilities, correct and timely adjustments to provide the needed number of staff members according to the admission rate is the dream-come-true for any hospital administration.

Alerting in real time

Clinical Decision Support software (CDS) is used for data analysis in hospitals. Doctors could receive information, analyze this on a spot to make the decision about certain prescriptions.

Source: wikimedia.commons.com

This is applicable at in-hospital areas, but it is costly for the patients to stay for the in-house treatments for a substantial time. When they out of the hospital, wearables that collect patient’s data and send it to the cloud storage come to rescue.

All this gathered data can be accessed by the doctor to assess the state of health of the general public – that is perfect for comparing such data in socioeconomic contexts and modifying the delivery strategies in a proper way. When massive data streams show a significant deviation from the existing norms, it could be a signal for the medical specialists for reaction to the disturbing results.

It could be used in private cases, for instance, if the blood pressure falls drastically, it could alert the doctor in real time and he’ll be able to reach out to the patient to normalize it, or call the ambulance if the condition becomes critical.

There is a startup that used to be called Asthmapolis which primary activity was in using inhalers with GPS trackers for the identification of asthma trends at the individual and large populations levels.

Now it is known as Propeller Health and they plan to go beyond asthma – help patients with other diseases. As for now, their data analytics allows developing some better treatment plans for asthmatics.

Conclusion

These were just some of the examples of the productive and effective application of big data analytics. We will continue this series of articles so stay tuned for the deeper exploration of big data analytics in healthcare.

We can once more say that the development of medical applications should be the high priority of data science, as they have the potential to save money and even lives. Contact us here if you are interested in the outsource development of your healthcare software to receive a free quote.

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