Why Hospitals Need to Start Adopting Automated Compliance and Big Data

calendar icon Posted on May 20, 2019

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In an era where nearly all our digital messages are carefully recorded and analyzed; the medical sector is experiencing a drought of continuous patient data documentation. Facebook and Google use big data to further their enterprise goals, but the medical industry lags behind even though its data could save lives and serve as the foundation of many powerful medical technologies.

The Challenge of Medical Big Data and Automated Compliance Data

As medical data is so important, it is strange that much of our hospital data is still collected using “spot checking,” a method that is highly outdated and problematic for three reasons.

Firstly, spot checks happen intermittently, usually only every 6-8 hours, creating a “data desert.” Clinicians and health staff are not being fed the latest patient data, impacting their decision-making process.

Secondly, when patients are undergoing spot checks, many experience “White Coat Syndrome,” which occurs when patient readings go awry due to the patient being aware that they are being measured. For instance, blood pressure and heart rates can rise suddenly during the check making the data gathered inaccurate.

Third, infrequent spot-checking leads to a lack of reliable data points. Without consistent and dependable data points, true big data generation is unattainable, preventing quality data analytics.

Rather than using spot checks, we should strive to collect data with volume, velocity and variety. This means collecting a large amount of data, quickly and at great range to ensure accuracy.  

A Solution in Automated Compliance Data

Fortunately, new technologies such as automated compliance data (ACD) is making this big data solution a possibility in hospitals. ACD provides a constant stream of quality data without inputs from patients or staff.

Subsequently, the data can be fed into systems for analysis and help guide medical professionals in their treatment of the patient. By tracking key vitals like heart rate, respiratory rate and movement, automated reliable data generation can provide more than 100 data readings per minute, giving health staff a more complete picture of a patient’s condition. With this data, trends in patient status as well as baseline changes can be easily tracked, analyzed and detected helping to create new opportunities for effective intervention.

The key to generating successful data collected for analytics also depends on the algorithms that gather these patient readings and filter out the ambiguous data. Only then can the reliable and accurate patient readings be digested by big data systems.

Many hospitals and skilled nursing facilities that opted to use EarlySense ACD have seen several positive outcomes. Hospitals reported reductions in code blue events by over 85%, pressure ulcers by 64% and a significant reduction in ICU and overall patients stays.

 

The world is accepting big data analysis as a standard. The healthcare industry should be leading this shift for the sake of both patients and doctors.

 

Guy Meger is the CTO and VP of Research & Development at EarlySense, the global leader in contact-free, continuous monitoring solutions for the healthcare continuum. He received his B.SC in Computer Engineering from the Technion Israel Institute of Technology and his MBA from Tel Aviv University. Guy is also the author of several issued and pending patents.

 

Read the full article atInside big data

 

 

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