Healthcare Data Trends to Keep an Eye On

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Not limited to health IT, healthcare data is driving innovation in intriguing areas.

Big Data has sparked the growth of new trends across a number of industries, including some notable new developments in the field of healthcare. Not limited to healthcare IT, Big Data is driving innovation in intriguing areas.

Here are few notable examples to watch out for in 2017 and beyond.

Imaging analytics creating improved diagnostics: Diagnostic images form a crucial part of medical assessments, yet until recently have been limited to the interpretation of doctors and other skilled clinicians. While people can be highly effective in catching abnormalities in images, powerful computers can do so faster and with a greater degree of precision.

The internet of things and patient-centered care: Healthcare’s internet of things (IoT) involves network-connected wearables like smart watches and fitness trackers. These gadgets can be tremendously powerful sources of health information and data analysis. With doctors attempting to increase the quality of patient-centered healthcare, health data and other trackers from wearable devices are proliferating.

Health information security: Analytics provided by Big Data are being used to significantly reduce cases of fraud, identity theft and compromised sensitive healthcare data. Cybersecurity remains a significant concern across industries, and healthcare is no exception. The past few years have seen a steady increase in the loss of patient records and financial extortion of healthcare facilities. Fortunately, Big Data can provide just as accurate a picture of malicious intent as other areas and is proving to be a strong factor in the fight against cybercrime.

Real-time patient monitoring: The wearable, IoT-connected devices mentioned above do more than allow medical providers to understand and access patient health data over time; they also allow real-time monitoring of vital signs and other health metrics for patients with certain diseases or conditions. Some apps can be used to monitor blood sugar levels in diabetics, for example, while others can track weight of patients suffering from heart disease. This monitoring allows up-to-the-minute care for sensitive populations and may prove to be a godsend for improving the quality of life of such patients.

Alternate payment models (APMs): APMs are a recent attempt to reduce the rising cost of healthcare for patients across the country. Where previously patients would be asked to pay for services as they utilize them, APMs ask healthcare providers to use healthcare data and other digital tools to provide diagnostic and analytical insights for at-risk patients. The cost savings in assessing healthcare issues before treatment prohibits some expenses from occurring and could trickle down into the system and reduce the cost of healthcare overall.

Cognitive computing will impact decision making: While IoT-connected devices, digital patient records and other sources of Big Data can help gather information, this information is meaningless if we lack the tools to analyze and understand it. Fortunately, cognitive computing can expand on the ability of people to conduct this analysis. Sometimes known as machine learning, cognitive computing refers to the ability for computers to learn to analyze information in much the same way a human would. Faster and often more effective than even the most trained analyst, computers can provide a detailed, comprehensive picture of healthcare data and how it can best be understood. This information can be used by policy makers and other administrators to tailor healthcare policy while reducing waste and increasing medical efficiency across the board.

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