The term “quadruple aim” entered the lexicon about 10 years ago to describe four criteria that US healthcare organizations could use to understand, evaluate, and improve their performance. They include:
Predictive analytics uses artificial intelligence (AI) and machine learning (ML) to examine immense data sets—far larger than any human could manage—in near-real time, then make predictions about future outcomes. The enabling technology is becoming accessible to more and more users almost daily, and does not require lengthy lead times or use of sophisticated IT resources.
Both the creation of predictive analytics in healthcare and the democratization of access will play an enormous role in helping healthcare organizations leverage data in the same way that businesses in other fields have done for decades.
When it comes to transforming healthcare with AI, the following use cases will be critical to organizations and society moving forward.
Businesses in heavily regulated industries have long used AI-enabled tools to secure their people, data and infrastructure. An important example of predictive analytics in healthcare, cybersecurity, uses these tools to detect anomalies and potential healthcare system security breaches.
Their AI-enabled models have been trained on billions of webpage visits, emails, device updates and other authentications across industries. This makes them ideal for safeguarding patient and provider data, protecting systems and maintaining regulatory compliance. These tools also help organizations prevent reputational damage, financial losses and other damage that results from security breaches.
The creation of predictive analytics in healthcare and the democratization of access will help healthcare organizations leverage data as other fields have done for decades.
Streamlining and accelerating the revenue cycle is a key function for many payors. Predictive algorithms enable accounting departments to assess claims data quickly and accurately, regardless of which vendor provided the EHR system in use. This enables administrators to detect fraudulent claims and more accurately predict their likelihood.
Similar algorithms enable accounting teams to identify and correct errors in coding and other documentation, all of which can help to eliminate time-consuming manual review processes. This greater efficiency and lower costs help free up funds and people for more important work.
Most equipment in commercial use is maintained on an assumptive basis, such as when an operator assumes the need to change the oil in an engine every 5,000 miles. Maintenance in the healthcare sector operates much the same way, though a bit more complex.
Predictive analytics in healthcare allow organizations to combine Internet of Things (IoT), data, hyper-automation and cloud computing to compare data gathered in real-time from one device to historical data from thousands of similar devices. With this IoT data modernization, the system predicts when and how that device might fail. Then, maintenance crews can service or replace a device just before failure rather than replacing it sooner than necessary. Owners see longer service lives and lower down time, which enhances patient safety as well as operational efficiency.
Predictive analytics must be combined with healthcare AI solutions and other emerging technologies to meet the quadruple aim.
The astonishing evolution of technology that we’ve seen in the last few years has immense potential to improve the health of the entire human race. The field of predictive analytics in healthcare is uniquely promising, but it must be combined with healthcare AI solutions and other emerging technologies like IoT, edge computing, data analytics, hyper-automation and cloud computing in order to meet the quadruple aim.
The nine use cases above are the tip of the iceberg. The future of predictive health analytics has already arrived, and healthcare AI solutions are within your grasp.
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