Machine learning is an emerging technology in the healthcare industry with exciting potential. From IBM Watson Health to Amazon HealthLake, companies are racing to solve healthcare’s most challenging problems with recent advancements in technology. While many people know machine learning as the technology that drives some consumer applications, like personal digital assistants Siri and Alexa, it’s also used by healthcare providers to extract value from vast quantities of patient charts and other documents to inform operations and care delivery.
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Implementing machine learning-driven solutions gives healthcare organizations the potential to access previously untapped patient data, improve operational efficiency and ultimately provide more effective patient care.
The solution to healthcare industry’s data problem
Machine learning has the potential to dramatically change how healthcare is delivered by providers and how patients receive care, but it’s only as effective as the data we’re able to access and analyze. As healthcare organizations continue to transition to electronic health records (EHR), they’ve gained access to mass quantities of patient data.
In the past, organizations have mined structured data—information that lives in defined and tagged fields such as billing or claims data—to inform care and operations. But structured data only accounts for 20% of all healthcare data, so it often doesn’t capture the nuances of patient-provider encounters. The remaining 80% of data is unstructured, meaning free-form text entries. While unstructured data contains critical details about patient health, conditions, past family history and treatments, it’s largely gone unused by payers and providers because it’s much more challenging to access and for computers to analyze.
All that is changing with recent advances in machine learning. We have the technology today to surface insights from unstructured text that was previously difficult to generate and use at scale. With this new advantage of machine learning-derived intelligence, physicians and administrators are empowered to make more timely, informed decisions about patient care and operational programs that impact millions of lives.
The impact on clinical outcomes
Unstructured data provides previously untapped information on social determinants of health (SDH). Health behaviors and socioeconomic factors, such as income and education, are known to have a greater effect on overall health than what happens inside the doctor’s office. In order to improve overall health, providers must address the whole person, taking into consideration their lifestyle, environment and even family history.
By combining data about a patient’s health and SDH, machine learning can be used to identify patients at higher risk for preventable, chronic conditions such as diabetes and heart disease.Health organizations can then more easily reach these patients to provide resources on nutrition, smoking cessation programs and community groups.
Machine learning also helps improve patient outcomes inside the hospital. A recent study investigated how machine learning can help healthcare providers measure the quality of care for heart failure patients.
Using the Congestive Heart Failure Information Extraction Framework (CHIEF) application, researchers extracted EHR data from over 1,000 patients at Department of Veteran Affairs (VA) medical centers to determine if physicians prescribed the appropriate medications upon discharge to select patients. Compared to the VA’s manual process, CHIEF identified mentions of heart failure medications and other metrics with a 99% success rate. The study showed that machine learning provided a cost-effective means to capture data and accurately assess care.
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Improving healthcare operations and outcomes
With the rising popularity of value-based care, healthcare organizations face more pressure than ever to speed up processes, improve health outcomes and lower costs.
According to an Annals of Internal Medicine study, for every hour a physician spends with patients, they spend nearly two hours on paperwork and manual EHR data entry. Machine learning applications that identify documentation gaps and prepopulate problem lists can enable physicians to spend more valuable time with patients and less time at the computer.
Machine learning algorithms also extract information from clinical charts more quickly and accurately than manual review processes, and even become smarter as they’re applied to more documents. This helps to identify hidden risk factors and gaps in care,giving healthcare providers the information needed to better manage risk and improve the quality of patient outcomes.
The future of machine learning
There is reason to be optimistic about the future of machine learning in healthcare industry. Healthcare leaders predict a widespread adoption of the technology in the near future, and the COVID-19 pandemic is only accelerating the process. The future of machine learning ultimately relies on the continued digital transformation and aligning on interoperability standards to better measure clinical activities, guide care and make life-changing discoveries.
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