End-of-life decisions are inherently difficult for all concerned. Fortunately for healthcare providers, and in turn the patients they serve, increasingly robust options offer support for data-driven decisions.
Clinical technology and data analytics have tremendous potential to overcome the obstacles to effective end-of-life care, according to Srinivasa Vegi, PhD, Bethesda, Maryland-based president of data analytics and artificial intelligence for IT services provider DMI.
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“Not only will these tools help doctors and patients determine whether treatment is the best course of action, but they also help to better identify appropriate patients for palliative intervention as part of their end-of-life care,” he says. “Moreover, clinical tools and analytics can help to reduce the length of stay for palliative care patients and decrease costs whether or not treatment is sought.
In fact, data analytics—particularly machine learning solutions that use big data—are radically transforming how physicians deal with palliative care, says Sapan Desai, MD, PhD, MBA, a Chicago-based vascular surgeon and CEO of Surgisphere, a firm offering an advanced healthcare data analytics platform.
Desai says that data analytics will change how physicians approach end-of-life care. Not only can machine learning and artificial intelligence pinpoint the causes for death, but depending on how the algorithm is set up, it can even make calculations regarding which of those causes will impact the quality of life the most.
“Good analytics can help direct the conversation with a patient by targeting specific medical problems and the projected efficacy of treatment,” he says. “It can help them understand, and come to terms with, how their quality of life will look in the near future.
For example, Desai describes an 83-year-old patient who has moderate kidney disease, poor leg circulation, and heart failure.
“Machine learning may tell me that he is likely to suffer kidney failure leading to dialysis if I do any major intervention for his circulation problems,” Desai says. “Armed with this information, my discussion with the patient may guide him toward palliative care rather than spending his last few months on dialysis, separated from his family.”
One of the strongest arguments for the use of data analytics in this area is improved efficiency.
“Machine learning and data science can help make end-of-life care services more efficient and accessible,” says Gabriel Bianconi, founder of Scalar Research, a New York AI and advanced analytics consulting firm. He notes that these tools can increase efficiency by automatically predicting which patients could most benefit from end-of-life care, without necessarily requiring referrals, inbound interest, or manual data reviews. “Automated predictions can help healthcare professionals proactively reach out to more patients and do so at the ideal time for addressing end-of-life needs.”
Bianconi says that machine learning could help patients and professionals make more informed decisions. “For example, the tools can help give more accurate estimates of survival rates and times for different interventions,” he says. “With this information, both parties can better make a decision and plan accordingly.”
In addition, machine learning could in theory be leveraged to make more accurate end-of-life predictions, Bianconi says. He points that hybrid approaches have already outperformed healthcare professionals on a number of diagnostics tasks, and a similar approach could be applied to end-of-life situations.
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At the same time, these tools have the potential to give insights that are tailored specifically for the patient. Instead of giving general information about survival rates, for example, a data-driven approach could leverage the patient's medical history, and other personal data, to give more accurate survival rate predictions for different treatments or conditions, according to Bianconi.
Such applications have moved beyond the theoretical to significant real-world applications.