What Your Hospice Needs From Predictive Analytics

Most platforms have similar features, but look for those that stand apart.

Artificial intelligence is causing quite the buzz in the hospice and palliative care spaces thanks to its ability to turn real patient data into actionable items for providers. These tools can help prognosticate more accurately, enable objective examination of a patient’s status, and much more.

When hospice or palliative care leadership dive into predictive analytics platforms, it can be difficult to determine which is right for your organization and what exactly you’ll need. While there are some similarities across predictive AI tools on the market today, there are also differences, and those are crucial to ensuring you are providing the best possible care.

Duane Feger, Director of Health Economics at Acclivity Health, explained that some AI/predictive analytics platforms can have features in common because of their shared origins and goals.

“All predictive models attempt to imitate human cognitive processes. In essence, they attempt to accomplish a goal, whether it is describing a complex relationship, winning a game, reaching a decision or predicting an outcome,” he said, adding that predictive models have existed for centuries in other industries like insurance, financial services, and meteorology. “Given their common background, all predictive tools possess two key similarities. The first is their use of limited historical data to identify complex patterns of relationships. The second is that they produce possibilities, not facts.”

In health care, most AI-based tools strive to empower physicians with objective, real-life data about their patients. This can help them make better clinical decisions, have access to benchmarks, spark conversations about end-of-life goals, and increase access to palliative or end-of-life care. So, when a hospice or palliative care organization is searching for a predictive analytics-based tool, what features would set one platform apart from the rest?

“The Acclivity Connected Care Platform delivers the benefits produced by a wide range of artificial intelligence tools, including several types of predictive models. However, the platform takes a major step forward by leveraging state-of-the-art machine learning tools such as deep learning. Using these types of artificial intelligence techniques, Acclivity implements networks of models that focus on extremely complex problems, such as predicting end-of-life scenarios,” said Feger. “While a few major academic institutions have done work in this area, very few healthcare tools have taken on this challenge and attempted to bring these types of benefits to the day-to-day provider environment.”

Acclivity’s platform is known for its unique abilities to predict the Palliative Performance Scale of each patient, inform providers of key health changes prior to appointments, manage transitions and referrals, and support connections with community partners. While these aren’t the only features that make Acclivity’s Connected Care Platform one-of-a-kind, they are a few that Feger feels hold great value.

“Examples of these capabilities include Acclivity’s three-month, six-month, and twelve-month end-of-life metrics,” Feger said. “These metrics greatly enhance the dashboards available to physicians treating advanced illness patients and populations. By highlighting specific patients who may warrant in-depth evaluation for palliative care or hospice services, both the patient’s longevity and the quality-of-life for patients and families alike are significantly improved.”

The minds behind Acclivity’s Connected Care Platform are constantly thinking of new features to add, with long-term goals of making data-driven patient care more accessible to all providers and improving the quality of care wherever it is implemented.

“As Acclivity continues to improve and enhance the use of artificial intelligence tools within the platform, both the efficiency and the effectiveness of the care delivered by providers will improve accordingly. For example, predicting the probability of end-of-life within a specific timeframe augments the other diagnostic tools available within the platform and helps inform the provider’s decision process,” said Feger. “The result is truly a win-win scenario for both providers and their patients.”

If you’d like more information about how Acclivity Health can serve you and your patients, please feel free to email us at info@8kw.3ce.myftpupload.com.