Wendler has focused on developing strategies to support surrogates in making informed decisions about their reproductive journeys. More than a decade ago, he pioneered the notion of creating algorithms that take into account factors such as age, gender, and individual insurance coverage status. The proposed software would primarily rely on a machine learning algorithm trained on population-wide survey data. While appearances may initially suggest crudeness, the underlying characteristics actually significantly impact people’s genuine sentiments towards healthcare. While a teenager might be more likely to seek drastic solutions compared to a 90-year-old, And analysis means that .
In 2007, Wendler and his team developed an initial, foundational model of the software, grounded in limited data. According to Wendler, the straightforward software struggled with accurately forecasting the type of care that individuals would require.
Now, Wendler, Earp, and their colleagues are pioneering a groundbreaking concept. The innovative software envisioned by researchers will depart from traditional approaches, instead being tailored to specific needs and characteristics. The proposal suggests harnessing the power of artificial intelligence and machine learning to predict a patient’s treatment preferences based on personal data such as medical history, comprising email correspondence, personal messages, internet search histories, social media postings, and even Facebook likes?
A cutting-edge innovation promises to yield a “digital psychological twin” of an individual, a sophisticated software that medical professionals and loved ones can consult to inform and optimize an individual’s healthcare. While unclear what this appears to be capable of, the team aims to develop and review the software before refining its features.
The researchers have dubbed their software a Personalized Affected Individual Desire Predictor (P4), a designation that is concise yet informative. According to Wendler, the concept has the potential to surpass its predecessors in accuracy, even outperforming human intermediaries, should it operate as intended. While potentially more reflective of a patient’s current thinking, an advance directive completed a decade ago may not accurately capture their current values or wishes.
A greater wager?
A software akin to the P4 could potentially alleviate the emotional toll surrogates often experience when making life-or-death decisions for their loved ones, which may lead to symptoms of post-traumatic stress disorder, notes Jennifer Blumenthal-Barby, a medical ethicist at Baylor College of Medicine in Texas.
According to Kaplan, some surrogates possess expertise in navigating the complex process of decision-making, which they can leverage to avoid “decisional paralysis” by utilizing software that provides guidance throughout the decision-making journey. In such situations, the P4 may help alleviate some of the burden that surrogate decision-makers may encounter, without necessarily providing definitive answers. The AI system would suggest that an individual “is likely” or “might not” genuinely experience a certain outcome following treatment, or provide a probability rating signifying how probable the response is to be accurate or inaccurate.
