In Affective.health’s three-part blog series there is a powerful theme that affects millions of us: the decisions people, particularly in the US but not limited to, are asked to make concerning healthcare are much more frequent, complicated, and expensive than ever before.
Modern U.S. Healthcare Consumers pay a hefty price for this participation in their care planning, with an increasing gating function at the payment process. U.S. Citizens pay the most per capita care, while providing an ever decreasing quality and quantity of care compared to other nations.
Over the past several decades, the responsibility to make a decision about treatment has transferred heavily to the patient. This responsibility shift makes understanding risks and potential outcomes highly important. In order to make the physician’s recommendations abundantly clear, care has to be taken in how it is communicated.
Patients then need to be able to self-regulate and remain adherent to prescribed care plans, treatment, therapy, or medication. This type of behavior shift requires a high level of care and support that is not always feasible in person; we’ve seen the effect of doctors having full schedules along with the added stress of a global pandemic.
Data for Better Health Outcomes
The largest problem to overcome in order to achieve improved health outcomes is overcoming human bias. Physicians interpret data based on their own perceptions. The myriad data points combined to make decisions for a patient need collected and sorted without the heavy cloud of bias fogging up the process.
However, using data, we can be accurate in predicting costs and survival chances. We can also use this data to accurately predict what a specific patient persona will need to have the most success. Using the Affective.health DXP allows for the best data to be collected, which, in turn, leads us to the patient journey with the highest rates of success.
“The findings support a blunt conclusion: simple models beat humans.”
The data collected in Affective.health experiences show us more than simple outcomes - the results illuminate areas of struggle and success, which then drive changes to the communication and support given for a care plan. Learning from this data improves outcomes for others and increases physician success rates overall. In Daniel Kahneman’s book, Noise: A Flaw in Human Judgement, on how and why people come to different conclusions with the same data to base it on, “the findings support a blunt conclusion: simple models beat humans.” In other words, data models beat the human brain in terms of accuracy. Removing Bias from Healthcare Decisions The human brain is too susceptible to bias to make repeatedly accurate predictions and conclusions. This is why collecting high quality data and then using models to make decisions is immensely valuable.
One of the bias to overcome is the Illusion of Validity; this is a cognitive bias that describes our tendency to be overconfident in the accuracy of our judgements. Technology allows us to clear the fog cognitive bias creates and make clear, rational decisions that are factual and consistent.
“...the evidence for the advantage of the mechanical approach to combining inputs was “massive and consistent.’”
Better Data with Affective.health DXP
Affective.health’s unique methodology provides a solution for the discovery of the particular hopes and fears of individual groups. Inducting, or collecting, the data with our DXP reduces bias so the data collected can be applied to better decisions. The data modeling becomes simpler, less of a specialist task, and more clear with our built-in functionality. As Daniel Kahneman discovered, “[...] the evidence for the advantage of the mechanical approach to combining inputs was “massive and consistent.’”
Our cloud-based solution allows rapid deployment with minimal required resources up front. Collect better data and make better decisions the first time and see the health outcomes skyrocket. We’re ready to help you have better days.
Kahneman, Daniel, Olivier, Sibony and Cass R., Sunstein, Noise: A Flaw in Human Judgment. New York: Little, Brown Spark, 2021.