Published on April 17th, 2018 | by Sunit Nandi0
Artificial Intelligence: Helping Make Healthcare More Affordable
According to a recent report by the National Public Radio, the United States spends the most on healthcare among all of the countries in the world. Citing data from the World Bank’s Institute for Health Metrics and Evaluation, the NPR reported that the U.S. healthcare industry shelled out $9,237 per person per year in 2014, compared to Somalia’s $33 per person per year—the lowest among the countries surveyed.
While it is true that less-developed nations spend the least on healthcare and high-income countries spend the most, high spending doesn’t necessarily equate to better health outcomes. For instance, other developed countries like Switzerland ($7,831), Germany ($5,356), Canada ($4,576), Japan ($3,816), and the United Kingdom ($3,749) all spend significantly less on healthcare per person than the United States, but their populations all have higher life expectancies than Americans. To put it plainly, Americans are not getting the best value for their money. Moreover, as the report pointed out, the U.S. tends to stick out among highly developed nations as a place where people routinely become impoverished due to having to pay calamitous medical expenses out of pocket.
The state of healthcare in the United States is a sobering reminder about the need to find innovative solutions for making the cost of care more affordable for people, and for the quality of healthcare to be elevated in the first place. One possible way to address the situation is by adopting the value-based model of care.
The Paradigm Shift to Value-Based Care
Conventional volume-based models of healthcare like the fee-for-service model pay health providers based on the volume of care they provide. This tends to have the undesirable effect of incentivizing the over-provision of healthcare services while doing nothing to improve actual health outcomes. A doctor that routinely orders patients to get costly medical exams or attempts to attend to a high number of patients everytime will logically get paid more than a doctor that takes time to study the best possible course of care for each of patient.
On the other hand, value-based care puts more emphasis on the outcome of care and focuses on the value obtained from top-quality care. Thankfully, it is looking like the paradigm shift to this accountable model of care is here to stay, as the U.S. Department of Health and Human Services has targeted—by 2018—to tie 50% of Medicare fee-for-service payments to quality or value through alternative payment models.
Artificial Intelligence: Helping Clinicians and Regulators Craft the Best Health Programs
Now, healthcare agency leaders and health providers are coming under intense pressure to discover and enforce best practices in order to deliver high-quality care at lower costs. In order to do this, they are turning to state-of-the-art solutions like healthcare-specific artificial intelligence in order to rapidly analyze huge volumes of data and turn them into actionable insights.
For instance, there are software solutions that utilize the power of machine learning to study not only electronic medical records but also financial information that represent tens of thousands of patient experiences and individual events. By analyzing clinical variation at such a granular level, the software is able to automatically surface groups of similar patient procedures and discover clinical routes that result in the best patient outcomes at the lowest possible costs.
With the help of artificial intelligence, health providers and health payers will also be able to develop a proactive strategy for managing population health. Some of today’s artificial intelligence solutions can automatically identify nuanced sub-populations and predict risk trajectories for people who fall under each patient group. By predicting who will advance to riskier states over time and what conditions are likely to be prevalent for each group, health organizations are able to determine the most cost-effective interventions that will deliver the best health outcomes.
Aside from clinical variation and population health management, artificial intelligence will also be increasingly useful in the future for other healthcare applications that will have a direct impact on the cost of care. These include the management of regulatory risks, the detection of healthcare fraud, and the reduction of healthcare claims denials.
As governments and health organizations around the world face increasingly more complex challenges, they are also becoming more open to studying alternative healthcare models and relying on innovative solutions to lessen the financial burden of care on populations and individuals. With the help of technologies like artificial intelligence and machine learning, they are well on their way to crafting better healthcare for everyone.