Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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The United States leads the world in artificial intelligence, but lags behind other countries in applying technical innovations to the field of healthcare. Globally, machine learning is used to increase efficiency, lower error rates, and decrease medical costs, but the fragmented marketplace and lack of universal healthcare in America disincentivize adoption of new technology as buyers often prioritize economics over patient care.
Sally Daub, CEO of Enlitic, a frontrunner in providing AI-based healthcare solutions, illuminates why healthcare technology adoption is stunted. "Here the conversation revolves around 'Who is going to pay for this? What are the economic incentives to use this technology?' rather than 'Will this technology result in better outcomes for patients?'," she explains. Payoff structures may even discourage insurers from footing the bill for early intervention treatments. If insurers don't approve a new technology, they don't issue insurance procedure codes necessary for healthcare providers to be reimbursed for services, which means providers won't adopt the solution either.
Private healthcare systems like America's allow costs to be funneled to consumers. Care providers, hospitals, insurance companies, and other players in the market often compete with each other to offload responsibility for medical bills. Public health systems like Canada's single-payer model operate under a unified government budget, incentivizing system-wide adoption of technologies that cut costs. Canadian citizens hold public healthcare entities and decision-makers accountable for high quality care, and the media fallout from failing to deliver to those standards is swift and severe.
Due to intermarket competition, healthcare entities in the U.S. are often unwilling to share medical data, but Daub believes their excuses about privacy and security are unreasonable. "Keeping healthcare records secure is obviously very important," she clarifies, "but in AI the true benefit will come from sharing data while respecting patient privacy. If everybody sits in their silos, we're not going to progress." Canada enforces that all medical records be kept in a central depository owned by the government and citizens. Hospitals within a province are also linked, so a technology tested at a single facility can be scaled quickly over the entire network. Ontario alone includes 20 million patients, more than the number covered by any single provider in America.
Daub recently joined Enlitic, which uses deep learning to analyze millions of clinical cases containing patient history, symptoms, lab tests, medical images, and other important data points required to arrive at optimal treatment decisions. Founded by former Kaggle President Jeremy Howard, the company's powerful technology allows doctors to derive actionable insights from the collective intelligence of the medical community. "The key insight that led me to creating Enlitic is that deep learning can be used to integrate nearly all types of medical data to diagnose nearly all kinds of disease," Howard shares.
Two areas in healthcare stand to benefit the most from AI solutions: triage and imaging. Diagnosing medical images requires years of practitioner training, leading to a shortage of skilled radiologists, while volume of patient data has surged, resulting in high error rates. Leading healthcare investor Robert Mittendorff, Partner at Norwest Ventures, sees healthcare imaging as "the most productive area for AI to be placed." He explains that radiologists are trained to work with "a very standardized data set and curated output," both of which are critical to training AI to replicate human diagnoses. As measured by internal benchmarking tests, the accuracy of Enlitic's software is already on par with, and in some cases better than, trained clinician decisions. In a clinical setting, Enlitic enhances provider productivity and provides secondary reviews to catch costly mistakes.
While optimistic, Mittendorff sees many AI startups in healthcare make the mistake of thinking too small. "It costs the same for radiologists to adopt an imaging technology that detects appendicitis or one that detects all anomalies across the region. Doctors aren't going to adopt single point solutions, but rather systems they believe deliver considerable value and improvement across multiple needs." Howard agrees, stating that "a diagnostic aid is far more useful to doctors if it covers nearly all diseases - since otherwise the doctor has to double check every piece of data themselves. This is particularly important in developing nations such as China, where there is a massive shortage of doctors." Read More