Ariana Zayuri Castaneda
AI has a high impact on healthcare. It offers multiple benefits such as identifying new techniques that can prevent diseases, perform clinical diagnoses. AI also has the capability of detecting meaningful relationships in a data set that can be used in meaningful situations. For instance, there are startups like Cerebro that focuses on improving nurse staffing.
Day and night, hospitals require high-quality clinical staff for their patients. To meet their needs, hospitals increasingly rely on inefficient, insufficient staffing agencies. The agencies rarely find all of the necessary staff or only after a long delay. At the same time, agencies treat themselves to high margins instead of rewarding the clinicians’ valuable work.
Cerebro is a SaaS enabled marketplace that disrupts the traditional means through which healthcare organizations connect with clinicians for contingent work. It offers a faster, responsive healthcare solution that connects healthcare facilities with verified, ready-to-work clinicians to book appointments, manage prescriptions, tracks patients diet and exercise, post and accept both per diem and contract shifts, without reliance on intermediaries. These activities go directly to the hundreds of nurses on the Cerebro mobile app. Therefore, allowing for immediate responses and shortening time to procurement to nearly zero. The app tracks and delivers quality nurses on time and makes supplemental staffing more capital efficient by better utilizing each dollar of spending from the healthcare organization.
The Real Estate industry is not the first thought of when it comes to technological advancements. However, that has changed over the last years. Real Estate tech investments have ballooned from $33M to $5B since 2010, mainly focusing in the property management. Perhaps this has been the topic for many Real Estate agents as they are fearing that it would replace high-value and negatively impact face-to-face interaction, technology and automation, when used thoughtfully, humanize the customer experience.
There are different ways we could look at the benefits of this market-industry integration. For example, real estate tech investments may provide solutions that systematize how properties and tenants are managed, while others contribute to the data insights that is being provided to landlords and tenants. From a data visualization perspective, the amount of information retrieved can be overwhelming, but it also led firms to focus on how to best visualize all kinds of constant streams of information. Leverton, in one hand, applies AI / Deep Learning technology to extract data from real estate firms and create data visualizations and analytics from unstructured data and converted into meaningful insights. Another emerging product that promises help visualize data is called Matterport, which allows people to virtually walk around the interior of an existing space using 3-D renderings.
Diabetes and heart diseases are the two most chronical conditions in the US. About $30 billion are spent in the US when it comes to hospitalizations. The majority of those costs are due to chronic diseases (30% heart diseases, 20% diabetes). Iaonnis Paschalidis, Professor of Engineering and Director of the Center for Information and Systems Engineering at Boston University, and his team promise to develop machine learning algorithms that will help to identify patients at higher risks of heart diseases or diabetes, and to prevent such hospitalization events. With machine learning and big data approaches, the team aims to deliver personalized predictions and recommendations to patients with the purpose of improving outcomes and reduce healthcare costs.
Despite this new method can benefit about 86 million Americans, these machine learning tools must be trustworthy and accurate with reliable data in order to work. An estimate of 33% of the US population already uses either fitness apps or smartwatches to track their wellness with. A lot of information can be retrieved from health trackers, using real-time health conditions, allowing Paschalidis and team to facilitate the development of such technology to make predictions.