Imagine if you could get arrested now for a crime you were going to commit in the future. Oh, wait. There’s already a movie about that. OK. Imagine that you had the gift of foreseeing the future, but all of your prophecies were doomed never to be believed. Whoops—that story is so 3,000 years ago. How about a cyborg assassin comes back from the future to kill you, because you’re pregnant with humanity’s savior against machines, but—

Noticing a trend? So often, the idea of supernatural foresight gets a bad rap. Of course, once any action is undertaken, its ripple effect may be difficult to counteract, especially when such actions are made on a national or global scale. And even if that foresight is 100 percent true, that doesn’t mean that the most thought-out preventive measures will succeed. But striving for more and more accurate visions of the future is certainly a start.

When it comes to artificial intelligence, predictive analytics is a quickly-evolving component in this vast innovative space. While other areas of AI may promise headier utopic visions, the beauty of predictive modeling is that it already enjoys a host of mainstream business applications. We usually talk about these models in terms of marketing, sales, and customer retention analytics, but our increasing ability to accurately see the future through data can dramatically alter both large sectors of our economy and the general business models themselves. It just so happens that the disruption we’re talking about is occurring in our three core innovation focuses here at tekMountain: medtech, HR tech, and edtech.

How can AI restructure the healthcare workforce?

We trumpet it ad nauseam: Make healthcare more efficient, save a ton of money. But what does that sort of efficiency look like in the world of AI? Deep learning, for starters. This past January, Stanford researchers announced that they had successfully created a diagnosis algorithm for skin cancer. The algorithm was trained to conduct a visual diagnosis for potential cancer throughout a database of 130,000 images of skin disease. Tested against 21 board-certified dermatologists who had diagnosed the same images, the algorithm results matched its human counterparts with stunning accuracy (Stanford News). This sort of news isn’t necessarily a matter of robots replacing humans, but rather it shows the promise of humans and AI working in tandem to make visual diagnoses, which then must be confirmed via biopsy—a strictly human task, for now.

The ultimate vision of Stanford’s project is to make the algorithm available for smartphones. Such a reality requires a lot more clinical testing, but it certainly falls in line with healthcare’s current evolution toward expanded mHealth and eHealth delivery. Let’s not get ahead of ourselves, but the cost-saving measures this sort of technology can create for healthcare providers will completely rehaul every CIO and CFO’s data structures.

How can AI reenvision the employee lifecycle?

IBM Watson is well on its way to becoming a household name, though it’s no longer the supercomputer that beat Ken Jennings on Jeopardy. Watson has become the brand name for IBM’s suite of cognitive computing solutions, one of which being Watson Analytics, a “cloud-based smart data discovery tool”. The HR component of this software, much like Careskore’s scope, covers the entire lifecycle of the employee, including attraction of talent, onboarding, improving employee satisfaction, and predicting future needs. More specifically Watson Analytics allows HR to

  • “Prioritize and target applicants who are most qualified for a specific position
  • “Forecast workforce requirements and determine how to best fill open positions
  • “Link workforce utilization to strategic and financial goals for improved business performance
  • “Identify the factors that lead to greater employee satisfaction and productivity
  • “Discover the underlying reasons for employee attrition and identify high-value employees at risk of leaving
  • “Establish effective training and career development initiatives”

How can AI better prepare students for the jobs of tomorrow?

As for the National Laboratory for Education Transformation, a Silicon Valley-based edtech nonprofit, predictive modeling should be extremely customizable and scalable. NLET’s vision is comprehensive and ambitious, developing AI to harvest actionable data on individual students, specific curricula, entire educational institutions, and the transition between school and career.

One of their most captivating efforts is what president Gordon Freedman calls “opportunity engineering,” as NLET strives to build “a data warehouse that brings together up-to-date information on what skills employees need and what colleges currently offer—and then applying artificial intelligence to attempt to predict when sectors or certain employment needs might be expanding” (EdSurge).

NLET enlists the help of a variety of organizations, too. From local educational institutions and county departments to other edtech software and solutions providers, the company’s belief is that such lofty goals can only be accomplished via cross-sector collaboration.

Integrate innovation without losing your business identity.

Through tekMountain’s constant search for the latest and greatest innovations in medtech, HR tech, and edtech, we know how complex it can be to integrate game-changing tech into a well-established business model, or heavily regulated spaces like education and healthcare. But, as we’re daily exposed to the incredible strides that CB Bridges, our parent company CastleBranch’s flagship product, has made in streamlining the clinical placement process of nursing students, we know how quickly tech can change a single market overnight.

Contact tekMountain today to see what collaboration can do for your business or institution.


This blog was produced by the tekMountain Team of Sean AhlumAmanda SipesBill DiNome and Beth Roddy with lead writer Zach Cioffi.

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