No one knows yet how the rise of smart machines will turn out for us in the end. However, when it comes to delivering healthcare solutions for humans more efficiently and quickly, it appears that, far from taking over, the machines are very helpful, at least for now.

The pace of evolution in eHealth application development is moving quickly. The innovations from startups and established businesses put pressure on all of the competitors in the market for new solutions based on the latest IT capabilities.

One of the most exciting technologies that disruptors are using to architect new apps is the power of machine learning. Companies are using machine learning to do everything from diagnosing illness and care planning, to studying physician performance, administration, and safety monitoring.

Multidimensional Solutions To Learn From Data

Machine learning is built on analytical models and designed to use automated data processing to extract information. As a technology, it is showing promise in the ability to recognize patterns and uncover hidden insights.

The principles behind machine learning are highly mathematical; they reduce complexities to step-by-step solutions. These are systems programed to break down data into multidimensional arrays and estimated initial conditions from which to make predictions iteratively and suggest conclusions.

Machines Learning About Human Activities

The list of things that you can do with machine learning is long and growing. A machine learning-based application can make recommendations and predictions about taste, or recognize patterns of impending failure due to faults or prevent fraud.

Netflix and Amazon provide examples of how learning algorithms can learn to read patterns and make recommendations to consumers, based on past behavior. There are many uses for the same learning algorithms to automate decision-making and information management; it is ideal for the healthcare industry, and it provides a diverse range of applications.

Machine learning based healthcare management for patient care, administration of health-focused companies, and other specialized fields, such as medical research, some of the problems it can solve are:

  • Diagnose diseases, even if the correct diagnosis is an exceptionally rare condition
  • Improve administration and patient safety, while reducing costs
  • Manage care platforms to create programs that deliver patient results
  • Encouraging patients to adhere to treatments to increase the probability of long-term recovery

Machines That Make Real-Time Inferences Without Intervention

The value in machine learning originates from the ability to predict outcomes and events, to do it in real-time and without human intervention. Intelligent systems improve accessibility for information, make decision-making more transparent, and enhance communication fidelity.

Smarter healthcare means better quality care delivered more efficiently. The power of learning software lies in its ability to translate the intentions and ideas of humans into systems. In medicine, Big Data and machine learning change the relationship between healthcare and patients by automating healthcare decisions.

If your company is an eHealth startup or enterprise, consider how you will develop your next business application. Machine learning is an excellent technology to explore, and on which to build the architecture of your first or next healthcare app.

The machines might not rise up anytime soon, but machine learning and software that changes the game in specific industries is already here. The rapidly evolving potential of IT in healthcare will create more opportunities as the technology advances over the next few years.

Vik is our Brand Journalist and Head of Online Marketing / PR with 11+ years of international experience in IT B2B. He's also a guest blog contributor to business2community, SitePoint, Journal of mHealth, Wearable Valley and other e-zines.

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