Artificial Intelligence implementation in healthcare domain is developing quickly as vendors and providers prioritize affordability, accessibility, accuracy, and speed. The use of Artificial Intelligence in healthcare will continue to draw focus in 2018, as developers work diligently to turn machine learning and advanced analytics into actionable clinical decision support for providers.

Healthcare is widely regarded as the test-bed for AI-led transformation. The market for artificial intelligence in healthcare was worth over $600 million in 2014 and by 2021, it is expected to rise to $6.6 billion. AI is broadly prescribed as a remedy for healthcare’s high cost and an enabler for better health outcome and experiences.

Healthcare providers focused on cost, health regulations (like HIPAA), life science and patient care can leverage GAVS expertise in health care analytics. It’s primary interest area in advanced analytics encompasses the technologies and skills used to deliver business, clinical and programmatic insights into the complex interdependencies that drive medical outcome, cost and oversight.

Using GAVS’ predictive analytics platform, GAVel, organizations can examine patterns in various healthcare data in order to determine how clinical care can be improved while limiting excessive spending. Input data is collected from four areas within healthcare mentioned below:

  • Claims and cost data
  • Pharmaceutical and research and development (R&D) data
  • Clinical data collected from electronic medical records (EHRs)
  • Patient behaviour and sentiment data (patient behaviours and preferences, retail purchases e.g. data captured in running stores)

Healthcare AI poised for explosive growth and big cost savings

The multibillion dollar healthcare landscape is proliferated by established tech giants and startups to support healthcare-focused AI tools used for genomics data processing, pharmaceutical discovery, imaging analytics, and clinical decision support. This market is expected to be more than $20 billion by 2020 exhibiting a compound annual growth rate (CAGR) of nearly 50 percent over the next five to seven years.

Speed, accuracy, and affordability are paramount to healthcare organizations looking to invest in systems that are driven by machine learning. Using AI to sieve through healthcare data may help providers to avoid unnecessary surgeries and develop more personalized treatment plans for patients. Expect robot assisted surgeries, virtual nursing assistants, workflow tools and more offerings potentially make patient care more efficient in 2018.

Pepper, a humanoid robot that can interpret human body language and read emotion is the classic example to showcase AI’s rise in healthcare.

Other use cases for AI based Robots are

  • RoBear is a nursing-care robot that can lift and move patients in and out of beds into a wheelchair, help those who need assistance to stand, and even turn patients in bed to prevent bedsores.
  • Da Vinci Surgical System, a robotic system designed to facilitate complex surgery using a minimally invasive approach and is controlled by a surgeon from a console.

Ubiquitous AI applications still have a long way to go in the healthcare industry that is known for its hesitancy to incorporate big data analytics into meaningful workflows. But there is a growing interest in the potential for advanced computing to shorten drug discovery cycles, improve the accuracy and speed of diagnostics, to create a more efficient and intuitive care environment.

Market research predicts that AI technology represent a significant opportunity for industry players to manage their revenue in an evolving landscape and examine different AI applications ranked by their potential for cost savings. They are:

Robot-assisted surgery, Virtual nursing assistants, Administrative workflow assistance, Fraud detection, Dosage error reduction, Connected machines, Clinical trial participant identifier, Preliminary diagnosis, Automated image diagnosis and Cybersecurity.

As ML and artificial intelligence progress, accessibility will join affordability and accuracy as key competitive differentiators for developers looking to commercialize and promote these algorithms.

Providers seeking to invest in advanced big data analytics and AI tools founded on machine learning principles can partner with GAVS Technologies to quickly make their way into the patient care environment.

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