Artificial Intelligence

AI is the use of data science techniques to capture and analyse huge and complex datasets.

What is Artificial Intelligence?

Within the digital health industry, AI refers to the use of data science techniques to capture and analyse huge and complex datasets in order to positively impact patient care outcomes, and optimise business processes.

While the term, big data, may seem to reference the volume of data, that’s not necessarily the case. Big data may also refer to the extent of technology that an organization requires to handle large amounts of data, as well as the needed facilities to store it.

The healthcare industry produces large amounts of clinical, financial, administrative and genomic data and needs big data techniques to manage it.

The use of big data in the digital health industry usually addresses the following six categories of information:

  1. Web and social media data – such as interaction data from Facebook, Twitter, LinkedIn, blogs, health plan websites, and smartphone apps
  2. Machine-to-machine data – such as information from sensors, meters, and other devices
  3. Transaction data – such as healthcare claims and billing records in both semi-structured and unstructured formats
  4. Biometric data – such as fingerprints, genetics, handwriting, retinal scans, X-rays and other medical images
  5. Human-generated data – such as Electronic Medical Records (EMRs), physicians’ notes, email, and paper documents
  6. Pharmaceutical R&D data related to a drug’s mechanism of action, target behaviour in the human body and side effects

How is big data used in healthcare?

The role of big data in the healthcare industry is continually expanding and includes the following foundational processes:

big data nuviun

  • Collecting and aggregating the vast amounts of patient data produced from a variety of sources.
  • Analysing the collected data for a variety of purposes – such as optimised patient care and business intelligence.
  • Applying the analysed data results to improve the effectiveness of patient care systems and ROI of business processes.

The goals for the use of big data in healthcare will continue to evolve and currently include:

  • Improved patient outcomes through the use of advanced clinical analytics to enhance proactive care
  • Enhanced clinical decision-support through rapid analysis of the most current knowledge databases
  • Improved clinical trial design with the use of statistical tools and algorithms
  • Enhanced models of personalised medicine through the analysis of large datasets
  • Optimised business decision-supporting to help ensure the appropriate allocation of resources

Current Market and Industry Trends for big data

According to an R&R Market Research report, the healthcare analytics market alone is estimated to grow at a rate of 23.7% from 2012 to 2017 to reach $ 10.8 billion.

McKinsey estimates that if the potential of big data is fully exploited across the healthcare value delivery chain, it could account for $300 billion to $450 billion in reduced health-care spending in the U.S. alone.

Factors driving the big data market in the healthcare sector are:

  • The need for improved clinical outcomes
  • The need for increased efficiency in managing healthcare data
  • The presence of Federal healthcare mandates in some segments
  • The double digit growth in the EHR
  • The increased focus on value-based medicine
  • The need for personalised medicine that’s based on analytics
  • The need for improved decision support
  • The need to reduce pharmaceutical research costs
  • The need to reduce clinical testing costs

Factors inhibiting the growth of big data include:

  • A resistance to a systems-approach by the medical community
  • The operational gap between payer & provider front office
  • The acute IT staff shortage in healthcare
  • A lack of comparable & transparent data in healthcare
  • Financial constraints
  • Concerns regarding ensuring patient confidentiality
  • The low costs of traditional analytics techniques
  • The lack of interoperability between healthcare systems

Artificial Intelligence is already storming its way across the globe in industries outside of healthcare, drilling into the details that make a difference in quality outcomes and profitability.

With the rapid expansion of technology within the healthcare world, organisations everywhere are waking up to the need to use the massive amounts of data being generated to serve their patients, their providers, and themselves.

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