Health professionals are asking “How can we use data more effectively?” – The answer is pretty simple: To give them insights to support better outcomes for patients. The available data for healthcare is growing through the introduction of more electronic systems to support the business of healthcare such as EMR’s and EHR’s. Hence, in the actual data we are capturing in health is increasing although there are still challenges to gaining insights from the data.
There are a number of real roadblocks to the healthcare industry making the use of the capability that include siloed approach to delivery and hence, capture of data, which makes it more difficult to aggregate data from its raw forms to a valuable database for health. Also, there are then the different views needed for the different health professionals to make the insights applicable to their practice. Finally, the available analytics tools are becoming more sophisticated and powerful to deliver these valuable insights.
So now, more than ever, it is becoming more viable to deliver valuable insights to the industry – but healthcare leaders are going to need to become smarter in how they approach the challenge of data analysis.
The traditional tools for analytics are effective for many of the applications healthcare require they “…cannot fully exploit the value of big data”: They are unable to adapt to new problem domains or handle ambiguity and are only suitable for structured and unstructured data with known, defined semantics (the relation of words and phrases and what they mean). Without new capabilities, the data paradox of having too much data and too little insight will continue.
“In healthcare, using technology to automate various parts of this data-driven decision-making is critical, as healthcare data (and its implications) is complex and intertwined. The goal of analytics is not to replace human decision-making, but to better arm clinicians, managers, and eventually patients with valuable knowledge tailored to their specific circumstances. Automation can help identify trends and issues, uncover new insights, and fine-tune operations to meet organizational and system goals much more easily. To that end, the best use cases of analytics in healthcare help specialists across various disciplines of healthcare to focus on solving the “last mile” challenge of data interpretation and subsequent action, rather than data gathering and storage. “- OVUM, The case for Making Analytics a First-Class Citizen in Health
This leads us to look at Cognitive Computing and how the industry can start to address the “last mile” issue of data interpretation and action. It is answering the questions of – How can the healthcare industry bridge the gap between untapped opportunities and current capabilities? How can hidden insights that reside in data – structured and unstructured – be fully harnessed for discovery, insight, decision support and dialogue?
Cognitive-based systems build knowledge and learn, understand natural language, and reason and interact more naturally with human beings than traditional programmable systems.
While the digital age has brought a massive amount of healthcare data brimming with insights, organizations still struggle to unlock its full value. Advances in the pioneering area of cognitive computing can help bridge the gap between data quantity and data insights.
To learn more click here: A booster shot for health and wellness: Your cognitive future in the healthcare industry
Come visit IBM at HIMSS16, Booth #5932 to see demos on how we are applying analytics to drive value based care.
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