Watson and the Evolution of Data Analytics and Healthcare
IBM, WellPoint and Memorial Sloan-Kettering Cancer Center are collaborating on a project using Watson as a means to develop an oncology treatment tool.
Once imagined ideas regarding the progress of healthcare are quickly becoming a part of the real world.
A majority of the value from Watson comes from its ability to aggregate massive amounts of medical journals.
Declared a 'clinical decision support system', it will operate in collaboration with physicians and specialists.
Although it is not taking over the entire doctor patient relationship, Watson is offering valuable data driven insight for medical professionals.
Big Data analytics have been brought to the forefront of popular culture thanks to IBM's Watson.
The advanced computer is a symbol of Big Data's proliferation and the future of data driven decision making in healthcare, business and government.
For instance, President Obama's 2012 reelection team boasted a 100 person analytics department to analyze voters, with its own internal 12 member team to conduct analysis regarding their operations.
The beauty of Watson, rests in its ability to analyze millions of pages of journals containing unstructured natural language data in a rapid fashion.
Analytics for unstructured data is the most challenging aspect of data analysis, Big Data analytics however are beginning to solve this challenge.
With the progression toward electronic medical and health records, analytics will become more practical and have added use cases.
Hospitals in control of their patient data can conduct powerful analysis through technology similar to Watson, to improve future decision making.
By creating a data friendly environment within an enterprise like a hospital, data will become less of a burden and more of a resource.
Having the ability to assess the past 20 years of treatment in the context of real-time patient data, will undoubtedly influence future scenarios and treatment.
One instance of practical analytics usage at University of Pittsburgh Medical Center (UPMC) occurred when the new IT team used analytics to highlight patients with propensity to staph infection.
Staph or MRSA is a contagious bacterium that can wreak havoc on a patient's fragile immune system and even cause death due to infection.
By conducting analytics to create a list of likely staph patients, the provider was able to specially cater to those patients in a way that prevented infection.
Also applying analytics to medication procedures has the potential to drastically reduce mistakes in drug administration.
Watson is a great step forward for Big Data analytics.
As the implementation of Watson within Sloan Kettering progresses, it will be exciting to see the improvements in treatment and the rate at which quality of care increases.
It is highly likely that commodity versions of Watson will be made available to general practitioners within their individual practice (and eventually for consumers on their smartphone).
IBM stresses that their brain-child is much more than an advanced search engine.
Its ability to process such massive amounts of unstructured data and provide contextual insight for decision makers puts it squarely in the territory of Big Data.
Once imagined ideas regarding the progress of healthcare are quickly becoming a part of the real world.
A majority of the value from Watson comes from its ability to aggregate massive amounts of medical journals.
Declared a 'clinical decision support system', it will operate in collaboration with physicians and specialists.
Although it is not taking over the entire doctor patient relationship, Watson is offering valuable data driven insight for medical professionals.
Big Data analytics have been brought to the forefront of popular culture thanks to IBM's Watson.
The advanced computer is a symbol of Big Data's proliferation and the future of data driven decision making in healthcare, business and government.
For instance, President Obama's 2012 reelection team boasted a 100 person analytics department to analyze voters, with its own internal 12 member team to conduct analysis regarding their operations.
The beauty of Watson, rests in its ability to analyze millions of pages of journals containing unstructured natural language data in a rapid fashion.
Analytics for unstructured data is the most challenging aspect of data analysis, Big Data analytics however are beginning to solve this challenge.
With the progression toward electronic medical and health records, analytics will become more practical and have added use cases.
Hospitals in control of their patient data can conduct powerful analysis through technology similar to Watson, to improve future decision making.
By creating a data friendly environment within an enterprise like a hospital, data will become less of a burden and more of a resource.
Having the ability to assess the past 20 years of treatment in the context of real-time patient data, will undoubtedly influence future scenarios and treatment.
One instance of practical analytics usage at University of Pittsburgh Medical Center (UPMC) occurred when the new IT team used analytics to highlight patients with propensity to staph infection.
Staph or MRSA is a contagious bacterium that can wreak havoc on a patient's fragile immune system and even cause death due to infection.
By conducting analytics to create a list of likely staph patients, the provider was able to specially cater to those patients in a way that prevented infection.
Also applying analytics to medication procedures has the potential to drastically reduce mistakes in drug administration.
Watson is a great step forward for Big Data analytics.
As the implementation of Watson within Sloan Kettering progresses, it will be exciting to see the improvements in treatment and the rate at which quality of care increases.
It is highly likely that commodity versions of Watson will be made available to general practitioners within their individual practice (and eventually for consumers on their smartphone).
IBM stresses that their brain-child is much more than an advanced search engine.
Its ability to process such massive amounts of unstructured data and provide contextual insight for decision makers puts it squarely in the territory of Big Data.
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