The Economist article, 'A
Different Game', is relevant because it addresses a need for deep
analysis of inert data to extract actionable information. Actionable
information is referred to as 'intelligence' because it provides a tangible blueprint to address significant, but hidden problems.
This novel approach to understanding
hidden problems that are lurking with in large populations, through data analysis, is at the
heart of cost reduction efforts within the healthcare industry.
Healthcare data is generated by a multitude of sources, including patients records, claims reports, hospital
equipment, mobile devices and insurance reports. This data contains hidden messages to address the cost and quality issues that plague healthcare.
One
interesting use of data mining techniques in healthcare has been to analyze population trends to identify low cost solutions to achieve high margin gains.
This represents the starting point in the healthcare supply chain.
The significance
of this area of research reveals itself in a New Yorker article entitled 'Hot Spotters' by A. Gwande. In Hot Spotters, the significant finding
and relevant issue to our study of information technology is that data analysis
of public health records has revealed a few individuals within
certain geographic areas that account for the most healthcare spend. The
key is using data analysis to identify these 'frequent fliers' or high spend
individuals. Again, this identification of high risk individuals is the
start of the healthcare supply chain. Later steps include allocating
resources and care models.
In Camden, New Jersey, “one percent of the hundred thousand
people who made use of Camden’s medical facilities account for thirty five
percent of its costs.”[1] These ‘frequent fliers’
showed higher rates of hospital admissions, emergency room visits and complex
chronic conditions. Providing intensive
outpatient care to communities where a relatively low number of high needs
patients generate the costliest medical bills will produce high value
results.
These high cost individuals
could only be identified through sophisticated data mining and GIS
techniques. Health IT, data analytics and geographic information systems
identified specific hot spot apartment buildings. Two buildings in
Camden, NJ accounted for $200 million dollars in health-care bills, and the
most expensive patient cost insurers $3.5 million.[2] These buildings were
identified using patient records, GIS and data analytics tools. The
relevance to our study is that this analysis of inert data led to
'intelligence' or actionable items and created a blueprint for action throughout the healthcare supply chain.
In light of the effective nature
of data analytics to predict trends in population health and target individuals
who create healthcare costs outside the normal distribution, are there
privacy issues that need to be addressed? Should we implement policies that allocate resources
towards highlighting high crime neighborhoods and sick neighborhoods that incur
disproportionate costs or is this a negative profiling technique? Can we justify these invasive measures by
explaining that this has the highest marginal benefit to society?
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