From the August 2013 issue of HealthCare Business News magazine
Looking forward, one of the biggest barriers to the development and use of personal analytics is data quality. Case in point: One group we worked with had been entering temperatures as heights in their electronic medical record until we pointed out how many people in their records were 98 inches tall. It was frightening that no one had noticed, but equally clear that this data had never been used for treatment decisions. With the increased use of patient data by computers to help guide decisions, methods will be needed to ensure that our individual health data is as accurate as our bank records. To anyone involved in health care this may sound like a Sisyphean task, but other industries have already led the way — wouldn’t you be shocked if you discovered that your bank had been recording your deposits as withdrawals?
Finally, the quality metrics chosen to determine health care reimbursement will be critical. They will need to accurately measure what is beneficial to patients — no easy challenge — or risk driving health care away from the mission of improving health to other parts unknown. Ultimately, if health care metrics are appropriately designed, economics and analytics should lead us down the path to the right treatment for the right person.
Our twins, by the way, still have their tonsils, and as of the last visit they were not quite so humungous.
About the author: Don Morris, PhD, is vice president of scientific product and technology development at Archimedes Inc., a healthcare modeling and analytics company based in San Francisco. He leads the development of IndiGO, Archimedes’ clinical-decision support tool, and other products for individualized risk prediction and decision support.
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