April 20199for those individuals discharged from the hospital after a cardiovascular admission (such as a heart attack), can we identify a score that will predict those at highest risk for readmission to the hospital?This is a standard question and applicable to the work that we do in healthcare. But we can be better. Most predictive algorithms look internally to within the four walls of our healthcare system to evaluate for clues for readmission. But readmission, like most events in healthcare, often occurs due to events and knowledge external to our healthcare system. Impacts of socioeconomic status and behavior patterns drive healthcare utilization as much as, if not more, than clinical characteristics. But how do we measure socioeconomic status and behavior? How do we leverage data external to the healthcare system?As a clinician, I know that I rarely capture socioeconomic or behavior data unless it directly impacts the clinical issue at hand. I will ask the parents about whether or not their child attended daycare to understand the exposure pattern of an infection but fail to ask if the family has faced recent financial difficulties resulting the child being under significant psychologic stress, thereby decreasing her ability to mount a robust immune response. I will ask about vaccinations but not ask about how often the family gets fast food due to the lower costs and high convenience since the parents both work two jobs.However, these nuggets of knowledge are just as important as clinical data. Applying these non-clinical variables to our analytic process can vastly improve our predictive algorithms. Capturing these variables can occur in a traditional way by having clinicians enter these into the electronic medical record. Alternatively, de-identified data sets can be applied to gather a proxy for socioeconomic and behavioral data. Governmental data such as the census data can give insights to patient population served by a healthcare organization by examining patient outcomes and metrics based on zip codes. Other de-identified and aggregated sources of data such as through CMS and AHRQ can also be teaching data sets that align existing clinical data to new variables predictive of behavior.Furthermore, non-traditional data such as social media data and consumer purchasing behavior can also be applied to clinical metrics and outcomes. Similar to retailers understanding your purchasing patterns to predict which other products may be of interest, healthcare organizations can utilize this data set to anticipate declining health. These data can be both identifiable, such as purchasing pattern tied to a specific household or aggregated, such as purchasing behavior tied to a zip code or county.Back to the example of readmission after a cardiovascular discharge, if clinical data of hypertension and high cholesterol and be aligned with socioeconomic data suggestive of low income and purchasing behavior suggestive of increased fast food purchasing pattern­a predictive algorithm based on this largely diverse data set can be countless times more sensitive and specific in predicting a cardiovascular event as opposed to an algorithm based solely on clinical data.The frontier of healthcare analytics no longer ends with the data at our disposal. Instead, by capturing or identifying proxies to socioeconomic and behavioral data external to our four walls of care, predictive algorithms can make the leap into targeted prediction of event thereby improving the way we can care for our population. Non-traditional data such as social media data and consumer purchasing behavior can be applied to clinical metrics and outcomes
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