BigData

Is Smart Data Better than Bigger Data for Predictive Analytics?

http://healthitanalytics.com/news/is-smart-data-better-than-bigger-data-for-predictive-analytics

Bigger isn’t always better when it comes electronic health record data and predictive analytics, according to a new study from the University of Texas Southwestern.

Researchers found that performing analytics with EHR data collected throughout a patient’s entire hospital stay was not significantly more accurate at predicting 30-day readmissions than data from just the first twenty-hours, suggesting that value over volume is an important mantra for more than just the financial aspects of healthcare.

A team of UT researchers examined nearly 33,000 patient admissions from 75 regional hospitals between 2009 and 2010, and found that 12.7 percent experienced a 30-day hospital readmission.

The study intended to examine how a patient’s in-hospital complications, such as hospital-acquired infections (HAIs), and their stability during discharge affected their risk of 30-day readmissions.  The researchers collected and analyzed EHR data from the entire course of the patient’s stay to test several different predictive analytics models.

They found that certain common risk factors, such as C. difficile infection, vital sign instability upon discharge, and longer length of stay in the hospital were positively correlated with an unplanned 30-day readmission.  Using full length-of-stay data resulted in a +2.4 likelihood ratio of readmission.

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However, when they examined data from just the patient’s first twenty-four hours in the hospital, which often did not include adverse events that may have taken place later in the stay, they found that the 24-hour data was nearly as good at predicting readmissions on its own, producing a likelihood ratio of between +1.8 and 2.1.

“Our group’s previous research found that using clinical data from the first day of admission was more effective in predicting hospital readmissions than using administrative billing data,” said lead author Dr. Oanh Nguyen, Assistant Professor of Internal Medicine and Clinical Sciences at UT Southwestern in a press release.

“We expected that adding even more detailed clinical data from the entire hospitalization would allow us to better identify which patients are at highest risk for readmission. However, we were surprised to find that this was not the case.”

The results suggest that non-clinical factors, such as patient health literacy, behavioral health issues, the degree of discharge planning, and socioeconomic challenges after leaving the hospital, may have a greater-than-expected impact on which patients are most likely to return within the costly 30-day window.

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Data on this issues is rarely included in the electronic health record, but may be vital for crafting truly effective predictive analytics and risk scores, added Dr. Ethan A. Halm, Chief of the William T. and Gay F. Solomon Division of General Internal Medicine and Chief of the Division of Outcomes and Health Services Research in the Department of Clinical Sciences at UT Southwestern.

“More ‘big data’ alone did not make much of a difference,” he said. “Better models for predicting readmissions will require ‘better data’ on things like psychosocial and behavioral factors that are not currently captured in electronic health records.”

The UT Southwestern study adds to a growing body of evidence indicating that sophisticated big data analytics aren’t always enough to truly impact a patient’s health status.;

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