Five Challenges in Using Predictive Analytics to Improve Patient Outcomes
Machine Learning Times
Machine Learning Times
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4 years ago
Five Challenges in Using Predictive Analytics to Improve Patient Outcomes


In the increasingly patient-centric world of healthcare, predictive analytics has taken firm hold as a means for healthcare organizations to improve patient outcomes. Predictive models for determining patient responses to medication, lowering hospital readmission rates, assessing risk of disease breakout, and other uses are being implemented for superior disease management and care delivery. Similarly, in the area of med-tech, predictive analytics is about determining the likelihood of device failures, responding proactively to improve machine downtime, assessing consumable sales forecasts, and other medical device and customer business issues. Innovations in predictive analytics are enabling med-tech companies to differentiate themselves from the competition and improve business results like never before.

Currently, med tech companies are strengthening their data collection and analytical capabilities to indirectly determine patient conditions, through devices such as heart monitors, biosensors and other wearable devices. They are also using these new capabilities to directly predict device performance and usage, such as machine failure, which is critical to maximize clinical availability and minimize costs due to downtime. The costs of downtime can be significant based on the Complexity of the treatment process, the scale of operation, the number of failure occurrences, and the opportunity cost of providing better care.

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