On a friendly and uncomplicated note, predictive modelling is some sort of mathematical tarot meant to answer one question: ‘Is it possible to forecast what will happen in the future by analysing known past behaviours?’ The answer is YES. But let’s lay our cards and see how this actually works.
What is predictive modelling?
Also known as predictive analytics, predictive modelling is used to forecast future events by analysing past patterns. Once all the data is collected, analysts use historical data to train statistical models and thus predict future outcomes. As more data becomes available, statistical analyses need to be revised or validated.
In case you are wondering to what extent predictive modelling affects our daily routine, then you should know that weather forecasting, personalized online advertising/marketing, or spam filters make our lives easier thanks to predictive modelling. Predictive modelling and ML are progressively becoming the New Norm in healthcare, transportation, finance, retail, heavy industry, etc.
How is predictive modelling used in healthcare software?
While it is true that technology cannot replace empathy, human warmth, and intuition, it is also true that predictive modelling can help health providers improve diagnosis and treatment quality, decrease readmission rates by identifying risks, and reduce costs. Based on multiple variables (such as age, sex, medical record, economic and social data, etc.), predictive modelling can identify a patient’s susceptibility to develop certain diseases (e.g. asthma, diabetes, heart conditions, etc.) On the whole, predictive modelling can significantly improve patient care, prevent serious medical problems, and – eventually - save lives.
In March 2019, the Society of Acuaries conducted a survey on 101 health provider executives and 100 health payer executives to pinpoint future trends in the use of predictive modelling in the healthcare industry. According to their findings, the targeted outcomes match the expectations.