A number of industries are effectively leveraging data science to realize greater efficiency and gain a number of benefits. When it comes to the healthcare industry, here are three advantages of using data science approaches:
1. Reliability, Verifiability, and Openness to Experiments
Much of the healthcare industry relies on the knowledge and experience of clinicians, administrators, and underwriters. Along with the insights and creativity of people, there is also the possibility of human error and inconsistency. Data-based approaches can be built to produce similar results under similar conditions, reliably, and importantly can be tested on millions of patients to verify performance—this also allows a fast cycle of experimenting with new methods.
2. Models Reveal Biases
Data science models are designed to identify complex patterns that can predict health outcomes or cost with minimal human intervention. In addition to improving predictions, such models can illuminate how traditional approaches might be biased. Are care programs selectively benefitting certain population groups better than others? Are certain patients not being reached? These questions can be as important as overall program effectiveness.
3. Predictions Often Improve with More Data
Perhaps the most exciting benefit of using machine learning-based models is that they can improve with more data, assuming the model has enough free parameters to discover new patterns and that the new data is sufficiently different from previous datasets. Much of the work in data science is in tuning models such that the number of features is high enough to capture important effects in the data, without introducing so many that the model overfits to the training set. In our experience, after model tuning, adding millions of new medical health records has significantly improved our predictive performance.
Stay tuned, as we take a closer look at each of these advantages and explore how healthcare can benefit from data science.
Senior Data Scientist, Lumiata