Application of Analytics in Health Care

1. Application of Analytics in Health Care Technology is the driving force in health care today, and data analytics are helping to drive better decisions. The ability to collect and analyze complete, accurate data enables decision-makers to make choices regarding treatment or surgery, predict the path of large-scale health events and plan long-term. The application of data analytics in healthcare has increased thanks to the widespread adoption of electronic health records. Most hospitals use electronic records to collect information on patient demographics, visits, diagnoses and treatments provided. This information can then be used to evaluate and develop practitioners by assessing how they perform relative to their peers. Data gathered from patients regarding their experiences with medical practitioners can be analyzed to reveal areas for improvement. One example is of Dr. Helen Riess’s research on empathy in physicians. After noticing that objective, scientific facts were often prioritized over a patient’s experience of their symptoms during appointments, she conducted a study in which patients rated their doctors on a scale of how empathetic they perceived them to be during visits. After gathering the data, half of the doctors received empathy training, while the other half did not. The doctors were again rated, and the group that received training was rated more empathetic than the control group. By analyzing patient-reported data, Riess discovered that training deploying the use of Health Care Analytics could increase physicians’ empathy. Medical practitioners are already turning to data analytics to improve efficiency and accuracy. One method that’s been proven effective is machine-learning algorithms applied to medical scans. In 2018, researchers at the Massachusetts Institute of Technology used an algorithm to detect differences in 3D medical images—like MRI scans—at a rate 1,000 times faster than humans alone . Data science expert Professor Dustin Tingley warns against relying too heavily on these algorithms without human thought and guidance throughout the entire process. However, when paired with your critical thinking and knowledge as a medical practitioner, algorithms based on Health Care Analytics could potentially save lives in many ways like detecting anomalies in scans more quickly. Health Care Analytics can be used to predict trends in the spread of illness, allowing doctors’ offices, hospitals, schools, and individuals to adequately prepare. The Centers for Disease Control and Prevention (CDC) leverages data to predict the next flu outbreak. This is an example of both predictive and prescriptive analytics because the CDC uses historic and current data to predict trends and drive action. However, data analytics goes beyond predictive analysis. It can also reveal patterns in data to help organizations prepare for the unknown. The important takeaway is that our world is becoming increasingly more data rich, and being able to harnesses it will lead to smarter decision-making and better service. Improved Quality of Care, Improved Patient Safety, Transparent and Repeatable Processes and Improved Cost-Effectiveness are just a few of the areas in which Analytics can help. But as with every new technology, there can be unsure challenges that need to be overcome. A widespread adoption of Data Analytics also requires a cultural shift in how healthcare professionals approach their work, from "fire fighting" to becoming "Data Aware".