Understanding Predictive Analytics

Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. One of its major uses is in healthcare. It predicts the effectiveness of new producers, medical tests, and medications and improves services or outcomes by providing safe and effective patient care. It also helps in detecting and handling insurance claim frauds, identifying which patients are at most risk of having a chronic disease and know which interventions make the most medical and financial sense. Some examples of the above applications can be the executives of Taipei Medical University that analyse and monitor performance across all hospitals in its system or Express Scripts, one of the largest pharmacy benefits companies in the US which uses analytics to identify the patients not adhering to their prescribed treatment, resulting in a savings of $1500 to $9000 per patient.

We distinctly remember the moment that scientists claimed victory against all nature of future disease after the human genome had successfully been decoded. However, over the ensuing decade-plus, it has become clear that our health is not quite that deterministic. Clinicians must weigh not just a string of nucleotides when making decisions about our care but must also incorporate a growing set of health data that is generated and controlled by patients. Incorporating this data into health care to enable better decisions is at the heart of this report. The benefits of using predictive analytics are the same as many categories of digital health: better care and lower costs. The difference is that the path to realizing these benefits—through personalized care—is only possible by implementing these technologies. The concern that care will be reduced to a set of algorithmically-derived probabilities is important and real. But the promise is as well.

Vinnie Ramesh, chief technology officer and the Co-founder of Wellframe said, “Predictive analytics is not reinventing the wheel. It’s applying what doctors have been doing on a larger scale. What’s changed is our ability to better measure, aggregate, and make sense of previously hard-to-obtain or non-existent behavioral, psychosocial, and biometric data.Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in the enhanced re-engineering of clinical pathways and truly personalized care.”

Investors certainly believe in the promise, pouring $1.9 Billion into companies that purport to use predictive analytics. The most active investors being Khosla Ventures, Merck Global Health Innovation fund, Norwest Venture Partners, Sequoia Capital and Social + Capital Partnership. Funded companies claiming to use predictive analytics are highly focused on providers, practically ignoring patients.

The keystone of any successful predictive analytics model is the ability to improve the prediction based on a feedback loop.Within seconds, Google knows whether its search engine prediction is correct. But in health care, the feedback loop—which is often measured in terms of impact on biometric or cost outcomes—can take years.

Startup companies are attacking the key challenges in predictive analytics, advancing the space and making a difference.

The challenges that this method faces is the ability of accessing meaningful, historical data sets and normalize for inherent biases and validity concerns; Integrating with current clinical workflow to collect real-time, point of care patient data; Learning to manage and process new and existing forms of unstructured soiled data and in addressing HIPAA and privacy related concerns to guarantee patient anonymity.

“We are underestimating the potential impact of predictive analytics in process tools to help physicians make better decisions.Every week, at the airport, I get on an airplane, and I don’t worry about flying at all. There are so many tools deployed to assist the pilot. I was talking with a pilot about the new 787–and the pilot said he basically monitors the plane. We’re going to see more of that in health care.Physicians will be monitoring algorithms”, saidKevin Fickenscher, President AMC Health and Former President, AMIA.

References-

https://en.wikipedia.org/wiki/Predictive_analytics

http://www.sas.com/en_us/insights/analytics/predictive-analytics.html

http://www.webopedia.com/TERM/P/predictive_analytics.html

http://www.predictiveanalyticsworld.com/predictive_analytics.php

The Future of Personalized Healthcare: Predictive Analytics

 

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