Opinion: Can Machine Learning (ML) Reduce Patient, Provider, and Insurer Administrative Burdens?

“What is Machine Learning?

If mechanical machines play an essential part in helping people to improve muscle strength, why can’t digital machines enhance man’s ability to increase the efficiency of the healthcare system (Thomas, 2019)? Machine Learning (ML) uses business processes to study patterns of data resulting in predictions and recommendations (Bresnick, 2017).

Health-care related administrative burdens include the business processes and associated costs that patients, providers, and insurers incur but are not directly related to the delivery of medical care (Gottlieb e Shephard, 2018). ML represents an approach to using data to make or recommend decisions with a focus on improving efficiency within the healthcare system and thus reducing administrative burdens.”

“Reducing Administrative Burdens

Recent studies into the impact of administrative burdens on patients, providers, and insurers have found that costs associated with administrative burdens (2019) may exceed $496m (Gottlieb e Shephard, 2018). According to this same study for every $1B in revenue collected, the health care system requires 770 full-time workers to handle the administrative burdens. Considering there was more than $900b in revenue within the private commercial sector in 2014 (Becker, 2019), the number of resources applied to handle administrative burden is substantial. If the application of ML can help to decrease the resources spent on administrative burden by 10%, the savings could be extensive.”

“Patient Administrative Burdens

The purpose of healthcare is to improve or enhance a person’s quality of life…” Read the full post here.

Source: Can Machine Learning (ML) Reduce Patient, Provider, and Insurer Administrative Burdens? – By Stuart Rabinowitz and Emmanuel Iroanya, HIMSS.



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