{"id":32489,"date":"2018-11-13T15:01:17","date_gmt":"2018-11-13T20:01:17","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/using-machine-learning-to-bend-the-healthcare-cost-curve\/"},"modified":"2018-11-13T18:10:23","modified_gmt":"2018-11-13T23:10:23","slug":"using-machine-learning-to-bend-the-healthcare-cost-curve","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/using-machine-learning-to-bend-the-healthcare-cost-curve\/","title":{"rendered":"Using machine learning to bend the healthcare cost curve"},"content":{"rendered":"

Academics and politicians commonly decry the \u201chealth care cost crisis\u201d in the United States. While spending more per capita on healthcare than any other country in the world, the United States has an average life expectancy in the middle of the pack among developed nations.[1]<\/a> Though much of the cost burden in the United States is driven by high prices for treatments, as much of 20% of healthcare spending is the result of clinical waste and administrative complexity.[2]<\/a> athenahealth, as a provider of electronic practice administration tools to small-medium-sized healthcare practices, has an important role to play in helping their clients reduce this wasted healthcare spending.<\/p>\n

Machine learning is a crucial tool for this task, due to the volume and structure of healthcare data. Hospitals generate a breathtaking amount of data. Everything from a patient\u2019s heartrate to the tone of a doctor\u2019s clinical notes could conceivably be used to improve patient outcomes or reduce the amount of time a doctor needs to spend on a given patient (thus improving the cost effectiveness of healthcare).[3]<\/a> However, this data is typically not structured in a way that is amenable to simple regression analysis or other less sophisticated methods of data analysis. Machine learning, then represents a huge opportunity for a company like athena to help their clients wade through the mountains of data that they generate.<\/p>\n

athenahealth has several machine learning pilots that they are testing in parallel. athenahealth has thus far avoided the extremely complex field of computer-aided diagnosis \u2013 opting to start with lower hanging fruit. As Girish Venkatachaliah, vice president of data strategy, analytics and machine learning at athenahealth put it, \u201cin the future, AI will benefit patients. But today it can help doctors.\u201d[4]<\/a><\/p>\n

athena\u2019s most prominent machine learning program deals with faxes. Due to regulatory requirements, faxes remain the most common mode of healthcare communication. athenahealth\u2019s 100,000 providers receive 120 million faxes every year.[5]<\/a> athenahealth is using machine learning to automate some of the processes of importing faxed data into a hospital\u2019s electronic medical record. Using machine learning to recognize and automatically transcribe common fields in faxed forms, athenahealth has been able to reduce the average time a clinic spends reading and importing a fax from over two minutes to one minute and 11 seconds.[6]<\/a> These time savings add up and represent cost savings for clinics and for the healthcare system more broadly.<\/p>\n

athenahealth has also begun using machine learning to automate the processing of prescription refill requests. A plugin for athenahealth\u2019s software can evaluate a refill request and determine whether it meets treatment guidelines for a given patient.[7]<\/a> The company claims that it can process up to 60% of prescription refills without involving a physician \u2013 again generating substantial time savings for clinics.<\/p>\n

Saving time for clinics is a worthy goal, but for athenahealth to truly help bend the healthcare cost curve they will need to delve into improving the way that physicians administer care. Through its athenainsight service, athena is already providing descriptive data to its clinics about how other clinics treat a given condition. However, there is a huge opportunity to build on this service and begin offering proactive treatment recommendations. Some of the most innovative hospital systems are beginning to apply machine learning to understand the likelihood of success and\/or complications for a specific procedure given everything they know about the patient\u2019s health profile.[8]<\/a><\/p>\n

Services like this are a natural next step for athena \u2013 and would be a huge value-add for their customer base. athenahealth\u2019s user base consists of over 100,000 physician practices who treat 55 million patients per year.[9]<\/a>\u00a0athenahealth typically serves clinics on the smaller end of the size spectrum –\u00a0 many of which are under-resourced and\/or located outside of major cities.[10]<\/a> This massive user base represents an opportunity to democratize access to some of the most impactful machine learning tools used by top national hospital systems.<\/p>\n

Obviously this sort of service comes with major ethical, legal, and logistical challenges. athenahealth would need to ensure that they implement safeguards on the system to preserve patient safety and patient privacy.<\/p>\n

As machine learning is increasingly used as a decision support and\/or automation tool in healthcare, several open questions remain:<\/p>\n