Despite leading the world in medical innovation, it’s no secret that Americans are paying more for less when it comes to healthcare []. In 2013, 17.1 percent of U.S. GDP was spent on a healthcare, far greater than any other high-income country, many of whom offer universal healthcare []. While there are many reasons for why healthcare is so expensive in the United States, one major driver of spending is the high administrative cost associated with the processing and payment of medical claims submitted by physicians to insurers [].
Long the bane of many a physician’s existence, the claims process begins with the submission of an insurer-mandated prior authorization form via phone or fax in order to obtain pre-approval for tests, medicines, and other necessary clinical services. The complexity of this labor-intensive process poses a tremendous burden on physicians, delays the delivery of necessary care for patients, and drives up what are already sky-high administrative costs []. While insurers believe this gate-keeping function reduces inappropriate healthcare spending and enhances patient safety by reducing unnecessary procedures, a 2012 study by the American Medical Association called into question the utility of spending an estimated $728 million dollars per year on prior authorizations since nearly all claims are approved []. As others sectors in the healthcare industry reap the benefits of era of “big data,” there is growing support amongst both payers and providers for a tool than can ease the prior authorization process with the hope of reducing the administrative burden involved while also improving the quality of care patients receive.
In order to fill this gap, physicians and insurers alike have turned to machine learning, the science of building algorithms that can complete tasks without being explicitly taught, in order to automate the process of prior authorization. A key example of how machine learning can be harnessed to reduce the hassle of prior authorization can be seen in Evicore’s “intellipath” system. Recently acquired by the nation’s largest pharmacy benefits manager for 3.6 billion dollars, Evicore’s system integrates directly into the electronic health record system where it automatically submits pre-populated prior authorization forms to any insurer the physician works with, thereby greatly reducing the need for manual data entry []. According to a recent study by Accenture, streamlining routine manual prior authorizations and other similar core administrative processes has the potential to save U.S. health insurers up to 7 billion dollars and benefit all stake holders in the healthcare system []. From the billing departments of major academic medical centers to the smallest physician practices and every payer they interact with, these tools can have an immediate impact on operating income and reduce the cost associated with the processing and payment of medical claims. For physicians, it means less time filling out paperwork and more time focusing on what matters, namely caring for patients. Taken together, these captured efficiencies can align financial and altruistic incentives by both reducing healthcare spending associated with administrative overhead while also improving the quality of care.
As more payers and providers move to adopt machine learning as a tool to automate the prior authorization process, new challenges to keeping the cost of healthcare down and ensuring that patients are receiving appropriate care will emerge. For instance, it is plausible that removing the gatekeeper function and making it easier to submit prior authorizations will actually lead to an increase in utilization and healthcare spending. Moreover, the success of an automated system depends on the integrity of the data set the algorithm is learning from. Due to limitations of the current state electronic health records, many physicians view these systems not as accurate repositories of patient information but merely as vehicles for billing. As a result, critical information that may be necessary for determining the clinical appropriateness of a test or procedure but not necessary for billing is often left out of the record. Without this information, machine learning may end up being another system where garbage in produces garbage out. That being said, further investment in machine learning as a tool to automate core administrative functions associated with medical billing can create significant value for our healthcare system by enabling physicians to provide better care to more people at a reduced cost.
[] Tyler Cowen, “Poor U.S. Scores in Healthcare Don’t Measure Nobels and Innovation,” The New York Times, October 5, 2006, https://www.nytimes.com/2006/10/05/business/05scene.html?module=inline, accessed November 2018.
[] Thomas Sullivan, “AMA’s National Health Insurer Report Card – $12 Billion Could be Saved Through Increased Claims Automation,” Policy and Medicine, July 18, 2013, https://www.policymed.com/2013/07/amas-national-health-insurer-report-card-12-billion-could-be-saved-through-increased-claims-automation.html, accessed November 2018.
[] Austin Frakt, “The Astonishingly High Administrative Costs of U.S. Health Care,” The New York Times, July 16, 2018, https://www.nytimes.com/2018/07/16/upshot/costs-health-care-us.html, accessed November 2018.
[] “Putting a price on the hassle of preauthorization,” American Medical News, January 21, 2013, https://amednews.com/article/20130121/business/130129986/6/, accessed November 2018.
[] Stephanie Baum, “As Express Scripts pays $3.6B for eviCore Healthcare, did Amazon make the PBM blink?,” MedCity News, October 10, 2017, https://medcitynews.com/2017/10/express-scripts-pays-3-6b-evicore-healthcare-amazon-make-pbm-blink/?rf=1, accessed November 2018.
[] Evicore, “Provider Solutions: Overview,” https://www.evicore.com/solution/pages/provider.aspx, accessed November 2018.
[] Rebecca Pifer, “AI can save US insurers $7B in admin costs, Accenture says,” Healthcare Dive, August 9, 2018, https://www.healthcaredive.com/news/ai-can-save-us-insurers-7b-in-admin-costs-accenture-says/529578/, accessed November 2018.