Although it's increasingly less often, every once in a while, I get asked to send a fax somewhere. A fax? And for a moment I wonder how anyone, anywhere could still be relying on faxes for a "job to be done." I hope they aren't still using those thermal machines that would roll up the paper and drop it on the floor . . . but I wonder if maybe they are.Not only does someone want to receive a fax, I have to find a way to send one. I still chuckle at the marketing of the "3 in 1" printers still sold today, amused by the incorporation of a Flintstone-era functionality with this otherwise relatively useful device.
My office for the last 10 years still had a fax, and I think I saw someone using it in 2014. I only noticed because it was beeping incessantly with error messages as I walked by. Poor soul forgot to dial 9 or 1 or did...something.
Kidding aside, it never ceases to amaze me that the business of prior authorizations in healthcare, whether drugs, procedures, or other needed equipment or services—the interminable back and forth between highly-trained physicians and payers—is still stuck in the stone age of facsimile machines. And we wonder why healthcare is not keeping up and is becoming increasingly unaffordable.
It's refreshing to see another rallying cry from the AMA to get a hold of this madness.
The AMA has drawn needed attention to the tremendous waste of providers' time, and even more tragic, the delays in patients receiving the care they need: "Nearly 90 percent of surveyed physicians reported that prior authorization sometimes, often, or always delays access to care." Their Consensus Statement, crafted with input from physicians, pharmacists, medical groups, hospitals, and health plans does provide a glimmer of hope that meaningful changes will be made to reduce the costs and the delays deeply embedded in these well-worn pathways.
The value of solving this problem and the emergence of machine learning and artificial intelligence advancements in computing creates a tremendous opportunity, and for the investment made, an opportunity to return benefits to both provider and patient.
McKinsey & Co. published a thoughtful article on artificial intelligence this month, endorsing an early focus on "enterprise micro-verticals—specific use cases within select industries." Such is the approach taken by Digitize.AI in the prior authorization sphere of the healthcare revenue cycle. McKinsey points out that "buyers aren't interested in AI just because it's an exciting new technology—instead, they want AI to generate a solid return on investment (ROI) by solving specific problems, saving them money, or increasing sales."
Nothing could be truer of healthcare providers, eager to solve specific problems on their journey toward the triple aim of better outcomes and an improved patient experience at a lower cost. If a patient is denied medication due to a glitch in the prior authorization process and then elects not to comply with recommended medications, the system has experienced a triple-fail.
During nearly 30 years working for and within large healthcare systems, I witnessed firsthand the tremendous need for innovation to reduce costs, making healthcare more affordable and easier to navigate while freeing up good people from mindless tasks that keep them from operating as we say "top of license."
I am inspired by innovation, and I don't fear that automation will endanger jobs. Rather, I view it as an opportunity for people to do more creative and rewarding work, the inevitable result of shedding critical but less inherently gratifying tasks. Consolidation and automation will bring greater labor efficiency over time, allowing organizations to meet the pace of growth without commensurate growth in the labor required to keep the wheels moving.
I'm encouraged that innovative automation engineers are discovering multiple points along the healthcare revenue cycle that are ripe for machine learning, allowing a reduction of administrative costs and an improvement in the cycle time to patients getting care.
Digitize is proudly introducing Lia™, a healthcare revenue cycle digital assistant that automates routine tasks such as launching a prior authorization or verifying coverage with the payer interface. Lia also uses machine learning to create an intelligent queue so that human workers can prioritize highest efforts (timeliness of care and revenue impact).
And let's put an end to faxes!