It is no secret that the U.S. healthcare industry is a highly complex ecosystem, with payers, providers, PBMs and technology and services providers catering to vast swaths of mid/back office processes such as billing, claims, appeals, provider credentialing, re-imbursements and CMS reporting.
In this $3.5T industry, nearly 15% is consumed by these administrative tasks. Furthermore, of the 85% of healthcare spending that is associated with the actual costs of care, nearly 10% of that is wasted due to inefficiencies in late/inaccurate treatment authorizations, inaccurate coding, mis-diagnoses that result in over $300B per year in insurance denials.
At the root of this waste is the fundamental structure of healthcare activity, whereby physician diagnosis and treatment is largely delivered, and then validated, accounted for, and paid for/reimbursed by a increasingly complex mix of commercial payors, employers, individuals and/or the government. Furthermore, all of this activity is constrained by a dizzying matrix of coverages, plans, deductibles, and patient payments. And lastly, adding to this complexity, these rules, options, along with developments in medicine and treatment, are in a state of constant flux.
The traditional approach by the industry to navigate this mid and back office complexity is to throw people at the problem, with business process outsourcers, clearinghouses, billing departments, collections agencies, claims departments all trying reconcile patient treatment regimens and costs against approved and accepted treatments. Despite this, over- and under-payments/ reimbursements very frequently occur, and the healthcare provider is typically left holding the bag and trying to get proper credit for their services, equipment and drugs. They often pay the price in revenue realization, DSO and cash management, which for smaller ambulatory providers and physician practices, can be the kiss of death.
Now with advances in artificial intelligence (AI) and machine learning (ML), it is possible to create a new model, one that prospectively, gives providers revenue intelligence and assurance to lower claim denials, increase payment accuracy and timeliness. This is achieved by sifting and mining through clinical, financial and claims data to give providers relevant and accurate recommendations on billing codes, prior authorizations, and clinical data submissions to payors – all resulting in a smoother activity flow from treatment to payment, with fewer loops of re-conciliation and re-submission.
Glide Health (www.glide.health) is leading the way in this massive digital transformation in the healthcare mid and back office, with its AI-ML solutions for intelligent revenue operations, AR forecasting and financially personalized care. Glide Intelligent solutions help level the playing field for providers, enabling them to run more financially efficient practices, focus on the delivery of care to patients and support the overall healthcare industry objective of better patient outcomes and lower cost of care.
This mission of Glide is what drew me to the opportunity to lead it during this highly disruptive and transformative time in the healthcare industry. With the COVID-19 pandemic raging, requirements for greater price transparency and data interoperability across the healthcare value chain, all the industry participants are under immense pressure to make the overall healthcare experience much better for the most important participant of all: the patient.
Along with my co-founders and industry veterans, Bhupesh Bajaj and Dan Lodder, we’re building an organization to bring the immense power of AI and ML to these critical challenges in healthcare, an industry we are all so highly dependent on as a society.