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RCM best practices and the potential for AI assistance


Practices need to have better analytics and more efficient workflows from the start to the end of the Revenue Cycle Management (RCM) pipeline.

revenue cycle management | © thodonal - stock.adobe.com

© thodonal - stock.adobe.com

Healthcare providers have long struggled to get paid for the services they render, but today’s reimbursement landscape makes past challenges pale in comparison. Payors continue to seek ways to deny claims and delay physician enrollment, while the after-effects of the pandemic on staffing and finances also continue to linger.

The challenges that healthcare organizations (HCO’s) face in collecting revenue from payors for serving patients require thoughtful solutions to both staffing shortages and technology shortcomings and adoption issues.

As organizations continue to have better data at their fingertips, they need to have better analytics and more efficient workflows from the start to the end of the Revenue Cycle Management (RCM) pipeline.

Three or four years ago, the biggest challenge was getting data from multiple clinical systems. Now, as more organizations are operating on one system, with one database, they have the data – but what are they going to do with it? For starters, it is necessary to better manage the available data and look for trends and systemic issues. It is also necessary for organizations to become more deliberate and timely with their analysis to make sure they are more quickly noticing the cause-and-effect relationships.

For instance, in a low-margin business such as healthcare, it is all about the details. As an example, if you can improve self-pay collections, that may translate to 1-2% improvement in overall performance. If providers are undercharging by 10% and you can help them correct that deficit by half or more, while maintaining compliance, that becomes a significant intervention.

Insights into physician productivity are limited

One of the most elusive components of productivity projects is the ability to perform predictive staffing based on historical and real-time data. Many physician practices could make smart use of the ability to integrate seasonal patient volume with current trends to tune tomorrow’s staffing matrix. If they integrated disparate datasets – such as physician scheduling, encounters, and charge distributions – they would also gain the insights needed to inform rational compensation plans.

Unfortunately, a significant proportionof organizations do not have sufficient insight into the metrics that drive physician productivity. This is a critical blind spot given the ongoing pressure on finances and the high-cost of physician labor.

Potential impacts of artificial intelligence (AI)

Across the industry, many are optimistic that natural language processing (NLP) and AI can introduce meaningful efficiencies and analytics to the practice of medicine and the business of healthcare.

Technology can free clinicians from repetitive, often spirit-breaking tasks and return that time for direct patient care—improving the margin and furthering the mission of many organizations as a result.

A broad range of clinical and revenue cycle functions could be impacted by AI solutions:

  • Eligibility, Benefits Verification, and Scheduling
  • Registration and Authorization
  • Treatment, Decision Support, and Transitions of Care
  • Charge Capture
  • Coding and Billing
  • Payment and Collections

In general, the benefits of AI in revenue cycle management (RCM) derive from improved timeliness, efficiency, accuracy, and predictive analytics and satisfaction. From less required “scrap and rework” to fix errors, to decreased complexity and delay for staff to follow-up on issues that inevitably still require person-to-person negotiation and resolution, there is reason to believe AI will help streamline operations.

Great potential, but not yet reality for most
Recent industry research has shown that though a small proportion of organizations currently use AI in RCM, most of them believe AI will play a larger role in the future. Adoption will likely depend on the size of the organization, with larger teams making the leap in the nearer-future.

As the capabilities of AI evolve and become clearer, organizations who understand how and where to use it are likely to benefit in both operational and financial performance. Organizations of all sizes will want to stay informed about the use cases that apply to them and involve stakeholders across their clinical, operational, and financial departments from the beginning.

Jason Stein, MD is the Chief Medical Officer and co-founder of Ingenious Med. Ingenious Med delivers easily implementable mobile and web solutions that improve physician productivity and hospital performance at the point of care.

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