Data quality is the top barrier to AI in revenue cycle management, according to a report from Black Book Research.
Black Book surveyed 149 revenue cycle leaders between Nov. 1-11 to examine how frontline teams “experience AI and analytics in practice, specifically, how ‘garbage in/garbage out’ issues affect adoption, trust, and impact,” according to a Nov. 20 news release.
Six things to know:
1. Seventy-four percent of revenue cycle leaders said poor data quality is a significant or critical barrier to successful use of AI and analytics. Leaders cited issues including coding inconsistencies, missing fields and timing problems in charge data and documentation.
2. Another top challenge is technology partners focusing on capabilities rather than foundations. Sixty percent of respondents said their major RCM and analytics partners avoid or gloss over data quality problems in joint initiatives. Just 20% said they feel these partners address data quality well or very well.
3. Sixty-six percent said that RCM data quality and governance work is unfunded or minimally funded while budgets are concentrated on tools, licenses or models.
4. Sixty-nine percent of revenue cycle leaders surveyed said that clinical and financial documentation in their system is shaped more by reimbursement and compliance requirements than by clinical or operational reality.
5. Seventy-six percent said integration between RCM AI/analytics tools and core EHR systems is a major or severe challenge. Those challenges included interface complexity, data mapping issues and breakage during system upgrades.
6. “What we’re hearing from revenue cycle leaders is not resistance to AI,” said Black Book founder Doug Brown. “They’re saying the next wave of progress will come less from adding more algorithms and more from doing the unglamorous work on data quality, governance and integration. Until that’s addressed, the risk is that AI becomes another promise that doesn’t match the reality on the RCM floor.”
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