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文章总结
Health systems rely on effective revenue cycle management to follow the patient journey, navigate claims, and ensure the organization collects payment for its services. In today’s complex and fluid healthcare industry, in which revenue cycle management is about much more than billing and collecting payment, traditional revenue cycle approaches can’t meet escalating demands. Additionally, with lost volume due to COVID-19, organizations can’t afford to miss an opportunity for payment.
当代医疗保健领域需要一个全面、标准化和数据驱动的收入周期流程。Health systems that leverage data to support revenue cycle management improve their financial outcomes in three significant ways:
1. Reduce denials.
2. Increase collections with propensity-to-pay insight.
3. Improve discharged-not-final-billed efforts.
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Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Operating Margins
Marlowe Dazley
Senior Vice President and Managing Director of Financial Advisory Services
在医疗保健行业，患者护理往往是最重要的，但许多幕后活动使每一次相遇、每一次手术和每一个生命都成为可能。例如，医疗保健收入周期管理是组织中保持业务运行并为社区服务的一项关键功能。收入周期包括从创建患者账户到为医疗保健服务收取费用的过程，以及其间的每个步骤。因此，卫生系统依靠有效的收入周期管理来跟踪患者的旅程，处理索赔，并确保组织为其服务收取费用。
今天的收入周期管理不仅仅是开票和收款。随着医疗保健行业的发展，管理收入周期的传统、耗时(通常是手工)的方法需要太多的资源，并留下了不可接受的误差空间。同时，开箱即用的收入周期解决方案不允许定制或集成付款人和EMR数据。2022卡塔尔世界杯赛程表时间
当代医疗保健领域需要一个全面、标准化和数据驱动的收入周期流程。此外，由于COVID-19造成的销量下降进一步增加了对收入周期管理进行转型的必要性，因为组织不能错过支付的机会。
Three Ways Data Improves Healthcare Revenue Cycle Management
Health systems that leverage data to support comprehensive and accurate revenue cycle management improve their financial outcomes in three significant ways:
Reduce Denials
Within the revenue cycle process, claim denials cost each healthcare provider an average of$5 millionevery year. Organizations can recover some of this loss by using an analytics solution to integrate data from multiple sources to understand the denials’ causes. With such insight, health systems can implement denial prevention plans and procedures for recovering the denials.
Billings Clinic, for example, found opportunities to reduce its denials when it pinpointed the sources of the denials. The organization leveraged the Health CatalystData Operating System (DOS™)platform and a robust suite of analytics applications to implement a revenue cycle analytics application. The resulting insights informed data-driven improvement efforts that have yielded positive outcomes, including a $4.5 million reduction in denials in just 12 months, resulting from an 8 percent relative reduction in overall denial dollars.
Increase Collections with Propensity-to-Pay Insight
With patients responsible for an increasing amount of their healthcare costs, self-pay accounts are now the top contributor to bad debt for hospitals and health systems. Bad debt accounts for more than$55 billionin healthcare revenue loss annually. Health systems need strategy-driven processes for patient collections—in other words, a reliable propensity-to-pay predictive model—to get ahead of bad debt.
Allina Healthleveraged DOS to navigate its bad-debt challenges by creating a predictive model to support a propensity-to-pay strategy. The resulting propensity-to-pay machine learning model uses artificial intelligence to predict the probability that the patient will pay their bill during the month in question. Propensity-to-pay predictions have helped Allina increase overall collections by $2 million in just one year, including collecting more than $660,000 in additional patient payments during the strategy’s first two months.
Improve Discharged-Not-Final-Billed Efforts
Managing discharged not final billed (DNFB) cases, where bills remain incomplete due to coding or documentation gaps, is one important way hospitals can improve revenue cycle performance. However, without analytics to support efforts, meeting a target for DNFB improvement remains a serious challenge.
Thibodaux Regional Medical Centerinvested in analytics and resources targeted at improving its DNFB rates. The health system deployed a DNFB analytics application on top of the analytics platform (DOS) to leverage the clinical, financial, operation, claims, and other data aggregated in the analytics platform. The analytics applications enabled quick and easy access to the information Thibodaux Regional needed to effectively manage its DNFB efforts and create a more efficient workflow process for coders and physicians. Two years after the organization launched its initial DNFB improvement effort, it has achieved $1 million in additional annual reimbursement and a 66.7 percent relative reduction in DNFB dollars, significantly improving cash flow.
Data and Analytics Reveal Critical Revenue Cycle Management Opportunities
如今，收入周期管理必须应对组织日益复杂的局面，同时在2019冠状病毒病等紧急情况下保持财务健康。卫生系统可以通过强大的分析来满足这些需求，这些分析可以帮助它们识别改善新闻流的机会，并根据数据制定战略，以在组织发展和挑战出现时持续改进。
Additional Reading
Would you like to learn more about this topic? Here are some articles we suggest:
- Healthcare Revenue Cycle: Five Keys to Financial Sustainability
- VitalCDM™ Ranked #1 in KLAS Revenue Cycle, Chargemaster Management Category for Third Year in a Row
- The 2021 Healthcare Financial Forecast: What to Expect, How to Prepare
- How to Optimize the Healthcare Revenue Cycle with Improved Patient Access
PowerPoint Slides
Would you like to use or share these concepts? Download this presentation highlighting the key main points.
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