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Best in Class Documentation: A Critical Way out of COVID-19 Financial Distress
5/28/2020
Prior to COVID-19, health systems were already operating on generally thin margins, with many finance leaders acknowledging that a significant root cause was leakage from their mid-revenue cycle and that “average performance” was still well below optimal results. Today due to COVID-19, there are several additional factors impacting hospital revenues, with health systems now facing inpatient reimbursement declines of up to 30% year-over-year:
- Patient volumes: On April 7, 2020, CMS recommended that all elective surgeries and procedures be limited to free up capacity for COVID-19 patients. Even as this restriction is removed in some states, limitations remain in effect across much of the country. Patient populations may also choose to defer elective surgeries out of fear. Given these challenges, the CARES Act’s aid to hospitals will not by itself keep hospitals and health systems afloat.
- Rising unemployment: The payer mix will further shift away from commercial coverage due to rising unemployment.
Recent analysis by the Iodine data science team found that as health systems exit the pandemic and COVID-19 admissions decrease, medical reimbursement is expected to decline. If surgical volumes do not recover, health systems could experience a second wave of financial distress.
To overcome the financial distress related to COVID-19, health systems need to explore revenue accretive solutions. The mid-revenue cycle contains significant untapped potential, with the average 250-bed hospital losing $4.7 million in mid-cycle revenue each year¹. One survey of healthcare leaders found that 84% believe clinical documentation and coding are at medium or high risk for errors leading to a negative impact on revenue². Accurate documentation results in revenue integrity, but even established CDI programs struggle to consistently capture all documentation opportunities. To realize the full potential of documentation integrity to decrease mid-cycle leakage and improve margins, CDI teams would need to review every single record continuously throughout the entire stay.
Iodine’s CognitiveML™ engine leverages proprietary artificial intelligence technology and machine learning algorithms to automatically analyze the full clinical record for each patient. Combined with a growing database, Iodine’s technology is able to identify more statistically relevant predictors of disease, helping teams quickly and accurately identify areas of potential opportunity to accelerate productivity, data accuracy, and financial return. This approach has helped hospitals realize a 75% increase in query volume.
As healthcare finance leaders seek creative solutions to slow mid-revenue cycle leakage, they should consider whether their CDI teams are armed with the right technology to be able to capture more revenue.
¹Advisory board study from 2016
²https://info.besler.com/hubfs/HIMSS%20Revenue%20Cycle%20Management%20Research%20FINAL%20SECURE.pdf?hsCtaTracking=b4b7691a-4990-48fd-ad6a-7585504a25d1%7C52d35be5-671a-4a59-ab80-123faf69c1c4
Prior to COVID-19, health systems were already operating on generally thin margins, with many finance leaders acknowledging that a significant root cause was leakage from their mid-revenue cycle and that “average performance” was still well below optimal results. Today due to COVID-19, there are several additional factors impacting hospital revenues, with health systems now facing inpatient reimbursement declines of up to 30% year-over-year:
Patient volumes: On April 7, 2020, CMS recommended that all elective surgeries and procedures be limited to free up capacity for COVID-19 patients. Even as this restriction is removed in some states, limitations remain in effect across much of the country. Patient populations may also choose to defer elective surgeries out of fear. Given these challenges, the CARES Act’s aid to hospitals will not by itself keep hospitals and health systems afloat.
Rising unemployment: The payer mix will further shift away from commercial coverage due to rising unemployment.
Recent analysis by the Iodine data science team found that as health systems exit the pandemic and COVID-19 admissions decrease, medical reimbursement is expected to decline. If surgical volumes do not recover, health systems could experience a second wave of financial distress.
To overcome the financial distress related to COVID-19, health systems need to explore revenue accretive solutions. The mid-revenue cycle contains significant untapped potential, with the average 250-bed hospital losing $4.7 million in mid-cycle revenue each year¹. One survey of healthcare leaders found that 84% believe clinical documentation and coding are at medium or high risk for errors leading to a negative impact on revenue². Accurate documentation results in revenue integrity, but even established CDI programs struggle to consistently capture all documentation opportunities. To realize the full potential of documentation integrity to decrease mid-cycle leakage and improve margins, CDI teams would need to review every single record continuously throughout the entire stay.
Iodine’s CognitiveML™ engine leverages proprietary artificial intelligence technology and machine learning algorithms to automatically analyze the full clinical record for each patient. Combined with a growing database, Iodine’s technology is able to identify more statistically relevant predictors of disease, helping teams quickly and accurately identify areas of potential opportunity to accelerate productivity, data accuracy, and financial return. This approach has helped hospitals realize a 75% increase in query volume.
As healthcare finance leaders seek creative solutions to slow mid-revenue cycle leakage, they should consider whether their CDI teams are armed with the right technology to be able to capture more revenue.
¹Advisory board study from 2016
²https://info.besler.com/hubfs/HIMSS%20Revenue%20Cycle%20Management%20Research%20FINAL%20SECURE.pdf?hsCtaTracking=b4b7691a-4990-48fd-ad6a-7585504a25d1%7C52d35be5-671a-4a59-ab80-123faf69c1c4
