Recent upturns in operating revenues haven’t shifted hospital finance executives’ laser-focus on revenue growth and retention, part of which is putting a stop to revenue leakage. That includes rooting out what are often unexpected sources as part of a holistic revenue cycle management (RCM) strategy – like inaccurate patient identification and information.
Patient misidentification issues cost the average healthcare facility $17.4 million per year in denied claims and lost revenue and cost the U.S. healthcare system over $6 billion annually. According to the Ponemon Institute, about 35% of denied claims incurred by hospitals each year can be attributed to inaccurate patient identification or inaccurate/incomplete patient information, adversely affecting both cash flow and AR days.
The financial impact of poor patient identification runs deeper than revenues. According to a survey from HIMSS and Patient ID Now, healthcare organizations spend an average of 109.6 hours per week resolving patient identity issues. Over half spend 21-80 hours per week and have an average of 10 full time employees dedicated to patient identity resolution. More than one-third said they spend more than $1 million annually on identification resolution, including the cost of full-time employee salaries and benefits, technology, and software.
Duplicates and Leakages
The revenue leakage problem is exacerbated when patient misidentification leads to duplicate or overlaid patient records. This can result in lost revenues when hospitals file claims or bill patients for the wrong amount and additional costs associated with correcting both the patient record and incorrect claim/bill, as well as increased days in A/R. This, in turn, slows cash flow and causes more bad debt write-offs. An incomplete medical record can also hamper efforts to correct and resubmit rejected or returned claims.
Duplicate and/or incomplete patient records can also hinder quality efforts and impact the metrics that drive value-based payment formulas. For example, incomplete patient records can lead to providers failing to recognize at-risk patients and missing opportunities to proactively schedule screenings or treatments, while incomplete information can contribute to clinical errors and adverse events that can reduce Medicare payments and/or reimbursements under value-based payment models.
Duplicates can also contribute to an increase in avoidable readmissions and related penalties, as well as impact patient satisfaction scores and make it more difficult for hospitals to accurately calculate quality metrics.
Duplicates and AI Investments
When considering the link between duplicate records and revenue leakage, it’s important to expand the focus to include the impact a compromised MPI/EMPI can have on investments into the latest health information management (HIM) and RCM technologies powered by artificial intelligence (AI), natural language processing (NLP), and machine learning (ML).
For example, AI enables automated analysis of patient chart contents and prioritizes those with the highest likelihood of requiring clinician queries for more targeted and impactful clinical documentation integrity (CDI) initiatives. When embedded into encoder and computer-assisted coding (CAC) software, AI suggests the most likely codes based on clinical indicators, which in turn allows coders to focus on validating or adjusting recommendations based on their review of appropriate chart elements. All of which trickles down to ensuring submission of claims at the highest appropriate reimbursement levels.
The value AI brings to the revenue cycle goes beyond addressing documentation and coding issues that cost the U.S. healthcare system an estimated $54 billion annually, however. It can also alleviate several significant administrative pain points by automating repetitive functions like insurance verification, claims submissions, and billing follow ups.
Equally important is the ability of advanced AI, ML, and NLP to harness and analyze the massive volume of healthcare data being collected daily by healthcare organizations. Overall, data analytics can be a highly effective strategy for reducing claim denials due to coding and documentation errors, which are the driving force behind an average annual loss of $5 million for hospitals and write-offs of up to 5% of a physician practice’s net patient revenue. Correcting these errors is urgent, as denial rates are trending upward.
According to Crowe RCA, 11% of claims were denied in 2022. That’s an 8% increase over 2021 and translates into approximately 110,000 unpaid claims annually for the average health system. Higher denial rates reduce revenues, slow the revenue cycle, and require additional resources to process corrections – a process that costs an average of $25 per claim for practices and $181 per claim for hospitals.
AI-enabled predictive analytics can identify performance gaps and coding/documentation issues that drive down reimbursements, drive up denials, and increase costs across the board. The result is an accelerated revenue cycle and healthier bottom line.
The reality is that the outcome of any AI investment will be directly impacted by the quality of the patient data flowing into the technology tools. A clean MPI/EMPI is foundational to a healthcare organization’s clinical and financial operations and a critical element of any AI strategy. Eliminating duplicates and overlays upfront and implementing complementary technologies to ensure the integrity of patient data going forward are imperative to realizing maximum ROI for the investment into AI-powered RCM technologies and optimizing their impact on the clinical and financial bottom lines.
The Clean MPI Impact
When it comes to stopping revenue leakage, protecting revenue integrity and optimizing the value of technology investments, an important element of any RCM strategy is a clean MPI/EMPI. Thus, a critical early step is to undertake a comprehensive MPI/EMPI cleanup coupled with deployment of technology tools capable of ensuring the integrity of patient data.
The ideal approach involves a combination of professional services and advanced technology to identify and resolve duplicate medical records that already exist within the MPI/EMPI and establish processes to help prevent new ones from occurring. This includes a thorough data assessment, followed by an expert determination of the most appropriate approach for identifying and correcting duplicates and other data integrity issues.
Once the MPI/EMPI is clean, deploy technology capable of catching and correcting future errors before they can lead to the creation of new duplicate and overlaid records. The goal should be technology that enables end-to-end MPI/EMPI protection by operating in multiple environments and at multiple stages throughout the patient record process.
The most impactful platforms use advanced deterministic and probabilistic matching algorithms to analyze and clean patient data before a record is updated or duplicate created – and before the misidentification can contaminate downstream systems including billing and collections. It should also integrate tools that address privacy and security concerns.
Ultimately, investing in a robust MPI/EMPI management system as part of a hospital’s RCM strategy delivers a rapid ROI by protecting cash flow and patient experience, providing accurate patient data to avoid costly delays and enabling more accurate management of high-risk patients with a longitudinal record. All of which stops revenue leakage and protects revenue integrity.
Rachel Podczervinski
Rachel Podczervinski, MS, RHIA, is vice president of professional services with Harris Data Integrity Solutions and a member of the AHIMA Board of Directors. The viewpoints expressed in the article are personal and are not being made on behalf of AHIMA.
