Artificial Intelligence (AI) is not a single technology, but rather a field of technology able to perform tasks that normally require human intelligence by using algorithms, heuristics, pattern matching, and other techniques within the realm of computer science. AI has been an area of significant interest for the health care industry for several years, and many health systems, payers, and life science organizations have (or plan to) incorporate AI and automation into their business strategy.
During a time of elevated physician burnout, Artifact is helping to ease administrative burden. “Giving providers faster, more convenient mobile solutions will be imperative as we redesign healthcare workflows with post-pandemic learnings.”
Iodine Software was interviewed at AHIMA 2020 on how HIM leaders can leverage AI and machine learning to reduce revenue cycle leakage. Iodine has pioneered a new machine learning approach called Cognitive Emulation™, and most recently launched the AwareCDI™ Suite. Listen to a recording of the interview here and read the full excerpt below.
AHIMA: Can you talk about the problems that Iodine is seeing when it comes to mid-revenue cycle leakage?
IODINE SOFTWARE: When it comes to the mid-revenue cycle, it’s critical that the full clinical picture as reflected in the evidence is correctly, accurately, and with detail documented, and then fully represented in the code. Unfortunately, this can cause problems due to the fact that humans are involved at every step, and that the underlying legacy software is focused only on workflows that aren’t holistically solving any of these problems.
- There aren’t enough CDI personnel to review every case every day, which is necessary to ensure documentation integrity.
- Even when pointed to and reviewing the right case, there’s a substantial loss of integrity at the point of decision to query.
- When the query is written, there are fall offs both in physician response and agree rates.
- And finally, there’s further loss of integrity at the coding step due to lack of clinical competency, poor communication, and failure to cross-connect evidence / documentation/code.
What this results in is lost “earned revenue”, which can significantly impact organizations.
AHIMA: Could you help us better understand the magnitude of this leakage?
IODINE SOFTWARE: Prior to the start of 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. For the average 250-bed hospital, that is $4.7-11M1 in revenue each year.
Today, the world is different. Complacency has been fast replaced by a new urgency, and the traditional approach to solving this problem — hiring more staff — is no longer feasible as highly trained and specialized staff to do clinical documentation are in short supply.
We can no longer afford to effectively ‘earn dollars’ only then not to realize them, solely because of unintentional, clerical and clinical human error in documentation and coding. Failing to get this right could mean the difference between positive or negative operating margin, which impacts our real mission – delivering the highest quality clinical care, sustainably.
With this new normal as our backdrop, finance leaders are looking at how to best leverage technology to do things differently – now – and ensure their organizations are financially resilient for the next decade and beyond.
AHIMA: Can you tell me about Iodine’s Cognitive Emulation approach, and what makes it different from others on the market?
IODINE SOFTWARE: Today, most healthcare technology solutions that support revenue cycle billing, coding and documentation teams use systems and workflows that “think” like computers – not clinicians. They leverage rules and check-lists, which only consider narrative documentation and can lead to unforeseen errors given the many nuances of the healthcare revenue cycle.
At Iodine, we take a different approach. Cognitive Emulation applies physician-like judgment to the clinical evidence in a patient’s chart and leverages previous learnings to more accurately determine the likelihood a condition exists. Conditions often present in a variety of ways, and by relying on clinical evidence rather than ambiguous thresholds, Iodine is able to identify and learn from these unique instances.
We’re the only organization with the capability of quantifying the magnitude of this problem with precision. And now, we’re the organization uniquely equipped to address it. For each of the leakage points that I talked about earlier, we’ve built and deployed software modules, with each one emulating clinical judgement to solve this earned revenue leakage problem. All these components seamlessly integrate in a unified suite that we call AwareCDI, and powered by our core AI/machine learning technology, Cognitive Emulation.
AHIMA: How could an HIM leader leverage the AwareCDI Suite?
IODINE SOFTWARE: One of our newest products, and an example of how we apply Cognitive Emulation to the mid-revenue cycle, is Retrospect. Retrospective reviews are often the last opportunity to resolve documentation and coding issues prior to final submission of codes for billing and quality reporting purposes. With up to 25% of post-discharge reviews resulting in meaningful education opportunities or code changes that can lead to revenue impact, this final inspection is business-critical. However, this would require the review of every single discharged record to ensure full integrity of each and every outgoing code—which is impossible to do without technology.
At Iodine, we ease the burden on CDI and coding teams by automatically reviewing every record prior to billing. Retrospect provides reconcilers with clear and actionable information to review the right cases at the right time, calling out specific opportunities to clarify documentation and/or final codes in order to improve review confidence and query quality.
We have several clients that are currently utilizing the first version of Retrospect, and the results are pretty amazing. What we are seeing in our early adopters is that about 30% of cases reviewed in Retrospect resulted in coding changes that impacted the final DRG. Through the use of our CognitiveML engine and prioritization, we were able to support a post discharge workflow that impacted final codes in greater than 60% of cases reviewed.
To learn more about Iodine and the AwareCDI Suite, click here.
¹ 2016 ACDIS Advisory Board Study
Working diagnosis-related group (DRGs) are utilized for a variety of functions across healthcare organizations, including case management, finance, and care coordination. In particular, working DRGs contribute to many system-wide forecasts, so accuracy is vital. This necessity is increasingly apparent during times of high uncertainty when the margin for error is even more slim than usual.
Today, the responsibility for generating concurrent working DRGs typically falls on CDI teams. This increases their already over-burdened workload and makes it difficult, if not impossible, for each patient to be assigned a working DRG. As a result, opportunity for DRG assignment and/or correction is often missed, decreasing the accuracy of both the overall clinical picture and DRG-based predictions.
Enter Forecast™, a fully-automated, clinically-driven module that assigns a final DRG prediction for every patient record, early in each patient stay, effectively obsoleting conventional working DRGs. Powered by the Iodine CognitiveMLTM Engine, Forecast automatically and concurrently predicts MS-DRGs for all inpatients based on the available patient record, including the clinical data and electronic documentation, even if no prior manual review has been performed.
In addition, geometric mean length of stay (GMLOS), relative weight, and other associated DRG information is included for each forecasted MS-DRG. This data can be leveraged for discharge planning, resource utilization, early identification of bundle DRG cases, revenue forecasting, and other relevant use cases. All Iodine Forecast data can be exported to and integrated with other systems, allowing it to be utilized by all relevant teams in their daily workflow and planning, and creating cohesiveness across the organization. All data is kept up-to-date from admission through discharge as new patient information is received.
Leveraging Forecast to help understand the reimbursement impact of COVID-19
Recently, Iodine applied its machine learning models to 60,000+ COVID-19 cases from more than 600 hospitals across the U.S. to better understand the reimbursement impact of COVID-19. In this analysis, Forecast enabled Iodine to consider all discharged patients without waiting weeks for final coded DRGs.
Timeliness of financial insights is critical–especially when navigating the challenges of a crisis such as COVID-19–and Iodine Forecast provides near-real-time access to the data that supports these insights. The Iodine Forecast Module enables hospitals to better understand trends such as the reimbursement impact of COVID-19 and develop strategies to get ahead of unforeseen challenges.
To learn how Forecast can empower your Case Management and Finance teams, reach out to email@example.com.
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. Given the unforeseen impact of COVID-19 on hospital revenue, healthcare finance leaders must now be even more protective of margins.
Retrospective reviews are the last opportunity to resolve documentation and coding issues for billing and quality reporting purposes. However, traditional reconciliation is inefficient and often ineffective for a number of reasons, including:
- Chart selection criteria that do not specifically identify all correction opportunities
- Manual processes, which force unnecessary chart reviews
- Complex, frequently changing documentation and coding guidelines
- Over- and under-documentation by physicians
- Understaffed CDI teams
- Strict productivity requirements for coders
Traditional approaches force clinical documentation integrity (CDI) specialists to unnecessarily review a significant number of charts in search of these opportunities, which extends DNFB to no benefit. Additionally, most CDI teams are unable to review every chart, leaving many documentation and coding issues unidentified prior to billing.
The broad scope of factors impacting CDI efficiency and accuracy require creative solutions beyond hiring more CDI specialists. Only technology can automatically review each record during the retrospective review process and direct CDI teams to the charts that are most likely to require their attention, allowing them to eliminate a significant, unproductive portion of their workload and focus on improving documentation integrity.
Iodine Software is redefining reconciliation through its Retrospect module, which eases the burden on CDI and coding teams by automatically reviewing every chart prior to billing. Iodine Retrospect combines the prioritization technology of Iodine’s SmartList™ with an integrated CDI review workflow for post-discharge records. Narrative documentation, clinical evidence, and patient demographics are all taken into consideration, allowing Retrospect to make accurate predictions and automatically identify both cases that do and do not require a review. Discharged patients are therefore prioritized based on their statistical likelihood of containing documentation opportunities.
Retrospect also helps quality-audit CDI staff. Residual documentation concerns are detected in records already reviewed by CDI teams, allowing CDI program leaders to isolate knowledge deficits and identify workflow and process opportunities to support continued CDI program growth and success.
To learn more about the Iodine Retrospect Module, contact: firstname.lastname@example.org.