Flourishing in the Current Financial Climate

The past few years have seen the further growth of longstanding macroeconomic challenges to which no hospital system is immune. From inflation, both generally and in wage growth, to labor shortages, particularly in nursing, to the continued impacts of the global pandemic, providers everywhere are familiar with the headwinds facing the industry. In a September 2022 report, McKinsey & Company referred to the circumstances healthcare systems face as a “gathering storm,” and no wonder: McKinsey suggests that inflation alone could add an additional $370 billion in healthcare spending above the expected baseline by 2027, with endemic COVID-19 adding another $222 billion to that increase.1  On top of these major forces growing costs, accelerated salary growth, turnover, and shortages in labor make the environment even more difficult.

Passing on costs and hiring are unlikely to fix the problem

With headwinds like these facing the industry, to whom can the burden of increased healthcare costs be passed? Employers, facing their own financial pressures, are unlikely to foot the bill, with 95% of employers stating they “would pass along any cost increase greater than 4 percent per annum to employees.”2 Patients themselves face challenges, with “more than 20 percent of consumers report[ing] having more than $1,000 in medical debt…will have difficulty absorbing these higher costs for much longer.”3 According to a WebMD survey, almost 7 in 10 Americans have deferred care due to a lack of affordability.”

The government doesn’t look to be in the best shape to step in, either. As McKinsey points out, “a range of factors indicate that it may be difficult for the government to absorb the additional medical-cost burden.”5 The United States is experiencing inflation rates that haven’t been seen since the 1970s, healthcare spending represents a record 20% of GDP6, and federal responses to the COVID pandemic drove the largest federal budget deficits ever in 2020 and 2021. Add on top of this a narrowly split Congress, and substantive progress on tackling increased healthcare expenditures is an uphill battle. 

Providers across the industry “cited revenue cycle management as a top priority for the next year, pointing to a broad set of specific priorities, including revenue integrity, charge capture, and complex claims, and underscoring a robust set of RCM needs across the provider ecosystem.”7

For systems who have managed to maintain strong enough finances to hire heavily, critical staff just aren’t there for the taking. An analysis on labor market data revealed a potential shortage of 3.2 million healthcare workers by 2026. This healthcare labor crisis cuts across a number of job categories, but is especially serious in nursing, with the United States facing a potential shortage of 200,000 to 450,000 registered nurses by 2025. By this time, the shortage of physicians could reach 50,000 to 80,000 physicians. This has major implications not only for care, but for areas directly impacting revenues, like clinical documentation integrity (CDI) and utilization management (UM).

In short, generating additional revenue by increasing costs and counting on someone else to pick up the tab is far from a secure bet, and hiring to solve the problem will remain an immense challenge. How, then, can healthcare leaders respond?

Systems can use technology to capture more revenue for the work they’re already doing

Clinician shortages and economic pressure are driving demand for solutions that enable existing teams to be more productive, efficient, and drive ROI. In an October 2022 report jointly developed by Bain & Company and KLAS Research, the authors found that over the past year, 45% of providers accelerated software investment, with only 10% slowing down and “forward-thinking providers doubling down on technology roadmaps.” Providers across the industry “cited revenue cycle management as a top priority for the next year, pointing to a broad set of specific priorities, including revenue integrity, charge capture, and complex claims, and underscoring a robust set of RCM needs across the provider ecosystem.”7

Not all solutions, however, are equal to the task at hand, and among the challenges providers face in responding to financial headwinds are vendor proliferation and an increase in tech stack complexity. Numerous solutions claim to solve for the pains ailing hospital systems, but in the current climate, an acute focus on ROI and long-term financial peace of mind is key. Systems can’t invest indiscriminately in technology for technology’s sake. Rather, a challenging financial landscape makes judicious, careful decisions on technology all the more important. Deciding on a nascent solution without proven ROI or a second-tier solution to save a few dollars up front can have real consequences.

Iodine is focused on driving real ROI to help systems find long-term financial resiliency

Throughout our history, Iodine has developed solutions with real-world, high-level value in mind. Our tools help systems go beyond merely weathering McKinsey’s “gathering storm” to something better: future readiness. We don’t merely contribute to financial stability, but serve as a full partner in revenue enhancement to help you capture the revenue you’ve already earned and achieve lasting, big-picture financial peace of mind. We do this by using our clinical machine learning AI suite of solutions to help staff spend their time on the work that most benefits from their skill and attention. 

This isn’t just a long-term play, either: Iodine solutions deliver value at speed. For example, a five-hospital system in the mid-Atlantic with 94k+ admissions saw a first-month financial impact of $2.2 million, with $27.1 million in annualized impact. This big-picture value is made possible by improvements to key metrics in the functions we support. Programs powered by Iodine solutions drive increased output per FTE, with higher query volume. Iodine-supported CDI programs saw a median productivity lift of 134% in our 2021 Productivity Cohort Analysis, with improvement seen in 92% of facilities. One four-hospital system in the Southeast saw major improvements to their CMI, with 9.8% growth in surgery and 14.6% overall within the first six months after implementation. In short, we have a proven track record of delivering in concrete ways on the promise of our solutions: driving real, financially meaningful ROI.

While the challenges that face healthcare systems aren’t going away, neither are we. We’re excited to build on the work we’ve done with our current clients to help as many providers as we can flourish, no matter the financial climate.

1 “The gathering storm in US healthcare: How leaders can respond and thrive,” McKinsey & Company, September 2022.
2 “Employers look to expand health benefits while managing medical costs.” McKinsey executive survey from July 2022
3 McKinsey Consumer Healthcare Insights, February 2022
4 “Cost of Medical Care Leads to Delays for Many Americans: Survey”, WebMD, May 2022
5 “The gathering storm in US healthcare: How leaders can respond and thrive,” McKinsey & Company, September 2022
6 National Health Expenditure Data: Projected, Centers for Medicare & Medicaid Services, April 27, 2022
7 “2022 Healthcare Provider IT Report: Post-Pandemic Investment Priorities,” Bain & Company, Inc. and KLAS Research, October 2022

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The Truth About Organizational Definitions

By Cheryl Ericson, MS, RN, CCDS, CDIP

Let me start by saying I’m a proponent of organizational definitions. I have long advocated them as a Clinical Documentation Integrity (CDI) best practice, but I do think their purpose is frequently misunderstood. Although we like to think organizational definitions are a tool to minimize denials, they are really just an organization-wide strategy to promote consistency and have little to no bearing outside your organization.   

Contrary to popular beliefs, Centers for Medicare & Medicaid Services (CMS) does not “define” conditions like sepsis or malnutrition or morbid obesity. CMS provides guidance around when a particular condition is considered medically necessary so it will be covered by Medicare through National Coverage Determinations (NCDs) (e.g., gastric bypass defining morbid obesity as a BMI of 35 with the presence of complications due to morbid obesity) or Local Coverage Determinations (LCDs). But these NCDs and LCDs do not necessarily “define” the referenced conditions when it comes to publicly reported data. The same is true for quality measures adopted by CMS.  

The CMS Quality Measure titled “Severe Sepsis and Septic Shock: Management Bundle” supports best practice for the treatment of severe sepsis and septic shock which includes processes associated with Sequential Organ Failure Assessment (SOFA); however, the population eligible for this measure is defined by the assignment of either a sepsis or severe sepsis code. If you need evidence of a lack of CMS guidance defining a particular condition, look no further than the recent Office of the Inspector General (OIG) findings related to severe malnutrition where the OIG audited cases to “determine whether providers are complying with Medicare billing requirements when assigning diagnosis codes for the treatment of severe types of malnutrition on inpatient hospital claims”.  It is even hard to pin down commercial payers other than CMS when it comes to defining conditions. Often organizations receive information informing them a diagnosis was removed with little, if any explanation, of the criteria used to make that determination.  

So why bother with organizational definitions? To promote consistency across physicians, CDI professionals and Coding professionals. Often organizational definitions are a great way to engage physician leadership so they can become CDI advocates and to train CDIs. The reality is that making a diagnosis is a complex process and there is often disagreement across providers treating the same patient. Something else to consider is whether or not organizational definitions are too stringent and promote under-coding within an organization which can negatively impact financial goals. Just think about the debates that are occurring within the CDI profession over the use of Systemic Inflammatory Response Syndrome (SIRS) criteria vs. SOFA criteria for sepsis. Many organizations made the shift to SOFA criteria when it was first released only to return to SIRS criteria after the volume of sepsis cases decreased within their organization due to the stricter criteria. And what about those diagnoses that don’t have an organizational definition? What criteria should be used? Are we getting too bogged down in discussions about how to define a condition that we can’t see the forest from the trees? In fact, the whole concept of clinical criteria was such an issue within the CDI and Coding professions that the Official Coding Guidelines added the Coding Assignment and Clinical Criteria guideline a couple of years ago:  

“The assignment of a diagnosis code is based on the provider’s diagnostic statement that the condition exists. The provider’s statement that the patient has a particular condition is sufficient. Code assignment is not based on clinical criteria used by the provider to establish the diagnosis.” (ICD-10-CM Official Guidelines for Coding and Reporting (FY 2021), Page 12 of 126).

Although this guideline was intended to provide clarity, I really think it just added another layer of confusion as some organizations mistook this advice as an excuse to stop clinically validating documented diagnoses. Really, this guideline only separated the coding function from the medical necessity function because those lines were getting blurred; however, the medical necessity requirement is still alive and well as demonstrated by OIG audits.  

It was once believed that organizational definitions could be helpful from a compliance standpoint, but that only occurs when the definitions are consistently used across all payers and for all situations. Unfortunately, what I’ve seen over the years is that CDI professionals often have one rigorous set of criteria they use before querying for a potentially missing diagnosis and a different threshold for clinical validation e.g., if the provider documents a diagnosis based on limited criteria. I get it, most CFOs view CDI departments through a financial lens and few CDI departments want to be responsible for removing a diagnosis complication/comorbidity (CC) or major complication/comorbidity (MCC), but this inconsistency is confusing and increases compliance risk.  

So that begs the question, “Have organizational definitions outlived their usefulness?” Perhaps it is time to re-evaluate the purpose of organizational definitions. Are they resulting in more harm than good? In particular, perform an analysis to determine if organizational definitions are yielding the desired impact, which is typically fewer denials. In addition, determine if organizational definitions have actually become a liability due to inconsistent application, or potentially leading to under-coding of valid diagnoses. Organizational definitions can be a great educational tool for CDI professionals, Coding professionals and providers, but they often have limited practical application outside of your organization, so be sure to evaluate their usefulness on a regular basis.  

Why You Need to Start Looking at Your Mid-Revenue Cycle for Lost Revenue

Originally published on Becker’s Hospital Review: https://www.beckershospitalreview.com/finance/why-you-need-to-start-looking-at-your-mid-revenue-cycle-for-lost-revenue.html

By: William Chan, CEO, Iodine Software

84% of healthcare leaders believe the root cause of lost or decreased revenue is inaccurate clinical documentation and coding.* But the challenge of managing a more accurate mid-cycle is that this accuracy is fundamentally driven by ensuring that the patient’s full clinical picture (as reflected in the evidence) is correctly documented in detail, then fully represented in the codes.

And despite massive investments in documentation/coding programs, earned revenue loss continues to persist — to the tune of $5-11M in leakage for an average 250-bed hospital1

Where and Why Leakage Happens 

The overall leakage problem is a combination of the fact that humans are involved at every step, and that most software is focused only on workflows and not solving any issues that may arise along a given workflow. Why leakage happens: 

  1. There are not enough clinical documentation integrity (CDI) resources to review every case, every day, which is necessary to ensure documentation integrity. Being able to identify discrepancies between clinical evidence and documentation is the first step in minimizing mid-cycle leakage.
  2. Even when CDI teams are pointed to and reviewing the right cases, there’s a substantial loss of integrity at the point of decision to query. The reason for this is multifold — a lack of competence or confidence, a worry about physician response, or a concern about potential for impact. Regardless, the result is that CDI specialists are frequently deciding not to query even when there is a clear inconsistency between evidence and documentation.
  3. When the query is written, there are fall-offs both in physician response and agreement rates far in excess of what would be expected given the confidence in the root evidence. The causes here are again numerous: poor relationships between physician and hospital, poorly constructed or supported queries, lack of understanding in the importance and impact of better documentation, and a lack of ease of use in reading, interpreting and responding to queries can all drive down integrity at these steps.
  4. Finally, there’s an alarming loss of integrity at the coding step. Lack of clinical competency, poor interaction and communication with CDI, and failure to cross-connect the implications of evidence, documentation, and code can all be drivers of lost opportunity at this step.

How You Could Fix This — If Money and Resources Were Unlimited

With unlimited resources, it would likely be quite easy. For example: 

  • You would provide continuous education and resources to ensure physicians were doing their best to document at the point of entry.
  • Then you would review every single case, fully, every single day, during the patient’s stay with only the most qualified, tenured and experienced CDI specialists in the market.
  • Then, you’d find the easiest, most seamless way to transmit queries to the physicians in a way that worked in their normal workflows and was a minimal burden — perhaps even a pleasure — to drive quick responses. And you would query everything.
  • And then, finally, once everything was coded, you would look at every single case again— and not just for DRG mismatches, but for any documentation opportunity lost…and you would again use only the best retrospective CDI specialists with experience and strong tenure.

However, no health system has the resources to deploy these four strategies, and most legacy software solutions are not even capable of finding many financial and quality accuracy improvement opportunities because: 1) they cannot determine when patient information that is supported by medical data is not written in a patient’s chart, and 2) they cannot perform clinical validation, in which clinical evidence does not support something that has been documented, which increases the risk of audit.  

Machine Learning: A New Way to Address Mid-cycle Leakage

As Iodine started further examining ways to stop mid-cycle leakage, we realized that current solutions and technologies did not solve the problem. The objective of CDI is to determine whether the written documentation aligns with a patient’s clinical reality, and this requires more than tools with natural language processing (NLP) alone. Instead, this is where machine learning comes in. Machine learning is capable of considering the entire clinical picture and can make connections and predictions based on learnings from other data including lab results, vital signs, medications, radiology results, and other sources.

Iodine has built proprietary artificial intelligence technology and machine learning algorithms that “think” the way a clinician thinks and emulates clinical judgement. We call this approach Cognitive Emulation

The result of combining NLP and machine learning technology is a solution that evaluates complex medical data similar to a physician’s approach to diagnosing and treating patients.

Iodine applies physician-like assessment to the clinical evidence in a patient’s chart and leverages previous learnings to more accurately determine the likelihood a condition exists.

Results Across 500+ Hospitals 

We started with Concurrent™ — our first product, designed to increase query rates by prioritizing records that contain inconsistencies between clinical evidence and documentation — all without the inherent limitations of rules-, marker- and NLP-based approaches.

Our customers’ results with Concurrent have been impressive to date: 

  • $1.5 Bn additional appropriate reimbursement recognized annually²
  • $2.55MM additional incremental revenue captured on average (per 10,000 admissions, $6,000 average base rate and 70%/30% med/surg split)² 
  • 86% of clients experienced a growth in query volume² 
  • 21% increase in MCC capture volume² 

We recently released the AwareCDI Suite to identify and capture additional mid-revenue cycle leakage, beyond the Review and Query stages, all the way through to final billing. You can learn more about AwareCDI here

*HIMSS and Besler Revenue Cycle Management Research Report- Insights into Revenue Cycle Management October 20

1  2016 ACDIS Advisory Board Study 

2 Figures are based on a $6000 modeled base rate and actual measured MCC capture performance from the 2019 Iodine Performance Cohort Analysis of 339 facilities that compared measured MCC capture and CMI impact for the Iodine usage period 9/1/2018-8/31/2019 against pre-Iodine baseline performance.

Finding All Queries — not Just the “Right” Query

By: Fran Jurcak, MSN, RN, CCDS, CCDS-O, Chief Clinical Strategist

The goal of a quality Clinical Documentation Integrity program is to support documentation that identifies ALL of the conditions being monitored and treated during a patient encounter. Clear and simple. Yet many CDI Specialists (CDIS) in our profession spend significant time “shopping” for queries that only impact financial or quality outcomes (SOI/ROM). Not only does this waste significant and valuable CDI time, but the true goal of CD work is to ensure documentation integrity of the complete medical record.

If the clinical evidence supports a condition that is not clearly and consistently documented, best practice would dictate that communication with the provider should occur to ensure accuracy of the medical record. Period. For documentation integrity, the impact of the intervention shouldn’t matter. The need to accurately capture documentation of all the conditions that were assessed, evaluated and cared for in the medical record is essential to accurate coding of the care provided and results in accurate payment and reporting of quality metrics.  

CDI Specialists spend countless precious minutes searching for queries with impact. Thinking that the only way to engage providers is to only “bother” them with queries that matter is short sighted and misses the bigger picture of the value of documentation integrity. In a world with diminishing resources and where integrity of the medical record is so important to capturing true acuity of every patients’ condition, every query opportunity to support documentation integrity is vital.  

If the documentation does not reflect the clinical evidence in the medical record, any and all queries should be communicated with the provider. Picking and choosing the “right” query creates inconsistency that is not only confusing to providers but allows for inaccurate coding and reporting, which can lead to incorrect reimbursement and poor performance in quality metrics.   

Let’s also talk about the time wasted on searching for the right query.  Knowing that resources are limited and time is short (average length of stay is typically below 5 days) there isn’t time to spare for a search-and-seek mentality. CDI Specialists need to stay focused on the job at hand and not spend upwards of twenty minutes hunting for the query that drives a particular metric. All conditions being cared for and monitored should be appropriately documented in the medical record so they can be accurately coded and reported. 

During the current COVID-19 pandemic, we discovered that across our clients, query rates on COVID-19 patients are 33-42% higher than on non-COVID-19 patients. It’s not likely that provider documentation is materially worse for these patients than others. Rather, this seems to point to the fact that due to the perceived need for greater scrutiny on these records to capture all appropriate comorbid conditions for accurate reimbursement and reporting, CDI staff are actually querying for every co-morbid condition. Why just these patient records and not all patient records?  

Many CDI professionals spend additional time concurrently coding the record thinking this will assist them in identifying a query opportunity. While there may be other good reasons to concurrently code a record, doing so to specifically identify impactful query opportunities is often wasted effort as the impact of a query may change due to additional documentation that results later in the stay.  For example, consider a CDIS that spends time searching for a condition that will impact the DRG, say a Major Comorbid Condition (MCC).   So, the CDS searches for a condition that qualifies as an MCC and queries that condition based upon the clinical evidence but does not query other conditions that are also clinically supported but do not qualify as MCC.  Then, later in the stay an additional MCC condition becomes accurately documented, which changes the impact of the query.  The end result is wasted time concurrently coding, often leading to missed query opportunities on other conditions and subsequent negative impact to  accurate documentation, coding and reporting.  If a condition is being monitored and/or treated and not clearly and consistently documented, the documentation should be clarified regardless of the potential impact.

It’s important for CDI Specialists to  utilize their clinical expertise and judgment to determine if there are documentation integrity concerns and communicate with providers to resolve those concerns. While basic knowledge of code language and coding guidelines is important to assist in accurately capturing documentation, CDI professionals should be assessing the clinical evidence in the medical record to identify missed or inaccurate documentation of conditions to support the integrity of medical record documentation. How the condition final codes should be driven by the documentation and completed by professional coders.

So the catch line is this:  While it is important for CDI Specialists to understand coding language and be able to identify appropriate codes for conditions being monitored and treated, CDI Specialists need to focus their attention on supporting documentation integrity and query all documentation concerns, not just those that have financial impact. 

Let’s focus on documentation integrity in every record, not just where we can measure impact.

 

 

Do I Really Need to Query for that Third (or Fourth or Fifth) MCC?

Written By: Rachel Mack, RN, MSN, CCDS, CDIP, CCS
Clinical Program Manager

We all tend to have a number of “firsts” we never forget when it comes to our CDI careers: 

  • First positive interaction with a physician
  • First educational session with a physician group – and it goes well
  • First coder interaction where you have a light-bulb Coding Clinic moment 
  • First time you felt as though you truly impacted patient care

My first example of impacting patient care is when I saw a patient with a slew of clinical indicators for  malnutrition diagnosis (including significant weight loss, decreased PO intake, a pressure ulcer, and a BMI of 12). I did not yet see an order for a dietitian evaluation, and the patient had been in the hospital for several days. I took a risk and sent the query anyway; the next day the physician I sent the query to put in an RD order. That dietitian went on to document that the patient met criteria for severe protein-calorie malnutrition. I thought, “wow, I helped do that.”

But another instance I’ll never forget is opening a denial email from Coding and seeing that an insurance company was denying all three MCCs for one case. 

….three MCCs. No wonder the Coding department had reached out for CDI help!

It’s commonplace for insurance companies to deny whatever they can, whether clinically sound or not. But all three MCCs? I had to dive in and take a look. After review, I discovered that they were partially correct: one diagnosis was incorrectly coded (acute respiratory failure final coded, but was part of a previous visit only), and in our defense letter I acknowledged that yes, we should remove it from the final bill. However, the other two diagnoses – metabolic encephalopathy and septic shock – were above and beyond clinically sound. The patient was confused with a GCS score of 11 that improved to 15 by discharge and was treated with a head CT, sitter, and safety restraints. And the patient was clearly in septic shock and required Levophed s/p aggressive fluid resuscitation to improve their blood pressure. 

So what can CDI specialists do to make sure records are as safe as possible from denials? 

Here at Iodine Software we have a few best-practice suggestions:

  1. Query consistently for clarification of conditions when the clinical indicators are present but there is no associated documentation (or vice versa when it comes to clinical validation).  When we do this consistently, we help teach our providers to consistently document with a higher level of specificity. 
  2. Query consistently regardless of the financial impact to the record. Only querying for a first CC or MCC is no longer acceptable practice in CDI. If we only query for conditions when they impact the DRG, we are doing our physicians and providers a disservice. This behavior has potential positive downstream effects on quality metrics beyond the scope of typical CDI programs.
  3. Query consistently to determine present-on-admission (POA) status of conditions. It’s very easy for us (as clinicians reading the record concurrently) to make assumptions and assume that the coder will realize something is POA.
  4. Query consistently if a condition or conditions are documented in such a way that they are unclear, inconsistent, vague, or non-specific. If a condition is not clear for us reviewing a record concurrently, it will likely not be clear for the coder and is at risk for not final coding or requiring a retro query.

I don’t think we should query simply out of fear of denials. That’s no way to live or to work. But we do have to be aware of the healthcare climate of today. Hospitals are depending on us to prevent denials as much as possible – and confirming accurate documentation of diagnoses irrespective of impact is our responsibility. 

I hope this spurs some critical thinking for CDI specialists. At the least, I hope next time you hear someone say “Yeah, I’m not going to query, this record is maxed out,” you might encourage them to think again.

 

Cognitive Emulation: Insights from Iodine Data Scientists

By Lance Eason, Chief Data Scientist & Jon Matthews, Data Science Manager 

Q: Iodine has pioneered a new machine learning approach called Cognitive Emulation. How would you summarize this approach?

Lance Eason (LE): There are two sides of the picture to consider when thinking about clinical documentation integrity (CDI): 1) what is really happening to the patient, and 2) what has been documented and written about the patient. CDI is all about making those two pictures align, specifically making sure the documentation actually reflects what is clinically going on with the patient. Iodine’s Cognitive Emulation approach helps identify where there are discrepancies between these two sides by looking at the entire clinical picture, not just what has been documented or what aligns with certain rules or thresholds.

Q: Iodine has iterated on its intellectual property over time as new technology becomes available. How have different types of artificial intelligence been utilized, and what has been the experience with each?

Jon Matthews (JM) and LE: Iodine started off with a rules-based approach just using NLP, which calculated the probability conditions were present based on a simple set of rules. We started identifying challenges and limitations – issues with false positives and false negatives, for example – so, we decided we needed a more advanced technique. We explored coupling  Machine Learning and advanced Natural Language Processing (NLP) to holistically review the entire patient record, and found the approach worked well. This allowed us to move past surface-level documentation improvements constrained to specificity and capture the full spectrum of possible discrepancies between documentation and clinical reality. 

Q: What underlying technology does Cognitive Emulation rely on, and how is it different from legacy solutions on the market?

LE: Iodine’s combination of machine learning and NLP allows it to make separate judgments about the clinical state of the patient versus what is documented. This is a more statistical approach and does not rely on specific hard factors to determine if the patient’s symptoms are above or below certain markers. Instead, it calculates a statistical likelihood that a documentation opportunity exists based on clinical evidence. We let the technology find the patterns and connections between the data rather than arbitrarily defining rules ourselves.

Q: What is a marker-based or rules-based approach, and how is Iodine different?

JM: Rules- or marker-based solutions require either the client or the software authors to define what each condition means, so there is not a lot of flexibility. These approaches follow “if, then” logic and make simple yes or no decisions based on whether inputs are above or below certain values. The problem with this strategy is that one could spend forever iterating on those thresholds manually and still get poor results. You lose a lot of nuance in the data because what is apparently useful might actually be missing a large subset of important features in the models. Machine learning algorithms are able to find these features on their own and do so automatically, which takes the guesswork and years of iteration out of the picture.

 Q: How is Iodine’s Cognitive Emulation approach impacting healthcare?

JM: The biggest advantage of Iodine’s Cognitive Emulation approach is that it is able to leverage the experiences and knowledge of physicians, coders, and CDI Specialists (CDIS) across the country into one product. Iodine’s very broad data set allows it to define new and interesting features that are helpful for predictions, which you would not be able to do if you were just coming up with rules on your own. In this way, Iodine helps hospitals work with the resources they have to capture more opportunities for documentation improvement. With Iodine’s vast dataset, experience, and clinical expertise, we are able to build products that drive revenue integrity and labor optimization. So many functions in a hospital require guessing or people to spend time doing unproductive work. The sky’s the limit for what Iodine can do for health systems with our machine learning models. 

LE: There are a lot of patients in the hospital at any given time, and their medical records are each constantly being updated with new data. The job of a CDIS is to survey all of the patients all of the time. With an infinite amount of CDIS you would review every case every day, but this is unrealistic without the help of technology. Iodine does the job of filtering and prioritizing, emulating what the CDIS would have been doing, but constantly and for every case, so that CDI teams are spending their time on the cases that most require human review. This allows them to do more productive work, not just searching through all of the cases to find the ones to isolate for review. If you think of CDI work as searching for needles in haystacks, queries would be the needles and the hay would be all of the cases that do not require a review. Iodine removes most of the hay so the needles can easily be identified.