Opportunities and Obstacles: A CFO Conversation on Health System Financial Resiliency

Key Takeaways:

  • Health systems need to move beyond cost cutting strategies to weather the current financial climate, they need new strategies for generating revenue, including relying on automation to scale scarce clinical resources. BJC Healthcare focuses on people, process, and technology: where can they automate where previously they relied on people to do the work manually
  • Whereas previously labor was the primary driver of economic growth in healthcare, there is real opportunity for leveraging technology to capture additional opportunity. Mueller cautions healthcare orgs to ensure they have processes in place to support new technology implemented; and Damschroder pushes the importance of standardizing work and embedding new tools into the workflow.
  • The onslaught of AI powered technologies on the market makes evaluating and selecting a tool for investment confusing. Both HFH and BJC leverage Project Management Teams to ensure they get returns on their investments

Iodine Intelligence tackles a new challenge in healthcare’s mid-revenue cycle every month, shedding light on problems and solutions and sharing valuable insights from industry experts. Listen to Episode 13 Opportunities and Obstacles: a CFO Conversation on Health System Financial Resiliency to learn more.

Financial headwinds and staffing shortages from 2022 have prevailed, carrying over into 2023, forcing healthcare leaders to re-envision their workforce, redesign processes, and rethink strategies for achieving financial stability.

This month’s episode of Iodine Intelligence brings you a conversation we had in March, Iodine’s Chief Revenue Officer Troy Wasilefsky was joined by Henry Ford Health’s Executive VP and Chief Financial and Business Development Officer Robin Damschroder and BJC Healthcare’s VP of Revenue Management Harold Mueller to discuss the challenges healthcare systems are currently facing and strategies for building financial resiliency in the face of economic uncertainty.

Leveraging Technology to Capture Opportunity

The financial challenges facing healthcare providers today has led to an uptick in the use of consultants, particularly in the revenue cycle space, to try and stabilize health system economics. Overwhelmingly, the recommendation from these consultancies is cost reduction. CFOs are looking to slash budgets, with 75% stating they were planning on decreasing operating budgets as a cost savings measure.1

However, cutting costs doesn’t generate revenue. McKinsey has mapped out impacts to profit pools if no new sources of revenue are introduced. While passing on higher costs to payors and patients lessens the impact, profit pools are eroded in all three scenarios. Hospitals and health systems need a new source of revenue, or at the very least to be effectively capturing the revenue they’ve already earned.   

This report from McKinsey is pushing the idea that cost reduction alone cannot be the answer here. We need to find a way to also generate growth and new revenue, either new revenue, or at least be capturing revenue appropriately for the work that we’re already doing.”

Troy Wasilefsky, Chief Revenue Officer at Iodine Software

When asked how he approaches balancing efforts to manage costs with growth strategies, Mueller responded, “I think from the standpoint of things that we can’t control. So, if you think about the last 24 months, the price of some of the travel nurses…you wouldn’t have believed them five years ago, if you were to look at the balance sheet. That being said, from a revenue cycle standpoint, we are focused on the people, process, and technology. So, how do we automate things that can be automated, that are manual?” Currently BJC has a number of projects in flight looking to automate tasks which are currently done manually, and hopefully, these projects will enable BJC to ultimately redeploy staff to other areas.

Damschroder echoed these thoughts on implementing automation; noting that as the cost to procure a patient has gone up and staffing shortages persist, opening the “digital front door” is more important than ever. “Patients want to be seen faster than we can often get them through our process,” says Damschroder, “So the health system that can get that gate open in a market faster, and turns the faucet on, I think, wins there.”

Prior to the COVID-19 pandemic, Henry Ford Health decreased their revenue cycle costs while improving their yield by tipping their revenue cycle on its side and inserting automation to cluster homogeneous work (ex. grouping denials by demographics, regardless of payor), enabling staff to complete work faster and more efficiently. Henry Ford Health implemented Iodine with the same idea: increasing the productivity of the staff they currently do have. Damschroder noted that they have a lot of openings in the revenue cycle, and they’re holding out on those openings in the hopes that Iodine’s product will enable them to cover their current workload without the need for additional hires. 

Healthcare Providers Need a New Driver of Economic Growth

These strategies of implementing automation to reduce workloads, improve processes and scale staffing aligns with McKinsey studies. Historically, labor has been a heavy driver of performance and growth in the healthcare industry, dramatically so when compared to other sectors of the US economy.

Research from the Bureau of Labor Statistics shows that 90% of the economic growth in the healthcare space comes from labor, with more than two-thirds of labor’s contribution coming from workforce expansion (4M net jobs were added) whereas other sectors’ growth was primarily driven by capital or innovation.2 However, McKinsey would argue that there is a $1T opportunity in healthcare stemming from accelerating and scaling innovation in four key areas: care delivery transformation, administrative simplification, clinical productivity, and technology enablement.3 

However, it’s not enough for healthcare providers to merely find and acquire the right technologies, they must also be mindful of how they’re implementing them. As Wasilefsky explained, “Innovation, technologies powered by AI and machine learning…they can bring a tremendous amount of opportunity to organizations, and yet, if you don’t use them, or build your processes around using them, and manage that change management, you’re not necessarily going to get all the results.”

Mueller was quick to point out that ensuring processes are in place to support the actual technology implementation is key. As an example, before 2019 the mid-revenue cycle at BJC was decentralized, many departments including CDI, HIM operations, and coding were not part of a shared service, and different hospitals deployed technology in different ways. BJC centralized services right after the start of the COVID-19 pandemic, and this enabled them to deploy Iodine, which BJC used as an opportunity to educate staff consistently, query physicians in a consistent manner, and gave them a tremendous lift in query volume. BJC facilities that deployed Iodine saw a 15%-20% lift in query rates, with physician response and agree rates staying the same. Although CDI specialists were reviewing the same volume of charts, Iodine’s prioritization ensured they reviewed the right charts at the right time, resulting in improved query volume.

“You want to make sure that your technology supports your actual administrative processes, and the processes that you have in place… From an Iodine standpoint, when we had facilities that we had centralized (and we centralized them in waves) we saw a 15%-20% increase in query rates, with physician response rates staying the same and agree rates staying the same. And these folks were reviewing the same volume of charts, but they were actually in the in the right charts, “

-Harold Mueller, VP of Revenue at BJC Healthcare

Damschroder touched on change management; while some may interpret “standardization” to mean “you don’t trust me to do my work,” she was quick to push back that standardization is not about lack of trust, but rather about elevating staff to their top of license and focusing them on where they can make the biggest impact. 

Damschroder also talked about the importance of ensuring new tools are embedded in the workflow and that the new workflow has buy-in. Henry Ford’s Health trick is robust monitoring and transparency: ensuring everyone can see how everybody is performing. “When you can barely find the other workflow or the workaround somewhere else, it’s been fully adopted.” said Damschroder, “And when new people come into the organization, they don’t even recognize that there was an old workflow out there.”

Realizing the Promise of AI

While there may be a lot of promise surrounding AI powered technology, and the majority of healthcare executives recognize they need it if they hope to weather the current financial challenges, there remains some skepticism, largely stemming from lack of literacy surrounding artificial intelligence.

In fact, 60% of healthcare leaders report being confused by the range of automation and AI solutions.4 In the words of Wasilefsky, “AI is the new shiny bauble, and everybody uses that term, probably to an exhaustive level.” The confusion surrounding AI can make selecting an AI powered tool, and ensuring that investment will have a financial return, challenging. 

These feelings were reflected in the webinar attendees. In an Iodine survey of those attending the webinar, only 19% felt very confident in their ability to effectively evaluate and select the best AI tech needed to improve their financial performance, and only 28% of respondents felt they were getting quantifiable ROI from their current mid-revenue cycle solution. In fact, 80% of respondents felt they are missing out on earned revenue in the mid-rev cycle.

“This is something we hear in the market a lot. There’s an inherent skepticism of: have we seen the ROI prove out on some of these AI applications?” 

– Troy Wasilefsky, Chief Revenue Office at Iodine Software

The confusion surrounding AI is exacerbated by the glut of AI-powered solutions on the market. Between buzzword inflation, AI’s nebulous definition, and the vast range in AI technologies, their capabilities, and results, it can be difficult for healthcare leaders to truly wrap their arms around: what am I buying, what is it doing, and what outcomes can I truly expect? 

“As a representative of the vendor community I think a lot of this falls on us to not obfuscate the terminology of AI, and what AI is being utilized.” said Wasilefsky, “But instead, be far more transparent about what these technologies are, and how they work, and how they’re different from others, and also the impact piece.”

Mueller touched on the onslaught of AI-powered tech in the market, saying “Every vendor that we deal with that has a computer is “AI” now.” When it comes to evaluating AI powered solutions and measuring their impact, BJC has implemented Project Management teams, including some specifically in the revenue cycle space with revenue management experience, who evaluate business cases for new technology investments, and post implementation do a look-back to ensure they are seeing a return on their investment.

Damschroder echoed this, discussing HFH’s rigorous due diligence process regarding implementing new solutions. Regarding ensuring you see return on your investment, Damschroder emphasized the importance of ensuring adoption and that the new tool is embedded in the workflow. Whenever Henry Ford Health evaluates a new solution, they pay particular attention to: what is the lift to get this embedded in the workflow. For healthcare leaders out there who believe they’re not seeing the promised return on a technology investment, Damschroder’s advice was look at the workflow and ensure it’s embedded in the process, and then look at your adoption rates, because staff may not be using the tool or may only be using parts of it.

  1. Academy IQ, CFO Forum Debrief, December 2022
  2. The Productivity Imperative for Healthcare Delivering in the United States. McKinsey & Company. February, 2019.
  3. Claiming the $1 Trillion Prize in US Health Care. McKinsey & Company, September, 2013.
  4. The Academy Research and Analysis

Interested in Being on the Show?

Iodine Software’s mission has been to change healthcare by applying our deep experience in healthcare along with the latest technologies like machine learning to improve patient care. The Iodine Intelligence podcast is always looking for leaders in the healthcare technology space to further the conversation in how technology and clinicians can work together to empower intelligent care. if that sounds like you, we want to hear from you!

The Spectrum of AI in Healthcare: Understanding the Levels of Intelligence in AI

Key Takeaways:

  • There is enormous potential for leveraging AI in healthcare, including removing toil from staff workloads, increasing efficiency and productivity, improving consistency in results, and
  • It can be difficult to truly evaluate AI powered solutions due to buzzword inflation, and the fact that AI is an umbrella term covering a wide range of technology
  • Asking some foundational questions can be key to truly understanding if an AI powered solution aligns with your business need.

Iodine Intelligence tackles a new challenge in healthcare’s mid-revenue cycle every month, shedding light on problems and solutions and sharing valuable insights from industry experts. Listen to Episode 12 The Spectrum of AI in Healthcare: Understanding the Levels of Intelligence in AI to learn more.

Artificial Intelligence (AI) has been an area of significant interest for the healthcare industry for years, and that interest is only growing in the face of prevailing financial headwinds and staffing shortages. However, in a crowded field of AI powered solutions promising to solve healthcare’s more pressing issues, it can be difficult to truly evaluate their capabilities and potential. In this month’s episode, Priti Shah, Iodine’s Chief Product and Technology officer, provides a framework for making sense of AI and its potential, and some of its applications in the mid-revenue cycle space.

The Promise of AI

The real world applications for leveraging AI to solve some of the pain points in healthcare can be sorted into a few broad buckets:

  1. Automation: One of the most basic applications is simply automating tasks that you don’t actually need a human to perform.
  2. Efficiency: If you have a task that can’t be automated entirely, it still requires human judgement, you can still supplement and enhance your staff with AI-powered tools.
  3. Timeliness: You can ask an AI model to actively evaluate your entire patient census 24/7, which, realistically, you will never be able to staff humans to that degree.
  4. Consistency: An AI model can help establish a baseline level of competence, rather than relying on strengths and weaknesses of individual staff with different experiences and clinical background.

AI is Confusing

Although recognizing the promise of AI is easy, evaluating an AI powered solution can become confusing quickly, because the truth is not all AI is the same, and not all AI can solve all of healthcare’s unique problems. 

Today, there are two main barriers to understanding and evaluating AI. The first is that AI is just a hot space right now and the term gets bandied around a lot. Everyone is trying to claim that they use some form of AI, and they all mean something slightly different when they make that claim. The second problem is even within the field of computer science AI is not well defined, it’s an umbrella term that covers a lot of different tools and technologies for solving a lot of different types of problems. The only real common theme is: applying computer systems to perform tasks that normally require human intelligence because they’re too hard or complicated for computers.

AI models are constantly walking that tightrope, balancing precision and sensitivity, and you can’t over-pivot on either axis because it essentially renders that model impractical to use. But that also means we have to understand no model is perfect, and you have to chose what balance of false negatives and false positives you can live with.
” AI’s accuracy, or success, is always on a spectrum, and there’s always tradeoffs that I think we should be aware of.


De-Mystifying AI

Knowing the benefits that AI can bring you, but acknowledging the challenges of truly understanding and evaluating AI, Priti Shah offers the following framework for thinking about AI, with some basic, fundamental questions to have in the back of your mind when thinking about making an investment in an AI powered tool.

Use Case

The first question to ask yourself is: what problem are you trying to solve?

This is important for two reasons, one, there’s a wide range of AI tools and they’re not all equally good at all tasks, and two, almost every AI model you’re going to encounter right now is trained to do one very specific task. 

There are different tools available in the AI space that are better suited for solving some types of problems than others. 

  • Documentation Interpretation: NLP or large language models
  • Classification Problems: Gradient boosted machines or neural networks
  • Image Recognition: Deep learning models

Different tools have different applicability to different problems, so you shouldn’t just focus on the latest and greatest. ChatGPT is currently the talk of the own, and while there are some things it does very well, its limitations have been well demonstrated. While powerful in its domain, no one’s trusting it to make a clinical diagnosis.

There is no silver bullet, you should not expect any one technology to solve all your problems, so when selecting an AI for investment, focus on: what is this AI actually trying to do for me, and does that match up with the business problem that I am trying to solve.


The second question to ask yourself is: how well does the model actually perform?

You can’t assume that just because a solution is powered by AI, the AI is highly successful. While some may think, “If the AI is trained to do this, it’s doing it perfectly,” that’s rarely the case. The reality is AI’s success is always on a spectrum, and generally that comes with trade-offs that you need to be aware of.

Artificial intelligence models are always balancing sensitivity and precision. You can create a model that’s so precise it has no false positives, but your criteria will be so narrow you’ll miss most of what you’re searching for, and have a ton of false negatives. Conversely, you can create a model that is incredibly sensitive, but by casting a wider net you’ll also catch a bunch of false positives.

Dialing up either precision or sensitivity too high can result in a model that is essentially useless for practical purposes, so you have to choose what balance of false positives and false negatives you can live with.


The third question to ask yourself when evaluating AI is: when is it capable of making its predictions. 

An example of this coming into play would be giving your sepsis coordinators an AI model that can predict sepsis. If the model is only capable of making its predictions post-discharge, it doesn’t actually fit the use case of your sepsis coordinators, who want to identify sepsis within 24 hours of a patient’s stay. Timeframe is critical when making a decision about deploying AI.


And the final thing you should consider is: how much insight can the model actually give you into why it’s making its predictions. 

There are use cases where all you care about is the answer and how confident the model is in the answer. But when it comes to healthcare, where you’re interacting with other people, and you’re dealing with someone’s health information, it cannot be a black box. You have to be able to discuss and explain why is the AI making this determination. 

Especially because, consider what we discussed earlier: that no AI is perfect, that it’s always a balance of sensitivity and precision, and therefore also a balance of false positives and false negatives. You need clear explanations of the predictions so you can know when to trust the system’s predictions, and when to apply your own judgment.

Iodine and AI

To further help conceptualize this, below are example of an AI company answering these four fundamental questions.

  • What problem are we trying to solve
    • At Iodine, we leverage machine learning models to take in raw clinical data (lab results, ordered medications, performed treatments, clinician observations, etc.) and look at all those disparate pieces of data in conjunction with one another to make predictions about various disease conditions. We leverage those clinical predictions in a variety of ways. In the CDI space, we compare the clinical reality of the patient against what’s documented, and then look for gaps in between that can be clarified. So when it’s time to bill and code, the documentation is complete and accurate, and health systems will get paid. In the utilization management space, we compare the clinical reality of the patient to the level of care we would expect a patient like that to receive, to aid UM nurses with determining the appropriate level of care and admission status for patients
  • How well do our models perform
    • Iodine is fortunate enough to have access to a vast clinical datasdet, and this enormous set of clinical data is fueling our models. Having more data enables the ability to target more rare diseases. We’ve also been iterating, and experimenting, and improving on our models for seven years. Data science is a process of discovery; for some of the more complicated disease states, we have gone through seven or eight different generations, each new version building upon previous advancements to increase performance. Across our client cohort, we’ve found that we’ve been able to substantially improve client’s metrics: 92% of facilities saw an increase in productivity (query volume) with the average facility generating more than twice as many queries per CDS as they did before Iodine. With our models surfacing those cases with the greatest likelihood of opportunity, CDI specialists were two-thirds more likely to query a reviewed patient. And this has real, measurable impact, including increased MCC capture rates, increased CMI, and, on average, an additional $3.5M in annual appropriate reimbursements per 10k admission.  
  • When are our models making their predictions
    • Our models are working concurrent with the patient stay, and our models are constantly reevaluating in real time as new information becomes available
  • How much insight do we give into why the model made its predictions. 
    • We bubble up the most relevant clinical evidence to our users so they can see why we think this way about a patient. We’re not speaking about a patient in the abstract, or this general type of patient, it’s specifically: why does the model think this patient, Jane Smith, has sepsis based on the way she’s presented so far. 

Hopefully this framework is helpful for evaluating AI solutions, and having these four fundamental questions in the back of your mind will help to demystify the hype around AI, explain the different types of AI, and why it should be something you’re considering, but considering for the right reasons. 

Interested in Being on the Show?

Iodine Software’s mission has been to change healthcare by applying our deep experience in healthcare along with the latest technologies like machine learning to improve patient care. The Iodine Intelligence podcast is always looking for leaders in the healthcare technology space to further the conversation in how technology and clinicians can work together to empower intelligent care. if that sounds like you, we want to hear from you!

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

Interested In Learning More?

Wait and see is no longer an option in healthcare, stay ahead of the curve and prepare for the unexpected by investing in innovation for long-term success and viability. Fill our the form below to take the next steps.

Iodine Intelligence: Should CDS Query If There’s No Impact

Key Takeaways:

  • Historically CDI has measured query impact primarily through financial metrics, but this has expanded over time to include quality related metrics such as severity of illness, Vizient drivers, and O:E ratio
  • While the administrative burden on physicians is real, not submitting a query due to lack of impact can have a variety of negative implications
  • A better solution to alleviating administrative fatigue is to focus on creating a consistent, streamlined workflow for physicians to review and respond to queries

Iodine Intelligence tackles a new challenge in healthcare’s mid-revenue cycle every month, shedding light on problems and solutions and sharing valuable insights from industry experts. Listen to Episode 9: Should CDI Specialists Query If There’s No Impact to learn more.

Recently Iodine Software hosted a webinar in partnership with ACDIS, and the most frequently asked question was: should CDI specialists submit a query if there won’t be any chart impact? This stems from a concern for physicians, who often complain of administrative burnout, and is an attempt to alleviate administrative fatigue.

Historically, CDI has measured the impact of queries by calculating the financial impact of the query to the DRG, either through movement to a CC/MCC or by changing family (ex. pneumonia to sepsis). Over time, as CDI scope has expanded, they’ve looked to other areas to measure their impact: severity of illness, risk of mortality, elixhauser comorbidities, patient safety indicators and hospital acquired conditions, to name a few.

Fran Jurcak, Iodine’s Chief Clinical Strategist spoke on how there are a few issues with only querying when there’s measurable impact.

First, the end goal of clinical documentation improvement is to accurately represent every patient and their clinical reality, both in the documentation and the final code set, and that cannot be accomplished if CDI specialists aren’t querying consistently and all diagnoses aren’t captured.

Second, picking and choosing when to query sends an inconsistent message to providers, and can actually hurt education efforts. As Fran Jurcak said, “What about on the physician side?…What he’s seeing is, sometimes you query me, sometimes you don’t, I’m just going to sit back and wait for when you need me to, as opposed to when I should.” Inconsistent querying can ultimately undermine CDI’s efforts overall.

Finally, there are cases where at the time of generation a query won’t have impact, but post-discharge it will. As Fran Jurack explains, “In the end, when things are final coded and the final documentation is in, and maybe the physician has ruled out some of the conditions that you thought initially were there, will that query now have a level of impact that you didn’t see on day two of your review, but now happens in the post-discharge space.”

“I think that perception is something we need to think about in the CDI space, because we are trying to capture the appropriate clinical picture for every patient and not pick and choose when something may have meaning or value.”


A better strategy for reducing physician administrative burden is by modifying the process and workflow for physicians. Fran Jurcak argues that the burden of documentation is not going away, and in fact may worsen in the future as additional conditions impact quality factors. Some key factors to consider when designing a query response process for physicians include:

  1. How are physicians finding the query – Where are the queries located? Is it easy for a physician to find that there’s been a question, or does it get lost in a sea of other queries?
  2. Do the physicians have all the information they need – Do they have the information necessary to quickly and efficiently answer the question? Are the forced to dig through the medical record for additional details?
  3. Are queries consistent – Are queries standardized so physicians know what to expect and where to look?
  4. Where is the answer going – Does it become part of the permanent medical record? Does the CDI specialist need to translate the physicians response into another format?

Luckily, technology is well positioned to help in all of the areas listed above and automation of tasks that don’t require clinical knowledge can remove work from over-burdened plates.

Iodine’s Interact leverages templates to both streamline the query authoring process and provide a consistent workflow. Physicians can easily find the information they need to answer a query. Interact’s mobile platform means physicians can review and respond to queries from their phone. All the clinical evidence they need is at their fingertips and their reply gets added to the medical record thanks to EMR integration. This allows health systems and hospitals achieve complete and accurate documentation and coding, capturing the full clinical picture of the patient.

“It’s about workflow, it’s about consistency, it’s about creating efficiency and templates that make this process easy, so it’s less of a burden. The burden is not going to go away…So there are two options for physicians: get it right in their documentation, which is where our educational programs come in, or when you happen to miss it, find a quick and easy way to get that answer into the medical record.” 


Interested in Being on the Show?

Iodine Software’s mission has been to change healthcare by applying our deep experience in healthcare along with the latest technologies like machine learning to improve patient care. The Iodine Intelligence podcast is always looking for leaders in the healthcare technology space to further the conversation in how technology and clinicians can work together to empower intelligent care. if that sounds like you, we want to hear from you!

Mid Revenue Cycle Management: How to Measure, Manage, and Minimize Leakage

Key Takeaways:

  • Clear, consistent and complete documentation is crucial to the bottom line: it drives the final reporting of codes, enables accurate reimbursement, and minimizes denials
  • Every step of the documentation review process presents opportunity for leakage, meaning leakage occurs even with high functioning CDI teams
  • Staffing shortages require CDI teams focus their work on the cases with the greatest likelihood of discrepancy between the clinical evidence and documentation – but without technology identifying these cases is an exercise in futility
  • Artificial intelligence and machine learning based on large data sets is the best kind of technology to assist in this space: it can understand patterns and recognize what’s happening in the clinical care

Iodine Intelligence tackles a new challenge in healthcare’s mid-revenue cycle every month, shedding light on problems and solutions and sharing valuable insights from industry experts. Listen to Episode 1: Mid Revenue Cycle Management: How to Measure, Manage, and Minimize Leakage to learn more.

As healthcare providers operate on tighter and tighter margins, paying close attention to both efficiency and appropriate use of resources becomes more crucial than ever. This necessitates greater accuracy and depth in documentation, and while the answer may seem to be daily patient record reviews to identify discrepancies between the clinical evidence and the documentation, the reality is there aren’t enough trained, human resources to do this. The challenge becomes: where do I deploy the staff that I do have, and how do I prioritize which cases to review.

However, between changes in clinical definitions, documentation and coding guidelines, annual updates, and quality metrics and benchmarks, knowing what to focus on and which area has the greatest return can be a daunting task for CDI teams.

“We can’t be targeting a particular metric or condition saying ‘This is how I’m going to solve all problems,’ because it’s only solving a very small problem. Documentation integrity is no longer just important to a single payer or a single type of patient, it’s important in every case”
– Fran Jurcak, Chief Clinical Strategist

Technology may hold the answer for overwhelmed CDI teams. Artificial intelligence (AI) coupled with machine learning (ML) can look for discrepancies between the clinical evidence and what is actually documented, and then highlight those cases for CDIS to review. Software solutions can introduce efficient and automated workflows. Leveraged appropriately, this trifecta allows CDI specialists to focus on the right charts, find discrepancies, and fix any problems.

‘Its not about replacing people it’s about augmenting their ability to do their job well. Creating efficiency in their workflow and really allowing them… to really focus in on what they can do to help. Because in the end it’s about ensuring that we’re able to provide quality care to patients” – Fran Jurcak, Chief Clinical Strategist

Interested in Being on the Show?

Iodine Software’s mission has been to change healthcare by applying our deep experience in healthcare along with the latest technologies like machine learning to improve patient care. The Iodine Intelligence podcast is always looking for leaders in the healthcare technology space to further the conversation in how technology and clinicians can work together to empower intelligent care. if that sounds like you, we want to hear from you!

Webinar Recording: Retrospective Reviews: The Last Line of Defense?

Each stage of the CDI documentation integrity process represents an opportunity for additional leakage of accurate and appropriate documentation, resulting in inaccurate coding of conditions being monitored and treated during the patient’s encounter. This results in inappropriate reimbursement for care provided as well as potentially imperfect quality reporting. And despite massive investments in documentation and coding solutions, earned revenue loss continues to persist — Medicare and Medicaid underpayments reached $75.8 billion in 2019*

Retrospective reviews are the last opportunity to resolve documentation and coding issues for billing and quality reporting purposes. Traditional reconciliation is inefficient and often ineffective for a number of reasons, including: inefficient process, understaffed CDI teams and lack of technology that supports accurate identification of opportunity. 

Listen to this webinar as Fran Jurcak, Iodine Software, and Dee Banet, Advent Health, discuss strategies for implementing a more robust retrospective review process, including: 

  • Why current approaches for retrospective reviews aren’t working 
  • Strategies for prioritizing what to review 
  • Solutions for automating the Retrospective review process 

*AHA Fact Sheet: Underpayment by Medicare and Medicaid January 2021