CDI: Compliant Technology Adoption and the Role of Clinical Documentation Specialists

AHIMA and Artifact, an Iodine solution convened a panel of experts from across the healthcare industry for a roundtable discussion exploring best practices in CDI and how advances in artificial intelligence (AI) technology are transforming clinical documentation integrity (CDI) and the role of clinical documentation specialists (CDSs).

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!

Beyond Buzzwords: Understanding the Real Potential of ChatGPT in CDI

Key Takeaways:

  • ChatGPT is a free online chat bot that creates custom content at users requests, debugging computer code, composing songs, writing student essays, and more
  • While some tout ChatGPT as the next solution for the CDI space, it has a long way to go before it will have real utility to clinical documentation integrity
  • ChatGPT is based on Natural Language Processing (NLP) which is not well suited to interpreting and analyzing data to determine what’s missing
  • In its current iteration, ChatGPT isn’t capable of producing documentation with enough detail or specificity to replace physician written progress notes or CDS composed 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 11 – Beyond Buzzwords: Understanding the Real Potential of ChatGPT in CDI to learn more.

OpenAI debuted ChatGPT in November of 2022, and since then there has been discussion on its potential, and the impactions on everything from schoolwork to medicine, from marketing to law school, and Clinical Documentation Integrity hasn’t been excluded from the conversation.

ChatGPT is currently a free online program that works like a chat bot: you type in questions or requests and it spits back answers. Although it’s designed to mimic human conversation, users have found it to be incredibly versatile, using it to write and debug computer codes, compose poems and songs, write student essays, and even take the bar exam.

While ChatGPT is impressive, and certainly fun, it has a long way to go before it’s ready to revolutionize the clinical documentation space.

ChatGPT is founded on Natural Language Processing (NLP), which is already deployed widely in the CDI space. And while NLP can be a powerful tool for taking written text and interpreting it into a form a computer can understand, it is not well positioned to help CDI with their chief concern, namely: what is missing from the documentation. NLP isn’t capable of looking at information and applying any kind of logic or understanding to the underlying meaning. A medical record can speak very specifically about signs and symptoms of a medical condition, but if that medical condition isn’t articulated in the documentation, NLP isn’t capable of finding it. ChatGPT has the same limitation: it’s not trained to identify what the documentation should be. A very different type of AI is needed to interpret and understand documentation.

Additionally, some users are encountering instances where ChatGPT has falsified its answers. Users will ask ChatGPT a question, and get an answer that sounds very scientific and even includes citations, but upon digging deeper, users will discover the answer provided was fake and ChatGPT invented the citations. Additionally, ChatGPT has revealed some concerning biases in the data it’s been trained on, for example answering “describe a good scientist” with “a white man.” Both of these issues can have serious consequences when applied to people’s health and medical records.

Perhaps the largest barrier to ChatGPT in the CDI space is it doesn’t get to the root of the problem – mimicking the way a physician writes progress notes is likely to produce the same gaps we find in documentation now, and with the physician a further step removed there’s the potential for the documentation to be even less accurate – because the physician never touched it. In it’s current iteration, if you ask ChatGPT to write you a progress note or a query, it’s not capable of producing the level of detail necessary because it doesn’t have the right data or learning as a foundation. It would require a huge clinical data set, composed of millions of medical records to effectively emulate a clinician’s brain when creating documentation.

Further, even if ChatGPT was capable of writing a progress note or a query, if its outputs are just large blocks of text, that has limited utility in a modern healthcare setting where data must flow downstream to multiple programs and softwares. Additionally, no matter what ChatGPT requires the author to review its outputs and ensure what was translated into the document is accurate – even with AI you can still end up with typos or words and phrasing not appropriate or accurate for the situation. (Just ask anyone who’s blindly relied on auto-correct in a text message, or auto-completion in an email).

Some better uses for ChatGPT in healthcare might be handling scheduling and frequently asked questions from patients, helping doctors and patients with translations, and writing or drafting emails, including for fighting insurance denials. Use cases in the outpatient setting might also have a lower hurdle to clear, as patient encounters in outpatient are much shorter, so the details needed to be included are more streamlined and limited to this specific encounter, rather than the overall picture of the patient. Even helping with the day-to-day communications between providers, coders, CDI nurses, can be helpful and a time-saver, but as it stands today, at best ChatGPT is getting you a first draft, a human is still required to refine and finalize.

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!

AwareCDI Recognized as Best in KLAS CDI Software for Second Year Running

Iodine Software is thrilled to announce that for the second year in a row, we have been named the market leader for CDI Software by KLAS in their 2023 Best in KLAS: Software and Services Report. Iodine ranked #1 in Clinical Documentation Integrity Software in KLAS 2023 Report with a score of 90.0; this achievement follows AwareCDI™ winning Best in KLAS in 2022.

KLAS is an independent, third-party organization known in the market for honest insights informed by robust research; a Best in KLAS award is a true marker of excellence, and Iodine is proud to be the leader in our industry with KLAS Research’s highly coveted distinction. In their 2023 report, 96% of respondents stated that Iodine “avoids charging for every little thing” and that AwareCDI was “part of long-term plans,” with 100% of respondents saying they would purchase Iodine again. We take our client partnerships very seriously and are honored to be part of their journey towards complete and accurate clinical documentation.

All rankings are a direct result of the feedback of thousands of providers over the last year. “Winning Best in KLAS two years in a row is a true indication of our commitment to continuous product innovation, and putting the needs of our customers first.” said William Chan, CEO and co-founder of Iodine. “KLAS bases their awards on direct customer feedback, making this award a true reflection of the exceptional service and support we at Iodine strive to provide to our clients every day.”

Iodine’s groundbreaking AwareCDI solution is a game-changer for organizations looking to unlock the full potential of their workforce in the face of staffing shortages. By automating time-consuming clinical tasks, AwareCDI enables CDI staff to work at top of license, achieving unprecedented levels of efficiency and productivity. Hospitals who have leveraged the AwareCDI suite to boost output, see a median lift in productivity of 134%[1], and increase earned revenue capture, with $1.5 billion in additional appropriate reimbursement annually.[2]

According to KLAS Research President Adam Gale, “The 2023 Best in KLAS report highlights the top-performing healthcare IT solutions as determined by extensive evaluations and conversations with thousands of healthcare providers. These distinguished winners have demonstrated exceptional dedication to improving and innovating the industry, and their efforts are recognized through their inclusion in this report.”

KLAS Research will honor Iodine and the other segment winners for 2023 in a ceremony held at HIMSS Global Conference April 17th in Chicago, IL.

About KLAS Research:

KLAS has been providing accurate, honest, and impartial insights for the healthcare IT (HIT) industry since 1996. The KLAS mission is to improve the world’s healthcare by amplifying the voice of providers and payers. The scope of our research is constantly expanding to best fit market needs as technology becomes increasingly sophisticated. KLAS finds the hard-to-get HIT data by building strong relationships with our payer and provider friends in the industry. Learn more at

[1] 2020 Iodine Cohort Study

[2] 2021 Iodine Cohort Study

Unlocking the Power of Concurrent

Key Takeaways:

  • Concurrent can be a disruptive technology requiring some key shifts in thinking and processes to unlock its full potential
  • Auto-assignment allows for complex cases to be distributed evenly across a team and for better coverage when there’s a gap in staffing
  • When setting quiet periods, shoot for a time range that’s appropriate 80% of the time
  • When artificial intelligence and machine learning are leveraged to provide a prioritized list of cases for review, priority review rate is a more important metric than number of initial reviews and re-reviews
  • In healthcare’s ever changing landscape, it’s key to periodically reassess your configurations to ensure they’re still best fit

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 10: Unlocking the Power of Concurrent to learn more.

Concurrent is Iodine’s flagship software, and it leverages artificial intelligence and machine learning to prioritizes cases for CDI teams to review concurrently before patients are discharged based on misalignment between the clinical evidence and existing documentation.

In this month’s episode we sat down with Iodine’s Client Services Operations Manager, Justin Gerardot, and Iodine’s Clinical Product Consultant Manager, Diana O’Connor, for some tips and tricks for our users to get the most out of Concurrent. Concurrent, and it’s prioritization, can be referred to as a “disruptive” technology, and there are some key shifts in thinking and processes required to unlocking the full potential and return of Concurrent.

1. Auto-Assignment

Concurrent distributes cases to CDS’s for review using auto-assignment, which can be a shift for CDI teams who are used to cases being distributed by location or service line.

Iodine advocates for a “generalist” rather than a “specialist” approach. Some service lines are, by their nature, more complex than others, and auto-assignment allows for those complex cases to be distributed evenly across a team. It also helps when a CDS is on vacation or there’s a gap in coverage: everyone is able to cover and review those cases.

2. Quiet Periods

A Quiet Period is the amount of time a case must “incubate” before it can be considered for auto-assignment. It can be a delicate balance walking the line between a quiet period that is too short, and CDS’s receive cases for review with very little information, and a quiet period that is too long, and the patient is discharged before the case gets a chance to be prioritized and reviewed.

When selecting a quiet period, Iodine recommends shooting for the 80/20 rule: 80% of the cases are prioritized at the appropriate time or within the appropriate window.

3. Metrics

Concurrent provides teams with an intuitive, prioritized work-list in order to get the right cases in front of CDS at the right time, and because of this, Iodine is not as concerned with how many initial reviews and re-reviews a CDS does on a given day or during a given week. Instead, Iodine focuses on priority review rate: how many high priority cases did you get to.

It can be difficult moving away from longstanding, traditional KPIs, especially when they’re used for projections and staffing needs. Iodine has a robust reporting platform to support these new metrics, allowing CDI teams to easily track their progress.

4. Reassess

Healthcare is an ever changing field, and as a result it can be very helpful for hospitals and healthcare providers to reassess their Iodine configurations on at least an annual basis. Changes to the size of a CDI team, adding additional service lines or payors, and software updates within Concurrent itself can all impact the way CDS interact with Concurrent. Reevaluating your configurations, especially once you have six month’s to a year’s worth data to help with your evaluation, can help ensure you’re staying at top functionality.

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!