CDI Solution Drives Documentation Excellence and Improves Revenue Performance

R1 RCM and Iodine Software’s comprehensive clinical documentation integrity solution connects managed services and intelligent technology

R1 published the following on October 16, 2023 announcing R1 and Iodine Software as solutions partners.

CDI can lead to improved patient outcomes as well as increased reimbursements for healthcare systems. But up until this point, CDI solutions have been siloed as separate approaches: Consulting and managed services are one approach; technology solutions are another. But what if these approaches joined their strengths together? How could that improve the completeness, accuracy, and specificity of the medical record and reduce documentation, coding, and billing errors?

CDI Total Performance, powered by Iodine, does just this. A joint solution, CDI Total Performance combines the domain expertise and deep data insights of R1 RCM clinical documentation specialists with the Best in KLAS AwareCDI platform from Iodine Software.

The need for a new CDI approach

Efforts to codify clinical care are as old as documentation itself, with the first “modern” disease classification system developed in the early 18th century. With quantum leaps in specificity and complexity of medical coding in recent times, CDI becomes more important than ever as a driver of revenue performance.

A strong CDI program is a critical imperative that comes with challenges

Released earlier this year, the 2023 Revenue Intelligence Data and Insights Report unveils some of the RCM issues most profoundly impacting healthcare providers. Of the 13 revenue cycle stages covered, seven have clinical documentation integrity as a key enabler for process and outcome improvement. From determining appropriate patient status and capturing legitimate charges to recovering underpayments and reversing denials, CDI plays a foundational role in facilitating and improving mid-cycle and back-end revenue processes.

There are several barriers that can hinder the effectiveness and sustainability of a CDI program. One is the industry perception of CDI programs as cost centers rather than revenue drivers. In fact, a national study of CDI leaders conducted last year by the Association of Clinical Documentation Integrity Specialists (ACDIS) for Iodine Software found 91% of CDI leaders track financial impact as a measure of success, while 68% track severity of illness (SOI)/risk of mortality (ROM) impact, and 53% make note of the observed-to-expected mortality rate. Even providers that do recognize CDI’s revenue opportunities can struggle to make headway because their programs lack transparency, accountability, or focused leadership, often due to strained resources. Additionally, many providers are reluctant to make changes to their CDI programs because they worry about the potential for disrupting regulatory compliance. Other factors preventing CDI excellence include:

  • Constant regulatory and coding changes
  • Difficulty measuring program effectiveness
  • Technology limitations and integration
  • Inefficient and low-quality physician queries

Ideally, CDI programs should align with the Quadruple Aim – improving the experience of care, improving the health of populations, improving physician satisfaction, and reducing per capita costs of health care. A key role for CDI leadership, then, is to determine program priorities considering the aim, anticipate the potential barriers to achieving documentation excellence, set the table for appropriate expectations, and ensure that CDI program performance is assessed on a rational basis.

Professional development drives CDI performance improvement

The strength of any quality CDI program is a well-trained and educated team. Unfortunately, with ongoing staffing shortages, budget constraints, and cost-cutting priorities, hospitals often struggle to make the investment necessary to support their staff and, thus, see real improvement. Additionally, it can be difficult to quantify and therefore justify a return on investment from staff education and training.

R1 CDI Total Performance engagements provide a caring, high-touch onboarding experience with new education and career development opportunities for the CDI team. These include monthly education sessions on clinical topics, quarterly coding clinics, regular quality audits of each CDI specialist and an annual review of Inpatient Prospective Payment System (IPPS) updates and rules. The model enables CDI teams to operate at top-of-license and just as important, reduces stressors that can lead to team and physician burnout.

Introducing a new kind of CDI partnership

R1 CDI Total Performance, powered by Iodine, brings to market a transformational solution built upon shared responsibility for collective CDI goals, deep clinical documentation expertise, and clinically intelligent technology. As an accountability partner, R1 invests in the success of their clients because their success depends on it. That begins with career growth for the team – R1 transforms CDI programs with best practices education gleaned from CDI engagements with leading hospitals and health systems that opens new professional opportunities for staff. Then R1 further empowers that team with Best in KLAS clinically intelligent technology to drive measurably better results and revenue.

“We’ve been in this market a long time and know CDI is a big driver of success in our partnerships with leading hospitals and health systems,” said Kyle Hicok, Executive Vice President and Chief Commercial Officer at R1. “From our vantage point, we see an unsolved need for a more holistic approach to CDI and a real opportunity to innovate in this space. CDI Total Performance allows us to serve more hospitals and health systems wherever they are on the CDI journey and help them achieve documentation excellence that improves the quality of care and captures more earned revenue.”

“CDI Total Performance is a unique comprehensive solution capable of delivering exceptional value to clients. Together, R1 and Iodine bring proven performance management prowess, documentation expertise, and impactful technology to deliver the financial, quality, and productivity gains demanded by health system executives at a speed that will set a new benchmark for industry expectations – right when health systems need it most,” said Troy Wasilefsky, Chief Revenue Officer for Iodine Software.

Achieve CDI excellence with best practices and Best in KLAS software

With 95 of the top 100 hospitals in the U.S. as customers, R1 is unique in its ability to develop, aggregate, and replicate CDI best practices at scale to improve clinical documentation performance. Iodine’s Best in KLAS AwareCDI technology provides new insights to CDI teams by strategically identifying and prioritizing cases for review and streamlining the query process to simplify and improve CDI, Coder collaboration, and physician response. As solution partners, R1 and Iodine deliver the only offering of its kind on the market, the closest thing there is to an easy button to fast-track CDI excellence.

To learn more about R1 CDI solutions, visit

Iodine Software to Harness the Power of Generative AI to Expand the Impact of Its Industry-Leading AI Solutions

Iodine Software will leverage generative AI to accelerate the impact of its solutions, which includes the 2023 Best in KLAS for Clinical Documentation integrity solution, AwareCDI

Iodine Software, a pioneer in healthcare AI technology, today announced an expanded relationship with OpenAI, an AI research and development company. As part of this collaboration, Iodine will gain access to OpenAI’s cutting edge artificial intelligence technologies, including its powerful language model GPT-4.

Iodine Software has a deep-rooted history in clinical AI technology, having developed sophisticated, industry leading solutions that enhance financial performance in the mid-revenue cycle.  This collaboration with Open AI allows it to further infuse generative AI and large language models across the breadth of its AwareCDI product suite to improve prediction accuracy, streamline query processes, and develop clinical automation tools that will further stem revenue cycle leakage by ensuring documentation accuracy. 

“We are thrilled to collaborate with a fellow pioneer in the field of artificial intelligence,” said William Chan, Iodine CEO and co-founder, “Strategic, fast paced yet cautious innovation has always been our guiding principle. The evolution towards generative AI is a natural next step for us. We are optimistic about its potential impact, yet cautious due to the significant impact on patient care, physician trust and patient reimbursements. Aware of its limitations, such as data hallucinations, we are committed to a responsible approach, ensuring we balance progress with prudence.” 

The efficacy of Artificial Intelligence is dependent on data on which the language models are trained. Iodine Software is an industry leader with an unmatched clinical dataset, giving the company an unprecedented opportunity to transform how it supports hospitals across a variety of functions. A recent market analysis of the volatility of CMI validated that Iodine Software’s data cohort is representative of the overall market, which makes it one of  the best and powerful assets for training machine-learning and large language models.

By incorporating the latest innovations in large language models, Iodine Software can ensure more specific patient documentation and coding efficiency, increase prediction accuracy, and improve its predictive analytics. These advancements will contribute to streamlining the physician documentation experience, allowing physicians to focus more on patient care, and helping hospitals and health systems to capture more earned revenue from the care they provide.

About Iodine Software

Iodine is an enterprise AI company that is championing a radical rethink of how to create value for healthcare professionals, leaders, and their organizations: automating complex clinical tasks, generating insights and empowering intelligent care. Iodine’s powerful predictive engine complements the skills and judgment of healthcare professionals by interpreting raw clinical data to generate real-time, highly focused, predictive insights that clinicians and hospital administrators can leverage to dramatically augment the management of care delivery – facilitating critical decisions, scaling clinical workforces through automation, and improving the financial position of health systems. For more information, please visit PR Contact:

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

Innovative AI Data Solutions in Healthcare

Freddy White, CEO of AI-MED attended key healthcare industry event HLTH in November 2022 and spoke to some exhibitors about the work they are doing in this field.