Achieving ROI at Speed

Key Takeaways:

  • The CDI profession has evolved and is no longer about capturing a single CC or MCC, but rather the true clinical picture of the patient
  • West Tennessee Healthcare saw an increase in query volume after implementing Iodine accompanied by a financial return 3x more than expected
  • Leveraging Interact allowed West Tennessee to improve physician response times and rates even while simultaneously increasing query volume

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 6: Achieving ROI at Speed to learn more.

The Clinical Documentation Integrity (CDI) profession has evolved along with documentation requirements, and it’s no longer about capturing a single CC or MCC, but rather an accurate and complete record of everything that happened during an inpatient encounter. Technology provides a unique opportunity to enhance the CDI workflow. Rather than CDI specialists aiming to review all cases (and wasting an inordinate amount of time reviewing cases with no opportunity) the technology reviews 100% of cases, and prioritizes those with the greatest likelihood of opportunity for specialists to review to validate the clinical information and confirm the query opportunity.



Historically, one of the biggest challenges within CDI is knowing when to review a cases. “So, I looked at it [a case] today, but how soon will something change? Whether it be additional clinical information for a condition I looked at today, or a whole new condition that develops during the patient encounter. The only way, historically, that CDI had an opportunity to identify that, was to review every case every day, which is impossible” Fran Jurcak explained, “At Iodine, we allow ourselves, through technology, to identify when something has changed of significant value that then requires a re-review.”


So for us, the new metric is not how many reviews do you actually get to, it’s how many of the priority reviews are you getting to, because with technology you’re getting at 100% reviews of all cases, so it’s really about which are the cases that CDI needed to look at, and then comparing that from the pre-Iodine performance to post-Iodine performance.

– Fran Jurcak, Chief Clinical Strategist

West Tennessee Healthcare has six facilities in the West Tennessee area, the largest being Jackson-Madison County General with 740 beds. Their CDI team encompasses 11 CDI reviewers (some of whom are general, some of whom are specialized), a CDI manager and educator, and CDI director.

Several years ago their program shifted to focus on quality and improving their CMS star rating which led to West Tennessee implementing a variety of products in Iodine’s AwareCDI suite: Forecast which automatically predicts final DRG and GMLOS, Concurrent which prioritizes cases in CDI specialist’s workflow for review, Interact (formerly Artifact) which is a physician engagement tool that eases the query response process, and Retrospect which prioritizes post-discharge, post-code, pre-bill review. In the words of Denise Humphreys, their Director of CDI, “I think this product, in total, has helped us evolve as just a day to day CDI program to something that really specializes in: can we help our facility, our whole system, in attaining the goals that we have.”

Industry recommendation for a seasoned CDI team is a query rate of 20%-25% if you’re reviewing all charts; after implementation Denise’s team reviewed only prioritized cases and their query rate went up to 35%-38%. This resulted in increases in their case mix index, CC and MCC capture rates, and a financial return on investment that was 3x more than expected, allowing West Tennessee to pay off the full cost of Iodine within 3 months.


We have a query escalation policy here where if the query is not answered after 14 days, it goes on a suspension list…The number one offender that was on the suspension list every week was one of the first to use Interact…the first to answer a query, and he did within 40 seconds of us sending it. And he’s not been anywhere near the suspension list for the query process since.”

– Denise Humphreys, Director of CDI West Tennessee Healthcare

While increased query volume could be cause for concern in some physician circles where administrative burden is already high, Denise stated that the largest impact she saw to her program after implementation was physician satisfaction. Pre-Iodine, they regularly had physicians on suspension lists for taking more than 14 days to respond to queries. After implementing Interact, their biggest offender responded to a query within 40 seconds of receiving it – and he hasn’t been on the suspension list since.

Interact creates a quick and easy workflow that is intuitive, doesn’t interrupt physicians during their standard patient process and allows them to communicate in a fashion that works best for them – within the EMR or via a mobile app. Discussing the advantages of Interact, Denise stated, “One of the main things we’re looking for, and we have experienced already, is an excess of additional time during our day. We’re not chasing queries like we were before.” The additional time during the day enables Denise’s team to expand their scope, and focus on impacting their O:E ratio, improving their star rating, physician engagement and education, increasing SOI and ROM and more.

West Tennessee is going live with Iodine’s Retrospect this July. Retrospect – a post-charge, post-code, pre-bill tool that helps CDI and coders identify discrepancies between the clinical evidence documented and the final code and capture additional leakage – will help West Tennessee monitor their backend and make sure their coding and documenting correctly. Having Iodine products end-to-end will help West Tennessee ensure documentation accuracy across all medical records.



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!

Progression of Accuracy with Lance Eason

Key Takeaways:

  • Iodine Software has been iterating on its models for the last seven years, with each new generation unveiling advancements and improvements in accuracy
  • Artificial Intelligence (AI) is an umbrella term, natural language processing (NLP) that merely scans the written record is not the same as machine learning (ML) which surfaces clinical predictions based on the clinical evidence

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 5: Progression of Accuracy with Lance Eason to learn more.

Data analytics is nothing new, and big data is leveraged by everyone from Google in targeting ads to Starbucks in picking the best location for a new store, however, surfacing clinical insights out of healthcare data is an incredible challenge. Iodine’s CognitiveML seeks to conquer this final frontier: processing raw clinical data to make precise predictions about specific disease conditions.

While there is an incredible range of tools at one’s disposal (with dozens of different model types that can be applied to a problem) each disease has its own idiosyncrasies, and the same model often doesn’t work equally well for every type of disease state. While some conditions, such as an electrolyte balance, may be relatively straightforward to predict, other conditions are incredibly complex.

The genesis of Iodine’s machine-learning models lies, in part, in the inadequacies of traditional methods in the face of complicated conditions, namely: sepsis. F1 scores are frequently used when measuring a model’s accuracy, and examine both the number of predictions made and the number of predictions which are correct; the profoundly limiting nature of a rules-based approach resulted in an F1 of only 0.21 in the original sepsis model. This spurred Iodine into investigating different technologies; the first machine-learning model applied to sepsis more than doubled the model’s accuracy from 0.21 to 0.53. Since its original launch, Iodine has gone through numerous iterations of the sepsis model, with each subsequent generation building upon previous advancements and introducing new improvements. Iodine’s F1 for sepsis is now in the 0.80 range, with plans to continue experimenting, iterating, and improving.

If we had gone and said, ‘We are using AI to predict sepsis’ seven years ago, and we say it today, we’re saying two different things, because our predictions at that time were 0.5 versus 0.8 nowadays. So there’s been a significant increase in the actual quality of the predictions we’re making over time”.”


These improvements stem from a variety of advancements and experiments,

  • a growing pool of data – Iodine’s database currently contains more than 27 million historical
  • mapping of input values (i.e. labeling lab values)
  • applying successful models to similar disease conditions (ex. UTIs and pneumonia are both infectious diseases, a technique that is successful for one might be similarly successful with the other)

These combine to create a ML-AI engine seven years in the making that is now

Competitors wishing to switch to a similar format, would be starting almost a decade behind, but the reality is, many of the other technologies in the clinical documentation integrity (CDI) space take a radically different approach to solving the problem of documentation leakage: focusing on interpreting the clinical documentation to determine what’s missing. “The problem with that approach,” says Lane Eason, Chief Data Scientist, “is the documentation itself is not where most of the CDI opportunity is. What CDI is about is making sure what is documented for the patient matches what’s actually going on clinically with the patient, and if you’re not looking at that other side of the equation…then just reading the documentation is not going to tell you all the things that aren’t documented.”

AI is a wide umbrella that encompasses everything from NLP to image recognition, and the degree of complexity and ability varies across technologies. Claiming to leverage AI because you use NLP to scan documentation is not equivalent to using machine-learning to make clinical predictions about specific disease states based on the clinical evidence. And as Iodine itself learned in the beginning, the method of AI applied can have dramatic affects on the efficacy and accuracy of the results.


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 Journey to KLAS with William Chan and Tim O’Hara

Key Takeaways:

  • One of the biggest challenges Iodine faced was awareness – overcoming obstacles often associated with a new approach to a problem
  • Delivering both a product that people want to use and fulfilling the promises you make in the product are key
  • Be hungry for feedback, and be humble enough to accept both positive and negative feedback in order to stay responsive to the industry

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 3: Journey to KLAS with William Chan and Tim O’Hara to learn more.

KLAS is an independent, third-party organization that reviews vendor solutions and publishes an annual report of its findings. KLAS is known in the market for honest insights informed by robust research. This year, Iodine Software’s AwareCDI Suite was named #1 Best in KLAS in Clinical Documentation Integrity software. This award represents the culmination of years of hardwork and dedication, as previously Iodine was a top performer in the annual KLAS Clinical Documentation Improvement reports for three consecutive years.

Whereas once Iodine was seen as a narrow part of the CDI workflow, the team has worked diligently to develop trust in the application and build out functionality to deliver a comprehensive CDI platform. Being named Best in Klas serves not only as validation that AwareCDI is a product people find value in, but is also a reflection of Iodine’s exceptional team, and their work to provide superior post-sales service. In the words of William Chan, CEO and co-founder, “Because it’s so all encompassing, a best in KLAS award is really a culmination of a lot of work and a lot of people coming together.” 

Tim O’Hara, Vice President of Client Experience, wanted to be sure to thank all those who made this achievement possible, saying “First of all, how incredibly humble I am, and we all are, to receive this award, and incredibly grateful for all our employees and customers, because we couldn’t do it without them.” 

It’s not enough for us to think we have a great product with great service, that needs to be validated and affirmed by healthcare professionals who hold us to a very high standard, as they should.

– Tim O’Hara, Vice President Client Experience

Iodine has a robust team of people surrounding, and providing front-line support, to end users, including everything from Client Success Managers and clinical experts supporting organizations strategic priorities, to a technical support team available to answer questions, to customized training and support. Staff work with clients to establish key goals and how they’ll be measured up-front, allowing for transparent conversations on KPIs and enabling Iodine to achieve one of its founding principles: delivering on commitments. William believes delivering both a product that people want to us, and on the promises you make in the product are key, “We have always done our utmost, and worked our hardest, to make sure that we deliver on as many promises as we have made. I think that’s what you build your product and your company on, that’s what takes you on that trajectory towards best in KLAS.”

KLAS’s annual report provides a unique opportunity for organizations to garner additional feedback from clients. Reviews are anonymous, which can result in more candid responses, and KLAS interviews go deeper than your average NPS survey, asking things like “does this vendor keep their promises” and “are they part of your long term plans. William calls KLAS’s drive to understanding the true insights that come out of customers an obsession, and interestingly believes that embracing a similar level of obsession to be a key trait moving forward. “We need to obsess over getting behind the scenes, peeling back the layer of the onion, to know where we’ve messed up, where we haven’t done the right things, and it’s that obsession with wanting to hit the mark with the customer that is going to allow us to continue to be best in KLAS. That’s the marching orders I give to the organization.” 



Documentation Accuracy: Quality as an Outcome or a Process

Key Takeaways:

  • Documentation translates into publicly available information that drives hospital reputation and accreditation
  • Consumers today have more freedom of choice than ever before, and are able to decide where they want to receive their care based on hospital scores, rankings, and other publicly available information
  • A Documentation Accuracy Index measures if the clinical evidence equates to the documentation, so organizational leaders don’t make the assumption that a quality metric doesn’t look as good as it should due to a documentation problem
  • Through the use of Iodine’s Concurrent prioritization tool, Brigham Health was able to review less cases while simultaneously increasing query volume and improving financial impact

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 4: Documentation Accuracy: Quality as an Outcome or a Process to learn more.

The Institute of Medicine (IOM) introduced the six domains of quality care in 2001 with the goal of improving healthcare overall, and since then the business of evaluating hospital quality has only grown. In addition to CMS’s homegrown system of star ratings, a variety of private organizations including US News and World Health, Vizient, Leapfrog, and more, have popped up all with the same goal: objectively evaluating healthcare providers and their quality of care.

These scoring and ranking systems are leveraged by healthcare consumers, who have more freedom of choice than ever before, when choosing where they’d like to receive their care. Every patient, and every patient’s family, wants the best quality care possible.

In addition to swaying public opinion and driving consumer behavior, these quality rankings are increasingly tied to reimbursements and hospitals face being penalized with fines or having payments withheld if they do not meet minimum benchmarks. 

While the methodologies powering these quality rankings may vary, one thing they all share in common is that they’re based off of claims data. In fact, the humble hospital bill has grown into a mighty metrics driver with far reaching effects for hospitals. To name a few:

  • The documented acuity of a patient effects hospital accreditations and risk and severity adjustment, which cascades to influence quality scores and reimbursements
  • Documentation of present on admission (POA) conditions can impact Hospital Acquired Conditions rates (HACs) and Patient Safety Indicators (PSIs), which are tied to reimbursements and fines 
  • Quality scores and hospital rankings are based on the documented care given and patient outcomes; they also inform hospital reputation, both within the community and at large

Claims data’s large sphere of influence makes documentation accuracy imperative. The same claims data that drives hospital scores, accreditations, community reputation, and consumer decisions passes through Clinical Documentation Specialists (CDS’s) hands, which means CDS’s have more to monitor than ever. In the words of Fran Jurcak, Chief Clinical Strategist at Iodine Software, “There are conditions that are now very important to these methodologies in terms of identifying risk that, historically in the documentation world, we didn’t worry about…so today it’s really about capturing the true clinical picture of what’s happening to patients today, so you can best reflect yourself as an organization to the outside world.”

Our rankings and scoring are based off of claims data, and that claims data is dependent upon accuracy of the documentation. And if you have bad documentation, or inaccurate, inconsistent, unspecified documentation, you are not going to reflect the type of patients you’re taking care of. 

– Fran Jurcak, Chief Clinical Strategist, Iodine Software

While the connection between CDI and quality metrics is undeniable, there remains debate about the best way for CDI to influence quality metrics. The impulse can be to have CDI teams focus on improving specific metrics. Unfortunately, while the individual metric under the spotlight may improve, it’s often at the detriment of others, which slip under the neglect. 

A more effective strategy is striving for truly accurate documentation. Documentation that completely captures all patient conditions, is accurate, and consistent, allows quality metrics to accurately reflect the type of patients a healthcare provider is caring for, and the outcomes they’re experiencing. Jurcak says, “It’s really about ensuring that the world can see the level of care that you provide, and what level of acuity your patients are experiencing, and whether or not they have positive outcomes.” 

It can be a struggle for organizations to truly gauge where they stand in the documentation improvement process; oftentimes, if a quality metric doesn’t look as good as expected deep dives in the medical record are required to determine if the root cause is a documentation issue, a quality of care issue, or a patient acuity issue. In response to this conundrum, Iodine has created a Documentation Accuracy Index which reviews the clinical evidence in a patient record and compares that against its data warehouse of millions of historic patient records to determine if there is a discrepancy between the evidence and the documentation. The likelihood that the documentation is complete and reflective of the clinical evidence is then measured in a ratio.  

The Documentation Accuracy Index is designed to allow CDI managers and healthcare leaders to determine at a glance both if the clinical evidence equates to the existing documentation, and how effective a CDI program is at capturing leakage.  Jurcak explains, “I think it’s been very easy in the healthcare industry to point the finger at documentation and coding, as opposed to the problems you would need to potentially solve from a clinical perspective…yes, we do have documentation problems, I’m not going to deny that….but at what point do you know you’re there? And I think that’s a component we haven’t really explored in our industry, that we are excited to be looking forward to at Iodine.”

Reviewing less cases, we’re finding more cases with opportunity


Brigham Health is a 1000 inpatient bed hospital located in Boston, Massachusetts that serves around 60,000 inpatient stays annually. Brigham and Women’s, a member of Brigham Health, is a large medical academic center consistently ranked among the top 20 hospitals in the nation by US News and World Health Report. Deb Jones, who has been their Director of Clinical Documentation Improvement since 2015, describes her team, “We felt like we were doing a really good job. We have a very seasoned CDI team. Eighteen CDI nurses…most of them have over 10 years of CDI experience. This is pre-2019, pre-2020, and then things started to shift.“

In 2020 Brigham Health was given a new goal of improving expected mortality, through which they could influence the hospital’s O/E ratio, US News & World ranking, and have peripheral effects on LOS and readmissions.  According to Jones, “All of this new work we’re charged with, but we’re not given any more staff. So we have 18 CDS’s and we were staffed at probably 1 for every 1500 discharges. So the big question was, how do we incorporate this new work without losing sight of the work we were doing that we were really good at and maintaining that performance as well.” 

Rather than focusing solely on this new metric to meet their goal of improved expected mortality, Jones and her team instead concentrated on achieving “true north” in documentation accuracy, and took a three-pronged approach to implementing effective, quality processes: new technology, new processes, and new metrics.

New Technology

To optimize their workforce, Brigham Health implemented Iodine’s Concurrent, which prioritizes cases for review based on the greatest likelihood of opportunity. Prior to implementation, the team was reviewing 95% of encounters, but querying on less than 40% of the cases they reviewed. This large volume of non-productive work was neither the most efficient use of their resources nor the best way to reach their goals. In the first month after rolling-out Concurrent, their review rate dropped to 75%, but their query rate rose. Even though the team was reviewing less cases, they were finding more opportunities and querying more.

By removing all the unnecessary reviews of cases without opportunity, the CDS’s were able to broaden their scope and spend more time in patient records, review for complex conditions that affect severity and risk adjustment, and investigate complex cases. 

New Processes

Concurrent allowed Brigham Health to scale their workforce, so much so that even though their workload had expanded, the new efficiencies in their process allowed them to free up a CDS for a Special Assignment role. 

The CDS in this rotating role is able to focus on mortality reviews, applying risk-adjustment methodology, and reviewing prioritized discharged cases, which the team had not always had time for previously. Jones stated, “In addition to that, they’re also having the ability to go out and do some education that we didn’t really have time for before and aligning the CDS’s with the service lines, they’re building relationships, and all of this has really led to a lot of satisfaction for the nurses.”

New Metrics

Whereas originally Brigham Health tracked individual CDS review rates, after implementation they started measuring their total chart impact in comparison to total discharges. Jones found that prior to implementation on average, they had 925 encounters with clarification a month, after implementation, this rate rose to around 1400 a month, stating, “​​That’s again reviewing less cases, we’re finding more cases with opportunity. It’s been exciting all these quality initiatives have really invigorated CDI, at least at our organization, with its new lifeblood and purpose, it’s an exciting time.” Her team has also seen movement on their expected mortality metric – the same metric which initially prompted them to seek improvements to their workflow. They started with a baseline of 2.5% expected mortality and are currently at 3%, well on their way to their goal of 4%. 

By expanding their scope from “improve expected mortality” to “achieve true documentation accuracy” Brigham Health was able to achieve a multitude of goals. 

  • Team efficiency improved, allowing less CDS’s to do more work: increasing query volumes and chart impact rate. 
  • CDS’s role expanded to include a Special Assignment role, allowing for further training and growth for the CDS’s
  • They achieved their initial goal by improving expected mortality from 2.5% to 3%, and continue to strive for additional improvement


Rules-Based Prioritization Versus Machine Learning with Lance Eason and Troy Wasilefsky

Key Takeaways:

  • Rules-based systems are fundamentally limited
  • Thresholds lead to patients with clinical evidence for a disease state being ignored because they don’t meet the cut-off
  • The mid-revenue cycle is inherently clinical in nature, and solutions lie in establishing a source of clinical truth for a patient
  • Machine learning allows for more nuanced, accurate predictions

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 2: Rules-Based Prioritization Versus Machine Learning with Lance Eason and Troy Wasilefsky to learn more.

Hospitals and healthcare providers are currently facing enormous staffing challenges, and Clinical Documentation Integrity departments are not exempt. These workforce difficulties are likely here to stay, and with limited human resources, CDI departments turn to technology solutions to help scale their teams and improve the process. (Listen to Episode 1 here to learn more about the staffing challenges CDI departments face, and how hiring more specialists isn’t the answer, but deploying technology with AI is). 

There are a wide variety of products on the market purporting to solve the issues CDI teams face. Many leverage AI to help, but not all AI is created equal. Some adopt a rules-based approach to help identify cases in need of review, but software based off rules, thresholds, and cut-offs is fundamentally limited. 


There are several core problems with a rules-based approach: 

  1. Arbitrary cutoffs. This type of technology is forced to create a confidence threshold, for example: how high do lactate levels need to be for me to be 80% confident this patient has sepsis? You will inevitably have patients who fall just below this threshold. The system ends up discarding patients who don’t have enough clinical evidence, despite the fact that there is some.
  2. A limited number of factors are examined. Typically rules-based systems only examine 4 or 5 criteria, providing a very narrow, limited view that doesn’t allow for the capture of the patient’s full clinical picture. 
  3. Factors are examined independently. Rather than examining patient symptoms in conjunction with one another, they are all examined independently (for example, lactic acid levels and heart rate are looked at separately). The combination of a small number of factors, and looking at these factors independently, means there is a lot of opportunity for patients who do have clinical evidence of a disease state, still not meeting the letter of the law in criteria. 
  4. Divorced from the clinical nature of the task. Rules-based systems approach the problem algorithmically, trying to diagnose a patient based on a set of rules, which is not how medicine works.

The failures of these systems lead to false positives, false negatives, and ultimately loss in confidence from the CDI team. CDI specialists stop using the product, go back to trying to review every case every day, and are essentially back to square one. 

“Because we are actually unlocking the ability to understand what’s clinically going on with each patient, that allows us to tell our customers: specifically for this patient here, here is the intervention that’s necessary, here’s what you need to look at for this patient. And we don’t speak in generalities.”


Machine Learning provides a solution/answer to each of the core failings of rules based systems. 

Machine learning doesn’t utilize thresholds or cut-offs, and is often examining dozens of factors, not just four or five. While these factors may not all be slam dunk criteria giving definitive answers, they all contribute to a much more nuanced understanding of the patient. Additionally, these factors are all examined in conjunction with each other. Ultimately, what machine learning excels at is recognizing patterns in large pools of data. Diseases have multiple ways they can present, and machine learning is capable of identifying disparate clusters of symptoms that nonetheless are all indicative of the same disease state. At Iodine, the goal is to establish a source of truth: a complete clinical picture of what ha[[ened to the patient. By unlocking the ability to truly and accurately understand what’s clinically going on with each patient, we’re able to guide our clients specifically, rather than speaking in generalities.     


While it’s critical for technology aimed at improving the CDI process to leverage AI, the type of AI powering the tools is just as important. Teams using software that relies solely on thresholds and cut-offs with a rules-based system are likely to find themselves sorely disappointed in their results – and that the tool purchased to streamline processes and introduce efficiency and accuracy into the department, does anything but.


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