In this article, the author examines the implications for the capture of Hierarchical Condition Codes (HCCs) in the inpatient setting.

By now, most clinical documentation improvement (CDI) programs have an appreciation for the use of cases reflecting how Hierarchical Condition Codes (HCCs) impact upon quality and reimbursement. For those who do not, here is a quick primer:

  1. HCCs are used inside of payment methodologies:
  • HCCs set per member per month (PMPM) capitation payments for managed Medicare plans.
  • HCCs are used in combination with fee-for-service reimbursement (FFS) to compensate Accountable Care Organizations (ACOs) for sicker patients and quality goals in the Medicare Shared Savings Program.
  1. HCCs are used for risk adjustment:
  • HCCs are used to risk-adjust in individual and small group markets on and off the health insurance exchanges.
  • HCC’s risk-adjust three cost and quality measures for value-base purchasing for both hospitals and providers.

What most CDI programs do not have an appreciation for is how their inpatient focus translates to impacting patient acuity, and why it matters. The inpatient lens is often clouded by the perceived impact that inpatient HCC capture has on programs beyond value-based purchasing for hospitals, as many other use cases do not seem to tie to the current focus and goals of the inpatient CDI program. Consider the following:

  • Valid HCCs are added to the 12-month claim history for a patient for each and every visit to the acute-care setting, including inpatient, observation, and emergency occasions of service.
  • In work with a current client, 25 percent of the patients in their ACO accumulated their only HCC codes from a service delivered by an acute-care provider, as opposed to a primary care or specialist in their network. Without those HCCs, the patient would have had no risk adjustment factor, resulting in potential loss of payment.
  • Most organizations are contemplating or already participating in a risk-sharing contract or alternative payment model like Bundled Payment for Care Improvement (BPCI) that also considers HCCs for risk adjustment.

Most if not all hospital systems have reached a tipping point wherein there is enough reimbursement at risk outside of the traditional fee-for-service models that taking a longer view of risk adjustment is imperative. Time is of the essence, as the lookback periods for benchmarking performance are often 1-2 years behind the commencement of the programs.

Why Proper Depiction of Patient Acuity Matters

While much has been written regarding a defined focus and goals for inpatient CDI programs, it is appropriate to acknowledge that many programs have plateaued or are being inundated with non-documentation improvement tasks that are diluting their focus on improving the capture of patient acuity. This is contrary to an organization’s best interests. As reimbursement models and outcome measures for the inpatient hospital setting are definitely a moving target, an underlying constant is some form of HCC risk adjustment. Furthermore, it is incredibly important for organizations to develop a core competence in this area, if for no other reason than being able to discern the difference between a quality-of-care problem and a risk-adjustment problem when they interpret their performance in the mandatory payment reform programs. Phrased another way, it is important to understand if an organization has too many low-risk mortalities because of quality-of-care issues, or because of how the patients were risk-adjusted in their cohort.

When thinking about risk adjustment, I am drawn to a concept I call the Clinical Documentation Integrity Axis. Pictured in Figure 1, the concept portrays a new trajectory: depiction of patient acuity. Proper depiction of patient acuity simply means that a patient’s burden of illness, both chronic and acute, is accurately and completely portrayed in the clinical documentation by the provider, and translated to an accurate and complete claim file that contains all reportable diagnoses for the purposes of care continuity, risk adjustment used for prediction of future resource need, and reimbursement that is matched to that need. Depiction of patient acuity is dependent upon both coding depth and documentation precision. Subpar performance in either will produce suboptimal depiction of patient acuity.

Coding depth is a term utilized to describe the number of diagnosis codes per claim. In our experience, between 25-30 percent of inpatient claims are lacking a clinical diagnosis that is present and documented in the record, but not coded. Most of these diagnoses are uncoded because they do not impact the MS-DRG, or are not recognized by the coding professional as reportable. We know that this is a significant issue as it relates to HCC capture because 40 percent of the HCC diagnoses that are risk-adjustors are not CCs or MCCs under the MS-DRG reimbursement methodology.

Documentation precision is a measure of how many claims are processed without documentation defects such as queries for documentation gaps, non-specific diagnoses, or undocumented conditions or procedures. Documentation precision is impacted by a number of factors, including the configuration of the electronic health record (EHR). Because of the sheer number of codes possible to be selected by the provider, vendors have narrowed down the codes provided in the model solution to those meeting the needs of most clinicians. This has resulted in a great number of non-specific codes populated in pick lists and dropdown menus. Further contributing to non-specific code utilization is the gap between terminology used for care communication between clinicians and that used for coding. An example of this is the use of the term “bronchitis.” Bronchitis, unspecified is not a valid HCC. But chronic bronchitis, simple chronic bronchitis, and aspiration bronchitis are. In this example, patient acuity would be improperly depicted if bronchitis was reported without specification or not reported at all.

What are the Symptoms of Under-Depiction of Patient Acuity?

ACO/Medicare Shared Savings Program Participants:

  • Many patients with no HCCs reported in a 12-month measurement period. Example: A patient is prescribed Metformin in their medication history, without non-insulin dependent diabetes in their RAF score detail.
  • “Falling off” of HCCs that were previously part of the claim history, but are no longer present. Example: A patient with a left below-the-knee amputation in the last measurement period data, but not in the current measurement period data.
  • Attribution RAF scores of less than 1. (The average Medicare patient has a RAF score of 1.)

Inpatient Hospitals:

  • Penalties in the Hospital Readmission Reduction Program and/or mortality outcome measures.
  • High Medicare spending per beneficiary in the Efficiency Measures for Value-Based Purchasing.
  • Medicare case mix index disparity to average length of stay (lower CMI to higher LOS in comparison to facilities with a similar scope of services).


Solving for the Under-Depiction of Patient Acuity

Proper depiction of patient acuity is a function of both coding depth and documentation precision. CDI programs must recalibrate their scope and processes to align with improving their capabilities in each area. This typically requires a high degree of synergy and connectivity between the health information management (HIM) coding function and the CDI function. Transitioning a CDI program that has functioned within a legacy framework entails both a disciplined approach and a willingness to collaborate systematically with other constituents such as providers, quality, IT, and of course, HIM.

Figure 2 illustrates some of the characteristics of legacy CDI programs in contrast to more contemporary practices of CDI programs that are focused on improving risk adjustment.


Attributes of a Legacy CDI Program versus a Contemporary CDI Program


Legacy Practice

Contemporary Practice

Integration with Coding

Often works asynchronous from coding.

Often works synergistically with coding on improving coding depth, performing pre-bill reconciliation reviews, and participating in educational initiatives to improve HCC capture.

DRG Reconciliation Process

Often a limited focus addressing clinical validation to support coding of CCs or MCCs, which impact the final MS-DRG assignment.

Often focuses on the completeness of the diagnosis array for each patient, irrespective of which ones improve the MS-DRG; often incorporating specificity queries and high value HCCs. Actively engaged in pre-bill activities such as mortality reviews.

Significance or Value of Clarifications or Queries in CDI Program

Often quantify the impact of the CDI program by the number of cases with CDI queries or the number of queries themselves with financial impact on each case.

Adopts measures such as documentation precision, with a goal of increasing the number of cases reviewed without a CDI query.

 Metrics and Outcome Measures

Often engages with quality and care management on a retrospective basis, i.e.; reporting of complications and clinical definitions versus coding guidelines for sepsis.

Works collaboratively with quality department to monitor performance in value-based purchasing measures. Adopts new success measures such as SOI/ROM, RAF score, HCC capture, cases without query, diagnoses per claim that are in line with decreasing queries, improving productivity of staff and provider satisfaction.

Physician Engagement

Often engage with providers with electronic or paper queries, and may provide scorecards to report on query response rate.

Engage with providers at the elbow to coach on documentation improvement synchronous to the patient encounter. CDI is represented in medical staff committees such as EHR, quality and care/resource management and often contributes to scorecards on documentation precision metrics.


Some Limitations: Small Steps Can Produce High Yields

Even the best- inpatient CDI programs are fraught with some limitations, not the least of which is the current state of software-utilized clinical documentation specialists and coders to identify high-value diagnoses in the record. Without an HCC grouper, it is difficult to discern which HCCs are valuable versus which ones may be “trumped” by others already reported in the claim file. And many current CDI solutions lack visibility of HCCs in the workflow utilized for concurrent review. Finally, the software platforms utilized by coding and CDI may not be integrated.

Other limitations may include gaining access to HCC data and RAF scores if an organization is not participating in a Medicare Shared Savings Program. And there is also difficulty recognizing and quantifying the impact of HCCs captured in the inpatient setting on the RAF score that is attributed to physician practices.

These limitations, however significant, should not prevent inpatient programs from taking small steps that can improve HCC capture. Here some ideas worthy of consideration:

  1. Organizations can start by reviewing the CMS website for information about risk adjustment: There is also a risk adjustment fact sheet available at These are higher-level overviews discussing how risk adjustment is deployed in general terms by CMS.
  1. Proceed from a more general overview to the measures of complications, deaths, and unplanned hospital visits at These will help illuminate some of the nuances with regard to the inclusion and exclusion criteria, as well as some of the HCCs that are used inside the mortality and readmissions measures.
  1. Another useful activity is downloading the ICD-10-to-HCC crosswalk from the CMS web page and comparing it to your claims data as it relates to the use of unspecified codes. This will help you look for opportunities to improve coding specificity for HCC capture and determine which diagnoses to prioritize for work with your providers. These priorities can be fanned out through your physician education program for different specialties, discussed with your IT team to help remediate pick lists that are populated with unspecified codes, and certainly can become integrated into the standard query toolkit for use by CDI and coding.
  1. Lastly, I’m also mindful of the impact that just spending a single hour rounding with a provider can have on identifying limitations in workflow or technology. There is nothing quite like determining the root cause for a documentation deficiency and helping to remediate it in real time. These encounters at the elbow are often the most impactful way to deliver educational content because they have a direct connection to workflow for any given provider. And perhaps the single most important motivation for a provider to engage with us directly on improving documentation precision is how it translates to their productivity. Time is a most precious resource, and making providers more productive should be a key consideration for any CDI program.

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Michelle M. Wieczorek RN RHIT CPHQ

Michelle Wieczorek is a senior manager in the DHG Healthcare CFO Advisory team and focuses on clinical documentation and revenue integrity initiatives. She is a Registered Nurse, Registered Health Information Technician and Certified Professional in Healthcare Quality with more than 30 years of experience in healthcare. She has served in leadership roles in Clinical Nursing, Health Information Management, Utilization Review, Clinical Quality, and Information Technology.

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