For inpatient clinical documentation to have integrity, it must be accurate, timely, and completely reflect the patient’s full clinical presentation and scope of services provided for each admission.

What Does Inpatient Documentation Impact?

Complete and accurate documentation helps to facilitate patient care and improved outcomes, and helps to justify medical necessity for the patient’s stay and services provided.

After inpatient documentation is completed, post-discharge, it is translated into a final coded abstract. Inpatient cases are classified by diagnostic severity, using Diagnosis Related Groups (DRGs). Appropriate inpatient coding and DRG classification depends upon an accurately documented and fully specified primary diagnosis (the reason for admission, after study), and documentation of all significant comorbidities.

Abstracted coded data is used for many purposes, including clinical research on patient populations, public health reporting, reputational rankings and quality models. Coded data based on clinical documentation also directly impacts operational metrics, including Mortality Index and Length of Stay Variance.

Documentation Integrity Key Points

For accurate capture of patient severity, secondary diagnoses and their significance/plan of care should be included in documentation, if pertinent to the patient’s admission. Significant diagnoses are those that are measured, monitored, assessed, evaluated or treated, or cause the patient’s stay to be extended.

Unlike coding for outpatient services, diagnoses included in inpatient documentation that are “possible”, “probable”, “likely”, “suspected”, “questionable” or “still to be ruled out” at the time of discharge are included on the final inpatient coded abstract. These are coded for inpatient cases because they help support medical necessity of the decision for admission, and are important to properly classify patients according to diagnostic severity.


NYP’s CDI Departments are charged with the mission of helping to capture the most full and accurate coded abstract possible for each patient stay. Traditionally, this has been approached by conducting manual chart reviews, sending providers queries to clarify medical record documentation, and conducting targeted inpatient documentation education sessions for individual providers and small groups.

CDI Technology

New Natural Language Processing (NLP) technology is now available to assist with clinical documentation integrity as Physicians and APPs document in NYP’s Epic electronic medical record. Appropriate, pre-defined CDI concepts are surfaced real-time, while documentation is being created. This provides timely, consistent feedback to a broader clinical audience than was previously possible with CDI’s manual chart review and query processes.

For inpatient clinical documentation to have integrity, it must be accurate, timely, and completely reflect the patient’s full clinical presentation and scope of services provided for each admission.

HCCs explained

Hierarchical condition categories (HCCs) are used by Medicare for risk adjustment. By Richard D. Pinson, MD, FACP

Developed in 2000, HCCs are part of a risk-adjustment model allowing Medicare to project the expected future annual cost of care. They're used for calculating payments to Medicare Advantage plans, accountable care organizations (ACOs), and certain Affordable Care Act (ACA) plans. Many chronic conditions are included.

Risk adjustment allows Medicare to “level the playing field” so plans that cover patients with more severe, complex, and costly conditions receive a larger capitated payment than plans with less costly patients.

HCCs group together ICD-10 codes for related diagnoses with similar clinical complexity and expected annual costs of care. Each HCC is assigned a relative weight proportional to the relative costs associated with its constituent diagnoses.

Higher-cost HCCs have higher relative weights. HCC relative weights are therefore similar to diagnosis-related group weights and to relative value units for CPT codes.

The Table gives a few CMS-HCC examples with relative weights and the number of constituent diagnoses.

Medicare calculates a Risk Adjustment Factor (or RAF, pronounced “raf” as in “raft” without the “t”) for each patient by combining relative weights for certain of the patient's demographic factors with the weights of all HCCs covering diagnoses submitted on Medicare claims for that patient from certain sites of service during the calendar year. The individual patient's RAF scores are then averaged and this average RAF is multiplied by the base payment rate established by Medicare for the organization.

An HCC will not be included if one of its constituent diagnoses is not included. Each HCC is included only once in the RAF calculation. Once a diagnosis from an HCC has been submitted, other diagnoses in the same HCC have no impact.

RAF calculations are derived from claims submitted for physician offices and hospital inpatient and outpatient departments.

Today, HCCs are also used for risk adjustment of many quality and pay-for-performance measures for clinicians and hospitals, including the Merit-based Incentive Payment System (MIPS), the Hospital Value-based Purchasing Program (VBP), the Hospital Readmissions Reduction Program (HRRP), and the Hospital-Acquired Condition Reduction Program (HACRP). Hence, it is important to capture all diagnoses comprising HCCs from the RAF sites of service (including hospitals) and ensure assignment of the correct RAF.
Dr. Pinson is a certified coding specialist, author, educator, and cofounder of Pinson and Tang, LLC, and is based in Chattanooga, Tenn. This content is adapted with permission from Pinson and Tang, LLC. The views expressed in this column are those of the author and not intended to replace authoritative sources for documentation and coding.

Information referenced from

CDI HCCs Chart

Open Notes refers to the automatic release of visit notes to patients and their proxies via the NYP Connect Patient Portal, unless a clinician blocks the release due to allowable exceptions to prevent physical harm or protect patient privacy.
The goal of Open Notes is to improve information transparency, empower patients to engage in their care, and improve communication between patients and providers.