In today’s healthcare environment, provider groups are constantly navigating the dual challenges of cost containment while capturing the full scope of patient care. If you feel like your team is drowning in patient charts, struggling to document conditions to the right level of specificity, all while keeping up with the constant pressure of compliance and reimbursement deadlines, then the combination of clinical documentation improvement (CDI) and technology may offer a lifeline.

Clinical documentation improvement (also called clinical documentation integrity) is the backbone of accurate patient records, ensuring that documentation reflects the full scope of a patient’s health status. This is crucial in risk adjustment, where documentation deficiencies can lead to inaccurate reporting of patient conditions affecting both patient care and reimbursements.

The main objectives of CDI include:

• Improved patient outcomes: Ensure proper documentation leads to better-informed patient care and risk assessment.
• Improved documentation accuracy: Ensure that providers document to the highest level of specificity and provide clinical support for conditions asserted.
• Accurate code assignment: Reduce errors in coding to ensure accurate patient risk profiles.

Leveraging Technology in Clinical Documentation Improvement Programs

Technology is playing an increasingly important role in CDI. Tools powered by artificial intelligence (AI) and natural language processing (NLP) are improving the efficiency of CDI teams by supporting pre-visit chart prep and automating the identification of diagnosis opportunities.

Despite these advancements, however, AI’s effectiveness depends on quality algorithms, which are still evolving. This means human expertise still matters. Any CDI team that leverages AI-powered tools must review their work for accuracy. This is especially important for scenarios in which the specificity of the diagnosis is being determined, missing documentation that may change the code is being evaluated, or disease progression or resolution is being assessed.

Without human oversight, relying too heavily on AI technology can lead to significant risks, including:

• Unnecessary noise: AI tools may erroneously present unnecessary diagnoses to providers for review.
• Overcoding or undercoding: Without proper CDI expertise, automated systems may misinterpret patient data.
• Non-compliant queries: Improper use of AI could generate queries that do not meet regulatory standards, resulting in compliance issues.
• Adverse impact on care management: Missing, incomplete or incorrect documentation may negatively affect patient care and overall outcomes.

Building a Strong Clinical Documentation Improvement Team

Establishing a strong CDI team is crucial to improving documentation accuracy and patient outcomes, but many provider groups face challenges, such as staffing shortages, lack of physician buy-in, and managing large volumes of patient charts. Solutions include:

• Identifying and training internal talent: Engage experienced coders, clinicians, or nurses already in your practice that may be able to do this work. Once you demonstrate the return on investment, then expand your staff.
• Educational initiatives: To combat a lack of physician buy-in, hold educational sessions for physicians to improve documentation practices, communicate physician query response rates, and identity physician champions within your organization.
• Utilizing analytics: If your team is faced with an overwhelming amount of charts, try leveraging analytics technology to prioritize charts based on the highest burden of illness.

The Role of CDI in Inpatient vs. Outpatient Settings

Traditionally, clinical documentation improvement programs have focused primarily on inpatient care settings and capturing diagnostic-related groups (DRGs). However, the shift from fee-for-service towards value-based care models has moved this focus towards outpatient care settings, with the goal of accurately capturing risk adjustment factor (RAF) scores. This has influenced the need for more pre-visit programs — with CDI playing an integral role.

For outpatient visits, the high volume of patient encounters presents unique challenges for small and large provider groups alike. Independent physician associations (IPAs) and smaller groups often don’t have the technology, budget, or resources to deliver pre-visit programs, while large health systems have the tools but need the expertise or clinical teams to lead the change. On top of this, there are staff-specific challenges, such as the burden of needing to work outside of the provider’s EMR workflow and compliance concerns. Technology can be helpful in alleviating some of this administrative burden and can help make these pre-visit programs successful.

Why Invest in Pre-Visit Chart Prep?

Pre-visit chart prep has a two-fold benefit: improved patient outcomes, and appropriate reimbursement. By reviewing patient data prior to a visit with a patient, providers can not only anticipate potential health risks, such as comorbidities and disease progression, but also determine medication compliance. This way, a patient’s clinical needs can be addressed prior to and during the visit, improving efficiency in care and resulting in a more focused and positive patient experience.
Pre-visit chart prep also helps solve the problem of missing or incomplete documentation, which negatively impacts risk scores. Common issues affecting documentation include:

• Failure to document to the highest specificity (e.g., CKD 3 to 4, MDD single ep mild to severe)
• Reluctance to document certain diagnoses (i.e. morbid obesity)
• Failure to recapture ongoing diagnoses with inadequate clinical support in the documentation

Moving Away from Spreadsheet-Based Coding Tools

Today, many provider groups use a spreadsheet for pre-visit coding work due to its ease-of-use and low cost. However, a spreadsheet has many limitations, including a lack of collaborative ability, no easy reporting feature, slow/manual functions, inability to ingest data for pre-population, and lack of ability to scale for large volumes of charts.

Here’s where a CDI workflow tool powered by NLP technology can help. This technology can quickly scan patient histories and prior visit documentation to identify potential gaps and deliver these to a provider for review before the patient visit. Here are three key features to look for in an effective CDI workflow tool:

• Flexibility: The clinical documentation improvement tool should be compatible with different EHR systems to reduce administrative burden for your team, and be adaptable to single- and multi-location practices, as well as small and large practice groups.
• Collaborative functionality: Does your tool have the ability to allow diagnoses to be flagged for internal review so that your CDI team can collaborate on complex diagnoses directly within the platform?
• Useful/actionable reporting: Your CDI tool should be able to give your providers insight into the CDI team’s operational metrics, as well as provider response metrics.

Implementing a Clinical Documentation Improvement Workflow Tool

For providers looking to implement a CDI workflow tool within their own pre-visit programs, consider taking the following steps:

• Problem Awareness: Review patient charts to identify areas where documentation can be improved.
• Leverage Existing Tools: Utilize your Electronic Medical Record (EMR) system to embed opportunities for CDI.
• Earn Trust: Showcase best practices and build trust with providers by demonstrating how CDI tools can enhance patient care.

The integration of technology into CDI programs is creating new opportunities for provider groups to improve both patient outcomes and financial performance. However, technology should complement — not replace — human expertise. By leveraging AI and NLP for automation while maintaining a strong foundation of CDI professionals, healthcare provider organizations can achieve greater accuracy, compliance, and efficiency in risk adjustment.