Medical coding is one of the fastest-growing healthcare AI applications in revenue cycle management, but beware: Healthcare leaders must choose their AI coding vendor carefully. The wrong partner can create hidden costs that are difficult to overcome, including:
Choosing a vendor for AI in medical coding is a strategic decision that can affect long-term return on investment and financial sustainability. That’s why it’s important to ask detailed questions before signing a contract. In this article, we’ll cover six of those questions.
Many organizations focus on medical coding accuracy and productivity, but experienced coding leaders must also understand how the healthcare AI solution arrives at its recommendations, how much control coders retain, and whether the vendor designed the system around real-world coding workflows. Having these conversations can help buyers distinguish between AI that serves as a trusted assistant and AI that functions as a ‘black box.’
Dig into these questions:
- How does the healthcare AI communicate its level of confidence? Some platforms for AI in medical coding distinguish between high-confidence medical coding recommendations that may require minimal review and lower-confidence cases that should be routed to experienced medical coders.
- How much coding expertise do the people behind the AI have? Do certified medical coders, auditors, CDI specialists, and HIM professionals participate in developing, validating, and refining the solution.
- How transparent is each coding recommendation? Ask the healthcare AI vendor to demonstrate how the system links recommended codes to the specific clinical documentation that supports them.
AI should streamline routine work while allowing experienced coders to review complex cases, resolve exceptions, and ensure coding accuracy.
Dig into the following questions:
- Can you customize the workflows? Some organizations may want solutions for AI in medical coding to handle only low-complexity outpatient encounters, while others may use these solutions across a broader range of services with different review thresholds.
- How does the healthcare AI vendor handle coding discrepancies? Can coders easily override AI-generated recommendations, document the rationale, and provide feedback that helps improve future performance?
- Which cases are automatically routed to human coders? Complex inpatient encounters, surgeries, trauma cases, and records with incomplete or conflicting documentation often require additional expertise.
A reputable medical coding AI vendor should be able to explain its quality assurance methodology rather than simply citing a single overall accuracy percentage.
Dig into the following questions:
- How do you track and report quality over time? Look for dashboards that monitor coding accuracy, agreement rates between healthcare AI and human coders, denial trends, and audit findings.
- How does the vendor improve the AI over time? The healthcare vendor should have a structured process for using audit results and coder feedback to refine algorithms and adapt to changing coding guidelines.
- How does the healthcare AI vendor measure coding accuracy? Determine whether accuracy is based on internal testing, independent audits, or production data from existing clients.
- What coding accuracy rates has the healthcare AI vendor achieved by specialty? Performance may differ significantly between inpatient, outpatient, emergency department, radiology, surgery, and other service lines.
The healthcare AI vendor should provide dashboards that track productivity, coding accuracy, turnaround time, denial trends, quality scores, and financial impact so you can evaluate both AI and human performance.
Dig into these questions:
- Can you customize reports? Different stakeholders often require different levels of reporting and different performance indicators.
- Can you measure AI performance separately from human performance? Understanding which recommendations were generated by AI, accepted by coders, modified by coders, or rejected altogether can help identify where the technology adds value and where it may need refinement.
- Do the healthcare AI vendor’s dashboards provide drill-down capabilities? Coding leaders should be able to analyze revenue cycle management performance by facility, specialty, provider, coder, payer, encounter type, or service line to uncover trends and target improvement efforts.
- How does the healthcare AI vendor measure financial impact? Ask whether reporting includes metrics such as reimbursement improvements, reduced coding-related denials, productivity gains, labor savings, or return on investment.
- How frequently does the healthcare AI vendor update reports? Real-time or near-real-time dashboards enable organizations to identify issues quickly rather than waiting for monthly reports.
- Which key performance indicators (KPI) do the healthcare AI vendor’s dashboards track? Look for revenue cycle KPIs such as coding productivity, coding accuracy, turnaround time, first-pass claim acceptance rates, coding-related denial rates, discharge not final billed days, and case backlog.
Before selecting a solution for AI in medical coding, healthcare organizations should understand how the vendor safeguards protected health information (PHI) and supports HIPAA compliance.
Dig into these questions:
- How do you protect PHI at rest, in transit, and during AI processing?
- Is our patient data used to train or improve your AI models, and can we opt out?
- What is your breach notification and incident response process?
- Who can access our data, and do you use role-based access controls and audit logs?
- Will you sign a Business Associate Agreement?
Healthcare organizations should look for a healthcare AI vendor that acts as a strategic partner rather than simply a software provider.
Dig into these questions:
- Can the healthcare AI vendor help you adapt as your needs change? Whether your organization expands into new specialties or acquires additional facilities, the AI solutions vendor should be able to scale its services and adjust workflows accordingly.
- How does the healthcare AI vendor train coding staff? Ask whether coders receive role-specific training on validating AI recommendations, interpreting confidence scores, documenting overrides, and using reporting tools effectively.
- What happens after implementation? Determine whether the vendor providing the solution for AI in medical coding conducts regular business reviews to evaluate performance, identify workflow bottlenecks, and recommend process improvements.
- What implementation support does the healthcare AI vendor include? Ask whether the vendor provides project management, workflow assessments, integration support, user acceptance testing, and assistance during go-live.
- Who will be your primary point of contact? Understand whether you’ll have a dedicated customer success manager, coding consultant, or implementation specialist available to address questions and support optimization efforts.
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