Como médico en ejercicio y ex director de innovación, experimenté de primera mano cómo el creciente número de tareas administrativas está llevando al agotamiento de los clínicos y se está convirtiendo en una barrera importante para la atención directa al paciente. 

La aplicación cuidadosa de la inteligencia artificial (IA) en la atención médica tiene el potencial de transformar por completo la forma en que se brinda atención en los Estados Unidos. Entre sus aplicaciones más transformadoras se encuentra el asistente médico de IA, que tuvo una gran adopción en 2024. 

Al escuchar en tiempo real las interacciones paciente-clínico y generar automáticamente documentación clínica detallada, los asistentes de IA ahora están ahorrando a los clínicos horas de papeleo, reduciendo el agotamiento y permitiendo más tiempo para la atención directa al paciente. 

However, as health systems rush to deploy these technologies, one critical feature is often overlooked — coding awareness..

What is coding awareness?

Como médicos, estamos entrenados para enfocarnos en brindar la mejor atención clínica a nuestros pacientes.

No necesariamente estamos entrenados en cómo nuestra documentación impulsa los procesos posteriores. La codificación de evaluación y manejo, la garantía de DRG y la puntuación de HCC no forman parte de nuestro currículo educativo. Sin embargo, estos procesos son esenciales para la integridad financiera de una institución, la calidad y el ajuste de riesgos de las poblaciones, y la conducción de investigaciones que salvan vidas, registros de enfermedades y resultados informados públicamente.

Las plataformas de IA deben ser entrenadas para apoyar los matices de los procesos críticos posteriores.  

First defined by the American Academy of Professional Coders (AAPC), coding awareness is the ability of an AI medical scribe to navigate the labyrinth of complex medical billing and coding systems and ensure that the documentation it generates is compliant with coding and billing rules. This involves more than just a superficial familiarity with billing codes. A truly coding-aware AI must:

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Comprehend the nuances of thousands of codes – From ICD-10 and CPT codes to the ever-expanding list of modifiers, medical billing is a detailed and dynamic field. An effective AI scribe must understand these intricacies to structure documentation appropriately.

Incorporate distinct logic for specific codes – Every medical code has its own logic and rules. For example, coding for a particular diagnosis might require documentation of a symptom with specific language, treatment plan, or follow-up recommendation. AI scribes must be able to apply these rules accurately.

Use precise, code-compliant language – The phrasing in clinical documentation must reflect the specific language required by billing codes. Ambiguity or imprecision can result in rejected claims or compliance issues. In certain billing arrangements, the output of clinical documentation must reflect the specific documentation required by certain diagnoses codes. HCC capture requires MEAT-compliant documentation.

Without coding awareness, AI scribes can — and do — produce documentation that is incomplete, non-compliant, or inconsistent with the selected billing codes. This oversight is more than a technical hiccup; it’s a systemic risk.

The risks of “coding naive” AI scribes

I’ve met with health system leaders across the country who have deployed “coding naive” medical scribes. At the beginning, these organizations experienced a major increase in clinician satisfaction and significant decreases in documentation time. However, as their deployments continued, they saw massive increases in Clinical Documentation Integrity (CDI) queries when documentation that was generated did not support selected codes. This results in major downstream issues for the organization. 

AI scribes that lack coding awareness introduce significant challenges for healthcare providers and administrators. When documentation doesn’t align with billing codes, it gets flagged by CDI and revenue cycle management teams. This leads to:

Increased administrative burden – Instead of alleviating the administrative load, a poorly designed AI scribe can exacerbate it. Clinicians and administrative staff are left to correct errors or fill in gaps manually, negating the intended efficiency gains.

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Compliance risks – Incorrect or incomplete documentation can lead to audits and claim denials. For health systems already struggling with tight margins, these risks are unacceptable.

Inaccurate billing and problem list adjudication – Documentation that does not accurately and thoroughly capture the encounter results in underbilling, whether it be reduction in E/M codes, or missing add-on CPT codes that were in fact substantiated by the care provided.

Delays in patient care – Documentation discrepancies may require clinicians to revisit notes or clarify details after the fact, delaying billing and potentially delaying follow-up care for patients.

The importance of coding awareness

Coding-aware AI scribes provide solutions to these challenges by embedding CDI knowledge directly into their systems. As AI tools are always accountable to humans, this technology allows both clinicians and professional coders to operate at their top of license. This proactive approach saves time, reduces administrative burdens, and supports better financial outcomes for healthcare organizations.

The path forward

To fully leverage the potential of AI medical scribes, healthcare organizations must prioritize coding awareness in their adoption strategies. This includes evaluating AI tools based on their ability to:

Support accurate coding decisions at the point of care

Proactively prompt if a selected code is not supported by documentation

Provide comprehensive compliance with payer requirements

Demonstrate proven impact on CDI metrics

AI scribes that lack coding awareness risk undoing years of CDI progress, increasing claim denials, and creating new administrative challenges. In contrast, coding-aware AI scribes can become indispensable tools for improving workflow efficiency, enhancing reimbursement accuracy, and ultimately delivering better patient care.

By insisting on coding-aware AI solutions, health systems can ensure that these technologies fulfill their transformative promise, reducing clinician burnout and administrative burden, improving patient care and communication, and accurately capturing what occurred during an encounter. The journey to smarter healthcare must prioritize compliance, precision, and process integrity as foundational elements of AI deployment. 

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Furthermore, any deployment of AI must always be accountable to the clinicians and healthcare workers that it was built to support, elevating and enabling their work. Clinical documentation serves a multitude of healthcare professionals, and so should our AI platforms.

Photo: FG Trade, Getty Images

William H. Morris, MD, MBA, es un distinguido médico y líder en tecnología de la salud, actualmente se desempeña como Director Médico en Ambience Healthcare. En este rol, impulsa la visión de la empresa para la IA clínica, colaborando con los principales sistemas de salud para transformar los flujos de trabajo de los clínicos y mejorar la atención al paciente.

Antes de unirse a Ambience Healthcare, el Dr. Morris fue Director Médico de Información en Google Cloud Healthcare and Life Sciences, contribuyendo a innovaciones en atención médica de vanguardia. Anteriormente ocupó puestos de liderazgo en la Clínica Cleveland, incluido el de Director de Innovación y Director Asociado de Información, supervisando sistemas clínicos de TI y avances en TI de la salud. El Dr. Morris está certificado en Medicina Interna. Obtuvo su título de médico en la Escuela de Medicina de la Universidad Case Western Reserve y completó su residencia en el Centro Médico Beth Israel Deaconess, Harvard Medical School. El Dr. Morris también tiene un MBA de la Escuela de Administración Weatherhead de la Universidad Case Western Reserve.

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