Artificial Intelligence in Healthcare: Operational Impact for Health Information Professionals

Famey Lockwood
2 days ago
5 min read

Updated: 20 hours ago

AI is impacting healthcare across the continuum of care at an unprecedented rate. The Healthcare Professional is not a passive user of AI—they are essential stewards of its application – data integrity, compliance, and ethical practice.

Artificial intelligence (AI) is no longer a conceptual innovation in healthcare—it is actively embedded in clinical and operational environments. What was once framed as a future disruptor is now a present-day force shaping how healthcare professionals diagnose, document, and deliver care.

Artificial Intelligence in Healthcare: Operational Impact for Health Information Professionals

How far have we come?

Growth & Adoption Trends

The acceleration of AI in healthcare is measurable:

🪞 AI-related healthcare publications increased significantly from 158 in 2014 to 731 in 2024

🪞 The global AI healthcare market is projected to grow at a compound annual growth rate (CAGR) of ~47.6%, from $11.2 billion in 2023 to an estimated $427.5 billion by 2032

Knowing these statistics, one might conclude that the life and career of the Healthcare Professional has accelerated however, increased adoption does not uniformly translate to improved efficiency across all clinical domains.

Is the healthcare professional operating more efficiently?

AI is beginning to deliver measurable improvements, particularly in workflow optimization and administrative burden. However, its impact is most visible in areas closely aligned with HIM functions.

Some valid points:

1️⃣ Clinical Decision Support and Operational Efficiency

The integration of AI enhances clinical decision-making while streamlining administrative processes, contributing to overall system efficiency.

For HIM professionals, operational efficiency is reflected in the daily demands of documentation review, coding accuracy, compliance oversight, and data quality management.

2️⃣ Automation of Administrative Workflows

AI applications now support workflows:

🪞 Automated EHR documentation capture

🪞 Natural language processing (NLP) for chart review

🪞 Computer-assisted coding (CAC) for “first-pass” code assignment

🪞 Clinical documentation improvement (CDI) prioritization

AI is increasingly performing initial chart analysis and code suggestion, allowing HIM professionals to shift from manual task execution to validation, auditing, and exception management.

3️⃣ Burnout and Cognitive Load

Administrative burden remains a significant contributor to burnout across healthcare roles, including HIM professionals managing high volumes of records and the continuously changing coding requirements.

AI-driven tools offer a meaningful intervention—not simply by increasing speed, but by reducing cognitive overload and preserving clinical focus.

In this context, AI functions as a tool for cognitive support and workload optimization, contributing to more sustainable professional practice.

Where Do We Go From Here?

As AI becomes embedded across EHR systems, coding platforms, and operational infrastructure, two domains become critical: data governance and ethics.

▶️ Data Governance: A Core HIM Responsibility

Effective AI implementation depends on robust governance framework—an area where HIM professionals are uniquely positioned to lead.

Key components:

🪞 Data quality and integrity

🪞 Patient privacy and security

🪞 Regulatory compliance

🪞 Standardization of coding and documentation practices

🪞 Bias mitigation and equity

Our current healthcare model affords much “trust” in the medical professional. As AI becomes integrated into documentation and decision-making processes, that trust must extend to the data and systems that support them.

HIM professionals play a critical role in ensuring that AI outputs are accurate, auditable, and compliant with regulatory and organizational standards.

▶️ Ethical Considerations: AI-Driven HIM Workflows

The medical community, along with many other professions, are asking valid questions about the use of AI in healthcare from an ethical perspective.

The integration of AI introduces complex ethical challenges that are still evolving:

↳ Regulatory gaps: Existing frameworks such as HIPAA were not designed to address AI-driven data processing and decision systems

↳ Documentation and coding risks: AI-assisted coding may introduce risks of upcoding or under-coding, with financial and legal implications

↳ Bias and fairness: AI systems trained on incomplete or unrepresentative data may reinforce disparities

↳ Accountability: Responsibility for AI-influenced coding and documentation decisions remains an evolving legal and operational question

Particularly in revenue cycle management, AI must be carefully governed to ensure that optimization does not unintentionally lead to non-compliant or unethical practices.

The Role of the Healthcare Professional:

In this evolving landscape, healthcare professionals are not passive users of AI—they are essential stewards of its application – data integrity, compliance, and ethical practice.

Critical to the HIM career:

📜 Maintain current knowledge of AI applications in healthcare

📜 Understanding how AI tools are implemented within their organizations

📜 Strengthening expertise in auditing, validation, and exception management

📜 Engaging in professional organizations such as the American Health Information Management Association and American Academy of Professional Coders

📜 Pursuing emerging AI-related certifications and education opportunities

Engaging in professional organizations during the rise of AI not only helps individuals stay relevant but also influential in shaping how AI is deployed across healthcare systems.

The Insight

Artificial intelligence is no longer a distant disruptor—it is a present and growing force in healthcare.

AI is not a replacement for the healthcare professional but will function as a powerful partner now and for the future.

Opportunities in healthcare are expanding as AI transforms the field — offering new roles, careers, and leadership opportunities. But with these opportunities comes a responsibility: each of us must actively engage in learning about both the benefits and limitations of AI, so we can apply it ethically, effectively, and confidently in our professional practice.

Case Study in Practice: Preventing Overbilling with AI

In a recent review, a provider submitted a claim for a leg brace billed at $30,000—far exceeding the Medicare benchmark of $800. Historical data revealed repeated submissions of the same procedure code over several years, resulting in thousands of dollars of overpayments before detection.

Traditionally, investigating such claims required a manual process: benchmarking, historical claim queries, pattern identification, and formal escalation. While effective, this retrospective approach delayed interventions and allowed financial loss to occur.

In an AI-enabled workflow, real-time anomaly detection and automated benchmarking could flag such high-risk claims before payment. Machine learning models could recognize repeated billing patterns across providers, generate risk scores, and prioritize claims for expert review.

The Clinical Informationist plays a pivotal role in this workflow, ensuring data integrity, validating flagged anomalies, and translating AI outputs into actionable decisions. This case underscores how clinical expertise, coding knowledge, and AI capabilities intersect to prevent financial loss and improve operational efficiency.