Readers Write: AI to the Rescue: Revolutionizing Efficiency in Healthcare Workflows

AI to the Rescue: Revolutionizing Efficiency in Healthcare Workflows
By Caleb Manscill

Caleb Manscill, MBA is president of Vyne Medical.

The healthcare industry is at a tipping point. With rising demands for high-quality care, increasing financial pressures, and widespread staffing shortages, healthcare providers face an uphill battle to maintain efficiency while meeting patient needs.

Operational bottlenecks and administrative burdens have long weighed down progress, but a game-changing shift is underway: the rise of AI and machine learning. These cutting-edge technologies are not just tools. They are a jumping point for innovation, set to change healthcare workflows, optimize decision-making, and deliver better care outcomes.

The Role of AI and Machine Learning

The adoption of automation technologies in clinical and administrative workflows is accelerating at a fast pace. By 2029, the global workflow automation market is projected to surge to $34 billion, up from $21 billion in 2024, reflecting the pivotal role these technologies play in healthcare transformation. At the heart of this revolution, AI and machine learning are taking on the most pressing inefficiencies, reshaping operations to unlock productivity, accuracy, and cost savings.

Impact on Administrative Workflows and Resource Optimization

AI-powered solutions address some of healthcare’s most persistent challenges by automating time-intensive administrative tasks, allowing staff to focus on higher-value activities. For example, data transcription, a necessary yet manual process, can now be completed in just 30 seconds with over 90% accuracy, compared to the five minutes it once required. These gains drastically reduce errors and boost productivity without sacrificing quality.

Though the front-end processes are critical to getting things right, they’re only half the story. Beyond administrative tasks, AI also optimizes documentation, scheduling, and claims processing to ensure that back-end operations run smoothly. By streamlining these processes, organizations can eliminate redundancies, reduce operational overhead, and achieve greater financial stability. AI further enables leaders to strategically allocate resources, improving patient flow and enhancing revenue cycle management. Together, these improvements drive measurable efficiency and cost-effectiveness.

Enhancing Decision-Making and Clinical Workflows

AI also enhances clinical workflows by enabling smarter, data-driven decision-making. Through advanced algorithms, AI analyzes patient data to identify patterns, predict outcomes, and recommend treatment options, supporting clinicians in providing more personalized care. Process automation helps streamline clinical workflows by reducing manual processes, allowing care teams to spend more time with patients and less on administrative tasks.

For example, AI can prioritize urgent tasks, reduce delays in patient care, and foster collaboration across departments. The impact of these efficiencies includes improved patient experiences, reduced clinician burnout, and better overall care delivery.

Take a surgery order workflow as an example. When a hospital system receives a surgery order, teams traditionally need to extract key details manually and link them to the patient’s electronic medical record (EMR). By using AI and machine learning, much of this process is now automated. AI extracts data from the order, indexes it to the appropriate patient record, and forwards it seamlessly to the EMR system.

However, the next step, leveraging Generative AI, takes this automation to a higher level. Gen AI can resolve more complex challenges, such as identifying and associating the correct patient record when multiple entries exist in the EMR. Traditionally, humans spend significant time verifying patient information, such as matching dates, MRNs, or account numbers, across hundreds of transactions daily. Gen AI can take over this decision-making process for straightforward cases, sending the data directly to the EMR.

By tackling inefficiencies, reducing administrative burdens, and empowering smarter decision-making, this technology is setting a new standard for operational excellence. As healthcare systems continue to navigate workforce pressures and resource limitations, the integration of AI is no longer a luxury — it is an urgent necessity. With its potential to streamline workflows, enhance outcomes, and drive sustainability, AI is the key to building a more resilient and efficient healthcare ecosystem.