Getting Started with AI Clinical Workflows

The healthcare industry is constantly under pressure to deliver better patient care while navigating rising costs, administrative burdens, and changing regulations. At the heart of this complex system are clinical workflows—these are processes that ensure patients receive timely and effective treatment. However, inefficiencies in traditional workflows can exacerbate challenges, with healthcare providers spending nearly 34% of their time on administrative tasks, leaving less time to focus on patient care.

Luckily, artificial intelligence offers a way to improve these workflows. With AI agents, healthcare organizations can optimize patient care, improve outcomes, and even reduce physician burnout.

Let’s explore how AI agents can reshape clinical workflows, address long-standing challenges, and provide a more efficient and patient-centered healthcare system.

What Are Clinical Workflows?

Clinical workflows refer to the structured processes that healthcare providers follow to deliver care to patients. These workflows ensure that every step—from identifying patient needs to monitoring outcomes—is carried out efficiently and effectively. At their core, they aim to maintain high standards of patient care and optimize healthcare delivery.

A well-designed clinical workflow integrates various elements, such as:

• Patient data
• Electronic health records
• Communication between teams

For example, a workflow might begin with gathering patient information, diagnosis, and treatment planning and end with follow-up care. These processes help healthcare organizations improve coordination, reduce errors, and enhance patient outcomes.

Four Key Components of Clinical Workflows

Every clinical workflow is made up of key elements that guide the process of delivering effective patient care. Notably, Arcee AI offers a cutting-edge solution to optimize these workflows. With Arcee Orchestra, healthcare organizations can create multi-step, purpose-built AI workflows tailored to each component of care. This end-to-end agentic AI solution is powered by specialized small language models (SLMs)–which ensure fast, accurate, and detailed results.

Here’s a closer look at the four essential components:

1. Identifying Patient Needs

Understanding what a patient requires is the first step in any clinical workflow. This involves gathering patient data through consultations, medical history, and diagnostic tools like medical imaging. Accurate identification means healthcare providers can prioritize and address urgent health concerns effectively.

Yet, diagnostic errors remain a major challenge. Research has revealed that 23% of patients admitted to ICUs or those who died in hospitals experienced a missed or delayed diagnosis. Among these cases, 17% of the missed or delayed diagnoses harmed patients, ranging from temporary setbacks to permanent injuries. The study highlights a grim reality: errors in testing and clinical assessments contributed to these diagnostic failures.

2. Engaging with Patients

Engaging with patients is about managing collaboration between patients and their healthcare providers. Using tools like electronic health records (EHRs), providers can share treatment plans, offer education, and ensure that patients feel empowered to participate in their care. This step often improves adherence to treatment and overall satisfaction.

For instance, for a patient going through post-surgery recovery, engagement begins with sharing a personalized rehabilitation plan via EHRs. AI tools can provide tailored exercise routines and dietary advice, while wearable devices can track progress via metrics like mobility and heart rate. Patients can receive automated reminders for follow-ups, and feedback on their recovery.

3. Providing Treatment

Treatment delivery combines medical expertise with efficient processes to address patient needs. AI systems can enable real-time decision-making, such as analyzing medical images, to assist with diagnoses or to tailor treatments based on patient-specific data.

For a patient with suspected lung cancer, AI-powered imaging tools analyze CT scans to detect abnormalities with high precision. Based on the results, an AI system can create a personalized treatment plan that includes chemotherapy, radiation, or surgery customized to the patient’s medical history and genetic profile.

4. Monitoring Outcomes

The final step involves evaluating the effectiveness of treatment. Monitoring tools, like AI-driven analytics, can track patient outcomes and highlight areas for improvement. For instance, healthcare organizations can use predictive analytics to identify patients at risk of complications–and be prepared to provide timely interventions if needed.

For example, imagine a clinical workflow for managing chronic illnesses like diabetes. It starts with collecting patient data during a consultation and educating the patient on lifestyle changes. Then, AI-powered systems assist in tailoring treatment plans based on the patient's specific condition, while ongoing monitoring tools check blood sugar levels and adjust care accordingly.

Challenges in Current Clinical Systems

While there are already established clinical workflows, healthcare providers still face significant challenges that can hinder patient care and operational efficiency–even when it comes to long-established clinical workflows. Let’s explore some of these challenges so we can better understand how to work around them. 

Inefficiencies in Patient Care

Overburdened healthcare providers often navigate disjointed processes, leading to delays in diagnosis and treatment. These inefficiencies can result in longer wait times, miscommunications, and reduced quality of care.

Fragmented Medical Records

Patient data is frequently scattered across multiple platforms and between multiple providers. This fragmentation increases the risk of errors. According to a recent study in the HIPAA Journal, 80% of severe medical errors stem from miscommunication between caregivers during patient handovers. 

A common scenario that illustrates this risk is a patient admitted to the hospital for chest pain who is discharged after receiving treatment for a suspected heart attack. However, the discharge summary fails to include results from a prior test showing signs of a blood clot. This leads to a delayed diagnosis of a pulmonary embolism, resulting in the patient’s condition worsening and requiring emergency care. This highlights the critical need for integrated medical records to prevent life-threatening errors.

Outdated Documentation Practices

Manual documentation still dominates many healthcare organizations, consuming valuable time and contributing to physician burnout. Incomplete or inaccurate records further undermine the quality of healthcare delivery.

Addressing these challenges can improve healthcare delivery. By integrating AI into clinical workflows, healthcare organizations can overcome these inefficiencies and deliver better patient outcomes.

How AI Agents Can Be Used for Clinical Workflows

AI agents can be used for clinical workflows to automate repetitive tasks, enhance decision-making with data-driven insights, and improve patient outcomes through personalized care. Let’s break down the key areas where AI agents are already making a difference:

Clinical Documentation

One time-consuming aspect of healthcare is maintaining accurate and comprehensive medical records. AI agents, such as those powered by specialized AI models with strong integration capabilities, can automate the process. For example, Arcee Orchestra supports hundreds of integrations, making it easy to centralize all healthcare data in one place. This ensures smoother workflows and more reliable access to accurate information. Specifically, you can use Arcee Orchestra to build up an intelligent AI agent to transcribe doctor-patient conversations in real time, generate customizable notes, and sync seamlessly with electronic health records (EHRs).

Clinical Decision-Making

AI agents can enable healthcare providers to make faster and more accurate decisions by analyzing large volumes of patient data in real-time. For instance, AO agents can process medical images to detect abnormalities like tumors, reducing diagnostic delays and improving patient outcomes.

For example, an AI agent powered by Arcee AI's model Spotlight (a specialized model for visual-text tasks) can bridge the gap between medical imaging data and textual information. This ensures that healthcare providers receive comprehensive insights to choose the most effective treatment plans.

Patient Outcomes

AI agents can enable faster diagnoses, personalized treatments, and proactive interventions, which all lead to improved patient outcomes. For example, when you build an AI agent powered by specialized models like CHIEF, which has achieved nearly 94% accuracy in detecting multiple types of cancer, you can significantly enhance diagnostic precision. These advancements reduce diagnostic errors, allow for earlier interventions, and improve patient recovery rates and quality of care.

Clinical Trials

AI agents can simplify the clinical trial process, from patient recruitment to data analysis. By analyzing patient data, AI agents can identify suitable trial candidates more efficiently than traditional methods. They also accelerate data processing, which helps researchers draw insights faster, and improves the overall efficiency of these crucial trials.

Other AI Workflows in the Healthcare Industry

The impact of AI agents extends beyond clinical workflows, transforming other aspects of the healthcare industry as well. Here are a few notable examples:

Healthcare Supply Chain Optimization

AI agents can improve supply chain management by predicting inventory needs, minimizing waste, and ensuring timely delivery of critical medical supplies.

For example, a forecasting AI agent can analyze past data and predict demand for medications or equipment. This reduces shortages and avoids excess stock.

Fraud Detection in Billing and Claims

AI agents can be used to analyze billing and claims data to identify patterns of fraud or misuse. Healthcare organizations can reduce financial losses and improve their operational integrity by flagging anomalies.

For instance, platforms like MediBuddy use AI-based systems like Sherlock to detect duplicate claims, manipulated documents, and pricing errors in real-time. Similarly, Optum employs an AI assistant to analyze large datasets, identify unusual billing patterns, and prevent improper payments throughout the claims process. These machine-learning models save time and resources, enhance accuracy, and reduce fraud.

AI as Surgical Assistants

Robotic systems powered by AI assist surgeons by providing better precision and reducing errors during procedures. They can also analyze real-time data during surgery to guide decision-making.

How to Start Implementing AI Workflows in a Healthcare Organization

As a healthcare leader, implementing AI workflows can be overwhelming. After all, the stakes are high—your workflows directly impact patients' health and well-being. But with the right steps, you can introduce AI into your clinical processes effectively. Here’s how you can get started:

Identify Key Areas for AI Integration

Begin by assessing your current workflows to pinpoint inefficiencies or bottlenecks. Areas like electronic health records (EHRs), patient data analysis, or clinical documentation are often prime candidates for AI integration.

For example, AI agents can automate repetitive tasks like data entry or improve patient care by providing real-time insights. Talk with your team and conduct an audit on all the repetitive and manual tasks. 

Ensure Data Readiness and Compliance

AI agents rely on high-quality data, so it’s important that your records are accurate, up-to-date, and comprehensive. Compliance with healthcare regulations like HIPAA is also crucial to protect patient data and maintain trust.

Select Appropriate AI Solutions

Choosing the right AI solution is essential to meet your organization’s goals and address specific healthcare challenges.

For example, tools designed for natural language processing can enhance clinical documentation, while AI agents for medical imaging improve diagnostic accuracy.

As mentioned,  Arcee Orchestra offers enterprise-grade agentic solutions, enabling organizations to build custom AI agents/workflows powered by purpose-built small language models (SLMs). Key capabilities include:

• No-Code Workflow Creation: Easily create sophisticated AI workflows with a no-code interface. Start with templates and customize them to fit your processes and data.
• Task-Specific AI Models:  Intelligent task routing ensures that the most suitable SLM handles each job to deliver accurate and reliable results tailored to your needs.
• Seamless Integrations: Integrates with hundreds of tools and platforms, such as Jira, Salesforce, and Dropbox, consolidating all data into one centralized system for easy access.
• Scalable Deployment Options: Deploy workflows on-premises, in your virtual private cloud, or in Arcee’s secure cloud environment, ensuring compliance with even the strictest data protection standards.
• Enhanced Productivity: Automate repetitive tasks like report generation, compliance monitoring, and issue escalation, achieving up to 50% efficiency gains across entire departments.

With Arcee Orchestra, healthcare organizations can tackle complex workflows securely and efficiently.

Develop a Comprehensive Implementation Plan

Create a detailed roadmap that outlines how AI will be integrated into your workflows. Include timelines, resource allocation, and potential challenges. Staff training should also be a priority to ensure healthcare providers are confident using AI tools effectively.

Monitor and Evaluate Performance

After deploying AI workflows, healthcare organizations should regularly assess their effectiveness to ensure they meet objectives. Monitoring key performance indicators, gathering user feedback, and analyzing patient outcomes can optimize AI integration.

According to a study by Microsoft and IDC, 79% of healthcare organizations are already using AI technologies, with many of them reporting measurable financial benefits. The study found that organizations achieved a return of $3.20 for every dollar invested in AI, often realizing ROI within just 14 months. These figures emphasize the importance of implementing AI and continuously evaluating its impact to maximize results.

Ready to explore AI solutions for your organization? Book a call with the Arcee team to see how we can help transform your workflows.

FAQs

How can AI workflows be used in the healthcare industry?

AI workflows improve patient care, streamline clinical workflows, enhance medical imaging, and automate documentation. They also support decision-making, fraud detection, and supply chain optimization.

How are AI agents used in clinical decision-making?

AI agents analyze patient data and medical images to offer real-time, data-driven insights. This supports healthcare providers in diagnosing conditions faster, identifying treatment options, and reducing diagnostic errors for better patient outcomes.

How is AI used in clinical documentation?

AI workflows automate data entry by transcribing conversations, summarizing notes, and populating electronic health records (EHRs). This reduces errors, saves time, and allows healthcare providers to focus more on patient care.

Transforming Clinical Workflows with AI

AI is no longer a futuristic concept; it’s a tool that's already reshaping how healthcare is delivered.

While the process of integrating AI workflows into healthcare can seem complex, it offers a unique opportunity to improve care delivery and operational efficiency. By adopting AI agents thoughtfully and addressing challenges like data privacy and staff training, organizations can take meaningful steps toward a future where care is more efficient, accurate, and accessible for all.