The Growing Role of Artificial Intelligence in Ambulatory Health Care

 

Introduction

Yesterday, I wrote about the use of Robotic Process Automation (RPA) in health care. Today, I want to discuss the growing role of artificial intelligence in ambulatory health care.

Artificial Intelligence (AI) is reshaping nearly every aspect of modern health care, and ambulatory care settings such as outpatient clinics, physician offices, and same-day surgery centers are no exception. Ambulatory health care has traditionally relied on high patient volume, efficient workflows, and rapid decision-making. With AI emerging as a key enabler of smarter operations, organizations are finding new ways to enhance efficiency, improve diagnostic accuracy, and strengthen patient engagement outside of hospital walls.

What AI Means for Ambulatory Health Care

AI in ambulatory care involves the use of machine learning, natural language processing, and predictive analytics to automate tasks, assist in clinical decision-making, and deliver personalized patient experiences. Unlike inpatient settings, ambulatory care focuses on long-term, short-term and preventive services where time and data coordination are critical. AI systems can quickly analyze patient data, detect patterns, and offer actionable insights that help clinicians make faster and more informed decisions.

Key Use Cases of AI in Ambulatory Care

1. Predictive Scheduling and Resource Management

AI algorithms can analyze historical patient flow, appointment types, and seasonal trends to optimize scheduling and reduce no-show rates. Predictive analytics helps allocate staff and resources more effectively, improving both operational efficiency and patient satisfaction.

2. Clinical Decision Support

AI-powered systems can assist clinicians by analyzing patient data, lab results, and medical history to recommend potential diagnoses or treatment options. In an outpatient setting, this can speed up consultations and ensure early detection of chronic conditions such as diabetes, hypertension, or COPD.

3. Patient Engagement and Virtual Assistance

AI chatbots and voice assistants can manage routine communication such as appointment reminders, pre-visit screenings, and post-visit follow-ups. This frees up administrative staff while improving accessibility for patients who prefer digital interaction.

4. Documentation and Coding Automation

Natural language processing (NLP) tools can transcribe clinical notes, extract relevant information, and automatically assign billing codes. This reduces manual documentation, minimizes errors, and allows clinicians to spend more time with patients.

Ambient listening technology such as that provided by Nuance DAX Copilot and Abridge can listen in on a patient encounter and transcribe the pertinent information into the note section of the patient’s chart in the electronic health record (EHR) system, completing 80-85% of the encounter note for the provider to later review, edit and sign. This frees the provider up to have a more positive interaction with the patient without having to be buried in a laptop taking notes. It also makes the encounter more efficient, allowing the patient to be able to discuss more of their issues with the provider leading to better overall outcomes.

5. Predictive Analytics for Identifying At-Risk Patients

AI algorithms can be applied data stored in an EHR system using a risk-scoring model looking at such data as medical history, social determinants of health, lab results and trended vital stats, treatment plans, medications and urgent care or emergency department and/or inpatient admissions to identify patients who may be at risk of being admitted or readmitted to the hospital. Identifying these patients allows the provider to reach out and potentially intervene to prevent an admission or readmission which could make a huge difference in the health of the patient. Waiting until a condition worsens to the point where the patient has to be admitted to the hospital could have adverse effects on the future health of the patient. Not to mention the huge risk of potential hospital-borne infections, especially for those patients who are immune-compromised. Additionally, these avoiding these preventable admissions or readmissions free up health care dollars and resources that can applied elsewhere to further the health of the community.’

This is rather new use that is still developing and will continue to evolve as more and more data is used to train and refine the models and experts continue to calibrate them. This could also lead to breakthroughs in identifying new protocols and further developing and applying existing ones.

6. Population Health and Risk Stratification

AI can aggregate and analyze data across patient populations to identify those at higher risk of hospitalization or disease progression. Ambulatory clinics can use these insights to implement targeted preventive interventions and reduce avoidable admissions.

Protecting Patient Health Information (PHI) in an AI Environment

As AI systems handle sensitive health data, maintaining privacy and compliance with regulations such as HIPAA is essential. The use of AI in ambulatory care introduces specific security challenges, including the protection of EHRs and the safe handling of data used for model training.

To mitigate these risks, organizations should adopt the following safeguards:

 

1. Data De-Identification: Remove personally identifiable information before using data to train AI models.
   
   
2. Encryption and Access Controls: Encrypt all PHI at rest and in transit and use role-based access to limit data exposure.
   
   
3. Audit Trails and Monitoring: Maintain detailed logs of who accesses data and when to ensure accountability.
   
   
4. Vendor Due Diligence: Evaluate AI vendors for compliance with HIPAA, HITRUST, and other relevant standards.
   
   
5. Human Oversight: Keep clinicians and compliance officers involved to review AI recommendations and identify potential biases or anomalies.
   
   
6. Local Storage and Execution of Models: Wherever and whenever possible, store and run AI models locally. While this requires more compute resources and deeper knowledge to train the models, it ensures that PHI doesn’t leave the organization through the use of a shared model. As models become more specialized to particular tasks and industries, they are also becoming much smaller. Something the size of ChatGPT is not required to run the use cases that we just discussed as models like ChatGPT, Gemini, Claude, etc. are created to handle a wide-open array of questions and requests which require an enormous breadth of data, while the uses we just discussed only require specialized data, such as patient data, charges, etc. making them substantially smaller in size. In fact, some models have been demonstrated that can be run on just a laptop and accomplish amazing tasks! Some vendors may also train and deliver models for on-premises use that can be delivered and retrained at regular intervals.
   
   
7. Create an Organizational Policy Covering the Use of AI: Just as most organizations have policies covering security, having an AI policy that governs the use of AI in the organization is key. Additionally, these policies should be written in such a way that they can be evolved and updated as the technology and use of it grows.

Security and privacy must be treated as foundational elements of responsible AI use in health care.

The Future of AI in Ambulatory Health Care

The future of AI in ambulatory care is deeply connected to interoperability and precision medicine. As electronic health records, wearable devices, and remote monitoring systems continue to generate vast amounts of data, AI will play a central role in integrating this information and making it clinically useful.

In the coming years, we can expect advances in:

• Personalized treatment recommendations that adapt to individual patient profiles.
  
  
• AI-Driven Preventive Screening Tools that analyze biometric, genomic, and lifestyle data to identify early indicators of disease before symptoms appear.
  
  
• Automated Clinical Workflow Optimization that continuously monitors clinic operations, identifies process bottlenecks, and recommends real-time adjustments to improve throughput and efficiency.
  
  
• Emotion and Sentiment Analysis for Patient Interaction that enables AI to assess tone and engagement during patient communications, helping clinicians identify patients at risk of anxiety, depression, or disengagement from care.
  
  
• AI-driven triage systems that guide patients to the appropriate care setting before they arrive at a clinic.
  
  
• Synthetic Data for Research and Model Training that allows developers and researchers to create realistic, privacy-safe datasets for testing and innovation without exposing real patient information.
  
  

The most effective AI systems will act as augmented intelligence, enhancing rather than replacing human clinicians. When designed responsibly, AI can empower health care teams to deliver more efficient, data-driven, and compassionate care.

Conclusion

AI is transforming ambulatory health care by reducing administrative burdens and allowing clinicians to focus on what matters most: patient well-being. By combining automation with strong data governance and security practices, health care organizations can confidently adopt AI while maintaining patient trust and privacy. As technology continues to advance, AI will help make outpatient care faster, more accurate, and more human-centered.