Digital channels continue to expand communication between health systems and patients, but the phone remains a critical point of contact across the entire patient journey. Patients frequently call their providers for everything from scheduling and appointment changes to insurance questions, prescription issues, and urgent concerns about their care.
Calls are especially important not only for prospective patients exploring services, but also for existing patients managing ongoing care, confirming appointments, or seeking clarification about next steps in treatment.
Despite the importance of the phone in helping callers navigate their health journey, these interactions are commonly undervalued. Why? Many healthcare leaders believe that extracting data from voice calls is hindered by HIPAA or perceive that retrieving meaningful information from calls is difficult or time-consuming. Both assumptions are incorrect.
Voice calls contain a wealth of unstructured data. Conversation intelligence (AI-powered analytics that interpret call content, sentiment, and outcomes) turns these conversations into insights that can help enhance patient experience and improve organizational performance. Instead of treating calls as isolated interactions, health systems can analyze them at scale to better understand patient needs, operational gaps, and the factors that influence whether patients successfully access care.
Healthcare organizations face constant challenges that directly affect patient access, satisfaction, and operational performance, such as missed appointments, unresolved patient questions, scheduling delays, and communication breakdowns. Understanding why these issues occur is often difficult because the root causes are buried within thousands of patient conversations. Traditional call handling systems rarely capture these insights, making it difficult for organizations to identify recurring barriers or understand why patients fail to complete key steps in their care journey.
Conversation intelligence can analyze calls at scale to uncover patterns such as frequent scheduling obstacles, unanswered insurance questions, or confusion around appointment preparation. For example, a patient may call to reschedule an appointment but encounter limited availability, long hold times, or unclear instructions. These interactions can ultimately contribute to appointment cancellations or no-shows. By identifying these patterns across thousands of calls, healthcare organizations can address operational gaps that lead to missed care opportunities for patients.
By transforming unstructured call data into actionable insights, healthcare teams can improve workflows and deliver a more responsive and personalized patient experience.
Missed appointments pose a major financial and operational challenge for U.S. health systems, costing the industry up to $150 billion annually. However, reducing no-shows requires understanding why patients miss appointments in the first place. Conversation intelligence helps uncover the underlying causes that contribute to missed visits, such as scheduling difficulties, unanswered questions about insurance coverage, or confusion about appointment logistics. With better visibility into these issues, health systems can proactively address barriers that prevent patients from completing their care.
Conversation intelligence helps surface pain points in patient calls, evaluate call interactions, and provide health systems with actionable data. Using AI-powered analytics, organizations can pinpoint where issues occur in the patient journey. For example, if a caller asks about scheduling but encounters obstacles or unresolved questions, the interaction can be flagged as a potential risk for appointment cancellation or disengagement, allowing teams to follow up before care is disrupted.
Health systems’ patient access and engagement teams can adjust workflows and redirect call volume to alternative channels within their network as needed to meet demand and sustain patient engagement. These insights can help organizations reduce bottlenecks in scheduling, improve responsiveness, and ensure patients receive timely access to care.
As health systems increasingly deploy AI-powered agents to handle patient calls, a new challenge is emerging: understanding how well those systems are actually performing. Without clear visibility into AI-driven interactions, organizations risk introducing new friction into the patient experience at scale. Measuring AI agent performance, including accuracy, resolution rates, and patient outcomes, is becoming just as critical as evaluating human staff. Marchex is developing capabilities in this space to help health systems monitor and optimize AI performance alongside human agents.
Healthcare marketers and patient engagement professionals can identify barriers in the patient journey that prevent individuals from successfully navigating the system. Leaders also can monitor critical call alerts, which occur when high-value patient interactions involve dropped calls, unresolved questions, or scheduling complications. Instead of relying on anecdotal feedback, health system leaders can access clear data showing where patients experience friction, what questions remain unresolved, and what operational gaps may contribute to missed visits or delayed care.
Another key benefit of analyzing call data is measuring frontline staff performance. Are they answering patient questions accurately? Are they guiding callers toward the appropriate next steps in their care journey? Are they removing friction points and making the patient experience more seamless?
AI-powered agent behavior scoring is critical. Health systems need consistent, objective performance measurement that evaluates every call whether handled by a human agent or an AI assistant and focuses on behaviors that promote positive patient outcomes.
AI call scoring can analyze thousands of patient communications in hours instead of weeks. By automating these processes, health system leaders can identify which agents excel and who needs more customer service training. With this insight, leaders can create targeted coaching programs to ensure every caller interaction is handled properly.
In addition, tracking which marketing campaigns generate calls and matching those calls to patient visits helps a health system’s marketing team measure the return on investment for their outreach efforts. While improving marketing attribution is valuable, the larger opportunity lies in understanding how patient conversations reveal barriers to care and opportunities to improve access.
Another major trend is the use of AI in real-time patient call scoring. Currently, AI is used to analyze data and identify patterns, but the next step is to implement models that evaluate calls in real time to instantly prioritize high-value patients (for example, those more likely to need urgent care, have a high potential for retention, or necessitate additional follow-up). Real-time scoring helps with call routing so the most urgent or high-risk patient calls receive rapid attention.
These insights can reveal whether staffing needs adjustment, marketing strategies need refinement, or patient communication processes need improvement.
Ultimately, healthcare depends on patient trust. Every phone call represents a moment where patients seek guidance, reassurance, or access to care. Organizations that analyze these interactions and use the insights to improve patient access, reduce operational friction, and support frontline staff will set the standard for the future of patient engagement.
About Lee Barth
Lee Barth is a seasoned executive currently serving as the Vice President of Enterprise Sales at Marchex, a company based in Seattle, Washington.12 With over 10 years of experience in sales, business development, and team leadership, Lee has established himself as a successful professional in the industry.2
