The Elion Briefing

In-depth healthcare technology insights from our team of expert researchers

Patient Scheduling: Can AI Overcome Scheduling Complexity?

Patient scheduling is full of contradictions. Wait times for specialists average 26 days in major metro areas, yet no-show rates can exceed 30%, leading to lost revenue and inefficiency estimated at $150B.

Yet no-shows rarely result in an open schedule and plenty of time for documentation. Instead, physician burnout remains a huge problem. Just one component of that burnout, of course, is the incredibly high stakes: Delayed cancer screening or surgery can be life-altering for patients, so staff are constantly shifting to work in urgent appointments, overbooking based on estimated no-show rates, and attempting to add patients from the waitlist when there are last-minute cancellations.

What Are Patient Scheduling Tools?

Within our patient scheduling category, we include tools to book, manage, and optimize patient appointments across settings—including administrative staff scheduling, patient self-scheduling, appointment reminders, re-scheduling, and cancellation functionality. Self-service tools also often include features to help connect patients with the right provider and appointment type.

How Did We Get Here?

Prior to 2000, paper-based or basic electronic calendars were the norm, managed by front desk staff or call centers. In the early-2000s, EHRs like Epic, Cerner, and athenahealth introduced more basic rule-based scheduling tied to clinical workflows. Online self-scheduling and patient portals like Zocdoc, Kyruus, and MyChart followed shortly thereafter, leveraging somewhat more complex rules and business logic.

Emerging AI-powered and automated scheduling tools leverage AI-driven triage, chatbots, and predictive scheduling. While these newer tools may offer more value in terms of catering to patient preferences and offering accessibility outside office hours, they have fundamentally the same constraints as early paper scheduling.

Why Patient Scheduling Is Difficult

Patient scheduling is a complex technical problem, because humans are complex. Demand can be unpredictable, based on urgent versus elective appointments, cancellations, and emergency conflicts on the part of providers. Patient preferences also vary; some want the first available appointment, some a specific provider, and others a particular day or time. Therefore, there is no one right way to model the “best” available appointment time. Additionally, while some patients prefer to schedule online or outside normal work hours, many patients prefer to call to schedule over the phone, forcing providers to maintain solutions for both modalities.

Additionally, depending on specialty, the surrounding care ecosystem can be very complex. For example:

• Visit types significantly impact the amount of time needed (e.g. routine checkups vs. complex specialty procedures, new vs. existing patient) and urgency.

  
  

• There may be room and equipment dependencies like MRI machines, ORs, or infusion chairs that each have their own schedules.

  
  

• Insurance can cause bottlenecks as some appointments require pre-authorization or referrals; insurance coverage can also restrict which providers are available to a given patient, and scheduling workflows must integrate with eligibility verification to avoid last-minute denials.

  
  

• Finally, there are clinician-specific constraints and preferences around availability, e.g. multiple office locations, on-call times, or scheduling rules.

  
  

Patient Scheduling Vendor Landscape

Clearly, patient scheduling tools have their work cut out for them, and aim to manage all of this complexity to varying degrees. Generally, solutions fall into a few buckets:

• EHRs: While functionality is relatively simple, it’s very convenient that scheduling is embedded into the primary clinical workflow tool. Examples include: Epic, Oracle, athenahealth, Healthie.

  
  

• Specialized Scheduling Tools: These are standalone, HIPAA-compliant scheduling tools that range in complexity, cost, and integration into other systems like the EHR. Systems focused on hospitals have to solve the most complex issues, but there are also tools that work well for simpler cases. Many—though not all—of these solutions are specifically designed for administrative staff use, as opposed to patient self-scheduling. Examples include: Kyruus, Zocdoc, Acuity Scheduling, Cal.com, Cronofy, 10to8, Dexcare, Nimblr.ai.

  
  

• Digital Front Door Platforms: These EHR-integrated systems handle workflows including scheduling, patient intake and health screeners, two-way communication, and payment processing. Examples include: Luma, Clearstep, Notable, Tellescope, Weave, Qure4U, Blockit, Clearwave, Fabric, Finthrive Patient Access, Mend, NexHealth, OpenDoctor, and PatientPop.

  
  

• Agentic Approach: These solutions include AI agents to help manage patient appointments and scheduling without administrative intervention, based on many of the factors defined above (generally vendors are starting with the simplest use-case and building in complexity over time). There’s significant overlap here with Patient AI phone calls. Examples include: Puppeteer, Hyro, Assort Health, Syllable Patient Assistant, Parakeet Health, Clarion, and AgentifAI.

Where Patient Scheduling Is Headed

The reality is that newer AI scheduling solutions are not likely to open up enormous swaths of time in providers’ schedules; patient scheduling remains a structurally rigid part of healthcare operations, where incremental improvements matter more than sweeping transformations. “Predictive optimization” of schedules has fundamental limitations. No-show risk models and AI-suggested schedule improvements can offer meaningful ROI by getting more patients seen, but the larger bottlenecks (clinical constraints, provider rules, insurance barriers) aren’t fundamentally AI problems; they’re workflow and policy problems.

As such, the vendors to succeed in this space need to check a couple distinct boxes:

• Where we see the most value accrued is at the enterprise software level, where the most complex business logic is applied. As a result, software platforms from the 2010s have continued to dominate, as they already have the product surface area.

  
  

• If a vendor can provide that level of integration and complexity, there's tangible ROI to be had through streamlining patient interactions, improving scheduling modality options, and lowering administrative costs, rather than fully replacing human schedulers or overhauling complex workflows.

In many ways, this mirrors the continued dominance of EHRs for clinical documentation and other key clinical workflows. It’s difficult for new vendors to catch up to product complexity, but there are specialties, workflows, and use-cases where newer AI-first vendors have seen success. There’s also the potential for smaller upstarts to win through deep integration with an existing player, if the AI vendor can offer functionality that is valuable enough and difficult enough to duplicate.

Elion Named to Digital Health New York’s DH100!

We’re incredibly proud to announce that Elion has been named to the Digital Health 100 list! This annual list, published by Digital Health New York, celebrates the most promising startups in the NY region. See the full #DH100 list and dive into the latest trends in the 2025 New York Healthcare Innovation Report here.

A glimpse into what’s top of mind for healthcare IT executives

Until next week!
Bobby & Team

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