How AI Can Help with Patient Self-Scheduling

As every patient knows, scheduling a doctor’s appointment can be a real nightmare. Patients are exasperated with irritating phone menus, endless wait times, and cumbersome scheduling procedures. Meanwhile, healthcare staff are drowning in relentless scheduling calls and laborious booking procedures that leave them exhausted and burnt out. 

Clearly, the current system isn’t working for anybody. Healthcare facilities are aware of the problem, but many struggle to do anything about it. Nonetheless, they remain committed – at least in theory – to taking some kind of action. Some 64 percent of healthcare organizations have said that scheduling is a top priority for 2023. The question is, what kind of action should they be taking? 

Self-scheduling 

The solution is self-scheduling, a process that dramatically improves the patient experience, slashes call volume, and streamlines workflows by freeing up time and resources. Patients seem totally on board with this switch: 80 percent of patients have said they strongly prefer providers who offer the convenience of online self-scheduling. What’s more, some 30 percent of patients say they would switch providers if the new provider offered self-scheduling.

However, for many providers, the prospect of implementing self-scheduling is fairly daunting. Many organizations think self-scheduling might strip control from their doctors, who often have a particular way of organizing their appointments. Others think they can rely on their EHR for self-scheduling, but this is rarely the case. EHRs are quite useful at the hospital bedside and for other in-person clinical functions, but they were not designed to address the specifics of scheduling. This means the burden still falls on overburdened staff members to book complicated appointments that EHR templates can’t automate.

The driving force behind all scheduling challenges is complexity. Most existing scheduling systems are constrained by the unique characteristics of the healthcare sector, an industry in which an unusually large set of dynamic variables constantly interact. For example, healthcare organizations often have multiple locations, which may not all offer the same services. Additionally, patients frequently require various consultations with different departments or specialties within one location. As a result of this complexity, 70 percent of patients, even if they begin to schedule their appointments online, are ultimately redirected to call centers. 

Leveraging AI

Because of health care’s complexity, many if not most organizations still rely on human staff to manually match patients with the optimal appointment and provider. But recent advancements in AI mean that, no matter how complicated your scheduling process is or how many variables you need to take into account, AI can help to streamline or even automate your scheduling process. Automating manual scheduling tasks allows your organization to offer patients a more robust, comprehensive scheduling service. 

To accomplish this, you’ll need to integrate AI into your existing EHR platform. The first step is aggregating all patient, provider, and practice data into a single system. This means combining details from sources like insurance, medication histories, patient surveys, and more. The consolidated data set provides a comprehensive view of resources, experiences, and population needs that AI requires to function properly. With integration at the data level, AI can achieve a meaningful understanding of care contexts and automate scheduling in a truly useful way.

Once you have a unified data system, develop an AI algorithm customized to your organization’s goals. For example, AI could help coordinate follow-up care, personalize health goals, or streamline appointment booking. But AI will likely never fully replace humans in these functions. Focus the algorithm on augmenting human capability rather than replicating it. Most importantly, get buy-in from your staff during the integration process. Work with providers and technical experts to determine how AI can enhance care in a way that feels natural and helpful. 

To integrate the AI into your EHR platform, create a carefully mapped architecture focused on security and compliance. Your technical team should understand EHR data pathways,  application programming interfaces (APIs), and the extract, transform, and load (ETL) process to build safe data transfer mechanisms. IT staff must create a framework ensuring that you’re meeting regulatory requirements like HIPAA and that all integration points minimize privacy risks. This architecture is the foundation supporting how technology and data interact. Without effective architecture grounded in compliance, AI integration will falter.

Your technical team must work to identify the specific EHR, APIs, and ETLs applicable, including any restrictions or security protocols your system needs to access them. Your team can then determine which options provide the best pathways for flowing data between the AI algorithm and EHR according to your organization’s needs and compliance standards. Choosing optimal APIs and ETLs is crucial to building an integration that protects data privacy and transfers information accurately. 

Applying APIs and ETLs strategically through a security-minded framework helps ensure data flows between systems safely and optimally. ETLs make information usable across technology stacks with different data formats and languages. APIs create connection points allowing platforms to share that information dynamically. Together, ETLs and APIs act as the infrastructure through which integrations either thrive or falter based on the architecture built on top of them.

However, healthcare organizations need to recognize that AI alone cannot solve their challenges without robust human governance, oversight, and a moral compass at every stage of progress. Progress depends on balance, and balance comes from focusing technology through a lens of human empowerment above all else.

Approached strategically, AI can help healthcare organizations achieve significant improvements in care quality, access, and efficiency. However, organizations need to focus on human needs and oversight. Focusing narrowly on technical advancement won’t work. But with diligent data architecture, compliance mechanisms, and continuous support, AI and EHR technology can unite to make healthcare scheduling feel personal and effective. 

Transforming healthcare

AI is the difference between 2.4 percent of appointments scheduled online – which is the sad industry standard – and more than 40 percent of appointments scheduled online – the AI-driven standard.

Despite the challenges of implementing AI, the benefits to patients and organizations are enormous. Given America’s aging population and exploding demand for care, self-scheduling is critical to improving access, reducing costs, and enhancing the patient experience. For healthcare organizations ready to make self-scheduling a reality, AI is the key to overcoming medical complexity and transitioning from a reactive call center model to a proactive self-service model. It’s time to modernize healthcare access.

About Stephen Dean

Stephen Dean is the Co-Founder of Keona Health, a health desk that makes omnichannel patient access fast and simple.

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