Predictive Scheduler: The AI-Driven Future of Healthcare Scheduling

Blog Posts  |  21 March 2024

Written by: Jonathon Graham, Director Business Operations, Veradigm, and Cheryl Reifsnyder, PhD

In the fast-evolving healthcare industry, efficient care delivery management has become more critical than ever. Resource allocation plays a crucial role in ensuring a practice’s ability to efficiently and effectively deliver care, but practices face frequent difficulties hiring and retaining key patient-facing and clinical support positions. These staffing shortages come amid a growing demand for healthcare services, placing an immense burden on practice providers and staff.

Meanwhile, numerous other factors are creating additional administrative burden, such as changing:

  • Healthcare reform
  • Healthcare laws and regulations
  • Payment policies
  • Medical coding and documentation requirements
  • Requirements for quality measurement and reporting

Studies show that half of all physicians experience burnout at some point; this increased administrative burden is a significant contributor. Physicians’ schedules—their lack of control and lack of flexibility—is another driving factor behind physician burnout.

In many cases, physician burnout negatively impacts patient safety.

One solution to these growing challenges lies in leveraging artificial intelligence (AI) technology to streamline and optimize healthcare scheduling. Keep reading to learn how AI can help your practice deliver high-quality care and improve patient outcomes—while simultaneously enhancing the patient experience and optimizing operational efficiencies.

The urgent need for innovation in healthcare

The healthcare sector’s struggle to balance growth with efficient resource allocation has created a pressing need for innovative solutions—and this need is particularly urgent in patient scheduling. Traditionally, patient scheduling has been an inefficient manual process requiring hours of staff time for scheduling appointments, confirming appointments, and returning patient calls and emails.

In addition, current healthcare systems for scheduling and workforce management are often disjointed, failing to integrate with other key operational systems. This lack of integration hampers provider efficiency and patient care. It makes it difficult to manage and deploy providers and staff effectively.

With its ability to enhance analytical capabilities, AI has emerged as a potential answer to these complex challenges.

The growing role of AI in medical care

AI has been viewed as a potential solution to these complex issues because of its ability to use technology to enhance humans’ analytical capacity. Recent advances in cloud infrastructure and the accumulation of vast electronic data have made it practical to apply AI broadly across healthcare, assisting in crucial decision-making processes.

Machine Learning (ML)

The clinical and operational data in electronic health records (EHRs) and scheduling systems provide valuable sources of actionable insights. Machine Learning (ML) is a subset of AI that can extract those insights. ML refers to algorithms that employ adaptive learning without requiring specific instructions. ML can be designed to discover data patterns or predict a specific output (such as a disease diagnosis) based on input data from large, high-quality datasets.

Recent advances in AI and ML technologies have increased the accuracy and scope of their applications to the point that AI systems can provide specialist-level accuracy in numerous diagnostic areas. For instance, AI-assisted medical image-based diagnosis has been used successfully in medical specialties such as radiology, pathology, and dermatology.

Predictive Analytics

Predictive analytics is an advanced statistical process that typically employs an ML algorithm plus methods such as data mining and predictive modeling to predict future outcomes by analyzing patterns found in large datasets. In healthcare, predictive analytics is used to analyze historical and current healthcare data to help users identify opportunities to make more effective, more efficient decisions in both clinical and operational arenas. Predictive analytics is currently used in multiple healthcare sectors, including clinical care, administrative tasks, and operational management—and its use is predicted to become even more prevalent in the near future.

By sifting through massive datasets, predictive analytics can be used to assess the likelihood of future risks and outcomes. The value of predictive analytics lies in its ability to transform large quantities of data into actionable insights, helping practices to make better, data-driven decisions in both patient care and resource management.

Predictive analytics offers several advantages for healthcare decision-making:

  • Complex Pattern Recognition: Predictive analytics harnesses the ability of technology to analyze a larger quantity of data and perform more in-depth analysis than a human analyst, enabling it to identify complex patterns and relationships beyond the human ability to distinguish.
  • More informed and more objective decisions: Use of extremely large datasets, including analysis of both structured data (e.g., data found in predefined EHR fields) and unstructured data (e.g., data such as encounter notes), enables decisions that are both more informed and more objective.
  • Real-time decision support: Predictive analytics’ ability to generate results in real time makes it a valuable tool to support real-time decision-making.

Benefits of AI-assisted scheduling

Applying predictive analytics to practice scheduling delivers numerous benefits. AI and predictive analytics can analyze vast amounts of data to identify patterns and help practices make better-informed decisions. For instance, the insights provided by predictive analytics can help practices reduce unnecessary expenses and facilitate optimal resource allocation.

Another notable benefit of predictive scheduling is its ability to help improve physician burnout. Physician burnout is a pressing—and growing—concern in the healthcare industry; however, recent data from the American Society of Anesthesiologists shows that AI-based scheduling both improves physician engagement and reduces physician burnout.

Leveraging AI-assisted scheduling enables practices to optimize provider schedules to allow effective patient flow and sufficient time for documentation and other administrative tasks. Making these changes has been shown to increase provider satisfaction and increase retention.

Predictive scheduling also enables practices to incorporate flexibility into schedules to accommodate unexpected patient needs and emergencies, helping provide patients with more timely care. A recent Experian Health report revealed that patients’ greatest healthcare challenge is getting an appointment in a timely manner. Longer patient wait times negatively affect the patient experience and can negatively impact patient health outcomes as well; more timely care has been shown to help reduce morbidity and mortality for chronic conditions.

Achieving scheduling excellence with Predictive Scheduler

Predictive Scheduler from Veradigm is an advanced scheduling solution that uses AI and predictive analytics to address several healthcare scheduling challenges:

  • Reworking schedules for high-need patients: AI prioritizes urgent patient needs, helping practices to manage daily fluctuations in patient volume.
  • Complex scheduling rules management: Predictive Scheduler ensures adherence to scheduling and reimbursement guidelines.
  • Adaptive schedule updates: By responding to predicted demand changes, Predictive Scheduler helps reduce no-shows and cancellations and their impact on practice operations.
  • Optimizing workforce deployment: Predictive Scheduler helps ensure the right provider availability at the right times, reducing physician burnout.

Predictive Scheduler also offers a centralized scheduling system, integrating data from your practice’s health network to enhance visibility and coordination. The results include improved schedule visibility across the entire system; seamless integration with existing systems; data-driven decision-making, utilizing predictive analytics to create informed scheduling strategies; and streamlined operations with reduced labor costs.

Veradigm’s unique analytic capabilities

Veradigm’s approach to predictive analytics is based on a closed-loop system in which Veradigm supplies both the AI-powered Predictive Scheduler and the Practice Management (PM) system used to generate datasets for the Predictive Scheduler’s use.

This creates two major advantages. First, Veradigm can provide greater control over the scheduling system’s user experience. Because Veradigm operates the PM data source, our developers can modify the PM to optimize data quality. This, in turn, leads to higher-quality analytics.

Second, Veradigm operates several extensive databases. This allows us to provide a range of large, detailed, and secure datasets for more accurate predictive analytics.

Applying predictive analytics with Predictive Scheduler

AI has established itself as a force in healthcare, with organizations expanding their AI initiatives to improve processes and care quality. Those harnessing their EHR data through AI will lead in improving human health.

Predictive Scheduler by Veradigm represents a significant advancement in healthcare scheduling. By adopting this AI-driven solution, healthcare organizations can streamline operations, achieve cost savings, and deliver superior patient care. It’s not just a scheduling tool; it’s a transformative force in the healthcare industry.

Is your practice interested in learning how your patients might have been seen earlier? Contact us for an Optimization Readiness assessment, a proprietary, multi-objective assessment designed to assist healthcare providers in identifying operational activities that impact both clinical and financial results. Veradigm Optimization Readiness pulls 12 to 24 months of practice and provider historical data to analyze practice performance across 40 key efficiency and effectiveness metrics, then uses this data to generate insights into potential areas for improving scheduling inefficiencies.

Contact us today to discover how Predictive Scheduler can revolutionize your healthcare scheduling. Take the first step toward a more efficient and effective healthcare system.

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Veradigm   Optimization Readiness   Predictive Scheduler   Artificial Intelligence   AI   healthcare  

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