The Importance of Effective Resource Allocation for Optimizing Patient Care

When it comes to getting the right patients in to see the right physicians at the right times, today's medical practices face an unprecedented-and growing-challenge. Globally, 42% of patients name their top complaints with the current healthcare system as the lack of access to treatment and excessively long wait times. Physicians are getting busier and busier, with wait times for new patients increasing from an average of 22 days in 2021 to 26 days in 2022.

In this article, we provide insight into some of the critical issues experienced in patient scheduling and how these issues affect the practice of healthcare and patient outcomes. We will also explore how some practices are harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML) to better optimize their resource allocation and streamline patient scheduling, enabling them to see more patients in a timelier manner.

Factors complicating patient scheduling

Multiple factors are behind the healthcare industry's difficulties in ensuring the right patients are seen at the right times. An aging patient population and the corresponding increase in patient demand for care certainly play roles. Another contributing factor is the increasing administrative burden currently facing both healthcare providers and staff-a burden caused by changing requirements for quality measurement and reporting, changes to medical coding and documentation requirements, and changing healthcare laws and policies, to name a few.

At the same time, the healthcare industry faces a critical shortage of workers. This shortage existed even before the COVID-19 pandemic, but the pandemic significantly worsened the problem. There are no signs that things are improving, either: According to results from the Physicians Practice 2023 Staff Salary Survey¹, half of medical practices report they are understaffed. Of those, 53% report they do not intend to increase staffing in the near future.

This worker shortage negatively impacts those remaining. It decreases the flexibility of physicians' schedules, further complicating patient scheduling. It negatively affects physicians' ability to do their jobs. According to a recent report from healthcare consultants Merritt Hawkins, 36% of physicians are falling behind on their schedules several times each week¹. The worker shortage also brings with it increased reports of anxiety, depression, and other mental health problems among healthcare workers.

These factors are associated with a rising burnout rate among healthcare professionals. A poll from the Kaiser Family Foundation and The Washington Post found that about 3 in 10 healthcare workers had considered leaving their profession and about 6 out of 10 reported that pandemic-related stress had harmed their mental health. Other studies show that approximately half of all physicians experience burnout at some point.

Increased burnout among healthcare professionals doesn't just affect healthcare workers, either: It's associated with decreased patient satisfaction, and in many cases, physician burnout negatively impacts patient safety.

Current state of patient scheduling

Scheduling is one area where evolving technology has not yet taken a large role in medical practice operations. Traditional methods of patient scheduling require inefficient manual processes, predominantly relying on practice staff to identify, contact, and schedule high-need patients. Traditional scheduling often requires staff to understand individual patients' needs well enough to prioritize which are more pressing but without the clinical background generally required to make those types of decisions.

As a result, manual patient scheduling demands hours of staff time for scheduling and confirming appointments and returning patient calls and emails. According to a recent Medical Group Management Association (MGMA) Stat poll, 26% of medical practices reported scheduling as their top front desk training challenge.

Manual intervention to schedule individual patients hampers the staff's ability to efficiently and effectively manage patient care. However, the consequences of failing to meet these scheduling challenges can be profound, at times resulting in the inability of providers and staff to effectively serve those patients with the greatest needs. This, in turn, results in worse health outcomes for those patients.

Effective resource allocation plays a crucial role in ensuring a medical practice's ability to deliver patient care efficiently and effectively-and effective resource allocation is beyond the skills of many practices' front desk staff.

A solution to patient scheduling challenges

One solution to these growing scheduling challenges is to leverage AI technology to streamline and optimize patient scheduling. Other industries have been using AI and the concept of "yield management" to optimize their scheduling needs for decades. This concept originated in the airline industry in the 1970s and 1980s as a means for revenue management and optimization.

Yield management is a technique for optimizing airlines' ticket revenue through strategic pricing combined with segmentation of customers based on factors such as behavior, demand levels, and time of booking. Yield management applies AI and Machine Learning to optimize airfare pricing to help maximize airline income. For instance, by segmenting airline tickets for sale to 2 predicted types of customers, leisure and business travelers, this process can reserve a certain number of higher-priced tickets for business travelers, who will pay more for their air travel, while calculating the optimal number of lower-priced fares to make available to maximize ticket sales.

Numerous other industries have also started using the basic concepts of "yield management" to optimize their revenue streams. It's past time to apply some of these technological advances and innovations to the healthcare industry, particularly in improving the allocation of often-limited resources to help practices deliver better care to patients.

Veradigm's Predictive Scheduler

Veradigm's Predictive Scheduler is the first solution to apply the concept of "yield management" to patient scheduling in the healthcare industry. Predictive Scheduler is an AI scheduling solution that takes a multi-pronged approach. Using population-level data and observable patterns, this tool analyzes the practice's patient panel data to identify those patients in greatest need of appointments. Predictive Scheduler also uses predictive analytics to analyze patterns in large datasets and assess the probabilities of specific outcomes.

With Veradigm Predictive Scheduler, practices are able to incorporate more flexibility into physicians' schedules-enabling them to accommodate unexpected patient needs and emergencies. Predictive Scheduler forecasts and prioritizes high-need patient visits and holds slots open based on predicted daily volume. At the same time, the tool is able to follow complex scheduling and reimbursement rules created to maximize efficiencies. Users can also update templates in response to patient no-shows and cancellations to minimize the final number of open slots. The result:

• Busy practice schedules flex in response to changes in demand.
• Patient visits are distributed across multiple resources to ensure effective resource use.
• High-need patients are seen in a timely manner. *Practice staff face a decreased scheduling burden.

To learn more about how the Veradigm Predictive Scheduler, AI, and predictive analytics can help your practice combat physician burnout while enhancing patient care, download our Advanced Scheduling whitepaper today.

Reference:

1. Whitepaper on file 2024, Enhancing Patient Care and Managing Burnout Through Advanced Scheduling: LEVERAGING AI AND PREDICTIVE ANALYTICS, Veradigm.

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