Automation of Scheduling in Healthcare

The COVID-19 outbreak has magnified some key scheduling challenges in healthcare delivery systems such as overcrowded ERs, long patient wait times, overworked frontline staff, and hidden capacity. Scheduling challenges will continue in the post-pandemic world as providers rush to reschedule previously cancelled procedures. These challenges are common for all types of health services. In units serving walk-in patients, such as ERs, it is a struggle to forecast volume and serve demand while addressing individual staff needs. Scheduling appointments can be just as difficult. It only takes a few no-shows or late arrivals to throw a wrench in a planned schedule. The silver lining is that scheduling is not unique to the healthcare industry, and there is no need to build solutions from scratch in this digital age.

Inspiration from the retail industry

Fluctuating customer demand, late or no-show customers, and complicated shift schedules are challenges faced by retail operators from cinemas to stores to restaurants. The retail industry has improved forecast and optimization capabilities through machine learning, automation, and other digital solutions. A Big Box retailer drastically improved their demand forecasting by layering weather data with individual store demand to their benchmark after observing a decrease in foot traffic on rainy days, enabling optimized shift schedules and improved e-commerce operations. Large retail stores like Walmart and BestBuy are using staff self-scheduling applications to streamline scheduling and employee communication. These lessons learned and tools used by the retail industry can spark innovation and be adapted for the healthcare industry.

Five key capabilities to address healthcare scheduling  challenges

Despite the commonality, each health service delivery unit has its unique challenges across patient and staff, from scheduling planning to daily schedule operations. We identified five key capabilities that address these challenges – each with varying need for healthcare specific sophistication.

Demand forecasting

Accurate demand forecasting lays the foundation for schedule optimization. Important attributes to look for when evaluating solutions are the level of granularity for patient inflow, models that can place more weight on recent data to capture changes such as a new competitor nearby, ability to run various simulations based on inputs such as acuity, treatment needs, and ability to incorporate evolving targets and metrics. The technology is widely available – the challenge lies in aggregating accurate internal, external, structure and unstructured  data and building the machine learning models to spot complex relationship between the datasets for each unique operation.

Schedule Optimization

Patient appointment scheduling is a game of Tetris. Successful appointment schedule optimization should smooth out demand variation, increase capacity to serve, and minimize the “domino” effect of late arrival and procedures running over schedule. Successful staffing schedule optimization needs to consider patient needs, certifications, skill levels, PTO, preferences, and overtime minimization, while balancing various regulations with schedule fairness, such as night shift rotation. State-of-the-art workforce scheduling tools have developed auto shift creation and assignment capabilities for retail customers – adding sophistication such as patient to staff ratio, machine learning and analytics for continuous improvement will be key. The optimization process should be user friendly; the recommended schedule should be executable without much disruption to patient care and clinical workflow by allowing for the right level of patient and staff preferences to balance with pure operational and financial metrics.

Self-scheduling

Self-scheduling empowers employees and patients to take ownership, commit to their shifts or appointment times, and reduces administrative effort. Staff features such as swap, open communication, and manager auto-approval largely streamline the process, while enabling accountability and accurate overclock tracking. Staff self-scheduling is best used as a complement to centralized shift assignment to balance between individual preferences and staffing need for the entire unit. Similarly, patient self-scheduling allows patients to schedule their appointments online using a website, app, or chatbots anytime without staff interaction. Patient self-scheduling is powerful when used with digital patient waitlists described below. Both are mature technologies in retail – user experience and interoperability with existing clinical and HR workflows and systems will be key to successful implementation.   

Intraday Management

There are multiple intra-day solutions to go after depending the issue at stake. No-show solutions include personalized reminders and confirmation, digital waitlist for automatic mass notification to fill slots upon confirmed cancellation, as well as proactive recall capability that tracks and alerts patients who miss and those who are overdue for a routine visit, to ensure that they come back to receive care. Another idea to borrow from retail is virtual queues – self-service check-in that allows patients to wait at home and enables better tracking of patient volume real time to inform utilization and bottlenecks. Intelligent intra-day optimization engine can recommend schedule changes in time of underperformance – adjusting staffing hours, redistributing workload and patients, etc. Some of these technologies can be found outside; most still require healthcare specific solutioning that incorporates patient safety requirement, staffing regulations, clinical workflow, etc.

Metrics that Matter

Metrics help track improvement and ensure implemented solutions have measurable benefits. When choosing metrics, it is important that they 1.) quantify what the organization really needs to know and 2.) align with and balance corporate priorities by ensuring the metrics are accurately measured and benchmarked against internal goals and competitor performance. Some factors that may be beneficial to track for your organization are patient time to service, resource utilization, real time budget performance, staff overclock and overtime. This will require a foundational data and analytical  infrastructure to collate operational metrics to enable front line staff to make sound decisions.

Ride the wave and make it count

The digital age is here for Healthcare, including clinical scheduling operations. In-depth assessment of your future state need and goal will help you sift through the wide range of capabilities listed above and identify ones worth the investment. Your patients and staff might be reluctant to change – until you’ve proven how much these technologies have made their experience better and their jobs easier.

Craig Kane is a partner and Sachin Mahishi is a principal in the Digital Transformation practice of global strategy and management consulting firm, Kearney.