EHR Patient Scheduling for Balanced Facility Resources
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Solution Overview
Problem
Patient scheduling at healthcare facilities is complex due to variable patient volumes, cancellations, and varying treatment durations, leading to suboptimal scheduling that results in long wait times, uneven resource utilization, and high staff stress, with existing methods failing to optimize resource allocation efficiently.
Innovation Solution
A computing system and method that utilizes an Electronic Health Record (EHR) system to determine optimal treatment times by considering treatment, provider, and resource information, providing exact and approximate matches for scheduling, and employing AI to balance resource allocation and patient preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scheduling methods are used by human schedulers, then flexibility in handling patient preferences and staff availability is improved, but scheduling efficiency and optimization are worsened due to the complexity beyond human capacity
Solution Approach 1:
An AI-based scheduling system acts as an intermediary between patients, providers, and facilities. The system receives scheduling requests with patient preferences and provider availability, processes them through optimization algorithms, and generates optimized schedules that balance multiple constraints, thereby maintaining flexibility while dramatically improving scheduling efficiency
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an AI-based computational system. The AI system processes scheduling data, evaluates multiple possibilities, and determines optimal schedules automatically, substituting human cognitive limitations with computational power to achieve both efficiency and flexibility
2Device complexity
If suboptimal scheduling is used due to limited information availability, then scheduling simplicity is improved, but resource utilization and wait times are worsened
Solution Approach 1:
The system performs preliminary actions by collecting and storing provider availability, patient preferences, and facility resource information before scheduling occurs. This advance preparation of data allows the AI system to make optimized scheduling decisions without increasing operational complexity during the actual scheduling process
Solution Approach 2:
The scheduling system incorporates feedback loops that continuously monitor scheduling outcomes, resource utilization metrics, and wait times. This feedback enables the system to learn from past scheduling decisions and continuously optimize future schedules, improving resource utilization without requiring increased system complexity
3Reliability
If complex treatment schedules are implemented to handle variable patient volumes, then patient care quality is improved, but staff stress and operational costs are worsened
Solution Approach 1:
The scheduling system is designed to be dynamic, automatically adjusting schedules in response to variable patient volumes, provider availability, and facility constraints. This dynamic adaptation ensures high-quality patient care is maintained while distributing workload evenly among staff, thereby reducing stress levels without compromising care quality
Data Source
AI summary
A system and method for facilitating patient scheduling at a healthcare facility is disclosed. The method includes receiving a request from one or more electronic devices associated with a scheduler to schedule an appointment of a patient, obtaining treatment information, provider information and resource information from an EHR system, and obtaining one or more available slots of provider from the EHR system. Furthermore, the method includes determining one or more optimal treatment times for the treatment date for the treatment profile of the patient and outputting the determined one or more optimal treatment times for the treatment date along with relevant patient information on user interface screen of the one or more electronic devices associated with the scheduler.