Clinician-Patient Colocation Modeling for Dynamic Appointment Scheduling
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Solution Overview
Problem
Existing methods for scheduling clinician-patient appointments and EHR documentation are inefficient, leading to wasted time, prolonged clinician days, and patient frustration due to improper scheduling and documentation accounting.
Innovation Solution
A system and method using wireless communication devices and receivers to determine clinician and patient colocation, analyze time data, and generate predictive models to optimize appointment durations and slots, replacing human subjectivity with specific rules and processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If appointments are scheduled for longer time, then patient care quality is improved, but clinician time is wasted and productivity decreases
Solution Approach 1:
The system dynamically adjusts appointment durations based on real-time factors such as patient acuity, clinician availability, and historical data. Instead of fixed time slots, the scheduling system adapts duration recommendations to match actual care needs while optimizing clinician throughput, resolving the contradiction between quality and productivity
Solution Approach 2:
The system incorporates feedback loops that analyze actual appointment outcomes, patient satisfaction, and clinician workload to continuously refine scheduling recommendations. This feedback mechanism ensures that appointment durations are optimized for both quality care and efficient clinician utilization over time
2Productivity
If appointments are scheduled for shorter time, then clinician productivity is improved, but patient care quality deteriorates and patient frustration increases
Solution Approach 1:
The system uses dynamic scheduling that adjusts appointment durations based on patient-specific factors such as acuity level, complexity of condition, and required services. This ensures that each appointment receives appropriate time allocation for quality care while maintaining overall high clinician productivity through optimized scheduling
Solution Approach 2:
Different appointment durations and resource allocations are assigned to different patient categories based on their specific needs. High-acuity patients receive longer, more specialized appointments while routine cases receive efficient shorter slots, ensuring quality care is provided where needed without compromising overall productivity
3Adaptability or versatility
If EHR documentation time is not accounted for, then scheduling flexibility is improved, but clinician time is wasted and inefficiency increases
Solution Approach 1:
The system performs preliminary analysis of EHR documentation requirements based on patient acuity, visit type, and clinician workflow patterns. By pre-calculating documentation time needs, the system can flexibly schedule appointments that account for both patient care and documentation tasks, preventing time waste while maintaining scheduling adaptability
Solution Approach 2:
The system merges patient appointment scheduling with EHR documentation task scheduling into a unified workflow. By combining these previously separate time allocations into an integrated schedule, the system eliminates wasted transitions and ensures that documentation time is efficiently accounted for within the overall appointment structure
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves scheduling efficiency by reducing human error and subjectivity, optimizing appointment times, and enhancing clinician productivity while minimizing patient wait times.
Implementation Method 1
Each of the plurality of wireless receivers is configured to receive the device identifier broadcast by the wireless communication device, determine a signal strength
Data Source
AI summary
Methods, systems, and computer-readable media are provided for analyzing amounts of time spent by clinicians caring for patients. Clinician locations, patient locations, patient data, and clinician electronic health record activity may be used to facilitate generation of one or more predictive models. Such predictive models will provide, among other things, a decision support tool for scheduling patient-clinician appointments to minimize, at least partially, patient wait times.