Intelligent Patient Scheduling System Using GPS and Clustering
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Solution Overview
Problem
Computerized scheduling systems with fixed timeslots and durations fail to accommodate the varied needs of different clinicians and patients, leading to inefficiencies and increased patient wait times, as they cannot automatically customize timeslots and durations based on individual medical needs.
Innovation Solution
A system that leverages near real-time geographic location information and historical appointment data to calculate tailored appointment start times and durations for each patient, using patient-specific medical data and cohort clustering to identify optimal scheduling options, thereby reducing wait times and providing flexible scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed timeslot and duration templates are used across different clinicians and patients, then system simplicity and ease of operation are maintained, but scheduling flexibility and adaptability to individual needs are lost
Solution Approach 1:
The system dynamically adjusts timeslot durations and start times based on real-time factors including patient travel time (calculated from GPS location data), clinician availability, and historical appointment data. Instead of static templates, the scheduling system continuously adapts parameters to match current conditions and individual patient needs.
Solution Approach 2:
The system changes scheduling parameters (timeslot duration, start time) based on varying conditions such as patient location, travel time, and historical data. The timeslot duration is not fixed but is adjusted as a variable parameter to optimize scheduling efficiency for each specific case.
2Device complexity
If fixed appointment templates are used, then scheduling process complexity is reduced, but patient wait times increase due to inability to customize to individual needs
Solution Approach 1:
The system automatically calculates optimal appointment times and durations using patient-provided location data and historical information, eliminating the need for manual scheduling adjustments. The system serves itself by making intelligent scheduling decisions based on algorithmic analysis of multiple data sources.
Solution Approach 2:
The system incorporates historical appointment data and actual patient travel times as feedback to continuously improve future scheduling decisions. By learning from past appointments and real-time location data, the system refines its predictions to minimize wait times while maintaining operational simplicity.
3Productivity
If standardized timeslots are applied to all patients, then administrative overhead is minimized, but time waste increases due to mismatched appointment durations
Solution Approach 1:
The system performs preliminary calculations of patient travel time and required appointment duration before finalizing the schedule. By pre-calculating these parameters based on location data and historical information, the system optimizes timeslot allocation in advance, reducing both administrative overhead and time waste.
Data Source
AI summary
Methods, systems, and computer-readable media are disclosed herein for intelligently identifying specific timeslots and identifying specific durations for the timeslots that can be recommended to a person seeking to make an appointment with a clinician. A scheduling module leverages real-time GPS data of the person and clustering techniques to identify the timeslots and predicted durations the person is predicted to utilize in that appointment based at least on a chief complaint and patient cohort data.