Patient Access System Predicting Lost Appointments

Solution Overview

Problem

Conventional methods for managing patient access in healthcare facilities often lead to overcrowded waiting rooms, long wait times, and compromised patient experience due to overbooking appointments, resulting in reduced quality of care and increased staff burnout.

Innovation Solution

A computer-implemented patient access determination system that uses machine learning models to analyze patient encounter and scheduling data, identifying recoverable lost appointments and optimizing appointment schedules to enhance revenue and patient access while minimizing wait times.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If overbooking appointments is used to minimize missed appointments, then the number of patient appointments increases, but waiting room overcrowding and long wait times occur

Engineering Contradiction:

Improvenumber of patient appointmentsVSAvoidpatient wait time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting patient attendance probability before appointments are scheduled. This allows the healthcare facility to proactively adjust scheduling decisions, such as sending reminders to high-risk patients or optimizing slot allocation, to prevent missed appointments before they occur, thereby reducing the need for overbooking and associated wait times.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops by monitoring actual attendance outcomes and using them to refine future predictions. The machine learning model is retrained with new data, and the system provides real-time feedback to schedulers about which time slots and patient groups are most likely to attend, enabling dynamic adjustment of appointment strategies to minimize both missed appointments and wait times.

Inventive Principle:

Principle #23Feedback

2Productivity

If overbooking appointments is used to minimize missed appointments, then the number of patient appointments increases, but patient experience and quality of care deteriorate

Engineering Contradiction:

Improvenumber of patient appointmentsVSAvoidquality of care

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting patient attendance probability before appointments are scheduled. This allows the healthcare facility to proactively adjust scheduling decisions, such as sending reminders to high-risk patients or optimizing slot allocation, to prevent missed appointments before they occur, thereby reducing the need for overbooking and associated wait times.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes key parameters of the scheduling process by using predicted attendance probabilities to dynamically adjust appointment slot allocation, provider scheduling, and patient grouping. Instead of using fixed overbooking rates, the system varies scheduling parameters based on real-time predictions, allowing optimization of both appointment volume and quality of care delivery.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If focus is placed on revenue maximization through quantity of patients, then the number of appointments increases, but provider burnout and staff turnover increase

Engineering Contradiction:

Improvenumber of patient appointmentsVSAvoidstaff retention

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting patient attendance probability before appointments are scheduled. This allows the healthcare facility to proactively adjust scheduling decisions, such as sending reminders to high-risk patients or optimizing slot allocation, to prevent missed appointments before they occur, thereby reducing the need for overbooking and associated wait times.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by automating the complex task of predicting attendance and optimizing schedules, removing this burden from providers. The machine learning model automatically analyzes data, generates predictions, and provides scheduling recommendations, allowing providers to focus on patient care rather than administrative optimization, thereby reducing burnout and improving retention.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20240161915A1System and method for determining patient access in a healthcare facility

Publication Date: 2024.05.16 KPMG LLP

AI summary

A patient access determination system configured to extract health related data from a data source to form extracted health data, generate a plurality of data pipelines for conveying the extracted health data, and store the extracted health data conveyed over one or more of the data pipelines in a data model to form stored health data. The data model includes tables for organizing and storing the extracted health data. The system also determines from at least the patient encounter data forming part of the stored health data a number of lost appointments that can be recovered by the healthcare facility and apply one or more machine learning models to the stored health data to generate predictions therefrom. The system can also generate one or more user interfaces for displaying selected portions of the stored health data and the predictions.