ML Attendance Prediction Integrating External Data
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Solution Overview
Problem
Existing systems for predicting no-shows in medical appointments are inaccurate due to reliance on electronic medical records alone, neglecting external variables like economic measures, travel distance, and nuanced weather factors, and fail to implement proactive mitigation strategies to improve attendance.
Innovation Solution
A computer-implemented method that uses a machine learning algorithm to predict appointment attendance by integrating appointment, population, external, and environmental data, including economic and weather data, and performs mitigation steps such as communication or transportation assistance when attendance probability is low.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing systems rely only on electronic medical records (EMR) data sources, then the system complexity is reduced, but the prediction accuracy deteriorates due to insufficient data completeness and inability to capture external variables affecting patient behavior
Solution Approach 1:
The patent combines multiple data sources including EMR data, socioeconomic data, travel data, and weather data into a unified predictive model. This merging of previously separate data streams enables comprehensive prediction accuracy while managing system complexity through integrated architecture.
Solution Approach 2:
The predictive system is designed to handle multiple types of data sources and prediction scenarios within a single framework. The system can process EMR data, socioeconomic indicators, travel information, and weather conditions, making it universally applicable to various prediction needs without requiring separate specialized systems.
2Device complexity
If existing systems use basic weather variables (rain vs. shine, absolute temperature), then the data collection complexity is reduced, but the prediction accuracy deteriorates due to lack of nuanced weather measures like visibility and humidity
Solution Approach 1:
The patent transforms basic weather parameters into nuanced measurements by incorporating visibility, humidity, and other detailed weather metrics. This parameter refinement enables more accurate prediction of patient attendance behavior while the system manages the increased data complexity through automated processing pipelines.
3Device complexity
If existing systems assume all patient journeys to healthcare are equal, then the analysis complexity is reduced, but the prediction accuracy deteriorates due to ignoring travel distance, cost, and time factors
Solution Approach 1:
The patent applies local quality analysis by examining individual patient travel characteristics including distance, cost, and time specific to each patient's journey. This localized analysis of travel factors enables more accurate prediction of attendance behavior for each patient while the system manages complexity through automated data processing.
4Device complexity
If existing systems fail to include economic measures as variables, then the model simplicity is maintained, but the prediction accuracy deteriorates due to ignoring economic health factors that impact patient behavior
Solution Approach 1:
The patent merges economic indicators including income, housing data, and local economic health metrics with clinical EMR data in a unified predictive model. This combination enables comprehensive prediction of patient attendance behavior while the system manages the integrated data through coordinated processing architecture.
Data Source
AI summary
A computer-implemented method of increasing appointment attendance comprises providing a processor and a non-transitory memory including computer program code for one or more programs, the memory and the computer program code configured to, with the at least one processor, perform steps comprising obtaining an appointment data structure, comprising an attendee and a corresponding appointment for the attendee, obtaining a set of population data, obtaining a set of appointment data, obtaining a set of external data, obtaining environmental data, inferring, using the population data, the appointment data, the external data, and the environmental data in a machine learning algorithm, a probability that the attendee will attend the appointment, and when the probability of attendance is below a threshold, performing a mitigation step to increase the probability that the attendee will attend the appointment. A system for increasing appointment attendance and a non-transitory computer-readable medium containing computing instructions are also described.