Passive Employee Wellness Prediction From Workplace Data
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Solution Overview
Problem
Current methods for measuring employee experience and operational outcomes are disjointed and discontinuous, leading to challenges in addressing psychosocial risk factors, resulting in significant costs and lost revenue due to disengagement, absenteeism, and turnover, with surveys being resource-demanding and prone to biases.
Innovation Solution
A system and method that passively collects electronic data using machine learning and natural language processing to predict employee wellness by analyzing communication and calendar data, eliminating the need for surveys and providing real-time insights through dashboards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surveys are used to measure employee experience and psychosocial risk factors, then measurement data is obtained, but response rates are low and survey fatigue occurs leading to reduced data quality and increased resource demands
Solution Approach 1:
The patent replaces the mechanical survey system with an electronic data collection and machine learning analysis system. Instead of relying on employees to complete surveys, the system passively collects electronic data from existing workplace systems (email, calendar, collaboration tools) and uses machine learning algorithms to analyze employee wellness, thereby eliminating survey fatigue while maintaining measurement capability
Solution Approach 2:
The patent creates a digital copy of survey measurement capability through machine learning models that predict survey responses based on electronic behavior data. The system learns from actual survey data and replicates the measurement function by analyzing patterns in electronic communications, meetings, and collaboration behaviors to infer wellness metrics without requiring new survey responses
2Loss of time
If frequent surveys are conducted to obtain timely employee wellness data, then updated measurements are achieved, but resource demands increase and survey fatigue worsens
Solution Approach 1:
The patent implements continuous passive collection of electronic data from workplace systems, creating an ongoing stream of wellness indicators without interruption. The machine learning system continuously processes electronic communications, calendar events, and collaboration data to maintain up-to-date wellness assessments, eliminating the periodic discontinuity inherent in survey cycles
Solution Approach 2:
The system enables self-service wellness measurement by automatically collecting and analyzing electronic data without requiring employee participation or HR administrative effort. The machine learning models autonomously process the data and generate wellness insights, freeing resources while maintaining continuous measurement capability
3Loss of information
If traditional survey methods are used to measure psychosocial risk factors, then employee feedback is collected, but the data is outdated by the time results are presented
Solution Approach 1:
The patent performs preliminary analysis of electronic data continuously in the background, so that wellness insights are already prepared and available when needed. The machine learning system pre-processes electronic communications and behaviors to generate real-time wellness indicators, eliminating the lag between data collection and result presentation inherent in traditional survey cycles
Data Source
AI summary
Systems and methods for predicting employee wellness using passively collected electronic data associated with one or more employees in a work environment using a model of the determined relationship between the collected survey data and predicted responses to a hypothetical survey directed to the one or more. Employee wellness of the one or more employees is predicted by applying predicted survey data to a framework for predicting employee wellness, the framework being based on actual survey data.