Predictive Workflow Analytics Engine for Healthcare No-Show Reduction
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Solution Overview
Problem
Healthcare environments face challenges in correlating and effectively utilizing vast amounts of siloed data from different information systems, leading to data overload and difficulties in predicting patient no-shows, which affects service efficiency and revenue.
Innovation Solution
A predictive workflow analytics system that combines healthcare workflow data with non-healthcare data using a machine learning inferencing engine to generate predictions and trigger corrective actions, such as reminders or rescheduling, based on patient no-show probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If healthcare workflow data from multiple siloed systems is combined and displayed together, then comprehensive patient information is available, but the data becomes overwhelming and confusing
Solution Approach 1:
The patent segments the overwhelming healthcare data into distinct functional categories (patient demographics, clinical information, scheduling data, financial information) and presents them in organized sections. The system divides the data display into modular components that can be independently accessed and manipulated, making comprehensive information manageable and understandable.
Solution Approach 2:
The patent adds temporal and hierarchical dimensions to data organization by displaying historical workflow data alongside current status, and by creating multi-level views that allow users to drill down from summary-level information to detailed records. This dimensional organization transforms flat overwhelming data into structured, navigable information spaces.
2Productivity
If predictive analytics are implemented to predict patient no-shows, then resource utilization improves, but system complexity increases
Solution Approach 1:
The patent implements predictive analytics that perform preliminary analysis of patient no-show risks before appointments occur. By calculating probability scores in advance based on historical data and patient characteristics, the system enables proactive resource allocation and scheduling adjustments, improving resource utilization without requiring complex real-time decision-making infrastructure.
Solution Approach 2:
The patent introduces an intermediary predictive analytics layer that sits between the raw healthcare data systems and the resource management decisions. This intermediary component processes complex analytical operations and presents simplified probability scores and recommendations to users, shielding the overall system from excessive complexity while enabling sophisticated resource optimization.
3Productivity
If corrective actions are triggered based on no-show probabilities, then service efficiency improves, but automation extent increases
Solution Approach 1:
The patent implements feedback mechanisms where predictive no-show probabilities automatically trigger corrective actions such as reminder notifications, rescheduling offers, or resource reallocation. The system continuously monitors outcomes of these actions and uses the results to refine future predictions, creating a self-improving automated workflow that enhances service efficiency while maintaining appropriate levels of automation.
Data Source
AI summary
Systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. An example predictive workflow analytics apparatus includes a data store to receive healthcare workflow data including at least one of a schedule or a worklist including a patient and an activity in the at least one of the schedule or the worklist involving the patient. The example apparatus includes a data access layer to combine the healthcare workflow data with non-healthcare data to enrich the healthcare workflow data for analysis with respect to the patient. The example apparatus includes an inferencing engine to generate a prediction including a probability of patient no-show to the activity by processing the combined, enriched healthcare workflow data using a model and triggering a corrective action proportional to the probability of patient no-show.