Predictive Workflow Analytics Engine for Patient No-Show Reduction
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Solution Overview
Problem
Healthcare environments face challenges in correlating and effectively utilizing vast amounts of siloed patient data due to technological limitations, leading to data overload and difficulties in predicting patient no-shows, which affects resource management and patient care.
Innovation Solution
A predictive workflow analytics system that combines non-healthcare information with healthcare workflow data using an artificial intelligence engine to generate predictions about patient no-shows, synchronizing these predictions with schedules, and providing interactive dashboards for adjusting appointments, thereby improving resource management and patient care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If healthcare data is aggregated from multiple siloed systems, then data completeness improves, but data overload and system complexity increase
Solution Approach 1:
The patent introduces an intermediary layer (analytics engine, data warehouse, or cloud-based platform) that sits between the siloed healthcare systems and the end users. This intermediary aggregates data from multiple sources (HIS, RIS, PACS, EMR) and presents it in a simplified, integrated manner, reducing the complexity burden on individual systems while maintaining data completeness.
Solution Approach 2:
The patent segments the healthcare data ecosystem into distinct functional layers: data collection layer (siloed systems), data integration layer (analytics engine/data warehouse), and data presentation layer (dashboards/interfaces). This segmentation allows each layer to operate independently, managing complexity locally while achieving comprehensive data aggregation across the entire system.
2Measurement precision
If predictive analytics are implemented using AI engines, then patient no-show prediction accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-aggregating healthcare data into structured formats before predictive analytics are needed. Historical data is cleaned, normalized, and stored in optimized data structures in advance, so when prediction is required, the AI engine can quickly process pre-prepared information rather than raw data, reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent applies parameter changes by adjusting the complexity and granularity of predictive models based on specific needs. Different AI algorithms (machine learning, deep learning, statistical models) are selected and tuned with varying parameters depending on the prediction task, allowing optimization between accuracy and processing speed for different clinical scenarios.
3Loss of information
If comprehensive patient data is displayed on interfaces, then information availability improves, but screen real estate requirements and user confusion increase
Solution Approach 1:
The patent applies local quality by providing different views and levels of data detail to different user roles and contexts. The interface presents customized information sets based on user needs: executives see high-level summaries, clinicians see patient-specific details, and administrators see operational metrics. This allows comprehensive information availability while maintaining interface clarity through role-based customization.
Solution Approach 2:
The patent transitions from two-dimensional screen display to multi-dimensional data presentation by incorporating temporal dimensions (historical trends, real-time updates), hierarchical dimensions (summary views drillable to detailed views), and interactive dimensions (filtering, sorting, and customization options). This allows comprehensive information to be presented in an organized, navigable manner that doesn't overwhelm screen real estate.
Data Source
AI summary
Systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. An example apparatus includes processor(s) to at least: generate a prediction including a probability of a patient no-show to a scheduled appointment using an artificial intelligence engine including a patient no-show model to predict the probability of the patient no-show based on a combination of healthcare workflow data and non-healthcare information; synchronize the prediction and a schedule including the scheduled appointment; and generate an interactive dashboard including the synchronized prediction and the schedule and at least one of: a) a first option to adjust the schedule to replace the patient based on the probability of the patient no-show orb) a second option to adjust the schedule to move the patient to a different time based on the probability of the patient no-show.