Intelligent Touch Care System for Community Risk Prediction
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Solution Overview
Problem
Early warning systems used in clinical settings fail to account for patients in community care settings, leading to overlooked risk factors and increased healthcare costs due to inefficient workforce management and unnecessary in-person appointments.
Innovation Solution
The implementation of a Community Early Warning Score (CEWS) system that utilizes contributing data elements to predict touchpoint discipline, recommend frequency and modality of care, and initiate follow-ups via notification tools and scheduling functionality, improving clinical workforce capacity and patient engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional early warning systems are used in clinical settings, then patient deterioration can be detected within the facility, but patients in community care settings are not monitored and risk factors are overlooked
Solution Approach 1:
The early warning system is extended to serve multiple settings (clinical facilities and community care) through a single unified platform. The system processes data from diverse sources including wearables, patient portals, and EHRs to provide deterioration detection across both inpatient and outpatient environments, eliminating the need for separate monitoring systems.
Solution Approach 2:
A centralized processing platform acts as an intermediary between various data sources (wearables, patient portals, EHRs) and the early warning detection algorithms. This intermediary consolidates data from community care settings and translates it into actionable risk assessments, bridging the gap between home-based data and clinical decision-making.
2Reliability
If in-person follow-up appointments are scheduled for all patients in community care, then comprehensive monitoring is achieved, but workforce efficiency decreases and unnecessary appointments increase
Solution Approach 1:
Instead of applying universal in-person follow-ups to all patients, the system implements partial monitoring by selectively engaging high-risk patients through targeted interventions. The continuous data processing identifies only those patients who cross risk thresholds, allowing providers to focus resources on patients who actually need in-person attention while reducing unnecessary appointments for low-risk patients.
Solution Approach 2:
The system enables self-monitoring through patient wearables and portal interactions that automatically feed data into the risk assessment algorithm. Patients effectively monitor themselves through continuous data collection from their devices, reducing the burden on clinical staff to manually schedule and conduct routine follow-up appointments while maintaining comprehensive oversight.
3Measurement precision
If continuous data processing from multiple sources is implemented, then comprehensive risk assessment is achieved, but system complexity increases
Solution Approach 1:
The system merges multiple data sources (wearable data, patient portal submissions, EHR records) into a unified risk assessment framework. By consolidating these diverse data streams through a single processing platform that applies consistent algorithms, the system achieves comprehensive risk assessment without proportionally increasing complexity, as the merging process creates synergistic value greater than the sum of individual data sources.
Data Source
AI summary
Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.