Patient Callback Tool Automating Post-Discharge Follow-Up
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Healthcare providers face inefficiencies and errors in manually tracking and conducting follow-up calls with patients post-discharge, including time-consuming list generation, potential errors, and privacy concerns with paper-based systems.
Innovation Solution
A mobile application and desktop solution that automatically generates patient lists for follow-up, allows for real-time addition of patients, automatic dialing, and tracking of phone calls, with features like Doximity dialer for caller ID masking and real-time feedback collection into a database dashboard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification and tracking of patients for follow-up calls is used, then providers can maintain simple systems with minimal technology, but provider time is lost in generating patient lists and logging completed calls
Solution Approach 1:
The system enables automatic generation of patient lists by querying the EMR system, automatically dialing patients, and automatically logging call outcomes. This self-service automation eliminates the need for manual list generation and logging, directly addressing the productivity issue while managing complexity through standardized automated processes.
Solution Approach 2:
The patent replaces manual mechanical processes (physically generating lists, manually dialing, handwriting notes) with automated electronic systems. The computer automatically queries patient data, executes dialing sequences, and records outcomes in spreadsheets, substituting human manual labor with automated computational processes.
2Productivity
If manual entry of follow-up notes in separate spreadsheets is required, then data can be systematically organized, but potential errors occur in manual entry and time is lost
Solution Approach 1:
The system performs automatic logging of call outcomes directly into structured spreadsheet formats. The computer captures call results automatically without requiring manual transcription, thereby eliminating entry errors while maintaining systematic data organization. The automated process ensures consistency in data recording.
Solution Approach 2:
The system creates automated copies of call outcome data directly from the interaction into structured formats. Rather than requiring providers to manually copy and paste or transcribe information, the system automatically replicates the data into the appropriate spreadsheet structure, reducing errors while preserving systematic organization.
3Ease of operation
If paper-based patient lists and stickers are used for tracking, then simple manual tracking is possible, but privacy concerns arise with patient information floating around
Solution Approach 1:
The patent replaces physical paper-based tracking systems with electronic computer-based systems. Patient information is stored and accessed digitally rather than through physical lists and stickers, eliminating the privacy risks associated with paper floating around while maintaining ease of tracking through electronic access and management.
Solution Approach 2:
The system creates controlled electronic copies of patient information accessible only through authorized systems, replacing uncontrolled physical copies (paper lists and stickers). The electronic copying mechanism ensures that patient data is replicated only within secure digital environments, eliminating the privacy hazards of physical documents being accessible to unauthorized persons.
Data Source
AI summary
A user interface for a physician or other health care provider to follow up with high-risk patients recently discharged from the emergency department (ED) and update the patient's health care record using the physician or provider's mobile or end user device is described. The user interface is generated after extracting patient detail batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database or other patient information database into a medical activity tracking batch database, and constructing a real-time database from streams of real-time Health Level 7 (HL7) clinical administrative data, wherein at least one stream of real-time HL7 clinical administrative data is associated with at least one high-risk patient in a recent medical encounter and at least one stream of real-time HL7 data comprises daily schedule information associating at least one clinician with the at least one high-risk patient.