Mapping the Human Decision Points-AI-Enabled 30-Day Readmission Risk
How human will be able to safely use their clinical judgement to question technology information for clinical safety and not just follwo a prompt. Winning would mean being able to look at our solution as it makes healthcare safer, more consistent and more efficient. We want to demonstrate not only what AI can do but that AI can be introduced into a complex healthcare environment without losing sight of the people it is designed to serve. For Pod Sirius: Winning is when innovation meets patient safety, and technology supports people rather than replacing them. AI supports. Humans decide.
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Project Information
Project Name
Mapping the Human Decision Points-AI-Enabled 30-Day Readmission Risk
One-Line Problem Statement
Two hospitals have merged, but how can they standardise their discharge and follow-up process while safely introducing AI-supported 30-day readmission risk assessment?
Project Description
We are designing a standardised, auditable discharge and readmission-risk workflow for two merged hospitals, where AI helps identify and prioritise patients at risk of 30-day readmission, while clearly defined human checkpoints ensure that clinical decisions, identity resolution and patient-management actions remain under human accountability.
Methodology
Lakeside Health Network has recently brought together two hospitals that previously operated with different systems, patient identification processes, and discharge workflows. The merger creates an opportunity to use AI to identify patients who may be at increased risk of 30-day readmission and help the follow-up team prioritise patients who may need additional support after discharge. However, introducing AI into a merged healthcare environment creates important Business Analysis and clinical-safety challenges. These two hospitals do not yet have one consistent discharge process. Patient records may contain different identifiers or duplicate/conflicting information, and responsibilities around reviewing and acting on AI-generated risk information are not sufficiently clear. How can Lakeside Health Network standardise its discharge and follow-up process while safely introducing AI-supported 30-day readmission risk assessment? As a pod, we must analyse the current state, identify process and data risks, define the future state, establish clear ownership, and translate the business need into testable requirements.
Team Bio
Our team has different group of espertise as part of the Health Tech Business Analysis course team at 10Alytics. We are united by a passion to explore how technology can solve real healthcare problems through innovation and implementable solutions.
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