Philips – Healthcare Solutions

Patient Flow Capacity Suite

Through an accessible interface, supports hospitals in managing the Pandemic in order to improve clinical outcomes and reduce mortality.

Challenge

Challenge
The users have to have the information in real-time and thus understand and predict the flow of patients admission, different levels of information and the manage the occupation of the hospital.

My Role

User research & Analysis
UI Design & Prototyping
Usability Testing
Evolve the Design System

Research & Problem Definition

I investigated and mapped the pain points of the users, most were the hospitalization flow (regular and or emergency cases) and the high demand for hospital beds.
Create the user flow to define the flow of patients and administration.

As the place where this will be used has low lighting and long periods, the decision to design a Dark Mode was the best definition.

MVP was designed to track low and high admission scenarios and quick decision making to transfer patients in the case of Covid 19.

Ideating Solutions

Designed charts for the Interfaces, which were the most suitable for a dynamic and quick reading, especially for decision making.

The use of colours, numbers and charts makes the Dashboard information for the users.

Notifications for critical statements, in overcrowded moments, and or notifications for predicted catastrophic moment.

Design System

I take the responsibility of converting to Dark Mode and integrating it into the Design System to continue the scalability.

Results

User visibility of flows was covered by the project and also at multiple levels (they can see and anticipate cases) helping to make the decision;

Medical staff will be able to make decisions in advance of the patient’s discharge, and help move the patient from hospital to home “Home Care”;

Predictive algorithms, make scenario predictions, which will help the hospital to make a decision that will impact the future flow;

The hospital can manage the number of staff professionals, increase the number of beds, and predict the number of patients that will be admitted.