ICU is the place with the most advanced monitoring and support machines in a hospital. But surrounded by all these machines, ICU patients are also like machines instead of humans. Scientific advances have been proved to greatly improve the cure rate. Meanwhile, patients' well-being has been gradually treated as important as curing the disease itself. With the trend of human-centred intensive care, new services are required to better satisfy patients’
needs.

Artificial intelligence can be a strong force to improve the quality of care in ICU. Apart from taking advantage of the existing monitoring data and EHR data to improve the cure rate, A.I. also has great potential to promote social support for ICU patients.

The purpose of this project is to design a roadmap for A.I. to provide social support for ICU patients, in order to improve the patient wellbeing and contribute to a humane ICU.The design solution is an application called Cricare that includes nine services: 1. Orientation; 2. Autocomplete; 3. Voice Simulator; 4. Music Creator; 5. Environment adjuster; 6. Chatbot; 7. Virtual Psychologist; 8. Peer support; 9. Goal setter.

A questionnaire evaluation with nurses was conducted to test the desirability, importance, and effect on providing social support and effect on patient wellbeing. The results show Cricare is the potential to contribute to a humane ICU. After feasibility evaluation, a primary roadmap was created to develop Cricare.

Background: In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking. Objective: The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services. Methods: A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map. Results: The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support. Conclusions: The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support.