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Warn Harm: Real-time warning system for avoiding patients and healthcare staff harm´s Profile image

Warn Harm: Real-time warning system for avoiding patients and healthcare staff harm

Project
01.09.2022 - 31.08.2027
Department of Nursing Science, Faculty of Health Sciences
Information People Publications

Funders

Academy of Finland

Leaders

Developing a predictive real-time warning system for avoiding patients and healthcare staff harm after medication safety incidents  

Medication incidents cause significant harm to patients, health professionals and healthcare organisations. Incident reporting systems have been utilised globally to increase safety by learning from incidents. Such systems are not used to their full potential. This study moves towards next level utilisation of incident reporting systems by using them in real-time to warn of likely harm. This will be accomplished by producing a novel ‘warn harm’ taxonomy and trigger tool that can be translated into a practical predictive tool. Early-warning systems that use data from incident reports do not currently exist. Thus, this study offers opportunities for scientific breakthroughs. It will produce an innovative ‘warn harm’ taxonomy and trigger tool. This could potentially alert to additional avoidable harm that will increase patients’ well-being and safety, bring cost savings for health systems and society, as well as support staff members by decreasing emotional stress of the staff involved in errors.

Cooperation

News

Warn harm logo

  • Two scoping review protocols in process (4/2024)
  • Data collection completed of a questionnaire study for health care professionals about perceptions of current incident reporting system. The results have been analysed and two articles about to go for review (4/2024)
  • Two systematic reviews published (2/2024 and 4/2024)
  • Two systematic reviews under review and one questionnaire study ongoing (5.2.2024)
  • Work for two systematic reviews started (8.2.2023)

Keywords

adverse event early detection harm patientsafety predictive predictive modeling prevention

Leaders

Post-doctoral Researchers

Doctoral Researchers

Publications

8 items