AI4Health Lecture Series: Markus Lingman and Mattias Ohlsson

Beschreibung

The next talk of the AI4Health lecture series will continue on November 11 with a talk on "Information driven healthcare in Halland" by Markus Lingman and Mattias Ohlsson (Region Halland/Halmstad University, Sweden).

Information driven healthcare in Halland

Region Halland in Sweden is the main healthcare provider for the county of Halland (about 330 000 inhabitants). Region Halland realized early on the potential impact of information driven healthcare; using data and data analytics to improve the healthcare system. Region Halland have during the last ten years developed and maintained a comprehensive healthcare data infrastructure covering clinical and administrative information pertaining to every consumer in Halland of healthcare with public funding. This means approximately 500 000 patients treated in Halland now and in the past and includes all the Region’s care delivery units and also the pharmacies. Work is ongoing to include the municipalities, who have responsibility for e.g. elderly care.
Halmstad (Regional Capital of Halland) University, and in particular CAISR (Centre for Applied Intelligent Systems Research), have had a longstanding and seamless collaboration with Region Halland with the focus on applying AI and machine learning towards information driven healthcare solutions. This work also includes collaborations with international partners (e.g. Harvard Medical School and Brigham Women’s Hospital in Boston).
Over the last years, Region Halland has been able to cut costs in the healthcare service at the same time as the population has grown and there has been a substantial increase in patient arrivals to the emergency departments. Also medical quality of care has improved. The efficiency improvement has been achieved e.g. by reducing hospital bed days without affecting occupancy levels, by decreasing the admission rates to the hospital, and increasing the fraction patients that can be discharged early. Many of these achievements were enabled by using information driven healthcare, by introducing data analytics and better prognostics for the management of the healthcare system. For 2019, Region Halland is one of only two regions in Sweden that do not show a large economical deficit in the healthcare service. Several regions are flagging for large staff layoffs in their healthcare systems for 2020.
Detailed and comprehensive care data, together with modern AI and analysis tools, play an important role in delivering effective care by facilitating healthcare providers to to create actionable insights and take better informed decisions. What is also required is a methodology and organization on how to systematically work with information driven improvement work around quality and productivity where the goal is to understand how the patient is affected in the healthcare system. Region Halland have developed a model for organization, working methods and a nine-step process on how to go from idea, to follow-up of an implementation of a change in the health care system. The model gives the decision maker a powerful tool to choose the initiatives that give the best results at the system level. This includes creating agile multidisciplinary teams around system issues and use the nine-step process for a data-driven improvement work that considers all the necessary aspects including production, quality and economy with the highest possible degree of detail.
We will present how Region Halland works with information driven healthcare, both how to find insights and to get them implemented, and show research projects and results that have emerged through the collaboration between Region Halland and CAISR at Halmstad University. We also present ongoing work in developing methods and infrastructure for distributed machine learning, such that medical databases located at different healthcare provides can be utilized when creating AI and machine learning solutions related to information driven healthcare.

- See more at: https://acc.uni.lu/ai4health/

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Meeting link:
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Meeting password: UL-AIForHCWebs

Host key: 478625 

See more at: acc.uni.lu/ai4health