Artificial intelligence and data-driven insights for enhancing multimorbidity and hip fracture care
Artificial intelligence and data-driven insights for enhancing multimorbidity and hip fracture care
As people live longer, more individuals develop several chronic conditions and face complex health problems. These patients often need help from many different doctors, which can make their care fragmented and less efficient. At the same time, hospitals are under pressure because of rising demand and limited staff and resources. This thesis of Hidde Dijkstra explores how data and artificial intelligence (AI) can help make healthcare more efficient, personal, and truly focused on patients’ needs.
The first part looks at emergency and surgical care. Using text from ambulance reports, an AI model could identify older people who are at risk of visiting the emergency department often. This may help organize care in advance. For patients with hip fractures, computer models predicted short- and long-term survival quite accurately. In the future, these models could support doctors and frail patients and caregivers in making better, more personalized treatment decisions.
The second part focuses on people with multiple chronic conditions. Analyses of hospital data showed that older and lower-income patients often have to travel long distances for care, suggesting a need for more remote and accessible healthcare. Another study found that the number of doctors involved in care does not say much about how well patients can manage their own health.
Overall, this thesis shows that AI and data can help improve hospital care by supporting prediction, coordination, and efficiency. To make a real difference, these technologies must be used responsibly and always keep patients at the centre of care and technology development.