How AI Can Improve Hospital Management
AI offers hospitals an important lever to optimize processes, improve quality and increase efficiency. Based on the data generated in a digital hospital, better therapy decisions can be made. At the same time, it is becoming increasingly difficult for personnel to master the flood of data they are faced with. The use of artificial intelligence can become a valuable support in making sense of this big data. However, it only realizes its full potential if data is available in a structured manner and AI applications are based on specific questions. But the technology is advancing where this is becoming easier.
AI Hospital Management
Artificial intelligence helps translate large amounts of data into action-relevant information.
Health systems in general and hospitals, in particular, are facing major challenges worldwide. With the demographic change, the number of older, multimorbid patients is increasing. At the same time, there is a shortage of skilled workers and cost pressure. Another important trend: Patients are increasingly interested and informed and want to be actively involved in treatment options and processes as a patient.
This constellation necessitates a fundamental rethink in care. People should no longer be treated only reactively episodically. Proactive and accompanying care structures are needed, with more information, service and discussion opportunities for the patient. However, this can only be realistically implemented with the right technology to support human care elements.
Digitization provides an answer. A digital healthcare system opens up a multitude of possibilities for communication and information, for increasing quality and efficiency and for relieving staff. Digitization also exponentially increases the amount of medical data. This is a challenge and an opportunity at the same time: a challenge because the already overloaded staff have ever greater problems converting available data into action-relevant information. An opportunity because large amounts of data can lead to new scientific knowledge and medical hypotheses can be checked. Artificial intelligence (AI), which offers helpful approaches for processing the amount of data, is being used more and more frequently.
AI in Healthcare
The term artificial intelligence is often associated with the image of an all-encompassing AI engine that can be built on top of healthcare and thus solves all possible clinical problems. However, the opposite is the case. Instead of aligning the care processes with the requirements of artificial intelligence, it should fit seamlessly into the care processes and be based on the needs of patients and users. The starting point must, therefore, be the clinical context in which AI methods are applied in a targeted manner to specific questions. Artificial intelligence should work with other technologies on these specific tasks. In order to create real added value, it must also have the domain knowledge of doctors.
Artificial intelligence needs data to work with. This applies not only to their use but also to their development. In order to be trained on specific questions, neural networks need well-structured data. Radiology, for example, has been working digitally for many years. That is why it is one of the areas in which AI applications are increasingly being used. Most of them are clearly defined tasks that use training data that clearly represent both the clinical question and the answer. A typical task is the detection of tumors or tissue structures. The training data contain a large number of images of the corresponding organ and the information on whether and where there is a tumour on the image.
Digitization as a basic requirement for the use of AI
The biggest challenge for more extensive use of artificial intelligence is the lack of compatibility of the collected data. No hospital works in a purely monolithic IT environment. Even the most modern hospital information systems do not meet all special requirements. In order to ensure an optimal flow of information and to avoid media breaks, a system landscape is required in which special systems can be integrated without any problems. That is why interoperability is a key issue in digitization. Closed, proprietary systems with interfaces that are difficult or expensive to operate stand in the way of the economic success of hospitals.
The quality of the data determines the potential that results from its use. When collecting data, it is therefore important to ensure that the data is acquired and stored in a consistent and reliable manner. This is not the case today, especially with data that has been collected - that is, collected from different organizations, processes or source systems. This is particularly relevant for AI applications outside of clearly defined, uniform subareas of care. In radiology, well-trained AI applications can recognize whether the quality of an image is sufficient to find it, or whether (based on) learned decision criteria. In both cases, the radiologist is undoubtedly supported in performing his work more efficiently and with higher quality. However, the effect could be even greater if artificial intelligence is used on longitudinal data. These are data that document the course of a disease or recovery over a longer period in a structured manner. They include information on the influence of the disease on quality of life or workability as well as the success of the therapeutic measures initiated.
Artificial intelligence is oriented towards the needs of patients and users and fits seamlessly into the care process.
The analysis of a large amount of data from similar clinical pictures can show which therapies are best suited for which patients. However, statements about the likelihood of long-term success in care can only be made if all relevant data, both in the training data record and in the care for the individual patient, is available in a structured manner over a longer period of time. This, in turn, requires digitization across the entire observation period.