Clinical decision support systems (CDSS): When AI advises physicians

In medical care, time pressure, staff shortages and regulatory requirements are growing noticeably. Doctors and nursing staff are faced with the daily challenge of reliably classifying a flood of information in the shortest time possible. This is where clinical decision support systems come in: They structure clinical and health data from a patient, provide orientation in complex day-to-day care and support medical decisions where every minute counts and knowledge cannot be leveraged quickly.

A clinical decision support system (CDSS) is a software solution that supports medical professionals in making diagnostic, therapeutic and organizational decisions. It does not replace medical decisions made by a physician in the care of patients, but provides context-sensitive information, warnings or recommendations based on available health data. In this way, it bundles knowledge and makes it more readily available.

CDSS can work based on rules and rely on existing medical guidelines. However, they are increasingly using artificial intelligence to recognize complex patterns in image, laboratory or medical history data. In these cases, their recommendations are based on real data from healthcare practice and outcomes with which they have been trained.

The main target groups in medicine are

• physicians from various specialties,
• nursing staff, for example in triage or monitoring scenarios,
• interdisciplinary treatment teams in hospitals,
• to a limited extent, outpatient care providers.

A clinical decision support system is either a stand-alone system, for example as an app, or it can be integrated into existing hospital or practice information systems such as electronic patient records (EHR). This means they can be used almost seamlessly in ongoing workflows. Typical use cases are

• diagnostic support, for example through AI-supported image analysis in radiology,
• therapy support through guideline-based suggestions and risk information,
• early warning systems, for example to detect sepsis or complications,
• prioritization of cases in emergency rooms or diagnostic processes,
• quick access to evidence, guidelines and studies.

The added value of clinical decision support systems for medicine lies primarily in the standardization of processes, the reduction of avoidable errors and the relief of staff. This gives staff greater confidence in their actions, even in stressful situations, increases patient safety and improves health outcomes.

The regulatory status and use of AI vary depending on the module, indication and market region. Information is based on publicly available manufacturer information.

CDSS are only useful if they are geared towards a clearly defined use case. Clinics and practices should determine in advance whether they are looking for diagnostic support, patient safety, workflow optimization or resource management. A target that is too broad or vague often leads to low usage in everyday life.

Integration into existing HIS, RIS or practice management systems is crucial for practical use. Media discontinuities or additional user interfaces increase the workload and reduce acceptance. Technical standards, interfaces and data quality should therefore be checked at an early stage.

CDSS with a medical purpose must fulfill regulatory requirements. These include, in particular, a CE marking in accordance with MDR or an FDA approval. Country or region-specific data protection rules also apply. Those responsible should also clarify the specific indications and modules for which the approval applies.

The benefits of a CDSS depend heavily on who uses it in everyday clinical practice. Medical staff, nursing staff or interdisciplinary teams have different requirements and working methods. The amount of training required, usability and integration into existing routines have a significant influence on acceptance.

AI-based CDSS in particular must make their recommendations comprehensible and documentable. Users should be able to understand the data basis or logic on which a recommendation is based. Transparency is not only relevant for user trust and patient health, but also for liability and documentation issues.