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The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query).

Antibiotic prescribing in ambulatory care centers is increasing. Previous research suggests that 20 to 50% of antibiotic prescriptions are either unnecessary or inappropriate. Unnecessary antibiotic consumption can harm patients by increasing antibiotic resistance and drug-associated toxicities, and the reasons for such use are multifactorial. Antimicrobial Stewardship Programs (ASP) were developed to guide better use of antibiotics. A core element of ASP is to provide feedback to clinical providers. To create [...]

Electronic health records (EHRs) are used in long-term care to document the patients' condition, medication, and care, thereby supporting communication among caregivers and counteracting adverse drug events. However, the use of EHRs in long-term care has lagged behind EHR use in hospitals. In addition, most EHR research focuses on hospitals.

Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening [...]

We characterize physician workflow in two distinctive emergency departments (ED). Physician practices mediated by electronic health records (EHR) are explored within the context of organizational complexity for the delivery of care.

Handoffs or care transitions from the operating room (OR) to intensive care unit (ICU) are fragmented and vulnerable to communication errors. Although protocols and checklists for standardization help reduce errors, such interventions suffer from limited sustainability. An unexplored aspect is the potential role of developing personalized postoperative transition interventions using artificial intelligence (AI)-generated risks.

Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types.

Limited research exists on patient knowledge/cognition or "getting inside patients' heads." Because patients possess unique and privileged knowledge, clinicians need this information to make patient-centered and coordinated treatment planning decisions. To achieve patient-centered care, we characterize patient knowledge and contributions to the clinical information space.