Continuous Vital Sign Monitoring Data in the General Ward: Exploratory Analysis.

This paper explores and analyzes a high-frequency vital sign- and event dataset from surgical ward patients to prepare for the training and application of predictive models.

Coding Clinic: A Multidisciplinary Approach Supporting Early-Stage Medical Data Science Research.

The Coding Clinic at Charité - Universitätsmedizin Berlin offers early-career researchers support in data science and software development, addressing challenges in medical research. Through consultations, pair programming, and open-source resources, the Coding Clinic enhances data management, EHR analysis, and fosters interdisciplinarity. Our team of several computer and data scientists developed an increasingly popular consultation format in a novel setting.

Optimizing Hospital Device Integration: Responsibility Mapping and Automation.

Implementation of technical hardware in hospitals is often delayed due to unclear responsibilities. In an interdisciplinary approach we developed a Technical Eligibility Items (TEI) list that maps technical requirements to responsible vendor and hospital departments. The TEI list is machine-readable and stored in an SQLite database, streamlining the implementation process and enabling future automated solutions.

Investigating the Implementation of Monitoring Technology: An Interdisciplinary Collaborative Process.

The complexity of implementing monitoring technology in healthcare presents numerous challenges, making it impractical for a small group of researchers to select observation windows and gather meaningful data. We developed a structured collaborative process involving about 20 individuals from informatics, data science, social science, practical implementation and healthcare to design protocols for investigating the implementation of monitoring technology in general ward and operating room settings through interviews, surveys and observations. Including practical implementation experts without research experience played a pivotal role, as their insights ground the protocols in practical realities.

Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach.

Background: Alarm fatigue, a multifactorial desensitization of staff to alarms, can harm both patients and health care staff in intensive care units (ICUs), especially due to false and nonactionable alarms. Increasing amounts of routinely collected alarm and ICU patient data are paving the way for training machine learning (ML) models that may help reduce the number of nonactionable alarms, potentially increasing alarm informativeness and reducing alarm fatigue. At present, however, there is no publicly available dataset or process that routinely collects information on alarm actionability (ie, whether an alarm triggers a medical intervention or not), which is a key feature for developing meaningful ML models for alarm management.

Association with perceived stress and feasibility of an app for patient-reported stressor experiences during treatment in an intensive care unit: a nonrandomized controlled study (ICU Feel Better App).

Background: Critically ill patients often experience substantial stress during their ICU treatment. The ICU Feel Better App is a novel mobile application that patients can use to evaluate ICU-related stressors during their stay. We aimed to investigate if using the app, without feedback to the ICU staff, would be associated with changes in perceived acute stress.

Multicenter validation of secondary hemophagocytic lymphohistiocytosis diagnostic criteria.

Background: Five fulfilled hemophagocytic lymphohistiocytosis (HLH)-2004 criteria, and the HScore are widely used and recommended by international expert consensus to diagnose secondary HLH. Both diagnostic scores have never been validated in heterogeneous patient cohorts of secondary HLH patients. We aimed to systematically optimize and validate diagnostic criteria of secondary HLH using a multicenter approach.

User Experience Design for Intensive Care Alarm Management.

Alarm fatigue is a pressing issue in intensive care units. Based on user experience design, including clinical shadowings and feedback loops, we developed a prototype for a redesigned patient monitor: The prototype moves away from today's threshold-based alarm systems. It combines a sleek design with machine learning driven clinical insights to mitigate alarm fatigue.

Value of hemophagocytosis in the diagnosis of hemophagocytic lymphohistiocytosis in critically ill patients.

Background: Ferritin is an established biomarker in the diagnosis of secondary hemophagocytic lymphohistiocytosis (HLH), which is diagnosed by the HLH-2004 criteria. Among these criteria, detection of hemophagocytosis through invasive procedures may delay early life saving treatment. Our aim was to investigate the value of hemophagocytosis in diagnosing HLH in critically ill patients.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study.

Background: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

Differential Diagnosis of Hyperferritinemia in Critically Ill Patients.

Background: Elevated serum ferritin is a common condition in critically ill patients. It is well known that hyperferritinemia constitutes a good biomarker for hemophagocytic lymphohistiocytosis (HLH) in critically ill patients. However, further differential diagnoses of hyperferritinemia in adult critically ill patients remain poorly investigated.

MIMIC-IV as a Clinical Data Schema.

Routinely collected electronic health records (EHR) in clinical information systems (CIS) are often heterogeneous, have inconsistent data formats and lack of documentation. We use the well-known open-source database schema of MIMIC-IV to address this issue aiming to support collaborative secondary analysis. Over 154 million data records from a German ICU have already been mapped and inserted into the schema successfully.

Utilizing Intensive Care Alarms for Machine Learning.

Alarms help to detect medical conditions in intensive care units and improve patient safety. However, up to 99% of alarms are non-actionable, i.e.

Influence of transfusions, hemodialysis and extracorporeal life support on hyperferritinemia in critically ill patients.

Background: Ferritin is the major iron storage protein and an acute phase reactant. Hyperferritinemia is frequently seen in the critically ill where it has been hypothesized that not only underlying conditions but also factors such as transfusions, hemodialysis and extracorporeal life support (ECLS) lead to hyperferritinemia. This study aims to investigate the influence of transfusions, hemodialysis, and ECLS on hyperferritinemia in a multidisciplinary ICU cohort.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions.

Background: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.

Objective: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system.

Hemophagocytic lymphohistiocytosis in critically ill patients: diagnostic reliability of HLH-2004 criteria and HScore.

Background: Hemophagocytic lymphohistiocytosis (HLH) is a rare though often fatal hyperinflammatory syndrome mimicking sepsis in the critically ill. Diagnosis relies on the HLH-2004 criteria and HScore, both of which have been developed in pediatric or adult non-critically ill patients, respectively. Therefore, we aimed to determine the sensitivity and specificity of HLH-2004 criteria and HScore in a cohort of adult critically ill patients.