[OPTINOFA-Intelligent assistance service for structured assessment in the emergency department]. / OPTINOFA ­ Intelligenter Assistenzdienst zur strukturierten Ersteinschätzung in der Notaufnahme.

BACKGROUND: Case numbers in central emergency departments (EDs) have risen during the past decade in Germany, leading to recurrent overcrowding, increased risks in emergency care, and elevated costs. Particularly the fraction of outpatient emergency treatments has increased disproportionately. Within the framework of the Optimization of emergency care by structured triage with intelligent assistant service (OPTINOFA, Förderkennzeichen [FKZ] 01NVF17035) project, an intelligent assistance service was developed. PATIENTS AND METHODS: New triage algorithms were developed for the 20 most frequent leading symptoms on the basis of established triage systems (emergency severity index, ESI; Manchester triage system, MTS) and provided as web-based intelligent assistance services on mobile devices. To evaluate the validity, reliability, and safety of the new OPTINOFA triage instrument, a pilot study was conducted in three EDs after ethics committee approval. RESULTS: In the pilot study, n = 718 ED patients were included (age 59.1 ± 22 years; 349 male, 369 female). With respect to disposition (out-/inpatient), a sensitivity of 91.1% and a specificity of 40.7%, and a good correlation with the OPTINOFA triage levels were detected (Spearman's rank correlation ρ = 0.41). Furthermore, the area under the curve (AUC) for prediction of disposition according to the OPTINOFA triage level was 0.73. The in-hospital mortality rate of OPTINOFA triage levels 4 and 5 was 0%. The association between the length of ED stay and the OPTINOFA triage level was shown to be significant (p < 0.001). CONCLUSION: The results of the pilot study demonstrate the safety and validity of the new triage system OPTINOFA. By definition of both urgency and emergency care level, new customized perspectives for load reduction in German EDs via a closer cooperation between out- and inpatient sectors of emergency care could be established.

Development of a Clinical Decision Support System for Smart Algorithms in Emergency Medicine.

The development of clinical decision support systems (CDSS) is complex and requires user-centered planning of assistive interventions. Especially in the setting of emergency care requiring time-critical decisions and interventions, it is important to adapt a CDSS to the needs of the user in terms of acceptance, usability and utility. In the so-called ENSURE project, a user-centered approach was applied to develop the CDSS intervention. In the context of this paper, we present a path to the first mockup development for a CDSS interface by addressing Campbell's Five Rights within the CDSS workflow.

Mobile Robotic Telepresence Between Hospital and School: Lessons Learned.

If a pupil falls seriously ill, it is not only a shock for the pupil himself or herself, but also for his or her family and classmates. The project "Virtual Classroom" of the Heilbronn University in cooperation with the foundation "Big Help for Little Heroes" therefore tries to maintain the daily school routine and thus the contact with friends as far as possible with the help of mobile telepresence robots in the classroom from home or from the hospital. Here there are both technical and human factors that contribute to success or failure. Based on a systematic literature research and practical experience, these factors are identified, discussed and weighted in this paper. If the mobile telepresence robot is used successfully, this pays off twice for the pupils affected: It has been shown that pupils can progress socially as well as in school, even if attendance at school is not possible for a long time.

Evaluation of Chatbot Prototypes for Taking the Virtual Patient's History.

In medical education Virtual Patients (VP) are often applied to train students in different scenarios such as recording the patient's medical history or deciding a treatment option. Usually, such interactions are predefined by software logic and databases following strict rules. At this point, Natural Language Processing/Machine Learning (NLP/ML) algorithms could help to increase the overall flexibility, since most of the rules can derive directly from training data. This would allow a more sophisticated and individual conversation between student and VP. One type of technology that is heavily based on such algorithmic advances are chatbots or conversational agents. Therefore, a literature review is carried out to give insight into existing educational ideas with such agents. Besides, different prototypes are implemented for the scenario of taking the patient's medical history, responding with the classified intent of a generic anamnestic question. Although the small number of questions (n=109) leads to a high SD during evaluation, all scores (recall, precision, f1) reach already a level above 80% (micro-averaged). This shows a first promising step to use these prototypes for taking the medical history of a VP.