[Prehospital perimortem cesarean section during cardiopulmonary resuscitation for traumatic cardiac arrest : Case report and lessons learned]. / Präklinische perimortale Crash-Sectio im Rahmen einer Traumareanimation : Fallbericht mit Darstellung gewonnener Erkenntnisse.

The following case report discusses the resuscitation of a pregnant woman in traumatic cardiac arrest after a fall from a height with consecutive resuscitative hysterotomy for maternal and fetal salvage. The report illustrates all lessons learned from critical appraisal amid new guideline recommendations and gives an overview of the published literature on the matter. Despite extensive resuscitation efforts, ultimately both the mother and the newborn were pronounced life extinct at the scene. Prehospital treatment of (traumatic) cardiac arrest in a pregnant patient as well as performing a perimortem cesarean section remain infrequent but challenging scenarios.

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed "CACulator" serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.

[Quo vadis joint emergency dispatch system?] / Quo vadis, gemeinsames Notfallleitsystem?: Standardisierte Notrufabfrage in der Berliner Leitstelle.

The call volume in emergency medical service (EMS) dispatch centers has seen a drastic increase for many years now, especially looking at urban regions of Germany. In this context, the control mechanisms of the EMS dispatch center can be utilized to break new ground regarding the handling of emergency calls and dispatch practice in order to manage incoming calls as efficiently as possible. This article clearly explains standardized protocol-based emergency medical call taking, internal structuring of control centers and pathways, also during the COVID-19 pandemic, using the Berlin EMS dispatch center as an example. The terms structured and standardized protocol-based emergency medical call taking should be differentiated, whereby the standardized call taking process is more binding and based on international standards with high reliability. Quality management measures ensure that the protocol is applied in accordance with the regulations. Improved collaboration and automated transfer of data between EMS dispatch centers and the control centers for non-life-threatening physician on-call services enable low-priority calls to be forwarded on a regular basis. Interprofessional teams in EMS can improve the care of specific patient groups in a targeted manner and avoid transport to emergency departments. Standardized protocol-based and software-based emergency call taking currently represents best practice according to medical science, supporting a nationwide implementation. Furthermore, an intensive collaboration between EMS control centers and control centers for non-life-threatening physician on-call services is recommended as well as the introduction of specialized EMS resources and app-based alerting of first responders.

Success Factors of Artificial Intelligence Implementation in Healthcare.

Background: Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare. Methods: A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database. The according search queries have been defined based on success factor categories for AI implementation derived from a prior World Health Organization survey about barriers of adoption of Big Data within 125 countries. The eligible publications and real life cases were identified through a catalog of in- and exclusion criteria focused on concrete AI application cases. These were then analyzed to deduct and discuss success factors that facilitate or inhibit a broad-scale implementation of AI in healthcare. Results: The analysis revealed three categories of success factors, namely (1) policy setting, (2) technological implementation, and (3) medical and economic impact measurement. For each of them a set of recommendations has been deducted: First, a risk adjusted policy frame is required that distinguishes between precautionary and permissionless principles, and differentiates among accountability, liability, and culpability. Second, a "privacy by design" centered technology infrastructure shall be applied that enables practical and legally compliant data access. Third, the medical and economic impact need to be quantified, e.g., through the measurement of quality-adjusted life years while applying the CHEERS and PRISMA reporting criteria. Conclusions: Private and public institutions can already today leverage AI implementation based on the identified results and thus drive the translation from scientific development to real world application. Additional success factors could include trust-building measures, data categorization guidelines, and risk level assessments and as the success factors are interlinked, future research should elaborate on their optimal interaction to utilize the full potential of AI in real world application.

The Economic Impact of Artificial Intelligence in Health Care: Systematic Review.

BACKGROUND: Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the health care industry. It is most relevant for the care provider and insurer as well as for the pharmaceutical and medical technology sectors. Although the broad economic impact of digital health solutions in general has been assessed many times in literature and the benefit for patients and society has also been analyzed, the specific economic impact of AI in health care has been addressed only sporadically. OBJECTIVE: This study aimed to systematically review and summarize the cost-effectiveness studies dedicated to AI in health care and to assess whether they meet the established quality criteria. METHODS: In a first step, the quality criteria for economic impact studies were defined based on the established and adapted criteria schemes for cost impact assessments. In a second step, a systematic literature review based on qualitative and quantitative inclusion and exclusion criteria was conducted to identify relevant publications for an in-depth analysis of the economic impact assessment. In a final step, the quality of the identified economic impact studies was evaluated based on the defined quality criteria for cost-effectiveness studies. RESULTS: Very few publications have thoroughly addressed the economic impact assessment, and the economic assessment quality of the reviewed publications on AI shows severe methodological deficits. Only 6 out of 66 publications could be included in the second step of the analysis based on the inclusion criteria. Out of these 6 studies, none comprised a methodologically complete cost impact analysis. There are two areas for improvement in future studies. First, the initial investment and operational costs for the AI infrastructure and service need to be included. Second, alternatives to achieve similar impact must be evaluated to provide a comprehensive comparison. CONCLUSIONS: This systematic literature analysis proved that the existing impact assessments show methodological deficits and that upcoming evaluations require more comprehensive economic analyses to enable economic decisions for or against implementing AI technology in health care.