Imagine…(a common language for ICU data inquiry and analysis)

European society for trauma and emergency surgery member-identified research priorities in emergency surgery: a roadmap for future clinical research opportunities

BACKGROUND

European Society for Trauma and Emergency Surgery (ESTES) is the European community of clinicians providing care to the injured and critically ill surgical patient. ESTES has several interlinked missions - (1) the promotion of optimal emergency surgical care through networked advocacy, (2) promulgation of relevant clinical cognitive and technical skills, and (3) the advancement of scientific inquiry that closes knowledge gaps, iteratively improves upon surgical and perioperative practice, and guides decision-making rooted in scientific evidence. Faced with multitudinous opportunities for clinical research, ESTES undertook an exercise to determine member priorities for surgical research in the short-to-medium term; these research priorities were presented to a panel of experts to inform a 'road map' narrative review which anchored these research priorities in the contemporary surgical literature.

METHODS

Individual ESTES members in active emergency surgery practice were polled as a representative sample of end-users and were asked to rank potential areas of future research according to their personal perceptions of priority. Using the modified eDelphi method, an invited panel of ESTES-associated experts in academic emergency surgery then crafted a narrative review highlighting potential research priorities for the Society.

RESULTS

Seventy-two responding ESTES members from 23 countries provided feedback to guide the modified eDelphi expert consensus narrative review. Experts then crafted evidence-based mini-reviews highlighting knowledge gaps and areas of interest for future clinical research in emergency surgery: timing of surgery, inter-hospital transfer, diagnostic imaging in emergency surgery, the role of minimally-invasive surgical techniques and Enhanced Recovery After Surgery (ERAS) protocols, patient-reported outcome measures, risk-stratification methods, disparities in access to care, geriatric outcomes, data registry and snapshot audit evaluations, emerging technologies interrogation, and the delivery and benchmarking of emergency surgical training.

CONCLUSIONS

This manuscript presents the priorities for future clinical research in academic emergency surgery as determined by a sample of the membership of ESTES. While the precise basis for prioritization was not evident, it may be anchored in disease prevalence, controversy around aspects of current patient care, or indeed the identification of a knowledge gap. These expert-crafted evidence-based mini-reviews provide useful insights that may guide the direction of future academic emergency surgery research efforts.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Thematic mapping of perioperative incident reporting data to relational coordination domains

Communication failure is a common root cause of adverse clinical events. Problematic communication domains are difficult to decipher, and communication improvement strategies are scarce. This study compared perioperative incident reports (IR) identifying potential communication failures with the results of a contemporaneous peri-operative Relational Coordination (RC) survey. We hypothesised that IR-prevalent themes would map to areas-of-weakness identified in the RC survey. Perioperative IRs filed between 2018 and 2020 (n = 6,236) were manually reviewed to identify communication failures (n = 1049). The IRs were disaggregated into seven RC theory domains and compared with the RC survey. Report disaggregation ratings demonstrated a three-way inter-rater agreement of 91.2%. Of the 1,049 communication failure-related IRs, shared knowledge deficits (n = 479, 46%) or accurate communication (n = 465, 44%) were most frequently identified. Communication frequency failures (n = 3, 0.3%) were rarely coded. Comparatively, shared knowledge was the weakest domain in the RC survey, while communication frequency was the strongest, correlating well with our IR data. Linking IR with RC domains offers a novel approach to assessing the specific elements of communication failures with an acute care facility. This approach provides a deployable mechanism to trend intra- and inter-domain progress in communication success, and develop targeted interventions to mitigate against communication failure-related adverse events.

Global healthcare fairness: We should be sharing more, not less, data

The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost-measured in terms of access to future medical innovations and clinical software-of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence's progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur-for the benefit of a global medical knowledge system.