Citations

Objectives This study aimed to explore clinicians’ perceptions of the occurrence of and factors associated with diagnostic errors in patients evaluated during a rapid response team (RRT) activation or unplanned admission to the intensive care unit (ICU). Methods A multicenter prospective survey study was conducted among multiprofessional clinicians involved in the care of patients with RRT activations and/or unplanned ICU admissions (UIAs) at 2 academic hospitals and 1 community-based hospital between April 2019 and March 2020. A study investigator screened eligible patients every day. Within 24 hours of the event, a research coordinator administered the survey to clinicians, who were asked the following: whether diagnostic errors contributed to the reason for RRT/UIA, whether any new diagnosis was made after RRT/UIA, if there were any failures to communicate the diagnosis, and if involvement of specialists earlier would have benefited that patient. Patient clinical data were extracted from the electronic health record. Results A total of 1815 patients experienced RRT activations, and 1024 patients experienced UIA. Clinicians reported that 18.2% (95/522) of patients experienced diagnostic errors, 8.0% (42/522) experienced a failure of communication, and 16.7% (87/522) may have benefitted from earlier involvement of specialists. Compared with academic settings, clinicians in the community hospital were less likely to report diagnostic errors (7.0% versus 22.8%, P = 0.002). Conclusions Clinicians report a high rate of diagnostic errors in patients they evaluate during RRT or UIAs.

Abstract Background: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for Palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. Methods: To that Aim we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist, and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary’s Hospital & Methodist Hospital, Mayo Clinic, Rochester, Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance.Discussion: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; Clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice whilst retaining scientific rigor.Trial registration NCT03976297 (ClinicalTrials.gov), registered on June 6, 2019 prior to trial start.

335 Background: Systematic reviews are outdated quickly when the evidence is rapidly evolving as the process is laborious and there is little incentive for primary author team of an index SRMA to update the evidence. Consequently, there is an epidemic of redundant SRMAs performed by different teams—sometimes with conflicted results—for treatment of first line mRCC. Methods: We have created a living, interactive systematic review (LISR)and network meta-analysis(LINMA) for the treatment of first line mRCC using an Artificial intelligence (AI) assisted framework for evidence synthesis (Living, Interactive evidence synthesis framework) (LIvE) . The framework is implemented in five-layered architecture (application layer, shared module layer, core service layer, middleware layer, and storage layer) which work together to automate the identification of new studies and analysis and semi-automate the screening and data extraction. Dynamic features such as interactive tables, figures and evidence maps are enabled using Python and JavaScript programming languages. Results: We have maintained a living, interactive evidence profile for the first line treatment mRCC since September 2019 ( LIVING WEBSITE) . Living search strategy identifies new studies as they become available. As of October 13, 2020 LISR, includes data 14 clinical trials ( PRISMA ). Baseline characteristics are summarized in an interactive table ( TABLE) . Cabozantinib& Nivolumab (Cabo-Nivo) is the highest ranked drug for improving Overall Response (OR), Progression Free Survival (PFS) and Overall Survival (OS) whereas Ipilimumab in combination with Nivolumab (Ipi-Nivo) is highest ranked drug for achieving complete response (CR). Ipi-Nivo and Atezolizumab & Bevacizumab (Ate-Bev) ranked highest and Cabo-Nivo ranked lowest for treatment related Adverse events (TRAEs). Results of network meta-analysis are summarized as interactive tables and plots ( NMA ), summary of findings tables ( MULTIPLE COMAPRISONS ) and evidence maps ( MAP ). Conclusions: LISRs can potentially reduce redundancy, increase transparency, reproducibility, enable shared-decision making (at a guideline level, or in a patient-clinician dyad) and support living guidelines.

AbstractObjective Video recording and video recognition (VR) with computer vision have become widely used in many aspects of modern life. Hospitals have employed VR technology for security purposes, however, despite the growing number of studies showing the feasibility of VR software for physiologic monitoring or detection of patient movement, its use in the intensive care unit (ICU) in real-time is sparse and the perception of this novel technology is unknown. The objective of this study is to understand the attitudes of providers, patients, and patient's families toward using VR in the ICU.Design A 10-question survey instrument was used and distributed into two groups of participants: clinicians (MDs, advance practice providers, registered nurses), patients and families (adult patients and patients' relatives). Questions were specifically worded and section for free text-comments created to elicit respondents' thoughts and attitudes on potential issues and barriers toward implementation of VR in the ICU.Setting The survey was conducted at Mayo Clinic in Minnesota and Florida.Results A total of 233 clinicians' and 50 patients' surveys were collected. Both cohorts favored VR under specific circumstances (e.g., invasive intervention and diagnostic manipulation). Acceptable reasons for VR usage according to clinicians were anticipated positive impact on patient safety (70%), and diagnostic suggestions and decision support (51%). A minority of providers was concerned that artificial intelligence (AI) would replace their job (14%) or erode professional skills (28%). The potential use of VR in lawsuits (81% clinicians) and privacy breaches (59% patients) were major areas of concern. Further identified barriers were lack of trust for AI, deterioration of the patient–clinician rapport. Patients agreed with VR unless it does not reduce nursing care or record sensitive scenarios.Conclusion The survey provides valuable information on the acceptance of VR cameras in the critical care setting including an overview of real concerns and attitudes toward the use of VR technology in the ICU.

Abstract Objective Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. Materials and Methods Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient’s corresponding care team. Results Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an “in-production” AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. Conclusions A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

ABSTRACT Introduction The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers’ ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation. Materials and Methods Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit. Results The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset. Conclusions We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic’s ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models.