Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist.

To facilitate real-world implementation of PROs in electronic health records (EHRs) for use in clinical practice, members of the EHR Access to Seamless Integration of Patient-Reported Outcomes Measurement Information System (PROMIS) Consortium developed structured PRO implementation planning tools. Each institution pilot tested the tools. Joint meetings led to the identification of critical sociotechnical success factors.

Three tools were developed and tested: (1) a PRO Planning Guide summarizes the empirical knowledge and guidance about PRO implementation in routine clinical care; (2) a Decision Log allows decision tracking; and (3) an Implementation Plan Template simplifies creation of a sharable implementation plan. Seven lessons learned during implementation underscore the iterative nature of planning and the importance of the clinician champion, as well as the need to understand aims, manage implementation barriers, minimize disruption, provide ample discussion time, and continuously engage key stakeholders.

Highly structured planning tools, informed by a sociotechnical perspective, enabled the construction of clear, clinic-specific plans. By developing and testing three reusable tools (freely available for immediate use), our project addressed the need for consolidated guidance and created new materials for PRO implementation planning. We identified seven important lessons that, while common to technology implementation, are especially critical in PRO implementation.

Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. Logistic regression–derived risk scores are common in the healthcare epidemiology literature. Machine learning–derived decision trees are an alternative approach for developing decision support tools. Our group previously reported on a decision tree for predicting ESBL bloodstream infections. Our objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.

Using a dataset of 1,288 patients with Escherichia coli or Klebsiella spp bacteremia, we generated a risk score to predict the likelihood that a bacteremic patient was infected with an ESBL-producer. We evaluated discrimination (original and cross-validated models) using receiver operating characteristic curves and C statistics. We compared risk score and decision tree performance, and we reviewed their practical and methodological attributes.

In total, 194 patients (15%) were infected with ESBL-producing bacteremia. The clinical risk score included 14 variables, compared to the 5 decision-tree variables. The positive and negative predictive values of the risk score and decision tree were similar (>90%), but the C statistic of the risk score (0.87) was 10% higher.

A decision tree and risk score performed similarly for predicting ESBL infection. The decision tree was more user-friendly, with fewer variables for the end user, whereas the risk score offered higher discrimination and greater flexibility for adjusting sensitivity and specificity.

The International Federation for Emergency Medicine (IFEM) Ultrasound Special Interest Group (USIG) was tasked with development of a hierarchical consensus approach to the use of point of care ultrasound (PoCUS) in patients with hypotension and cardiac arrest.

The IFEM USIG invited 24 recognized international leaders in PoCUS from emergency medicine and critical care to form an expert panel to develop the sonography in hypotension and cardiac arrest (SHoC) protocol. The panel was provided with reported disease incidence, along with a list of recommended PoCUS views from previously published protocols and guidelines. Using a modified Delphi methodology the panel was tasked with integrating the disease incidence, their clinical experience and their knowledge of the medical literature to evaluate what role each view should play in the proposed SHoC protocol.

Consensus on the SHoC protocols for hypotension and cardiac arrest was reached after three rounds of the modified Delphi process. The final SHoC protocol and operator checklist received over 80% consensus approval. The IFEM-approved final protocol, recommend Core, Supplementary, and Additional PoCUS views. SHoC-hypotension core views consist of cardiac, lung, and inferior vena vaca (IVC) views, with supplementary cardiac views, and additional views when clinically indicated. Subxiphoid or parasternal cardiac views, minimizing pauses in chest compressions, are recommended as core views for SHoC-cardiac arrest; supplementary views are lung and IVC, with additional views when clinically indicated. Both protocols recommend use of the “4 F” approach: fluid, form, function, filling.

An international consensus on sonography in hypotension and cardiac arrest is presented. Future prospective validation is required.