INTRODUCTION: Automation of clinical orders in electronic health records (EHRs) has the potential to reduce clinician burden and enhance patient safety. However, determining which orders are appropriate for automation requires a structured framework to ensure clinical validity, transparency, and safety. OBJECTIVE: To develop and validate a framework of desiderata for assessing the appropriateness of automating clinical orders in EHRs and to demonstrate its operational value in a live health system dataset. MATERIALS AND METHODS: The study comprised 4 phases to move from concept generation to real-world demonstration. First, we conducted focus group analyses using ground theory to identify themes and developed desiderata informed by these themes and existing literature. We validated the desiderata by surveying clinicians at a single institution, presenting 10 use cases to and assessing perceived appropriateness, cognitive support, and patient safety using a 4-point Likert scale. Survey results were compared to a priori appropriateness designations using t-tests. To evaluate operational impact, we analyzed one year of order-based alerts and orders (1.4 million firings alert and 44.1 million orders, respectively) using filtering rules and association rule mining to identify candidate orders for automation and their impact. RESULTS: We identified 8 desiderata for automated order appropriateness: logical consistency, data provenance, order transparency, context permanence, monitoring plans, trigger consistency, care team empowerment, and system accountability. Use cases deemed appropriate based on these criteria received significantly higher scores for appropriateness (3.13 ± 0.84 vs 2.30 ± 0.99), cognitive support (3.08 ± 0.82 vs 2.25 ± 0.94), and patient safety (3.08 ± 0.86 vs 2.21 ± 0.98) (all P < .001) compared to those considered inappropriate. Operational analysis revealed an alert firing 19 109 times annually, with a 96% signed order rate, where automation could save an estimated 26.5 provider hours per year. Additionally, an association rule with 16 628 occurrences (68.4% confidence) suggested automation could save 15.8 hours annually and yield 8000 additional appropriate orders. DISCUSSION: The desiderata align with clinician perceptions and provide a structured approach for evaluating automated orders. Our findings highlight the potential for automation of certain clinical orders to improve cognitive support while maintaining patient safety. CONCLUSION: Healthcare systems should use these desiderata, coupled with data mining techniques, to systematically identify and govern appropriate automated orders. Further research is needed to validate operational scalability.

OBJECTIVE: There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions. MATERIALS AND METHODS: Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from 2 medical centers. RESULTS: Our method was able to classify clinical severity for 100% of patient days for 2756 patient encounters across 2 institutions. DISCUSSION: Implementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS. CONCLUSION: We describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR.

OBJECTIVE: The electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV). METHODS: Simple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem. RESULTS: With POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001). DISCUSSION: The study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load. CONCLUSION: EHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.

Adequate sampling of the conformational space is a central challenge in ab initio protein structure prediction. In the absence of a template structure, a conformational search procedure guided by an energy function explores the conformational space, gathering an ensemble of low-energy decoy conformations. If the sampling is inadequate, the native structure may be missed altogether. Even if reproduced, a subsequent stage that selects a subset of decoys for further structural detail and energetic refinement may discard near-native decoys if they are high energy or insufficiently represented in the ensemble. Sampling should produce a decoy ensemble that facilitates the subsequent selection of near-native decoys. In this paper, we investigate a robotics-inspired framework that allows directly measuring the role of energy in guiding sampling. Testing demonstrates that a soft energy bias steers sampling toward a diverse decoy ensemble less prone to exploiting energetic artifacts and thus more likely to facilitate retainment of near-native conformations by selection techniques. We employ two different energy functions, the associative memory Hamiltonian with water and Rosetta. Results show that enhanced sampling provides a rigorous testing of energy functions and exposes different deficiencies in them, thus promising to guide development of more accurate representations and energy functions.