Citations

Abstract Background Surgical performance, provider health, and patient safety can be compromised when workload demands exceed individual capability on the surgical team. The purpose of this study is to quantify and compare intraoperative workload among surgical team members. Methods Observations were conducted for an entire surgical day for 33 participating surgeons and their surgical team at one medical institution. Workload (mental, physical, case complexity, distractions, and case difficulty) was measured for each surgical team member using questions from validated questionnaires. Statistical analyses were performed with a mixed effects model. Results A total of 192 surgical team members participated in 78 operative cases, and 344 questionnaires were collected. Procedures with high surgeon mental and physical workload included endovascular and gastric surgeries, respectively. Ratings did not differ significantly among surgeons and residents, but scrub nurses physical demand ratings were 14–22 (out of 100) points lower than the surgeons, residents, and surgical assistants. Residents reported the highest mental workload, averaging 19–24 points higher than surgical assistants, scrub nurses, and circulating nurses. Mental and physical demands exceeded 50 points 28–45 % of the time for surgeons and residents. Workload did not differ between minimally invasive and open techniques. Conclusion The workload questionnaires are an effective tool for quantifying intraoperative workload across the surgical team to ensure mental and physical demands do not exceed thresholds where performance may decrease and injury risk increase. This tool has the potential to measure the safety of current procedures and drive design of workload interventions.

The legacy of human-Neandertal interbreeding Non-African humans are estimated to have inherited on average 1.5 to 4% of their genomes from Neandertals. However, how this genetic legacy affects human traits is unknown. Simonti et al. combined genotyping data with electronic health records. Individual Neandertal alleles were correlated with clinically relevant phenotypes in individuals of European descent. These archaic genetic variants were associated with medical conditions affecting the skin, the blood, and the risk of depression. Science , this issue p. 737

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Abstract The American Medical Informatics Association convened the 2014 Health Policy Invitational Meeting to develop recommendations for updates to current policies and to establish an informatics research agenda for personalizing medicine. In particular, the meeting focused on discussing informatics challenges related to personalizing care through the integration of genomic or other high-volume biomolecular data with data from clinical systems to make health care more efficient and effective. This report summarizes the findings ( n = 6) and recommendations ( n = 15) from the policy meeting, which were clustered into 3 broad areas: (1) policies governing data access for research and personalization of care; (2) policy and research needs for evolving data interpretation and knowledge representation; and (3) policy and research needs to ensure data integrity and preservation. The meeting outcome underscored the need to address a number of important policy and technical considerations in order to realize the potential of personalized or precision medicine in actual clinical contexts.

Workload experienced by health care workers continues to be a challenge to define and quantify. The purpose of the current study is to present descriptive data on the application of sociometers in an emergency care environment during simulated hand-off scenarios and discuss their potential and limitations for quantifying individual’s workload in health care settings. Sociometer devices, worn by nine actors, collected body movement, interactions, and speech data during four simulated hand-off scenarios in the emergency department wards. Results found that sociometers distinguished body movement differences between sitting, standing, lying, and walking individuals. Interactions quantified by the devices were limited by obstructions, distance, and angles between the sociometer devices. The data collected by these devices show promise in providing human factors researchers a tool for quantifying the dynamic exposures experienced by health care workers over time.

Surgical trainees undergo demanding training to achieve high surgical task proficiency. Abounding clinical and educational responsibilities mandate efficient and effective training. This research measured resident workload during laparoscopic skills training to identify excessive workload and how workload impacted task performance. Twenty-eight surgical trainees performed a standardized surgical training task and completed a workload questionnaire while observers measured physiological stress, posture risk assessment, and task performance. Participants self-reported mental demands, physical demands, temporal demands, performance, effort, and frustration. Effort (12±4) and frustration (12±5) were the highest subscales while physical demand (8±4) was the lowest. All participants were observed performing the task in at-risk postures, with 21% exhibiting risk levels requiring immediate intervention. Physical demand was associated with posture risk assessment scores (p<0.05). Mental demand was positively (R 2 =0.20, p<0.05) and frustration was negatively (R 2 =0.18, p<0.05) associated with skin conductance range. A point increase in physical demand was associated with a six second increase in performance time (β=6.0, p=0.01). These results support the fact that human factors and ergonomic tools can be used to relate surgical skills performance with workload, stress, and posture risks.

AbstractBackground Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Abstract Objective To review and evaluate available software tools for electronic health record–driven phenotype authoring in order to identify gaps and needs for future development. Materials and Methods Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. Results Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. Discussion Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. Conclusions Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.