Usability of human-computer interaction in neonatal care.

While a goal for Electronic Health Record (EHR) technologies was to improve quality, efficiency, and safety, the usability of EHRs has remained poor. The relation to patient harm and user satisfaction cannot be ignored. Optimization of EHR usability is imperative to improving the outcomes for critically ill patients, especially neonates who are at the extremes of physiologic variability. Further development and integration of metadata with predictive modeling and clinical protocols can support provider decision making, increase efficiency and safety, and reduce clinician burnout. This paper reviews EHR usability and identifies opportunities to improve the EHR specific to neonatal care.

Developing video education materials for the return of genomic test results to parents and adolescents.

Objective: To describe the development, implementation, and revision of a video to provide information about genomic testing and the return of genomic research results to adolescents and parents. Methods: Formative, community-engaged research was conducted in three stages: development, implementation, and revision. Existing research participant advisory groups were used for focus groups and convenience sampling was used for interviews. Participants included parents, young adults without children, and adolescents. Transcripts of recorded sessions were used for formative analysis. Results: Video was the preferred format for delivering genomic testing information to adolescents during the development stage. During implementation, adolescents identified video length as an impediment to recall. During the revision stage, participants preferred the video in separate short segments, supported plan to require only one short video and leaving other short videos optional. Participants were divided on whether the required short video provided enough information, but all participants reported that watching additional videos would not have changed their decisions about receiving test results. Conclusion: Genomic education videos should be brief (<4 mins) to improve the odds that participants will view the entirety of any required video. Innovation: The development of participant materials should incorporate plans for monitoring implementation and plans for revising materials.

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants.

BACKGROUND: In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs. OBJECTIVES: This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC. METHODS: Radiographs, containing a PICC line in infants under 6 months of age, were manually classified into 12 anatomical locations based on the radiologist's textual report of the PICC line's tip. After categorization, we performed a 70/30 train/test split and benchmarked the performance of seven different (neural network, support vector machine, the naïve Bayes, decision tree, random forest, AdaBoost, and K-nearest neighbors) supervised ML algorithms. After optimization, we calculated accuracy, precision, and recall of each algorithm's ability to correctly categorize the stated location of the PICC tip. RESULTS: A total of 17,337 radiographs met criteria for inclusion and were labeled manually. Interrater agreement was 99.1%. Support vector machines and neural networks yielded accuracies as high as 98% in identifying PICC tips in central versus noncentral position (binary outcome) and accuracies as high as 95% when attempting to categorize the individual anatomical location (12-category outcome). CONCLUSION: Our study shows that ML classifiers can automatically extract the anatomical location of PICC tips from radiology reports. Two ML classifiers, support vector machine (SVM) and a neural network, obtained top accuracies in both binary and multiple category predictions. Implementing these algorithms in a neonatal intensive care unit as a clinical decision support system may help clinicians address PICC line position.

Risk Calculator to Predict Cesarean Delivery Among Women Undergoing Induction of Labor.

OBJECTIVE: To develop and validate a predictive risk calculator for cesarean delivery among women undergoing induction of labor. METHODS: We performed a population-based cohort study of all women who had singleton live births after undergoing induction of labor from 32 0/7 to 42 6/7 weeks of gestation in the United States from 2012 to 2016. The primary objective was to build a predictive model estimating the probability of cesarean delivery after induction of labor using antenatal factors obtained from de-identified U.S. live-birth records. Multivariable logistic regression estimated the association of these factors on risk of cesarean delivery. K-fold cross validation was performed for internal validation of the model, followed by external validation using a separate live-birth cohort from 2017. A publicly available online calculator was developed after validation and calibration were performed for individual risk assessment. The seven variables selected for inclusion in the model by magnitude of influence were prior vaginal delivery, maternal weight at delivery, maternal height, maternal age, prior cesarean delivery, gestational age at induction, and maternal race. RESULTS: From 2012 to 2016, there were 19,844,580 live births in the United States, of which 4,177,644 women with singleton gestations underwent induction of labor. Among these women, 800,423 (19.2%) delivered by cesarean. The receiver operating characteristic curve for the seven-variable model achieved an area under the curve (AUC) of 0.787 (95% CI 0.786-0.788). External validation demonstrated a consistent measure of discrimination with an AUC of 0.783 (95% CI 0.764-0.802). CONCLUSION: This validated predictive model uses seven variables that were obtainable from the patient's medical record and discriminates between women at increased or decreased risk of cesarean delivery after induction of labor. This risk calculator, found at https://ob.tools/iol-calc, can be used in addition to the Bishop score by health care providers in counseling women who are undergoing an induction of labor and allocating appropriate resources for women at high risk for cesarean delivery.

Electronic Health Record-Embedded Decision Support Platform for Morphine Precision Dosing in Neonates.

Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long-term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life-sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record-integrated, model-informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model-informed dosing guidance and has functionality to incorporate real-time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data-informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.

Neonatal Lung Disease Associated with TBX4 Mutations.

Variable lung disease was documented in 2 infants with heterozygous TBX4 mutations; their clinical presentations, pathology, and outcomes were distinct. These findings demonstrate that TBX4 gene mutations are associated with neonatal respiratory failure and highlight the wide spectrum of clinicopathological outcomes that have implications for patient diagnosis and management.

A Randomized Trial Comparing Classical Participatory Design to VandAID, an Interactive CrowdSourcing Platform to Facilitate User-centered Design.

BACKGROUND: Early involvement of stakeholders in the design of medical software is particularly important due to the need to incorporate complex knowledge and actions associated with clinical work. Standard user-centered design methods include focus groups and participatory design sessions with individual stakeholders, which generally limit user involvement to a small number of individuals due to the significant time investments from designers and end users. OBJECTIVES: The goal of this project was to reduce the effort for end users to participate in co-design of a software user interface by developing an interactive web-based crowdsourcing platform. METHODS: In a randomized trial, we compared a new web-based crowdsourcing platform to standard participatory design sessions. We developed an interactive, modular platform that allows responsive remote customization and design feedback on a visual user interface based on user preferences. The responsive canvas is a dynamic HTML template that responds in real time to user preference selections. Upon completion, the design team can view the user's interface creations through an administrator portal and download the structured selections through a REDCap interface. RESULTS: We have created a software platform that allows users to customize a user interface and see the results of that customization in real time, receiving immediate feedback on the impact of their design choices. Neonatal clinicians used the new platform to successfully design and customize a neonatal handoff tool. They received no specific instruction and yet were able to use the software easily and reported high usability. CONCLUSIONS: VandAID, a new web-based crowdsourcing platform, can involve multiple users in user-centered design simultaneously and provides means of obtaining design feedback remotely. The software can provide design feedback at any stage in the design process, but it will be of greatest utility for specifying user requirements and evaluating iterative designs with multiple options.

Lethal neonatal meningoencephalitis caused by multi-drug resistant, highly virulent Escherichia coli.

Neonatal meningitis is a rare but devastating condition. Multi-drug resistant (MDR) bacteria represent a substantial global health risk. This study reports on an aggressive case of lethal neonatal meningitis due to a MDR Escherichia coli (serotype O75:H5:K1). Serotyping, MDR pattern and phylogenetic typing revealed that this strain is an emergent and highly virulent neonatal meningitis E. coli isolate. The isolate was resistant to both ampicillin and gentamicin; antibiotics currently used for empiric neonatal sepsis treatment. The strain was also positive for multiple virulence genes including K1 capsule, fimbrial adhesion fimH, siderophore receptors iroN, fyuA and iutA, secreted autotransporter toxin sat, membrane associated proteases ompA and ompT, type II polysaccharide synthesis genes (kpsMTII) and pathogenicity-associated island (PAI)-associated malX gene. The presence of highly-virulent MDR organisms isolated in neonates underscores the need to implement rapid drug resistance diagnostic methods and should prompt consideration of alternate empiric therapy in neonates with Gram negative meningitis.