Methods to expose subsurface objects of interest identified from 3D imaging: The intermediate sample preparation stage in the correlative microscopy workflow.

The correlative imaging workflow is a method of combining information and data across modes (e.g. SEM, X-ray CT, FIB-SEM), scales (cm to nm) and dimensions (2D-3D-4D), providing a more holistic interpretation of the research question. Often, subsurface objects of interest (e.g. inclusions, pores, cracks, defects in multilayered samples) are identified from initial exploratory nondestructive 3D tomographic imaging (e.g. X-ray CT, XRM), and those objects need to be studied using additional techniques to obtain, for example, 2D chemical or crystallographic data. Consequently, an intermediate sample preparation step needs to be completed, where a targeted amount of sample surface material is removed, exposing and revealing the object of interest. At present, there is not one singular technique for removing varied thicknesses at high resolution and on a range of scales from cm to nm. Here, we review the manual and automated options currently available for targeted sample material removal, with a focus on those methods which are readily accessible in most laboratories. We summarise the approaches for manual grinding and polishing, automated grinding and polishing, microtome/ultramicrotome, and broad-beam ion milling (BBIM), with further review of other more specialist techniques including serial block face electron microscopy (SBF-SEM), and ion milling and laser approaches such as FIB-SEM, Xe plasma FIB-SEM, and femtosecond laser/LaserFIB. We also address factors which may influence the decision on a particular technique, including the composition, shape and size of the samples, sample mounting limitations, the amount of surface material to be removed, the accuracy and/or resolution of peripheral parts, the accuracy and/or resolution of the technique/instrumentation, and other more general factors such as accessibility to instrumentation, costs, and the time taken for experimentation. It is hoped that this study will provide researchers with a range of options for removal of specific amounts of sample surface material to reach subsurface objects of interest in both correlative and non-correlative workflows.

Body Mass Index Increased at a Large Midwestern Children's Hospital During the COVID-19 Pandemic.

Background: The COVID-19 pandemic has presented a great challenge to children and their families with stay-at-home orders, school closures, decreased exercise opportunities, stress, and potential overeating with home confinement. Our study describes the body mass index (BMI) changes over an entire decade, including a year of the COVID-19 pandemic at a large children's hospital. Methods: With our retrospective observational study, data were extracted from Cincinnati Children's Hospital's Epic electronic medical record, a free-standing children's hospital with 670 inpatient beds and >1.2 million patient encounters per year. Children aged 19 years and under with at least one height and weight were included in the analysis. Results: In all, 2,344,391 encounters were analyzed with 712,945 visits in years 2018-2021. The prevalence of overweight/obesity was relatively stable with a gradual rise from 35% to 36.4% from 2011 to 2020. However, the year of the COVID-19 stay at home and restrictions (2020-2021) showed an increase in overweight/obesity to 39.7% (8.3% increase), with the greatest increase in those with Class 3 obesity from 3.0% to 3.8%. When viewing the change in BMI percentile during the pandemic year compared with the 2 years prior, there was a significantly increasing trend (p < 0.0001). Conclusions: Children attending a large children's hospital showed an increase in overweight/obesity during the COVID-19 pandemic. These data suggest greater efforts are needed to reverse the increase in weight status from the COVID-19 pandemic as obesity is a risk factor for poor outcomes with COVID-19.

User-Centered Evaluation of a Visual Annotation Tool for Rapid Assessment of Pediatric Weight Entry Errors.

Weight entry errors can cause significant patient harm in pediatrics due to pervasive weight-based dosing practices. While computerized algorithms can assist in error detection, they have not achieved high sensitivity and specificity to be further developed as a clinical decision support tool. To train an advanced algorithm, expert-annotated weight errors are essential but difficult to collect. In this study, we developed a visual annotation tool to gather large amounts of expertly annotated pediatric weight charts and conducted a formal user-centered evaluation. Key features of the tool included configurable grid sizes and annotation styles. The user feedback was collected through a structured survey and user clicks on the interface. The results show that the visual annotation tool has high usability (average SUS=86.4). Different combinations of the key features, however, did not significantly improve the annotation efficiency and duration. We have used this tool to collect expert annotations for algorithm development and benchmarking.

Development and Evaluation of an Automated Approach to Detect Weight Abnormalities in Pediatric Weight Charts.

Inaccurate body weight measures can cause critical safety events in clinical settings as well as hindering utilization of clinical data for retrospective research. This study focused on developing a machine learning-based automated weight abnormality detector (AWAD) to analyze growth dynamics in pediatric weight charts and detect abnormal weight values. In two reference-standard based evaluation of real-world clinical data, the machine learning models showed good capacity for detecting weight abnormalities and they significantly outperformed the methods proposed in literature (p-value<0.05). A deep learning model with bi-directional long short-term memory networks achieved the best predictive performance, with AUCs ≥0.989 across the two datasets. The positive predictive value and sensitivity achieved by the system suggested more than 98% screening effort reduction potential in weight abnormality detection. Consequently, we hypothesize that the AWAD, when fully deployed, holds great potential to facilitate clinical research and healthcare delivery that rely on accurate and reliable weight measures.

Feedback at the Point of Care to Decrease Medication Alert Rates in an Electronic Health Record.

Frequently overridden alerts in the electronic health record can highlight alerts that may need revision. This method is a way of fine-tuning clinical decision support. We evaluated the feasibility of a complementary, yet different method that directly involved pediatric emergency department (PED) providers in identifying additional medication alerts that were potentially incorrect or intrusive. We then evaluated the effect subsequent resulting modifications had on alert salience. METHODS: We performed a prospective, interventional study over 34 months (March 6, 2014, to December 31, 2016) in the PED. We implemented a passive alert feedback mechanism by enhancing the native electronic health record functionality on alert reviews. End-users flagged potentially incorrect/bothersome alerts for review by the study's team. The alerts were updated when clinically appropriate and trends of the impact were evaluated. RESULTS: More than 200 alerts were reported from both inside and outside the PED, suggesting an intuitive approach. On average, we processed 4 reviews per week from the PED, with attending physicians as major contributors. The general trend of the impact of these changes seems favorable. DISCUSSION: The implementation of the review mechanism for user-selected alerts was intuitive and sustainable and seems to be able to detect alerts that are bothersome to the end-users. The method should be run in parallel with the traditional data-driven approach to support capturing of inaccurate alerts. CONCLUSIONS: User-centered, context-specific alert feedback can be used for selecting suboptimal, interruptive medication alerts.

Development and Preliminary Evaluation of a Visual Annotation Tool to Rapidly Collect Expert-Annotated Weight Errors in Pediatric Growth Charts.

Patient weights can be entered incorrectly into electronic health record (EHR) systems. These weight errors can cause significant patient harm especially in pediatrics where weight-based dosing is pervasively used. Determining weight errors through manual chart reviews is impractical in busy clinics, and current EHR alerts are rudimentary. To address these issues, we seek to develop an advanced algorithm to detect weight errors using supervised machine learning techniques. The critical first step is to collect labelled weight errors for algorithm training. In this paper, we designed and preliminarily evaluated a visual annotation tool using Agile software development to achieve the goal of supporting the rapid collection of expert-annotated weight errors. The design was based on the fact that weight errors are infrequent and medical experts can easily spot potential errors. The results show positive user feedback and prepared us for the formal user-centered evaluation as the next step.

Inpatient Communication Networks: Leveraging Secure Text-Messaging Platforms to Gain Insight into Inpatient Communication Systems.

OBJECTIVE: This study attempts to characterize the inpatient communication network within a quaternary pediatric academic medical center by applying network analysis methods to secure text-messaging data. METHODS: We used network graphing and statistical software to create network models of an inpatient communication system with secure text-messaging data from physicians, nurses, and other ancillary staff in an academic medical center. Descriptive statistics about the network, users within the network, and visualizations informed the team's understanding of the network and its components. RESULTS: Analysis of messages exchanged over approximately 23 days revealed a large, scale-free network with 4,442 nodes and 59,913 edges. Quantitative description of user behavior (messages sent and received) and network metrics (i.e., importance of nodes within a network) revealed several operational and clinical roles both sending and receiving > 1,000 messages over this time period. While some of these nodes represented expected "dispatcher" roles in our inpatient system, others occupied important frontline clinical roles responsible for bedside clinical care. CONCLUSION: Quantitative and network analysis of secure text-messaging logs revealed several key operational and clinical roles at risk for alert fatigue and information overload. This analysis also revealed a communication network highly reliant on these key roles, meaning disruption to these individuals or their workflows could lead to dysfunction of the communication network. While secure text-messaging applications play increasingly important roles in facilitating inpatient communication, little is understood about the impact these systems have on health care providers. Developing methods to understand and optimize communication between inpatient providers might help operational and clinical leaders to proactively prevent poorly understood pitfalls associated with these systems and build resilient and effective communication structures.

A Comparison of Existing Methods to Detect Weight Data Errors in a Pediatric Academic Medical Center.

Dosing errors due to erroneous body weight entry can be mitigated through algorithms designed to detect anomalies in weight patterns. To prepare for the development of a new algorithm for weight-entry error detection, we compared methods for detecting weight anomalies to human annotation, including a regression-based method employed in a real-time web service. Using a random sample of 4,000 growth charts, annotators identified clinically important anomalies with good inter-rater reliability. Performance of the three detection algorithms was variable, with the best performance from the algorithm that takes into account weights collected after the anomaly was recorded. All methods were highly specific, but positive predictive value ranged from < 5% to over 82%. There were 203 records of missed errors, but all of these were either due to no prior data points or errors too small to be clinically significant. This analysis illustrates the need for better weight-entry error detection algorithms.

The Effects of Medication Alerts on Prescriber Response in a Pediatric Hospital.

OBJECTIVE: More than 70% of hospitals in the United States have electronic health records (EHRs). Clinical decision support (CDS) presents clinicians with electronic alerts during the course of patient care; however, alert fatigue can influence a provider's response to any EHR alert. The primary goal was to evaluate the effects of alert burden on user response to the alerts. METHODS: We performed a retrospective study of medication alerts over a 24-month period (1/2013-12/2014) in a large pediatric academic medical center. The institutional review board approved this study. The primary outcome measure was alert salience, a measure of whether or not the prescriber took any corrective action on the order that generated an alert. We estimated the ideal number of alerts to maximize salience. Salience rates were examined for providers at each training level, by day of week, and time of day through logistic regressions. RESULTS: While salience never exceeded 38%, 49 alerts/day were associated with maximal salience in our dataset. The time of day an order was placed was associated with alert salience (maximal salience 2am). The day of the week was also associated with alert salience (maximal salience on Wednesday). Provider role did not have an impact on salience. CONCLUSION: Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective.

Stick or twist? Career decision-making during contractual uncertainty for NHS junior doctors.

OBJECTIVES: To examine the extent, and nature, of impact on junior doctors' career decisions, of a proposed new contract and the uncertainty surrounding it. DESIGN: Mixed methods. Online survey exploring: doctors' future training intentions; their preferred specialty training (ST) programmes; whether they intended to proceed immediately to ST; and other plans. Linked qualitative interviews to explore more fully how and why decisions were affected. SETTING: Doctors (F2s) in second year of Foundation School (FS) Programmes in England. PARTICIPANTS: Invitations sent by FSs. Open to all F2s November 2015-February 2016. All FSs represented. Survey completed by 816 F2s. Sample characteristics broadly similar to national F2 cohort. MAIN OUTCOME MEASURES: Proportions of doctors intending to proceed to ST posts in the UK, to defer or to exit UK medicine. Proportion of doctors indicating changes in training and career plans as a result of the contract and/or resulting uncertainty. Distribution of changes across training programmes. Explanations of these intentions from interviews and free text comments. RESULTS: Among the responding junior doctors, 20% indicated that issues related to the contract had prompted them to switch specialty and a further 20% had become uncertain about switching specialty. Switching specialty choice was more prevalent among those now choosing a community-based, rather than hospital-based specialty. 30% selecting general practice had switched choice because of the new contract. Interview data suggests that doctors felt they had become less valued or appreciated in the National Health Service and in society more broadly. CONCLUSIONS: Doctors reported that contract-related issues have affected their career plans. The most notable effect is a move away from acute to community-based specialities, with the former perceived as more negatively affected by the proposed changes. It is concerning that young doctors feel undervalued, and this requires further investigation.

Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records.

OBJECTIVES: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications. MATERIALS AND METHODS: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs. RESULTS: Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts. DISCUSSION: Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts. CONCLUSION: The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.

Meaningful Use of Electronic Health Records: Experiences From the Field and Future Opportunities.

BACKGROUND: With the aim of improving health care processes through health information technology (HIT), the US government has promulgated requirements for "meaningful use" (MU) of electronic health records (EHRs) as a condition for providers receiving financial incentives for the adoption and use of these systems. Considerable uncertainty remains about the impact of these requirements on the effective application of EHR systems. OBJECTIVE: The Agency for Healthcare Research and Quality (AHRQ)-sponsored Centers for Education and Research in Therapeutics (CERTs) critically examined the impact of the MU policy relating to the use of medications and jointly developed recommendations to help inform future HIT policy. METHODS: We gathered perspectives from a wide range of stakeholders (N=35) who had experience with MU requirements, including academicians, practitioners, and policy makers from different health care organizations including and beyond the CERTs. Specific issues and recommendations were discussed and agreed on as a group. RESULTS: Stakeholders' knowledge and experiences from implementing MU requirements fell into 6 domains: (1) accuracy of medication lists and medication reconciliation, (2) problem list accuracy and the shift in HIT priorities, (3) accuracy of allergy lists and allergy-related standards development, (4) support of safer and effective prescribing for children, (5) considerations for rural communities, and (6) general issues with achieving MU. Standards are needed to better facilitate the exchange of data elements between health care settings. Several organizations felt that their preoccupation with fulfilling MU requirements stifled innovation. Greater emphasis should be placed on local HIT configurations that better address population health care needs. CONCLUSIONS: Although MU has stimulated adoption of EHRs, its effects on quality and safety remain uncertain. Stakeholders felt that MU requirements should be more flexible and recognize that integrated models may achieve information-sharing goals in alternate ways. Future certification rules and requirements should enhance EHR functionalities critical for safer prescribing of medications in children.

Analysis of electronic medication orders with large overdoses: opportunities for mitigating dosing errors.

BACKGROUND: Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes. OBJECTIVES: To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users' responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them. METHODS: We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action. RESULTS: We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with » of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4-10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation. CONCLUSIONS: Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses (≥500%) and explain the sociotechnical factors that drove the error. Some of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.

Evaluating the accuracy of electronic pediatric drug dosing rules.

OBJECTIVE: To determine the accuracy of vendor-supplied dosing eRules for pediatric medication orders. Inaccurate or absent dosing rules can lead to high numbers of false alerts or undetected prescribing errors and may potentially compromise safety in this already vulnerable population. MATERIALS AND METHODS: 7 months of medication orders and alerts from a large pediatric hospital were analyzed. 30 medications were selected for study across 5 age ranges and 5 dosing parameters. The resulting 750 dosing rules from a commercial system formed the study corpus and were examined for accuracy against a gold standard created from traditional clinical resources. RESULTS: Overall accuracy of the rules in the study corpus was 55.1% when the rules were transformed to fit a priori age ranges. Over a pediatric lifetime, the dosing rules were accurate an average of 57.6% of the days. Dosing rules pertaining to the newborn age range were as accurate as other age ranges on average, but exhibited more variability. Daily frequency dosing parameters showed more accuracy than total daily dose, single dose minimum, or single dose maximum. DISCUSSION: The accuracy of a vendor-supplied set of dosing eRules is suboptimal when compared with traditional dosing sources, exposing a gap between dosing rules in commercial products and actual prescribing practices by pediatric care providers. More research on vendor-supplied eRules is warranted in order to understand the effects of these products on safe prescribing in children.

Transitioning from a computerized provider order entry and paper documentation system to an electronic health record: expectations and experiences of hospital staff.

OBJECTIVES: To examine healthcare worker's perceptions, expectations, and experiences regarding how work processes, patient-related safety, and care were affected when a quaternary care center transitioned from one computerized provider order entry (CPOE) system to a full electronic health record (EHR). METHODS: The I-SEE survey was administered prior to and 1-year after transition in systems. The construct validity and reliability of the survey was assessed within the current population and also compared to previously published results. Pre- and 1-year post-implementation scale means were compared within and across time periods. RESULTS: The majority of respondents were nurses and personnel working in the acute care setting. Because a confirmatory factor analysis indicated a lack of fit of our data to the I-SEE survey's 5-factor structure, we conducted an exploratory factor analysis that resulted in a 7-factor structure which showed better reliability and validity. Mean scores for each factor indicated that attitudes and expectations were mostly positive and score trends over time were positive or neutral. Nurses generally had less positive attitudes about the transition than non-nursing respondents, although the difference diminished after implementation. CONCLUSIONS: Findings demonstrate that the majority of responding staff were generally positive about transitioning from CPOE system to a full electronic health record (EHR) and understood the goals of doing so, with overall improved ratings over time. In addition, the I-SEE survey, when modified based on our population, was useful for assessing patient care and safety related expectations and experiences during the transition from one CPOE system to an EHR.

Clinical genomics in the world of the electronic health record.

The widespread adoption of electronic health records presents a number of benefits to the field of clinical genomics. They include the ability to return results to the practitioner, to use genetic findings in clinical decision support, and to have data collected in the electronic health record that serve as a source of phenotypic information for analysis purposes. Not all electronic health records are created equal, however. They differ in their features, capabilities, and ease of use. Therefore, to understand the potential of the electronic health record, it is first necessary to understand its capabilities and the impact that implementation strategy has on usability. Specifically, we focus on the following areas: (i) how the electronic health record is used to capture data in clinical practice settings; (ii) how the implementation and configuration of the electronic health record affect the quality and availability of data; (iii) the management of clinical genetic test results and the feasibility of electronic health record integration; and (iv) the challenges of implementing an electronic health record in a research-intensive environment. This is followed by a discussion of the minimum functional requirements that an electronic health record must meet to enable the satisfactory integration of genomic results as well as the open issues that remain.

Assessing the reliability of an automated dose-rounding algorithm.

OBJECTIVE: Pediatric dose rounding is a unique and complex process whose complexity is rarely supported by e-prescribing systems, though amenable to automation and deployment from a central service provider. The goal of this project was to validate an automated dose-rounding algorithm for pediatric dose rounding. METHODS: We developed a dose-rounding algorithm, STEPSTools, based on expert consensus about the rounding process and knowledge about the therapeutic/toxic window for each medication. We then used a 60% subsample of electronically-generated prescriptions from one academic medical center to further refine the web services. Once all issues were resolved, we used the remaining 40% of the prescriptions as a test sample and assessed the degree of concordance between automatically calculated optimal doses and the doses in the test sample. Cases with discrepant doses were compiled in a survey and assessed by pediatricians from two academic centers. The response rate for the survey was 25%. RESULTS: Seventy-nine test cases were tested for concordance. For 20 cases, STEPSTools was unable to provide a recommended dose. The dose recommendation provided by STEPSTools was identical to that of the test prescription for 31 cases. For 14 out of the 24 discrepant cases included in the survey, respondents significantly preferred STEPSTools recommendations (p<0.05, binomial test). Overall, when combined with the data from all test cases, STEPSTools either matched or exceeded the performance of the test cases in 45/59 (76%) of the cases. The majority of other cases were challenged by the need to provide an extremely small dose. We estimated that with the addition of two dose-selection rules, STEPSTools would achieve an overall performance of 82% or higher. CONCLUSIONS: Results of this pilot study suggest that automated dose rounding is a feasible mechanism for providing guidance to e-prescribing systems. These results also demonstrate the need for validating decision-support systems to support targeted and iterative improvement in performance.