Micapipe: A pipeline for multimodal neuroimaging and connectome analysis.

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

A Videogame-Based Approach to Measuring Information Processing Speed in Multiple Sclerosis Patients.

Objective: Slowing information processing speed (IPS) is a biomarker of neuronal damage in patients with multiple sclerosis (pwMS). A focus on IPS might be the ideal solution in the perspective of promptly detecting cognitive changes over time. We developed a tablet-based home-made videogame to test the sensitivity of this device in measuring subclinical IPS in pwMS. Materials and Methods: Forty-three pwMS without cognitive impairment and 20 healthy controls (HCs) were administered the videogame task with a tablet. Response times (RTs) and accuracy were recorded. Results: PwMS (mean RTs = 505.5 ± 73.9 ms) were significantly slower than HCs (mean RTs = 462.3 ± 40.3 ms, P = 0.014) on the videogame task. A moderate but significant correlation (r = -0.35, P = 0.03) between mean RTs and the Symbol Digit Modalities Test was observed. Conclusion: Our videogame showed good sensitivity in measuring IPS in apparently cognitive normal pwMS. Computerized testing might be useful in screening initial cognitive dysfunction that should be monitored as a marker of underlying disease progression. IRB approval Number is 2332CESC.

Assessment of automated clinical trial recruitment and enrolment using patient-facing technology.

OBJECTIVE: Interactive patient care systems (IPCS) at the bedside are becoming increasingly common, but evidence is limited as to their potential for innovative clinical trial implementation. The objective of this study was to test the hypothesis that the IPCS could feasibly be used to automate recruitment and enrolment for a clinical trial. METHODS: In medical-surgical units, we used the IPCS to randomise, recruit and consent eligible subjects. For participants not interacting with IPCS study materials within 48 hours, study staff-initiated recruitment in-person. Eligible study population included all caregivers and any patients >6 years old admitted to medical-surgical units and oncology units September 2015 to January 2016. OUTCOMES: randomisation assessed using between-group comparisons of patient characteristics; recruitment success assessed by rates of consent; paperless implementation using successful acquisition of electronic signature and email address. We used χ2 analysis to assess success of randomisation and recruitment. RESULTS: Randomisation was successful (n=1012 randomised, p>0.05 for all between-group comparisons). For the subset of eligible, randomised patients who were recruited, IPCS-only recruitment (consented: 2.4% of n=213) was less successful than in-person recruitment (61.4% of n=87 eligible recruited, p<0.001). For those consenting (n=61), 96.7% provided an electronic signature and 68.9% provided email addresses. CONCLUSIONS: Our results suggest that as a tool at the bedside, the IPCS offers key efficiencies for study implementation, including randomisation and collecting e-consent and contact information, but does not offer recruitment efficiencies. Further research could assess the value that interactive technologies bring to recruitment when paired with in-person efforts, potentially focusing on more intensive user-interface testing for recruitment materials. TRIAL REGISTRATION NUMBER: NCT02491190.

Improving Utilization of Vaccine Two-Dimensional (2D) Barcode Scanning Technology Maximizes Accuracy Benefits.

BACKGROUND: Recording vaccine data accurately can be problematic in medical documentation, including blank and inaccurate records. Vaccine two-dimensional (2D) barcode scanning has shown promise, yet scanner use to record vaccine data is limited. We sought to identify strategies to improve scanning rates and assess changes in accuracy. METHODS: Between January and June 2017, 27 pilot sites within a large health system were assigned to one of four groups to test strategies to maximize scanner use: training only, commitment card, scanning report, or combination. Seventy-two thousand vaccine records were assessed for completeness, accuracy, and scanning. RESULTS: Significant increases in vaccinator scanning rates found with commitment card and scanning report inclusion (alone and paired) compared with the training-only group. Record completeness and accuracy significantly improved with use of scanning. When manually entered, about 1 in 9 records had a missing or inaccurate expiration date; when scanned, this dropped to 1 in 5,000. CONCLUSIONS: Pilot findings indicate 2D scanning has the potential to eliminate most omissions and inaccuracies in vaccine records. Such data are critical during a recall or need to trace specific vaccines or patients. IMPLICATIONS: Consistent use and expanded adoption of 2D scanning can meaningfully improve the quality of vaccine records and clinical practices.

Progressive tracking: a novel procedure to facilitate manual digitization of videos.

Digitization of video recordings often requires the laborious procedure of manually clicking points of interest on individual video frames. Here, we present progressive tracking, a procedure that facilitates manual digitization of markerless videos. In contrast to existing software, it allows the user to follow points of interest with a cursor in the progressing video, without the need to click. To compare the performance of progressive tracking with the conventional frame-wise tracking, we quantified speed and accuracy of both methods, testing two different input devices (mouse and stylus pen). We show that progressive tracking can be twice as fast as frame-wise tracking while maintaining accuracy, given that playback speed is controlled. Using a stylus pen can increase frame-wise tracking speed. The complementary application of the progressive and frame-wise mode is exemplified on a realistic video recording. This study reveals that progressive tracking can vastly facilitate video analysis in experimental research.

The impact of integrating electronic referral within a musculoskeletal model of care on wait time to receive orthopedic care in Ontario.

An MSK model of care for hip and knee patients integrated with an electronic referral solution (eReferral) has been deployed within four subregions across Ontario. Referrals are sent from primary care offices to a central intake (CI), where the referral forms are reviewed and forwarded, if appropriate, to a rapid access clinic (RAC) where patients are assessed by an advanced practice clinician (APC). The pragmatic design of eReferral allows for a seamless flow of electronic orthopedic referrals from primary care to CI. It also enables CI to process and transcribe faxed referrals into the eReferral system for a smooth flow of data electronically to the RACs. In general, wait time is the time interval between receiving the patient's referral at CI or the surgeon's office until receiving the orthopedic surgeon's first consultation. Wait time is further broken down into wait 1 a and wait 1 b. Wait 1 a is the time between the receipt of the referral at CI until the date of the first initial assessment at the RAC. This study aimed at: a) assessing the processing time of orthopedic referrals at central intakes (CI) to be forwarded to the RAC, b) assessing the wait time (wait 1 a) of orthopedic referrals processed through the eReferral system to receive an initial assessment at the RACs. c) comparing the ability of the RACs to meet the target wait time for assessment (four weeks) by the method of referral (eReferrals vs. fax). d) evaluating patients' satisfaction with the length of time they waited to receive care at the RACs with eReferral. We used Ocean eReferral database to access MSK hip and knee referral data processed through the system. Patients whose referrals were initiated electronically through the system and opted to receive email notification of their referral status had the opportunity to take an online satisfaction survey embedded in the booked appointment notification message. There were 1,723 patients initially referred electronically for hip, and knee pain consults, while 13,780 referrals started as paper-based and transcribed into the system to be forwarded later electronically by CI to a RAC. Higher mean processing time at CI by 21.76 days for paper-based referral was detected as opposed to referrals received electronically (p<0.001). RACs took significantly less time to book appointments for referrals initiated electronically with a shorter average wait 1a of 21.42 days for eReferrals compared to paper-based referrals (p<0.001). RACs timeframe to book an appointment was significantly shorter for eReferrals versus fax referrals. A total of 393 patients completed the patient satisfaction survey with a response rate of 16%. Overall, 87.7% were satisfied with their experience with the eReferral process, and 81% agreed that they had waited a reasonable time to receive the needed care. eReferral can elicit faster processing of referrals and shorter wait time for patients, which improved patient satisfaction with the referral process.

Improving Portfolio Assessment: Addressing Challenges in Transition to Eportfolio.

BACKGROUND: As health care education programs adopt electronic portfolios (eportfolios), a midwestern university baccalaureate nursing program faces challenges in transitioning from a 25-year-old paper-based system to eportfolio. METHOD: Process improvements and faculty development were implemented through collaboration with the information technology (IT) department and the involvement of students as ambassadors. Pre- and posttransition surveys and portfolio benchmarks were used to evaluate the efficacy of the new eportfolio system. RESULTS: Results of faculty and student surveys revealed improved positive experiences regarding the value, clarity, and ease of use of the eportfolio system. Benchmark deficits decreased from six of eight portfolio criteria in Fall 2017 to none in Fall 2019. CONCLUSION: Buy-in through faculty and student involvement, along with a user-centered design, are essential for a successful eportfolio transition. Student and faculty engagement would be secured when they are well-informed in the transition process. [J Nurs Educ. 2020;59(12):705-708.].

Testing the Effects of Modality and Narration Style on Patients' Information Use in a Lung Cancer Treatment Decision Aid.

BACKGROUND: Risk information in patient decision aids (PDAs) is often difficult for older patients to process. Providing audiovisual and narrative information may enhance the understanding and use of health-related information. We studied the effects on patients' information processing and use of audiovisual and narrative information of an early-stage non-small-cell lung cancer treatment decision aid explaining surgery and stereotactic ablative radiotherapy. We further investigated differences between older and younger patients. METHODS: We conducted a 2 (modality: textual v. audiovisual) × 2 (narration style: factual v. narrative) online experiment among cancer patients and survivors (N = 305; Mage = 62.42, SD = 11.68 y). Age was included as a potential modifier: younger (<65 y) versus older (≥65 y) age. We assessed 1) perceived cognitive load, 2) satisfaction with information, 3) comprehension, 4) information recall, and 5) decisional conflict. Analysis of variance was used for data analysis. RESULTS: Irrespective of patient age, audiovisual information (compared with textual information) led to lower perceived cognitive load, higher satisfaction with information, and lower decisional conflict (subscale Effective Decision). Narrative information (compared with factual information) led to reduced decisional conflict (subscale Uncertainty) but only in younger patients. Combining audiovisual information with factual information also resulted in lower perceived cognitive load in younger patients as compared with older patients. LIMITATIONS: Patients who actually face the decision, especially older patients, might be more motivated to process our decision-aid information than the present study participants who responded to a hypothetical situation online. CONCLUSIONS: Providing participants with audiovisual information, irrespective of their age, improved their processing and use of information in a decision aid. Narratives did not clearly benefit information processing.

Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation.

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.

Improving medication safety practice at a community hospital: a focus on bar code medication administration scanning and pain reassessment.

INTRODUCTION: In the USA over 30% of medication errors occur at the point of administration. Among non-surgical patients in US hospitals exposed to opioids, 0.6% experience a severe opioid-related adverse event. In September 2018, Sierra View Medical Center identified two areas of opportunity for quality improvement: bedside bar code medication administration (BCMA) and pain reassessments. At baseline (April 2018 to September 2018) only 81% of medications were scanned prior to administration with pain reassessments completed only 41% of the time 1 hour postopioid administration. OBJECTIVE: To improve BCMA scanning rates (goal ≥95%) and pain reassessments within 1 hour postopioid administration (goal ≥90%). METHODS: Implementation methods included data transparency, weekly dashboards, education and plan-do-study-act (PDSA) cycles informed by feedback from key stakeholders. RESULTS: Following a series of PDSA cycle implementations, barcode medication administration (BCMA) scanning rates improved by 14% (from 81% to 95%) and pain reassessments improved by 50% (from 41% to 91%), sustained 17 months postproject implementation (October 2018 to February 2019). The number of adverse drug events (ADEs) related to administration errors decreased by 17% (estimated annual cost savings of $120 750-239 725 per year) and opioid-related ADEs decreased by 2.6% (estimated annual cost savings of $72 855-80 928 per year). CONCLUSION: Adopting John Kotter's model for change, developing performance dashboards and sustaining engagement among stakeholders on a weekly basis improved bar code medication scanning rates and pain reassessment compliance. The stakeholders created momentum for change in both practice and culture resulting in improved patient safety with a favourable financial impact.

Twenty-five years later: has ISBT 128 fulfilled its promise?

Traceability is essential to any quality program for medical products of human origin (MPHO). Standardized terminology, coding, and labeling systems that include key elements for traceability support electronically readable information on product labels and improve the accuracy and efficiency of data collection. ISBT 128 is such a system. The first specification for ISBT 128 was published 25 years ago, and since that time it has become the global standard for labeling and information transfer for MPHO. Additionally, standardization of granular product description codes has supported hemovigilance and other activities that depend on aggregated data. This review looks back over the development, current status, and potential future applications of the ISBT 128 Standard.

Gold Standard Evaluation of an Automatic HAIs Surveillance System.

Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%).

An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model.

BACKGROUND: Verbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts. The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input. RESULTS: The new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults. The InterVA-5 model's capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively. CONCLUSIONS: Despite the inherent difficulties of determining "truth" in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.

Information processing in electronic medical records: A survey validation.

OBJECTIVE: The purpose of this study was to validate the Clinical Information Processing Instrument. This instrument attempts to examine information processing in electronic medical records (EMRs). We drew upon the theory of swift and even flow to guide survey development and construction. MATERIALS AND METHODS: We used a mixed-methods approach to gather data from registered nurses. Nurses were invited to participate in focus groups, an expert panel, and the survey validation process. A semi-structured questionnaire addressed the following themes: method of communication, quality of information, and usability of the system. RESULTS: We conducted a confirmatory factor analysis using structural equation modelling. The Kaiser-Meyer-Olkin measure was greater than 0.7 (0.90), and the Bartlett's test of sphericity was significant (X2  = 1519.03, df = 105, P < 0.001). The proposed structural equation model was analysed and revised to a final model that was statistically significant. The final survey, Clinical Information Processing Instrument, contained 18 Likert scale questions that supported the tenets of the theory of swift and even flow. DISCUSSION: The nurses perceived EMRs as efficient for medication management, time management, and communication. The Clinical Information Processing Instrument is a validated survey tool that assesses information flow in EMRs. CONCLUSIONS: The Clinical Information Processing Instrument was validated as an approach to analyse the utility of EMR in disseminating information among clinical staff. To increase the utility and meaningful use of EMR systems, it is important to consider factors that affect the distribution of information among clinicians.

e-Roster policy: Insights and implications of codifying nurse scheduling.

Following a decade of dissemination, particularly within the British National Health Service, electronic rostering systems were recently endorsed within the Carter Review. However, electronic rostering necessitates the formal codification of the roster process. This research investigates that codification through the lens of the 'Roster Policy', a formal document specifying the rules and procedures used to prepare staff rosters. This study is based upon analysis of 27 publicly available policies, each approved within a 4-year period from January 2010 to July 2014. This research finds that, at an executive level, codified knowledge is used as a proxy for the common language and experience otherwise acquired on a ward through everyday interaction, while at ward level, the nurse rostering problem continues to resist all efforts at simplification. Ultimately, it is imperative that executives recognise that electronic rostering is not a silver bullet and that information from such systems requires careful interpretation and circumspection.

Evaluation of a Barcode Medication Administration Information System.

When a medication administration error occurs, patient safety is endangered. Barcode medication administration system usage has been implemented to reduce medication errors. The purpose of this study was to evaluate barcode medication administration system usage outcomes. A survey based on DeLone and McLean's model of information systems success was utilized. The questionnaire, composed of 27 items, explored system quality, information quality, service quality, user satisfaction, and usage benefits. It was completed by 232 nurses. User satisfaction received the highest average score, and quality of information was the most critical factor related to this result (r = 0.83, P < .01). Medication errors occurring before and after barcode medication administration use were collected, and the reasons for errors related to work process were explored. Medication errors decreased from 405 at preimplementation to 314 at postimplementation (t = 77.62, P < .001). The main reason for medication errors related to work process was "not following the standard procedure," followed by "other factors." While technology is deployed to support individual practice, organizational elements also remain important to technology adoption.

Examining the Relationship Between Nursing Informatics Competency and the Quality of Information Processing.

The purpose of this study was to examine nursing informatics competency and the quality of information processing among nurses in Jordan. The study was conducted in a large hospital with 380 registered nurses. The hospital introduced the electronic health record in 2010. The measures used in this study were personal and job characteristics, self-efficacy, Self-Assessment Nursing Informatics Competencies, and Health Information System Monitoring Questionnaire. The convenience sample consisted of 99 nurses who used the electronic health record for at least 3 months. The analysis showed that nine predictors explained 22% of the variance in the quality of information processing, whereas the statistically significant predictors were nursing informatics competency, clinical specialty, and years of nursing experience. There is a need for policies that advocate for every nurse to be educated in nursing informatics and the quality of information processing.

Automatic liver detection and standardised uptake value evaluation in whole-body Positron Emission Tomography/Computed Tomography scans.

BACKGROUND AND OBJECTIVE: Standardised Uptake Value (SUV), in clinical research and practice, is a marker of tumour avidity in Positron Emission Tomography/Computed Tomography (PET/CT). Since many technical, physical and physiological factors affect the SUV absolute measurement, the liver uptake is often used as reference value both in quantitative and semi-quantitative evaluation. The purpose of this investigation was to automatically detect the liver position in whole-body PET/CT scans and extract its average SUV value. METHODS: We developed an algorithm, called LIver DEtection Algorithm (LIDEA), that analyses PET/CT scans, and under the assumption that the liver is a large homogeneous volume near the centre of mass of the patient, finds its position and automatically places a region of interest (ROI) in the liver, which is used to calculate the average SUV. The algorithm was validated on a population of 630 PET/CT scans coming from more than 60 different scanners. The SUV was also calculated by manually placing a large ROI in the liver. RESULTS: LIDEA identified the liver with a 97.3% sensitivity with PET/CT images only and reached a 98.9% correct detection rate when using the co-registered CT scan to avoid liver misidentification in the right lung. The average liver SUV obtained with LIDEA was successfully validated against its manual assessment, with no systematic difference (0.11 ±â€¯0.36 SUV units) and a R2=0.89 correlation coefficient. CONCLUSIONS: LIDEA proved to be a reliable tool to automatically identify and extract the average SUV of the liver in oncological whole-body PET/CT scans.