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The newly synthesized imidazole derivative namely, 4,5-bis[(E)-2-phenylethenyl]-1H,1′H-2,2′-biimidazole (KA1), was studied for its molecular geometry, docking studies, spectral analysis and density functional theory (DFT) studies. Experimental vibrational frequencies were compared with scaled ones. The reactivity sites were determined using average localized ionization analysis (ALIE), electron localized function (ELF), localized orbital locator (LOL), reduced density gradient (RDG), Fukui functions and frontier molecular orbital (FMO). Due to the solvent effect, a lower gas phase energy gap was observed. Through utilization of the noncovalent interaction (NCI) method, the hydrogen bond interaction, steric effect and Vander Walls interaction were investigated. Molecular docking simulations were employed to determine the specific atom inside the molecules that exhibits a preference for binding with protein. The parameters for the molecular electrostatic potential (MESP) and global reactivity descriptors were also determined. The thermodynamic characteristics were determined through calculations employing the B3LYP/cc-pVDZ basis set. Antimicrobial activity was carried out using the five different microorganisms like Escherichia coli, Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae and Candida albicans.

Web services are moving toward a new emerging technology lead to the migration of SOAP to RESTful API, which is an Architectural Style that holds Lightweight, Stateless, Uniform Interface, etc., as its constraints. Various sources of clusters of resources, entity, database relations are access throughout the distributed environment across the internet. Generative Power of the RESTful API witnesses the emergence of many companies whose whole business process is based upon the building applications. Since the Syntactic essentials of RESTful Web Services are mainly concerned with the RESTful API, there is a need for evaluation of those essentials whether they are computable or not. The proposed work is carried out on taking resources as simple and effective using Primitive Recursive Function (PRF). Primitive Recursive Function (PRF) uses Turing Machine for REST API capability evaluation and the Service Invocation along with the Application Logic and AppState Logic in order to handle manageability of the RESTful resources via computability evaluation with or without security. To demonstrate the effectiveness of our evaluation process, we conduct a case study on the available REST web services using Primitive Recursive Resources (PRR). The results of our case study show that our evaluation process achieves greater portability, reliability, scalability, etc., which in turn results in high performance.

Friction stir processing is a surface modifying technique that allows microstructural modification and is being applied to control the behavior of a number of alloys. Surface composites of AA5052 alloy were fabricated by reinforcing zirconia (ZrO2) and silica (SiO2) particles by this process to improve the mechanical and corrosion properties. The developed surface composites of AA5052-ZrO2 and AA5052-SiO2 had a homogeneous distribution of particles with tensile strength equivalent to that of the base material. However, the hardness and corrosion behavior was different for both the composites. A significant improvement in microhardness was observed in the AA5052-ZrO2 surface composite while AA5052-SiO2 had microhardness similar to that of the base alloy. In the case of corrosion rate, AA5052-SiO2 had the least among the tested specimens while AA5052-ZrO2 showed no significant change. Fractography analysis and corrosion mechanism of the corroded specimens are also presented comprehensively.

Friction stir welding is one of the recent solid state joining processes that has drawn the attention of the metal joining community. In this work the effects of tool rotation speed (TRS) and welding speed (WS) on the tensile strength of dissimilar friction stir welded AA2024-AA7075 joints are investigated. Response surface methodology is used for developing a mathematical model for the tensile strength of the dissimilar aluminum alloy joints. The model is used to investigate the effect of TRS and WS on the tensile strength of the joints. It is seen that the tensile strength of the joint increases with the increase in TRS up to a limit of 1050 rpm and decreases thereafter. The tensile strength of the joints is also seen increasing with the WS up to 15 mm/min. Further increase in WS results in a reduction of the tensile strength of the joints.

The effect of electrical stimulation on changes in NPN content of beef stored at ambient and refrigeration temperatures was studied. The NPN content increased both in C and ES samples on storage probably due to the enhanced proteolysis. NPN values were higher for ES samples at every point of observation compared to the corresponding C samples

The present study was undertaken to study the effect of electrical stimulation on tenderness of beef stored at ambient and refrigeration temperatures at different intervals. Significant improvement in tenderness scores was obtained at 8 h of storage at ambient and refrigeration temperature and at 24 h 5f storage wider refrigeration temperature for electrically stimulated samples. Storage of beef at ambient temperature has got an additive effect on tenderness

The effect of electrical stipulation on changes in pH of meat stored at ambient and refrigeration temperatures showed that electrical stimulation has brought down the pH and the pH of ES samples was significantly lower than flat liar the C samples at 0, 1, 2, 4 and 8 h of storage both at ambient and refrigerated temperatures. At 24 h of storage, the pH values of ES and C samples were not significantly different. Electrical stipulation followed by storage at ambient temperature helped to bring down the pH of meat below 6.00nt a shorter tine.

The present study was designed to evaluate the effect of electrical stimulation on TVC of beef samples stored at ambient and refrigeration temperatures at different intervals. Significant lower counts were obtained in ES samples at 8 and 12 h at ambient temperature and 12 h tinder refrigeration. But this difference was not significant immediately after electrical stimulation and at 8 and 24 h of storage tinder refrigeration.

Competing Interests: Similar to Phase 1, in order to minimize conflicts of interest, CMSS, WHO, and NAM took steps to ensure the independence and objectivity of the advisory committee and this paper, in that authors were required to disclose financial and non-financial conflicts of interest (Kington et al., 2021). This paper represents the opinions of the authors and does not reflect a consensus position of CMSS; NAM; the National Academies of Sciences, Engineering, and Medicine; WHO; or the authors' organizations. The advisory committee did not receive payment for their contributions to this paper.Conflict-of-Interest Disclosures: Megan L. Ranney discloses receiving financial compensation from the National Opioid Abatement Trust II. Vineet Arora discloses receiving honoraria from the Journal of Hospital Medicine and UpToDate and receiving royalties from McGraw Hill. Don Dizon discloses receiving financial compensation from AstraZeneca and Clovis and stock options from Midi. Efrén J. Flores discloses receiving honoraria from Medscape and financial compensation from Journal of the American College of Radiology. Anjali Jain discloses sole proprietorship of Anjali Jain Research & Consulting, LLC. Richard Mularski discloses receiving grants from Gilead Sciences, Merck, and Pfizer. Claude Pinnock discloses former employment at Meta/Facebook.

Background: The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities., Objective: This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients' hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction., Methods: We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute care hospitals in the state of Florida, merged with the SDoH data from the American Community Survey. A case-control design was adopted to construct stroke and stroke mimic cohorts. We compared the algorithm performance and feature importance measures of the ML models (ie, gradient boosting machine and random forest) with those of the logistic regression model based on 3 sets of predictors. To provide insights into the prediction and ultimately assist care providers in decision-making, we used TreeSHAP for tree-based ML models to explain the stroke prediction., Results: Our analysis included 143,203 hospital visits of unique patients, and it was confirmed based on the principal diagnosis at discharge that 73% (n=104,662) of these patients had a stroke. The approach proposed in this study has high sensitivity and is particularly effective at reducing the misdiagnosis of dangerous stroke chameleons (false-negative rate <4%). ML classifiers consistently outperformed the benchmark logistic regression in all 3 input combinations. We found significant consistency across the models in the features that explain their performance. The most important features are age, the number of chronic conditions on admission, and primary payer (eg, Medicare or private insurance). Although both the individual- and community-level SDoH features helped improve the predictive performance of the models, the inclusion of the individual-level SDoH features led to a much larger improvement (area under the receiver operating characteristic curve increased from 0.694 to 0.823) than the inclusion of the community-level SDoH features (area under the receiver operating characteristic curve increased from 0.823 to 0.829)., Conclusions: Using data widely available at the time of patients' hospital presentations, we developed a stroke prediction model with high sensitivity and reasonable specificity. The prediction algorithm uses variables that are routinely collected by providers and payers and might be useful in underresourced hospitals with limited availability of sensitive diagnostic tools or incomplete data-gathering capabilities., (©Min Chen, Xuan Tan, Rema Padman. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.01.2023.)

Objectives: To describe a flexible common data model (CDM) approach that can be efficiently tailored to study-specific needs to facilitate pooled patient-level analysis and aggregated/meta-analysis of routinely collected retrospective patient data from disparate data sources; and to detail the application of this CDM approach to the DISCOVER CKD retrospective cohort, a longitudinal database of routinely collected (secondary) patient data of individuals with chronic kidney disease (CKD)., Methods: The flexible CDM approach incorporated three independent, exchangeable components that preceded data mapping and data model implementation: (1) standardized code lists (unifying medical events from different coding systems); (2) laboratory unit harmonization tables; and (3) base cohort definitions. Events between different coding vocabularies were not mapped code-to-code; for each data source, code lists of labels were curated at the entity/event level. A study team of epidemiologists, clinicians, informaticists, and data scientists were included within the validation of each component., Results: Applying the CDM to the DISCOVER CKD retrospective cohort, secondary data from 1,857,593 patients with CKD were harmonized from five data sources, across three countries, into a discrete database for rapid real-world evidence generation., Conclusions: This flexible CDM approach facilitates evidence generation from real-world data within the DISCOVER CKD retrospective cohort, providing novel insights into the epidemiology of CKD that may expedite improvements in diagnosis, prognosis, early intervention, and disease management. The adaptable architecture of this CDM approach ensures scalable, fast, and efficient application within other therapy areas to facilitate the combined analysis of different types of secondary data from multiple, heterogeneous sources., Competing Interests: This manuscript, including medical writing and editorial support, was funded by AstraZeneca. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Heart failure (HF) is a prevalent chronic disease and is associated with increases in mortality and morbidity. HF is a leading cause of hospitalizations and readmissions in the United States. A potentially promising area for preventing HF readmissions is continuous remote patient monitoring (CRPM)., Objective: The primary aim of this study is to determine the feasibility and preliminary efficacy of a CRPM solution in patients with HF at NorthShore University HealthSystem., Methods: This study is a feasibility study and uses a wearable biosensor to continuously remotely monitor patients with HF for 30 days after discharge. Eligible patients admitted with an HF exacerbation at NorthShore University HealthSystem are being recruited, and the wearable biosensor is placed before discharge. The biosensor collects physiological ambulatory data, which are analyzed for signs of patient deterioration. Participants are also completing a daily survey through a dedicated study smartphone. If prespecified criteria from the physiological data and survey results are met, a notification is triggered, and a predetermined electronic health record-based pathway of telephonic management is completed. In phase 1, which has already been completed, 5 patients were enrolled and monitored for 30 days after discharge. The results of phase 1 were analyzed, and modifications to the program were made to optimize it. After analysis of the phase 1 results, 15 patients are being enrolled for phase 2, which is a calibration and testing period to enable further adjustments to be made. After phase 2, we will enroll 45 patients for phase 3. The combined results of phases 1, 2, and 3 will be analyzed to determine the feasibility of a CRPM program in patients with HF. Semistructured interviews are being conducted with key stakeholders, including patients, and these results will be analyzed using the affective adaptation of the technology acceptance model., Results: During phase 1, of the 5 patients, 2 (40%) were readmitted during the study period. The study completion rate for phase 1 was 80% (4/5), and the study attrition rate was 20% (1/5). There were 57 protocol deviations out of 150 patient days in phase 1 of the study. The results of phase 1 were analyzed, and the study protocol was adjusted to optimize it for phases 2 and 3. Phase 2 and phase 3 results will be available by the end of 2022., Conclusions: A CRPM program may offer a low-risk solution to improve care of patients with HF after hospital discharge and may help to decrease readmission of patients with HF to the hospital. This protocol may also lay the groundwork for the use of CRPM solutions in other groups of patients considered to be at high risk., International Registered Report Identifier (irrid): DERR1-10.2196/36741., (©Courtney Reamer, Wei Ning Chi, Robert Gordon, Nitasha Sarswat, Charu Gupta, Safwan Gaznabi, Emily White VanGompel, Izabella Szum, Melissa Morton-Jost, Jorma Vaughn, Karen Larimer, David Victorson, John Erwin, Lakshmi Halasyamani, Anthony Solomonides, Rema Padman, Nirav S Shah. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 25.08.2022.)

Importance: Patient-reported financial hardship is an increasing challenge in cancer care delivery. Health insurance literacy and its association with financial hardship in patients with cancer, especially after controlling for financial literacy, have not been well examined., Objective: To examine the prevalence of and factors in the association between health insurance literacy and financial literacy as well as the overall and individual domains of financial hardship and their association with health insurance literacy, both independently and when adjusted for financial literacy, in patients with cancer., Design, Setting, and Participants: This cross-sectional survey study recruited and enrolled patients from 2 separate ambulatory infusion centers at Mayo Clinic Arizona in Phoenix, Arizona, and the University of Mississippi Medical Center in Jackson, Mississippi. Adult patients aged 18 years or older were enrolled from December 2019 to February 2020 and from August to October 2020 at Mayo Clinic Arizona (n = 299) and from September 2020 through January 2021 at the University of Mississippi Medical Center (n = 105). Survey respondents received a $5 gift card., Exposures: Surveys included questions about sociodemographic characteristics, health insurance literacy and financial literacy, financial knowledge, and financial hardship and its domains (material hardship, psychological hardship, and behavioral hardship)., Main Outcomes and Measures: Financial hardship was assessed using the COST-FACIT (Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy) measure and National Health Interview Survey questions to capture information about the domains of financial hardship. The Health Insurance Literacy Measure is a validated 21-item measure of a consumer's ability to select and use health insurance. Five questions from the National Financial Capability Study assessed financial literacy., Results: A total of 404 participants were enrolled in the study. Median (IQR) age of the respondents was 63 (54-71) years, and 219 were women (54%), 307 were non-Hispanic White individuals (76%), 153 (38%) had private insurance, and 289 (72%) had solid tumors. Overall financial hardship (denoted by median COST-FACIT score <27 points) was reported by 49% (95% CI, 44%-53%) of the cohort. Prevalence of financial hardship was higher using the National Health Interview Survey questions, with 68% (95% CI, 63%-72%) of respondents reporting at least 1 hardship domain (n = 276). Sixty-six percent (95% CI, 60%-69%) of respondents (n = 265) had a high level of financial literacy. The mean (SD) Health Insurance Literacy Measure score was 64.9 (13.3) points. In multivariable analyses, each 10-point increase in the Health Insurance Literacy Measure score was associated with lower odds of financial hardship (odds ratio, 0.82; 95% CI, 0.68-0.99; P = .04). However, this association was no longer significant after adjusting for financial literacy., Conclusions and Relevance: Results of this study showed that, despite a high level of health insurance literacy and financial literacy, the prevalence of financial hardship was high. Although there were lower odds of financial hardship with increased health insurance literacy, the association was no longer significant when financial literacy was added to the model, suggesting that a high level of financial literacy may help mitigate the adverse outcome of lower health insurance literacy levels in patients with cancer.

Objective: We report on our experience of deploying a continuous remote patient monitoring (CRPM) study soft launch with structured cascading and escalation pathways on heart failure (HF) patients post-discharge. The lessons learned from the soft launch are used to modify and fine-tune the workflow process and study protocol., Methods: This soft launch was conducted at NorthShore University HealthSystem's Evanston Hospital from December 2020 to March 2021. Patients were provided with non-invasive wearable biosensors that continuously collect ambulatory physiological data, and a study phone that collects patient-reported outcomes. The physiological data are analyzed by machine learning algorithms, potentially identifying physiological perturbation in HF patients. Alerts from this algorithm may be cascaded with other patient status data to inform home health nurses' (HHNs') management via a structured protocol. HHNs review the monitoring platform daily. If the patient's status meets specific criteria, HHNs perform assessments and escalate patient cases to the HF team for further guidance on early intervention., Results: We enrolled five patients into the soft launch. Four participants adhered to study activities. Two out of five patients were readmitted, one due to HF, one due to infection. Observed miscommunication and protocol gaps were noted for protocol amendment. The study team adopted an organizational development method from change management theory to reconfigure the study protocol., Conclusion: We sought to automate the monitoring aspects of post-discharge care by aligning a new technology that generates streaming data from a wearable device with a complex, multi-provider workflow into a novel protocol using iterative design, implementation, and evaluation methods to monitor post-discharge HF patients. CRPM with structured escalation and telemonitoring protocol shows potential to maintain patients in their home environment and reduce HF-related readmissions. Our results suggest that further education to engage and empower frontline workers using advanced technology is essential to scale up the approach., Competing Interests: The study devices, monitoring platform, and tech support are non-financial support by physIQ. K.L. is an employee of physIQ. All other authors do not have any conflict of interest. K.L. reports employment and equity from physIQ, during the conduct of the study; other from physIQ, outside the submitted work. N.S.S. reports non-financial support from physIQ, during the conduct of the study. J.E. reports and I serve on the ACCMedAxiom Board of Trustees. This is the for-profit arm of the ACC. While I have no direct relationships with vendors, the Board approves partnerships with vendors. U.R. is funded by the Daniel F. and Ada L. Rice Foundation. The foundation is not involved in the research design, implementation, or production of the manuscript in any way. Therefore, there is no conflict of interest. W.N.C. is funded by the Daniel F. and Ada L. Rice Foundation. The foundation is not involved in the research study design, implementation, or production of the manuscript in any way. Therefore, there is no conflict of interest., (Thieme. All rights reserved.)

Background: This study employed machine learning approaches to analyze sequences of adverse events (AEs) after left ventricular assist device (LVAD) implantation., Methods: Data on patients implanted with the HeartWare HVAD durable LVAD were extracted from the ENDURANCE and ENDURANCE Supplemental clinical trials, with follow-up through 5 years. Major AEs included device malfunction, major bleeding, major infection, neurological dysfunction, renal dysfunction, respiratory dysfunction, and right heart failure (RHF). Time interval and transition probability analyses were performed. We created a Sankey diagram to visualize transitions between AEs. Hierarchical clustering was applied to dissimilarity matrices based on the longest common subsequence to identify clusters of patients with similar AE profiles., Results: A total of 568 patients underwent HVAD implantation with 3590 AEs. Bleeding and RHF comprised the highest proportion of early AEs after surgery whereas infection and bleeding accounted for most AEs occurring after 3 months. The highest transition probabilities were observed with infection to infection (0.34), bleeding to bleeding (0.31), RHF to bleeding (0.31), RHF to infection (0.28), and bleeding to infection (0.26). Five distinct clusters of patients were generated, each with different patterns of time intervals between AEs, transition rates between AEs, and clinical outcomes., Conclusions: Machine learning approaches allow for improved visualization and understanding of AE burden after LVAD implantation. Distinct patterns and relationships provide insights that may be important for quality improvement efforts., (Copyright © 2021 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683-0.730] versus 0.740 [0.717-0.762] and 1-year: 0.691 [0.673-0.710] versus 0.714 [0.695-0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression., Competing Interests: The authors have read the journal’s policy, and the authors of this study have the following competing interests to declare: AK is on the Medical Advisory Board for Medtronic, Inc and receives personal fees in this capacity, although there are no direct conflicts with the present manuscript nor does this alter adherence to journal policies on sharing data and materials. None of the other authors have any disclosures. There are no patents, products in development, or marketed products associated with this research to declare.

Background: This study delineates the sequences of adverse events (AEs) preceding mortality attributed to multisystem organ failure (MSOF) in patients with a left ventricular assist device (LVAD)., Methods: We analyzed 3765 AEs after 536 LVAD implants recorded in The Society of Thoracic Surgeons Intermacs data registry between 2006 and 2015 that resulted in MSOF death. Hierarchical clustering identified and visualized quantitatively unique clusters of patients with similar AE profiles. Markov modeling was used to illustrate the AE sequences that led to MSOF death within the clusters. Cox proportional hazard models determined the risk-adjusted, preimplant predictors of MSOF., Results: We identified 2 distinct MSOF clusters based on their proportion of AE types and survival time. The early-death cluster (418 patients, 2304 AEs) had a median survival of 1 month (interquartile range, 3-6 months), whereas the late-death cluster (118 patients, 1,461 AEs) had a median survival of 11 months (interquartile range, 6-22 months). The predominant AE sequences in the early-death and late-death clusters were renal failure, to respiratory failure, to death (62%) and bleeding, to infection, to respiratory failure, to death (45%), respectively. Significant risk-adjusted preimplant predictors of MSOF included line sepsis (hazard ratio [HR] 3.0; 95% confidence interval [CI], 1.1-8.2), extracorporeal membrane oxygenation (HR, 2.2; 95% CI, 1.2-3.9), and dialysis or ultrafiltration (HR, 2.1; 95% CI, 1.5-3.0)., Conclusions: This analysis identified 2 AE clusters and the predominant sequences that result in MSOF-associated mortality. MSOF develops in 1 cluster of patients after chronic bleeding and repeated infections but has prolonged survival, while another group dies early after renal and respiratory complications., (Copyright © 2021 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjective and requires manual intervention. This study utilizes MIMIC III and associated matched waveform datasets across the entire ICU stay duration of each patient to develop prediction models for assessing fluid responsiveness in sepsis patients. We developed a pipeline to extract high frequency continuous waveform data and included waveform features in the prediction models. Comparing across five machine learning models, random forest performed the best when no waveform information is added (AUC = 0.84), with mean arterial blood pressure and age identified as key factors. After incorporation of features from physiologic waveforms, logistic regression with L1 penalty provided consistent performance and high interpretability, achieving an accuracy of 0.89 and F1 score of 0.90., (©2020 AMIA - All rights reserved.)

Background: Video and mobile games have been shown to have a positive impact on behavior change in children. However, the potential impact of game play patterns on outcomes of interest are yet to be understood, especially for games with implicit learning components., Objective: This study investigates the immediate impact of fooya!, a pediatric dietary mobile game with implicit learning components, on food choices. It also quantifies children's heterogeneous game play patterns using game telemetry and determines the effects of these patterns on players' food choices., Methods: We analyzed data from a randomized controlled trial (RCT) involving 104 children, aged 10 to 11 years, randomly assigned to the treatment group (played fooya!, a dietary mobile game developed by one of the authors) or the control group (played Uno, a board game without dietary education). Children played the game for 20 minutes each in two sessions. After playing the game in each session, the children were asked to choose 2 out of 6 food items (3 healthy and 3 unhealthy choices). The number of healthy choices in both sessions was used as the major outcome. We first compared the choice and identification of healthy foods between treatment and control groups using statistical tests. Next, using game telemetry, we determined the variability in game play patterns by quantifying game play measures and modeled the process of game playing at any level across all students as a Markov chain. Finally, correlation tests and regression models were used to establish the relationship between game play measures and actual food choices., Results: We saw a significant main effect of the mobile game on number of healthy foods actually chosen (treatment 2.48, control 1.10; P<.001; Cohen d=1.25) and identified (treatment 7.3, control 6.94; P=.048; Cohen d=.25). A large variation was observed in children's game play patterns. Children played an average of 15 game levels in 2 sessions, with a range of 2 to 23 levels. The greatest variation was noted in the proportion of scoring activities that were highly rewarded, with an average of 0.17, ranging from 0.003 to 0.98. Healthy food choice was negatively associated with the number of unhealthy food facts that children read in the game (Kendall τ=-.32, P=.04), even after controlling for baseline food preference., Conclusions: A mobile video game embedded with implicit learning components showed a strong positive impact on children's food choices immediately following the game. Game telemetry captured children's different play patterns and was associated with behavioral outcomes. These results have implications for the design and use of mobile games as an intervention to improve health behaviors, such as the display of unhealthy food facts during game play. Longitudinal RCTs are needed to assess long-term impact., Trial Registration: ClinicalTrials.gov NCT04082195; https://clinicaltrials.gov/ct2/show/NCT04082195, registered retrospectively., (©Yi-Chin Kato-Lin, Uttara Bharath Kumar, Bhargav Sri Prakash, Bhairavi Prakash, Vasini Varadan, Sanjeeta Agnihotri, Nrutya Subramanyam, Pradeep Krishnatray, Rema Padman. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 18.11.2020.)

Objective: This integrative review identifies and analyzes the extant literature to examine the integration of social determinants of health (SDoH) domains into electronic health records (EHRs), their impact on risk prediction, and the specific outcomes and SDoH domains that have been tracked., Materials and Methods: In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a literature search in the PubMed, CINAHL, Cochrane, EMBASE, and PsycINFO databases for English language studies published until March 2020 that examined SDoH domains in the context of EHRs., Results: Our search strategy identified 71 unique studies that are directly related to the research questions. 75% of the included studies were published since 2017, and 68% were U.S.-based. 79% of the reviewed articles integrated SDoH information from external data sources into EHRs, and the rest of them extracted SDoH information from unstructured clinical notes in the EHRs. We found that all but 1 study using external area-level SDoH data reported minimum contribution to performance improvement in the predictive models. In contrast, studies that incorporated individual-level SDoH data reported improved predictive performance of various outcomes such as service referrals, medication adherence, and risk of 30-day readmission. We also found little consensus on the SDoH measures used in the literature and current screening tools., Conclusions: The literature provides early and rapidly growing evidence that integrating individual-level SDoH into EHRs can assist in risk assessment and predicting healthcare utilization and health outcomes, which further motivates efforts to collect and standardize patient-level SDoH information., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In this paper, we report on the development of an analytical model and a decision support tool for meeting the complex challenge of scheduling dialysis patients. The tool has two optimization objectives: First, waiting times for the start of the dialysis after the patients' arrivals must be minimized. Second, the minimization of lateness after the scheduled finish time, which is relevant for transport services, are pursued. We model the problem as a mathematical program considering clinical pathways, a limited number of nurses managing the patients, and dialysis stations. Furthermore, information about patients' drop-off and pick-up time windows at/from the dialysis unit are considered. We develop a platform in Microsoft Excel and implement the analytical model using an Open Source optimization solver. A case study from a dialysis unit in the UK shows that a user can compute a schedule efficiently and the results provide useful information for patients, caregivers, clinicians and transport services., (©2019 AMIA - All rights reserved.)

Repeated emergency department visits have become a serious challenge worldwide. Despite prior research indicating that laboratory results may provide early alerts about such patients on their upcoming adverse events, few studies have examined their role as a critical indicator of the stability of a patient's medical condition over time. We model and analyze the developmental trajectories of patients' creatinine levels, a key laboratory marker of serious illness, as a potential risk stratification mechanism across many emergency department visits. We apply group-based statistical methodology to electronic health record data of 70,385 patients, with 3-15 emergency department visits, to identify and profile these trajectories for the entire population, for males and for females. Results reveal three distinct creatinine-based trajectory groups over time with significantly differing characteristics that may enable targeted interventions for each group. Future research will incorporate additional disease markers to identify longitudinal factors leading to repeated emergency department visits.

To examine whether the Medicare Part D program had an impact on the generic drug prescription rate among residents in long-term care facilities.We analyzed prescription data for 3 drug classes (atypical antipsychotic, proton pump inhibitor, and statin) obtained from a regional online pharmacy serving long-term care centers in Pennsylvania from January 2004 to December 2007.Difference-in-difference is used as a primary analysis method, and different regression methods (probit and multinomial) are used to accommodate different types of outcome measures.Contrary to expectations, the Part D program did not have a statistically significant impact on the generic prescription rate in the long-term care setting during the study period. Only the statin class showed a dramatic increase in generic drug prescriptions, mainly due to the loss of patent protection for one of the most popular brand-name drugs in the class.The complex dynamics of the prescription drug market, particularly the availability of generic versions of popular prescription medications, had a bigger role in increasing the prescription rate of generic drugs than the Part D program. This warrants the need to relax prescription medicines' patent policies and for further study on the impact of such policies.

Risk-stratifying chronic disease patients in real time has the potential to facilitate targeted interventions and improve disease management and outcomes. We apply group-based multi-trajectory modeling to risk stratify patients with chronic kidney disease (CKD) and its major complications into distinct trajectories of disease development and predict acute kidney injury (AKI), a serious, under-diagnosed outcome of CKD that is both preventable and treatable with early detection. Utilizing Electronic Health Record data of 1,947 patients, we identify eight risk groups with distinct trajectories and profiles. We observe that a higher estimated probability of AKI generally coincides with a higher risk group. Overall, at least 75% of patients stabilize into their final groups within less than two years from diagnosis of CKD Stage 3. Model calibration confirms that the estimated outcome probabilities are highly correlated with AKI incidence, providing group-specific and individual level predictions to improve clinical management of AKI in CKD patients.

Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.

Background: Diagnosis is complex, uncertain, and error-prone. Symptoms such as nonspecific abdominal pain are especially challenging. A diagnostic path consists of diagnostic steps taken from initial presentation until a diagnosis is obtained or the evaluation ends for other reasons. Analysis of diagnostic paths can reveal patterns associated with more timely and accurate diagnosis. Visual analytics can be used to enhance both analysis and comprehension of diagnostic paths., Objective: This article applies process-mining methods to extract and visualize diagnostic paths from electronic health records (EHRs)., Methods: Patient features, actions taken (i.e., tests, referrals, etc.), and diagnoses obtained for 501 adult patients (half female, half ≥50 years of age) presenting with abdominal pain were extracted from an EHR database to construct diagnostic paths from a hospital system in suburban Chicago, Illinois, United States. A stable diagnosis was defined as the same diagnosis recorded twice in a 12-month period; a working diagnosis was recorded only once. Three different types of path visualizations were obtained., Results: A stable diagnosis was obtained in 63 (13%) patients after 12 months. In 271 (54%) patients, a working diagnosis was obtained. Mean path duration was 145.3 days (standard deviation, 195.1 days). These 63 patients received 75 stable diagnoses., Conclusion: Structured EHR data can be used to construct diagnostic paths to gain insight into diagnostic practices for complaints such as abdominal pain., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

This study empirically examines the opinion leader effects on a mobile clinical information technology implementation by physicians in an American community health system using a fixed effect regression model. The model result suggests that the opinion leader effects are statistically significant during this information technology implementation process. Quantitatively, if opinion leaders increase their technology usage by 10 percent, the physicians who work closely with those opinion leaders would increase their technology usage by 3.5 percent, after controlling for physician individual-level fixed effects, time effects, working environment, and workload. This empirical result of opinion leader effects provides policy implications such as, if a healthcare system wants to promote a new information technology or a new mobile information technology implementation within their organization, they should leverage this opinion leader effects.

The diagnostic process is a complex, uncertain, and highly variable process which is under-studied and lacks evidence from randomized clinical trials. This study used a novel visual analytics method to identify and visualize diagnostic paths for undifferentiated abdominal pain, by leveraging electronic health record (EHR) data of 501 patients in the ambulatory setting of a single institution. A total of 63 patients reached diagnoses in the study sample. We illustrate steps in identifying diagnostic paths of the study patients, both individually and collectively, and visually present the diversity in their diagnostic processes. Patients in whom diagnoses were obtained generally had more clinical encounters and health services utilization, although their diagnostic paths were more variable than those of the undiagnosed group. The capability of identifying diagnostic paths demonstrated from this study allows us to study larger data sets to determine diagnostic paths associated with more timely, accurate, and cost-efficient diagnosis processes.

The increased adoption of Electronic Health Record (EHR) systems offers new opportunities for clinical research. The Health Insurance Portability and Accountability Act (HIPAA) mandates that medical records need to be stripped of personal identifiers in order to be shared. One particular challenge is how to handle free-text medical records. While many methods have been developed, there is a dearth of software tools than can be easily used by practitioners. We present deidentify, a new de-identification tool, which comes with a graphical user interface and runs on all operating systems. Evaluating its algorithm on a gold-standard corpus of nursing notes, we demonstrate its adequate performance with a recall of 0.919 and a precision of 0.645. Since deidentify comes with a pre-trained model, it can be used when no training data is available, but can also be manually configured. This way, it should be convenient to use for de-identification tasks.

Despite substantial advances in the treatment of end-stage renal disease, mortality of hemodialysis patients remains high. Several models exist that predict mortality for this population and identify patients at risk. However, they mostly focus on patients at a particular stage of dialysis treatment, such as start of dialysis, and only use the most recent patient data. Generalization of such models for predictions in later periods can be challenging since disease characteristics change over time and the evolution of biomarkers is not adequately incorporated. In this research, we explore dynamic methods which allow updates of initial predictions when patients progress in time and new data is observed. We compare a Dynamic Bayesian Network (DBN) to regularized logistic regression models and a Cox model with landmarking. Our preliminary results indicate that the DBN achieves satisfactory performance for short term prediction horizons, but needs further refinement and parameter tuning for longer horizons.