Search

Abstract Background Patients who no-show to primary care appointments interrupt clinicians’ efforts to provide continuity of care. Prior literature reveals no-shows among diabetic patients are common. The purpose of this study is to assess whether no-shows to primary care appointments are associated with increased risk of future emergency department (ED) visits or hospital admissions among diabetics. Methods A prospective cohort study was conducted using data from 8,787 adult diabetic patients attending outpatient clinics associated with a medical center in Indiana. The outcomes examined were hospital admissions or ED visits in the 6 months (182 days) following the patient’s last scheduled primary care appointment. The Andersen-Gill extension of the Cox proportional hazard model was used to assess risk separately for hospital admissions and ED visits. Adjustment was made for variables associated with no-show status and acute care utilization such as gender, age, race, insurance and co-morbid status. The interaction between utilization of the acute care service in the six months prior to the appointment and no-show was computed for each model. Results The six-month rate of hospital admissions following the last scheduled primary care appointment was 0.22 (s.d. = 0.83) for no-shows and 0.14 (s.d. = 0.63) for those who attended (p p Conclusions No-show to a primary care appointment is associated with increased risk for hospital admission among diabetics recently hospitalized.

Abstract Background Non-pharmaceutical interventions (NPI) are the first line of defense against pandemic influenza. These interventions dampen virus spread by reducing contact between infected and susceptible persons. Because they curtail essential societal activities, they must be applied judiciously. Optimal control theory is an approach for modeling and balancing competing objectives such as epidemic spread and NPI cost. Methods We apply optimal control on an epidemiologic compartmental model to develop triggers for NPI implementation. The objective is to minimize expected person-days lost from influenza related deaths and NPI implementations for the model. We perform a multivariate sensitivity analysis based on Latin Hypercube Sampling to study the effects of input parameters on the optimal control policy. Additional studies investigated the effects of departures from the modeling assumptions, including exponential terminal time and linear NPI implementation cost. Results An optimal policy is derived for the control model using a linear NPI implementation cost. Linear cost leads to a "bang-bang" policy in which NPIs are applied at maximum strength when certain state criteria are met. Multivariate sensitivity analyses are presented which indicate that NPI cost, death rate, and recovery rate are influential in determining the policy structure. Further death rate, basic reproductive number and recovery rate are the most influential in determining the expected cumulative death. When applying the NPI policy, the cumulative deaths under exponential and gamma terminal times are close, which implies that the outcome of applying the "bang-bang" policy is insensitive to the exponential assumption. Quadratic cost leads to a multi-level policy in which NPIs are applied at varying strength levels, again based on certain state criteria. Results indicate that linear cost leads to more costly implementation resulting in fewer deaths. Conclusions The application of optimal control theory can provide valuable insight to developing effective control strategies for pandemic. Our findings highlight the importance of establishing a sensitive and timely surveillance system for pandemic preparedness.

We present a case of suspected hepatotoxicity secondary to an etonogestrel contraceptive implant in which the patient presented with vomiting, jaundice, pruritis, elevated transaminases, and hyperbilirubinemia. An extensive work-up, including liver biopsy, was unremarkable. The implant was removed and the patient's symptoms and transaminitis resolved, suggestive of drug-induced liver injury., (Copyright © 2024. Published by Elsevier Inc.)

Objectives: We evaluated the effect of immediate postpartum use of depot medroxyprogesterone acetate (DMPA) on the timing of lactogenesis stage II (LS-II)., Study Design: The initial design randomly assigned adults who delivered a full-term infant in 2019-2021 to receive within 48 hours of delivery: (1) DMPA, (2) placebo injection, or (3) no injection. Due to low enrollment, we changed in 2021-2023 to a nonrandomized design using matching at recruitment for obesity and delivery method and propensity score weighting for analysis. We combined data from both designs to compare immediate postpartum DMPA use (N = 55) vs control (placebo or no injection) group (N = 95). We defined noninferiority a priori as being met if the upper bound of a two-sided 95% CI for mean difference in time to LS-II between groups was <6 hours., Results: The unweighted mean time to LS-II was 57.8 hours in the DMPA group (SD, 29.4) and 64.1 hours in the control group (SD, 36.1). Using propensity score weighting to make the groups comparable with respect to age, race, delivery method, and previous live births, the mean time to LS-II was 5.5 hours shorter (95% CI, -16.4, 5.5) for women in the DMPA relative to control group., Conclusions: We found no evidence that DMPA use inhibits the onset of LS-II. Findings support immediate postpartum DMPA initiation among those intending to engage in human milk feeding., Implications: A controlled trial (N = 150) did not detect any difference in time to lactogenesis stage II ("milk let-down") between injectable contraception use within the first 48 hours postpartum and those without this exposure., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Microbes that use the single-carbon substrates methanol and methane offer great promise to bioindustry along with substantial environmental benefits. Methanotrophs and other methylotrophs can be engineered and optimized to produce a wide range of products, from biopolymers to biofuels and beyond. While significant limitations remain, including delivery of single-carbon feedstock to bioreactors, efficient growth, and scale-up, these challenges are being addressed and notable improvements have been rapid. Development of expression chassis, use of genome-scale and regulatory models based on omics data, improvements in bioreactor design and operation, and development of green product recovery schemes are enabling the rapid development of single-carbon bioconversion in the industrial space., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Utilization of telemedicine care for vulnerable and low income populations, especially individuals with mental health conditions, is not well understood. The goal is to describe the utilization and regional disparities of telehealth care by mental health status in Texas. Texas Medicaid claims data were analyzed from September 1, 2012, to August 31, 2018 for Medicaid patients enrolled due to a disability., Methods: We analyzed the growth in telemedicine care based on urban, suburban, and rural, and mental health status. We used t-tests to test for differences in sociodemographic characteristics across patients and performed a three-way Analyses of Variance (ANOVA) to evaluate whether the growth rates from 2013 to 2018 were different based on geography and patient type. We then estimated patient level multivariable ordinary least square regression models to estimate the relationship between the use of telemedicine and patient characteristics in 2013 and separately in 2018. Outcome was a binary variable of telemedicine use or not. Independent variables of interest include geography, age, gender, race, ethnicity, plan type, Medicare eligibility, diagnosed mental health condition, and ECI score., Results: Overall, Medicaid patients with a telemedicine visit grew at 81%, with rural patients growing the fastest (181%). Patients with a telemedicine visit for a mental health condition grew by 77%. Telemedicine patients with mental health diagnoses tended to have 2 to 3 more visits per year compared to non-telemedicine patients with mental health diagnoses. In 2013, multivariable regressions display that urban and suburban patients, those that had a mental health diagnosis were more likely to use telemedicine, while patients that were younger, women, Hispanics, and those dual eligible were less likely to use telemedicine. By 2018, urban and suburban patients were less likely to use telemedicine., Conclusions: Growth in telemedicine care was strong in urban and rural areas between 2013 and 2018 even before the COVID-19 pandemic. Those with a mental health condition who received telemedicine care had a higher number of total mental health visits compared to those without telemedicine care. These findings hold across all geographic groups and suggest that mental health telemedicine visits did not substitute for face-to-face mental health visits., (© 2024. The Author(s).)

Exclusive enteral nutrition (EEN) is effective in inducing remission in pediatric Crohn disease (CD). EEN alters the intestinal microbiome, but precise mechanisms are unknown. We hypothesized that pre-diagnosis diet establishes a baseline gut microbiome, which then mediates response to EEN. We analyzed prospectively recorded food frequency questionnaires (FFQs) for pre-diagnosis dietary patterns. Fecal microbiota were sequenced (16SrRNA) at baseline and through an 18-month follow-up period. Dietary patterns, Mediterranean diet adherence, and stool microbiota were associated with EEN treatment outcomes, disease flare, need for anti-tumor necrosis factor (TNF)-α therapy, and long-term clinical outcomes. Ninety-eight patients were included. Baseline disease severity and microbiota were associated with diet. Four dietary patterns were identified by FFQs; a "mature diet" high in fruits, vegetables, and fish was linked to increased baseline microbial diversity, which was associated with fewer disease flares ( p < 0.05) and a trend towards a delayed need for anti-TNF therapy ( p = 0.086). Baseline stool microbial taxa were increased ( Blautia and Faecalibacterium ) or decreased ( Ruminococcus gnavus group) with the mature diet compared to other diets. Surprisingly, a "pre-packaged" dietary pattern (rich in processed foods) was associated with delayed flares in males ( p < 0.05). Long-term pre-diagnosis diet was associated with outcomes of EEN therapy in pediatric CD; diet-microbiota and microbiota-outcome associations may mediate this relationship.

Globally, there is a growing acknowledgment of Indigenous Peoples' rights to control data related to their communities. This is seen in the development of Indigenous Data Governance standards. As health data collection increases, it's crucial to apply these standards in research involving Indigenous communities. Our study, therefore, aims to systematically review research using routinely collected health data of Indigenous Peoples, understanding the Indigenous Data Governance approaches and the associated advantages and challenges. We searched electronic databases for studies from 2013 to 2022, resulting in 85 selected articles. Of these, 65 (77%) involved Indigenous Peoples in the research, and 60 (71%) were authored by Indigenous individuals or organisations. While most studies (93%) provided ethical approval details, only 18 (21%) described Indigenous guiding principles, 35 (41%) reported on data sovereignty, and 28 (33%) addressed consent. This highlights the increasing focus on Indigenous Data Governance in utilising health data. Leveraging existing data sources in line with Indigenous data governance principles is vital for better understanding Indigenous health outcomes., (© 2024. The Author(s).)

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

Importance: Blood pressure monitoring is critical to the timely diagnosis and treatment of hypertension. At-home self-monitoring techniques are highly effective in managing high blood pressure; however, evidence regarding the cost-effectiveness of at-home self-monitoring compared with traditional monitoring in clinical settings remains unclear., Objective: To identify and synthesize published research examining the cost-effectiveness of at-home blood pressure self-monitoring relative to monitoring in a clinical setting among patients with hypertension., Evidence Review: A systematic literature search of 5 databases (PubMed, MEDLINE, Embase, EconLit, and CINAHL) followed by a backward citation search was conducted in September 2022. Full-text, peer-reviewed articles in English including patients with high blood pressure (systolic blood pressure ≥130 mm Hg and diastolic blood pressure ≥80 mm Hg) at baseline were included. Data from studies comparing at-home self-monitoring with clinical-setting monitoring alternatives were extracted, and the outcomes of interest included incremental cost-effectiveness and cost-utility ratios. Non-peer-reviewed studies or studies with pregnant women and children were excluded. To ensure accuracy and reliability, 2 authors independently evaluated all articles for eligibility and extracted relevant data from the selected articles., Findings: Of 1607 articles identified from 5 databases, 16 studies met the inclusion criteria. Most studies were conducted in the US (6 [40%]) and in the UK (6 [40%]), and almost all studies (14 [90%]) used a health care insurance system perspective to determine costs. Nearly half the studies used quality-adjusted life-years gained and cost per 1-mm Hg reduction in blood pressure as outcomes. Overall, at-home blood pressure monitoring (HBPM) was found to be more cost-effective than monitoring in a clinical setting, particularly over a minimum 10-year time horizon. Among studies comparing HBPM alone vs 24-hour ambulatory blood pressure monitoring (ABPM) or HBPM combined with additional support or team-based care, the latter were found to be more cost-effective., Conclusions and Relevance: In this systematic review, at-home blood pressure self-monitoring, particularly using automatic 24-hour continuous blood pressure measurements or combined with additional support or team-based care, demonstrated the potential to be cost-effective long-term compared with care in the physical clinical setting and could thus be prioritized for patients with hypertension from a cost-effectiveness standpoint.

As noncontact health interventions have become critical during the Covid-19 pandemic, our study aimed to systematically review the published literature for barriers and facilitators influencing the adoption and use of remote health intervention and technology, as perceived by adult patients with diabetes or cardiovascular diseases (CVD) belonging to groups that are socially/economically marginalized and/or medically under-resourced. We searched Medline, Embase, CINAHL, and PsychINFO for peer-reviewed articles published from 2010 to 2018. We employed content analysis to analyze qualitative patient feedback from the included studies. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A total of 42 studies met the inclusion criteria. The design of the remote health technology used was the most frequently mentioned facilitator and barrier to remote health technology adoption and use. Our results should draw the attention of technology developers to the usability and feasibility of remote technology among populations that are socially/economically marginalized and/or medically under-resourced., (©2022 AMIA - All rights reserved.)

Background: Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in the task of analyzing Fast Healthcare Interoperability Resources (FHIR ® ) data and associated clinical terminology., Results: A server implementation was developed, featuring a FHIR API with new operations designed to support exploratory data analysis (EDA), advanced patient cohort selection and data preparation tasks. Integration with a FHIR Terminology Service is also supported, allowing users to incorporate knowledge from rich terminologies such as SNOMED CT within their queries. A prototype user interface for EDA was developed, along with visualizations in support of a health data analysis project., Conclusions: Experience with applying this technology within research projects and towards the development of analytics-enabled applications provides a preliminary indication that the FHIR Analytics API pattern implemented by Pathling is a valuable abstraction for data scientists and software developers within the health care domain. Pathling contributes towards the value proposition for the use of FHIR within health data analytics, and assists with the use of complex clinical terminologies in that context., (© 2022. The Author(s).)

Objective: Women are underrepresented in radiology overall, in radiology subspecialties, and in radiology leadership and academic positions. It is unclear why this disparity persists despite greater gender diversification in medicine. We sought to determine if a correlation exists between the proportion of female faculty at an institution, and the proportion of female residents in the associated residency program across Canada. Methods: Faculty gender for each Canadian Diagnostic Imaging Residency Program was obtained through publicly available sources (departmental websites and provincial physician registries) in the fall of 2020. Resident gender data was obtained through a survey emailed to programs following the April 2021 CaRMS match. Data was analyzed using Pearson's correlation coefficient. Research ethics approval was obtained. Results: Faculty information was available for 15 of the 16 Canadian radiology residency programs (94%) and resident information was obtained for 16 programs (100% response rate). Overall, women accounted for 31.4% of radiologist faculty and 31.9% of radiology residents, with a wide range between institutions (19.5-47.8% for faculty and 13.3%-47.1% for residents). There was a strong positive correlation between the proportion of female faculty and the proportion of female residents within individual programs (r=0.73; R2=0.54; p=0.002). Conclusion: Approximately one third of faculty and residents at Canadian Diagnostic Radiology residency programs were female but there was a wide range across the country with a strong correlation between faculty and resident gender distribution. Further exploration is warranted to determine causes of this correlation including the possible influence of role modeling, mentoring, female-friendly culture, and bias.

Mosquitoes transmit several infectious diseases that pose significant threat to human health. Temperature along with other environmental factors at breeding and resting locations play a role in the organismal development and abundance of mosquitoes. Accurate analysis of mosquito population dynamics requires information on microclimatic conditions at breeding and resting locations. In this study, we develop a regression model to characterize microclimatic temperature based on ambient environmental conditions. Data were collected by placing sensor loggers at resting and breeding locations such as storm drains across Houston, TX. Corresponding weather data was obtained from National Oceanic and Atmospheric Administration website. Features extracted from these data sources along with contextual information on location were used to develop a Generalized Linear Model for predicting microclimate temperatures. We also analyzed mosquito population dynamics for Aedes albopictus under ambient and microclimatic conditions using system dynamic (SD) modelling to demonstrate the need for accurate microclimatic temperatures in population models. The microclimate prediction model had an R 2 value of ~ 95% and average prediction error of ~ 1.5 °C indicating that microclimate temperatures can be reliably estimated from the ambient environmental conditions. SD model analysis indicates that some microclimates in Texas could result in larger populations of juvenile and adult Aedes albopictus mosquitoes surviving the winter without requiring dormancy., (© 2021. The Author(s).)

Background: Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures., Methods: A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake., Results: The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified., Conclusions: Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.

Background: Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences., Objective: This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods., Methods: Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods)., Results: This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies., Conclusions: Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods., (©Darpit Dave, Madhav Erraguntla, Mark Lawley, Daniel DeSalvo, Balakrishna Haridas, Siripoom McKay, Chester Koh. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 29.04.2021.)

The FHIR specification provides a mechanism to access clinical terminologies using a standard API, and many existing terminologies, such as SNOMED CT, are well supported. However, in areas such as genomics, terminologies from other domains are starting to be used in clinical settings. Many of these are authored or distributed in Web Ontology Language (OWL) format. In this paper we describe a transformation between OWL ontologies and FHIR terminology resources. The results show that there are several challenges in implementing the transformation, with the major one being the lack of a modularisation mechanism in the FHIR code system resource that resembles the import mecha nism available in OWL. A workaround with minimal drawbacks was successfully implemented in this solution. The availability of this transformation is significant because it enables a broad range of terminologies that are currently available in OWL to be available using the FHIR API., (©2019 AMIA - All rights reserved.)

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

Background: The use of post-acute care (PAC) for cardiovascular conditions is highly variable across geographical regions. Although PAC benefits include lower readmission rates, better clinical outcomes, and lower mortality, referral patterns vary widely, raising concerns about substandard care and inflated costs. The objective of this study is to identify factors associated with PAC referral decisions at acute care discharge., Methods: This study is a retrospective Electronic Health Records (EHR) based review of a cohort of patients with coronary artery bypass graft (CABG) and valve replacement (VR). EHR records were extracted from the Cerner Health-Facts Data warehouse and covered 49 hospitals in the United States of America (U.S.) from January 2010 to December 2015. Multinomial logistic regression was used to identify associations of 29 variables comprising patient characteristics, hospital profiles, and patient conditions at discharge., Results: The cohort had 14,224 patients with mean age 63.5 years, with 10,234 (71.9%) male and 11,946 (84%) Caucasian, with 5827 (40.96%) being discharged to home without additional care (Home), 5226 (36.74%) to home health care (HHC), 1721 (12.10%) to skilled nursing facilities (SNF), 1168 (8.22%) to inpatient rehabilitation facilities (IRF), 164 (1.15%) to long term care hospitals (LTCH), and 118 (0.83%) to other locations. Census division, hospital size, teaching hospital status, gender, age, marital status, length of stay, and Charlson comorbidity index were identified as highly significant variables (p- values < 0.001) that influence the PAC referral decision. Overall model accuracy was 62.6%, and multiclass Area Under the Curve (AUC) values were for Home: 0.72; HHC: 0.72; SNF: 0.58; IRF: 0.53; LTCH: 0.52, and others: 0.46., Conclusions: Census location of the acute care hospital was highly associated with PAC referral practices, as was hospital capacity, with larger hospitals referring patients to PAC at a greater rate than smaller hospitals. Race and gender were also statistically significant, with Asians, Hispanics, and Native Americans being less likely to be referred to PAC compared to Caucasians, and female patients being more likely to be referred than males. Additional analysis indicated that PAC referral practices are also influenced by the mix of PAC services offered in each region.

The review of pathology test results for missed diagnoses in Emergency Departments is time-consuming, laborious, and can be inaccurate. An automated solution, with text mining and clinical terminology semantic capabilities, was developed to provide clinical decision support. The system focused on the review of microbiology test results that contained information on culture strains and their antibiotic sensitivities, both of which can have a significant impact on ongoing patient safety and clinical care. The system was highly effective at identifying abnormal test results, reducing the number of test results for review by 92%. Furthermore, the system reconciled antibiotic sensitivities with documented antibiotic prescriptions in discharge summaries to identify patient follow-ups with a 91% F-measure - allowing for the accurate prioritization of cases for review. The system dramatically increases accuracy, efficiency, and supports patient safety by ensuring important diagnoses are recognized and correct antibiotics are prescribed.

Clinical terminologies play an essential role in enabling semantic interoperability between medical records. However, existing terminologies have several issues that impact data quality, such as content gaps and slow updates. In this study we explore the suitability of existing, community-driven resources, specifically Wikipedia, as a potential source to bootstrap an open clinical terminology, in terms of content coverage. In order to establish the extent of the coverage, a team of expert clinical terminologists manually mapped a clinically-relevant subset of SNOMED CT to Wikipedia articles. The results show that approximately 80% of the concepts are covered by Wikipedia. Most concepts that do not have a direct match in Wikipedia are composable from multiple articles. These findings are encouraging and suggest that it should be possible to bootstrap an open clinical terminology from Wikipedia.

Concern about the integrity of empirical research has arisen in recent years in the light of studies showing the vast majority of publications in academic journals report positive results, many of these results are false and cannot be replicated, and many positive results are the product of data dredging and the application of flexible data analysis practices coupled with selective reporting. While a number of potential solutions have been proposed, the effects of these are poorly understood and empirical evaluation of each would take many years. We propose that methods from the systems sciences be used to assess the effects, both positive and negative, of proposed solutions to the problem of declining research integrity such as study registration, Registered Reports, and open access to methods and data. In order to illustrate the potential application of systems science methods to the study of research integrity, we describe three broad types of models: one built on the characteristics of specific academic disciplines; one a diffusion of research norms model conceptualizing researchers as susceptible, "infected" and recovered; and one conceptualizing publications as a product produced by an industry comprised of academics who respond to incentives and disincentives.

Background: Even though several high-quality clinical terminologies, such as SNOMED CT and LOINC, are readily available, uptake in clinical systems has been slow and many continue to capture information in plain text or using custom terminologies. This paper discusses some of the challenges behind this slow uptake and describes a clinical terminology server implementation that aims to overcome these obstacles and contribute to the widespread adoption of standardised clinical terminologies., Results: Ontoserver is a clinical terminology server based on the Fast Health Interoperability Resources (FHIR) standard. Some of its key features include: out-of-the-box support for SNOMED CT, LOINC and OWL ontologies, such as the Human Phenotype Ontology (HPO); a fast, prefix-based search algorithm to ensure users can easily find content and are not discouraged from entering coded data; a syndication mechanism to facilitate keeping terminologies up to date; and a full implementation of SNOMED CT's Expression Constraint Language (ECL), which enables sophisticated data analytics., Conclusions: Ontoserver has been designed to overcome some of the challenges that have hindered adoption of standardised clinical terminologies and is used in several organisations throughout Australia. Increasing adoption is an important goal because it will help improve the quality of clinical data, which can lead to better clinical decision support and ultimately to better patient outcomes.

Objectives: Exclusive enteral nutrition (EEN) is an effective induction treatment for pediatric Crohn disease. Given the center-based variation in use and diversity in practice, we constructed a survey aimed at sharing experience and strategies in administering EEN, stimulating further research, and optimizing therapy., Methods: This survey was constructed after consultation with experts and designed to address key knowledge gaps. The survey was disseminated through the Pediatric IBD Porto Group of ESPGHAN, Canadian Children IBD Network, selective experts, and was sent twice through the Pediatric Gastrointestinal-Bulletin Board (PEDGI-BB). Data were collected into REDCap and analyzed using descriptive statistics., Results: In total, 146 participants from 26 countries completed the survey. Sixty-five percentage of participants were general, non-inflammatory bowel diseases (IBD)-focused pediatric gastroenterologists, 21% were IBD-focused, and 10% were dietitians. The most common indications (∼90% use) were for ileocecal and ileocolonic disease (Paris L1 and L3). The most common duration was 8 weeks and 66% preferred oral to nasogastric administration. Most (63%) did not allow any additional intake and 69% instructed patients to continue partial enteral nutrition (EN) after completing treatment. Dietitians were identified as essential to EEN success while the primary challenges of EEN programs were adherence and lack of support. Regional and professional practice differences were observed in EEN indication, age, exclusivity, program structure/support, and cost coverage., Conclusions: We found significant variation in practice and use of EEN with several regional and professional differences. Global variation offers opportunities for research and improving care. This survey establishes a framework and provides resources for collaboration and information sharing.

Background: Posttraumatic stress disorder (PTSD) is a prevalent mental health issue among veterans. Access to PTSD treatment is influenced by geographic (ie, travel distance to facilities), temporal (ie, time delay between services), financial (ie, eligibility and cost of services), and cultural (ie, social stigma) barriers., Objective: The emergence of mobile health (mHealth) apps has the potential to bridge many of these access gaps by providing remote resources and monitoring that can offer discrete assistance to trauma survivors with PTSD and enhance patient-clinician relationships. In this study, we investigate the current mHealth capabilities relevant to PTSD., Methods: This study consists of two parts: (1) a review of publicly available PTSD apps designed to determine the availability of PTSD apps, which includes more detailed information about three dominant apps and (2) a scoping literature review performed using a systematic method to determine app usage and efforts toward validation of such mHealth apps. App usage relates to how the end users (eg, clinicians and patients) are interacting with the app, whereas validation is testing performed to ensure the app's purpose and specifications are met., Results: The results suggest that though numerous apps have been developed to aid in the diagnosis and treatment of PTSD symptoms, few apps were designed to be integrated with clinical PTSD treatment, and minimal efforts have been made toward enhancing the usability and validation of PTSD apps., Conclusions: These findings expose the need for studies relating to the human factors evaluation of such tools, with the ultimate goal of increasing access to treatment and widening the app adoption rate for patients with PTSD., (©Carolina Rodriguez-Paras, Kathryn Tippey, Elaine Brown, Farzan Sasangohar, Suzannah Creech, Hye-Chung Kum, Mark Lawley, Justin K Benzer. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 26.10.2017.)

Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, nonattendance can affect continuity of care and result in higher emergency department (ED) and hospital use. Nonattendance also impacts appointment scheduling, patient access, and clinic work load. While no show has received significant attention, little research has addressed the prevalence and impact of appointment cancellation. Data on 46,710 appointments for 7586 adult diabetic patients was used to conduct a prospective cohort study examining primary care appointment behavior. The independent variable was the status of the INDEX appointment, which was attended, cancelled, or no showed. Dependent variables included the dates of (1) the last attended appointment, (2) scheduling the NEXT appointment, (3) the next attended follow-up appointment, and (4) ED visits and hospitalizations within six months of the INDEX. Cancellation was more prevalent than no show (17.7% vs 12.2%). Of those who cancelled and scheduled a next appointment, 28.8% experienced over 30 days delay between the INDEX and NEXT appointment dates, and 59.9% delayed rescheduling until on or after the cancelled appointment date. Delay in rescheduling was associated with an 18.6% increase in days between attended appointments and a 26.0% increase in ED visits. For diabetic patients, cancellation with late rescheduling is a prevalent and unhealthy behavior. Although more work is necessary to address the health, intervention, and cost issues, this work suggests that cancellation, like no show, may be problematic for many clinics and patients.

Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients. To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: medication, symptom, and background. Each sentence is projected into an interpretable feature space consisting of labeled sequential patterns, UMLS semantic types, and other heuristic features. A forest-based model is developed for categorizing OHF posts. An interpretation method is also developed, where the decision rules can be explicitly extracted to gain an insight of useful information in texts. Experimental results on real-world OHF data demonstrate the effectiveness of our proposed computational framework.