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Objectives: Long-term care combined with complex follow-up processes is among the essential needs of lung transplantation. Therefore, Telemedicine-based strategies can provide an effective approach for both patients and clinicians by applying remote patient monitoring. Hence, the main objective of this study was to investigate Telemedicine and telehealth usage in lung transplantation.Method: A systematic review was conducted in four databases using keywords. Eligible studies were all English papers that developed Telemedicine-based programs to enhance patient care in lung organ transplantation. The interventions were analyzed analysis to determine the main descriptive areas. The quality of the included articles was evaluated using Mixed Methods Appraisal Tool (MMAT) tool by two authors.Results: Of the 261 retrieved articles, 27 met our inclusion criteria. Of these, 22 studies were devoted to the post-transplantation phase. All articles were published from 2002 to 2021 and the trend of publications has increased in recent years. Most of the studies were conducted in the United States and Canada. All eligible studies can be categorized into five types of Telemedicine interventions, 15 (55.56%) articles devoted to Telemonitoring, four (14.81%) for Teleconsultation, four (14.81%) articles for Telerehabilitation, three (11.11%) articles for Telespirometery, and one (3.70%) article were done regarding Tele-education.Conclusion: This integrated review provides researchers with a new understanding of Telemedicine-based care solutions. Findings show that remote patient care in lung transplantation includes various aspects, especially self-care improvement. [ABSTRACT FROM AUTHOR]

Aim: To evaluate YouTube videos on vital pulp capping (VPC) for content, quality, source, usefulness, and reliability., Materials and Methods: This study assessed 249 English-language videos on vital pulp therapy using the Total Content Score (TCS), Video Information and Quality Index (VIQI), Global Quality Scale (GQS), Journal of the American Medical Association (JAMA) score and modified DISCERN score. Videos were categorized based on communication quality, duration, likes and dislikes, views, source, and video type. Of the videos analyzed, 22.1% met the inclusion criteria. Data were analyzed using Shapiro-Wilk, Kruskal-Wallis, and post hoc Bonferroni tests. Sperman's correlation, Kendal tau tests for correlations, and Fisher's exact test were used to assess associations between categorical variables., Results: The study identified significant correlations between various parameters. A higher TCS was correlated with increased VIQI (p = 0.005) and GQS scores (r = 0.685, p < 0.05). A moderate positive correlation was found between GQS scores and TCSs (r = 0.577, p < 0.05). VIQI scores were significantly and positively correlated with TCS (r = 0.573, p < 0.05) and comment count (r = 0.306, p < 0.05). JAMA scores were positively correlated with upload time (r = 0.304, p < 0.05), comment count (r = 0.337, p < 0.05), and likes (r = 0.301, p = 0.05)., Conclusions: YouTube videos provided low-to-average quality VPC information and tended to be inaccurate. Therefore, public videos may be incorrect, incomplete, and low-quality. Clinicians and patients should seek reliable information from specialists., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Backgrounds: The National Cancer Institute (NCI) conducts Patterns of Care (POC) studies for selected cancer sites under a Congressional Mandate. These studies aim to collect treatment information beyond what is typically collected by the NCI's Surveillance, Epidemiology, and End Results (SEER) Program. The 2019 POC study focused on non-small cell lung cancer (NSCLC) and melanoma cancer sites. For the NSCLC cases, one of the primary sampling objectives was to oversample patients who tested positive for EGFR/ALK mutations, but initial information on mutation test results was unavailable prior to selecting the study sample., Methods: To address this, text mining algorithms were developed to screen all eligible NSCLC cases from the SEER database. These algorithms were designed to identify the mutation test status, allowing for stratified sampling based on SEER registry, sex, race/ethnicity, and tumor mutation test results., Results: The final NSCLC sample included 2,434 patients aged 20+ with advanced stage (IIIB-IVB) NSCLC diagnosed in 2017 and 2018. Among this sample, 692 cases (13.2%) tested positive for EGFR/ALK mutations. An evaluation of the text mining algorithms performance, based on cases where both algorithm results and known EGFR/ALK status from medical chart abstraction were available, showed good results: sensitivity of 77.6%, specificity of 90.8%, and an overall accuracy 84.8%., Conclusions: The adaption of text mining algorithm proved effective in oversample patients with uncommon conditions in studies where electronic medical records are accessible. The 2019 POC study provides valuable data for researchers to evaluate cancer therapy details and patient characteristics, particularly among those with EGFR/ALK test positive cases., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Objective: Widespread electronic health information exchange (HIE) across hospitals remains an important policy goal for reducing costs and improving the quality of care. Meanwhile, cybersecurity incidents are a growing threat to hospitals. The relationship between the electronic sharing of health information and cybersecurity incidents is not well understood. The objective of this study was to empirically examine the impact of hospitals' HIE engagement on their data breach risk., Materials and Methods: A balanced panel dataset included 4,936 US community hospitals spanning the period 2010-2017, which was assembled by linking the American Hospital Association annual survey database and the Information Technology (IT) supplement, and the Department of Health and Human Services reports of health data breaches. The relationship between HIE engagement and hospital data breaches was modeled using a difference-in-differences specification controlling for time-varying hospital characteristics., Results: The percentage of hospitals electronically exchanging information has more than tripled (from 18% to 68%) from 2010 to 2017. Hospital data breaches increased concurrently, largely due to the rise in hacking and unauthorized access. HIE engagement was associated with a 0.672 percentage point increase in the probability of an IT breach three years after the engagement. Hospitals actively engaging in a health information organization and exchanging data with outside providers were associated with a higher risk of IT related breaches in the long run; however, hospitals actively engaging in HIE and exchanging data with inside providers were not associated with any significant risk of IT related breaches., Discussion: Over time, the increasing amount and complexity of patient information being exchanged can create challenges for cybersecurity if data protection is not up to date. Additionally, data security depends on the weakest link of HIE, and providers with fewer resources for data governance and infrastructure are more vulnerable to data breaches., Conclusion: Moving toward widespread health information exchange has important cybersecurity implications that can significantly impact both patients and healthcare organizations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Hospitals may adopt various information transmission methods to participate in health information exchange (HIE) programs. However, even if they adopt multiple mechanisms, they may not actively use all of them to send inside information. This study analyzes the frequently used methods for sending data and the common barriers blocking sending practices in hospital settings., Methods: Our sample included 3,015 community hospitals that reported their methods of sending patient health information in the 2019 American Hospital Association Information Technology Supplement Survey. The relationship between obstacles hospitals experienced and their use of the information-sending method was analyzed by using robust Poisson regression models., Results: Many-to-many exchanges that involve intermediaries such as a health information service provider (HISP), electronic health record (EHR) vendor-based network, and national network, once adopted, were more often used by hospitals than one-to-one exchange methods such as provider portals and direct access to EHR by login credentials. Hospitals that lacked the technical capability to electronically send patient health information were less likely to use any of the methods (p <.01), while hospitals located in a more concentrated market were more likely to send information to outside providers by using provider portal, interface connection and national network (p <.01)., Discussion: There is still a notable gap between hospitals' adoption and the actual use of different HIE methods to send inside information to outside organizations. Results argue that even if hospitals adopted an HIE method, they might not necessarily participate in the actual sharing of information, and the method may remain unused due to several usage barriers., Conclusion: Hospital and market-level barriers associated with using one-to-one and many-to-many HIE methods for sharing information may affect progress in interoperability. Examining the barriers to using multiple HIE methods and their impact on interoperability could offer implications for health information technology (IT) policy and inform health system leaders., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: The COVID-19 pandemic was an international systemic crisis which required an unprecedented response to quickly drive the digital transformation of hospitals and health care systems to support high quality health care while adhering to contagion management protocols., Objective: To identify and assess the best practices during the COVID-19 pandemic by Chief Information Officers (CIOs) about how to build resilient healthcare IT (HIT) to improve pandemic preparedness and response across global settings and to develop recommendations for future pandemics., Methods: We conducted a qualitative, interview-based study to sample CIOs in hospitals. We interviewed 16 CIOs from hospitals and health systems in the United States and Abu Dhabi, United Arab Emirates. We used in-depth interviews to capture their perspectives of the preparedness of hospitals' information technology departments for the pandemic and how they lead their IT department out of the pandemic., Results: Results showed that healthcare CIOs were ambidextrous IT leaders who built resilient HIT by rapidly improving existing digital business practices and creating innovative IT solutions. Ambidextrous IT leadership involved exploiting existing IT resources as well as exploring and innovating for continuous growth. IT resiliency focused on four inter-related capabilities: ambidextrous leadership, governance, innovation and learning, and HIT infrastructure., Conclusions: We propose conceptual frameworks to guide the development of healthcare IT resilience and highlight the importance of organizational learning as an integral component of HIT resiliency., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objective: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD., Materials and Methods: We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes., Results: Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008., Discussion: Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior., Conclusion: Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: Research participants have a growing expectation for transparency with their collected information; however, there is little guidance on participant preferences for receiving health information and how researchers should return this information to participants., Methods: We conducted a cross-sectional online survey with a representative sample of 502 participants in the United States. Participants were asked about their preferences for receiving, sharing, and the formatting of health information collected for research purposes., Results: Most participants wanted their health information returned (84 %) to use it for their own knowledge and to manage their own health. Email was the most preferred format for receiving health data (67 %), followed by online website (44 %), and/or paper copy (32 %). Data format preferences varied by age, education, financial resources, subjective numeracy, and health literacy. Around one third of Generation Z (25 %), Millennials (30 %), and Generation X (29 %) participants preferred to receive their health information with a mobile app. In contrast, very few Baby Boomers (12 %) and none from the Silent Generation preferred the mobile app format. Having a paper copy of the data was preferred by 38 % of participants without a college degree compared to those with a college degree. Preferences were highest for sharing all health information with doctors and nurses (77 %), and some information with friends and family (66 %)., Conclusion: Study findings support returning research information to participants in multiple formats, including email, online websites, and paper copy. Preferences for whom to share information with varied by stakeholders and by sociodemographic characteristics. Researchers should offer multiple formats to participants and tailor data sharing options to participants' preferences. Future research should further explore combinations of individual characteristics that may further influence data sharing and format preferences., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Purpose: To evaluate the number, characteristics, and outcomes of patients identified hospitalized with coronavirus disease 2019 (COVID-19) using two different case definitions., Procedures: Electronic Health Record data were evaluated from patients hospitalized with COVID-19 through May 2020 at 52 health systems across the United States. Characteristics of inpatients with positive laboratory tests for SARS-CoV-2 were compared with those with clinical diagnosis of COVID-19 but without a confirmatory lab result., Findings: Of 14,371 inpatients with COVID-19, 6623 (46.1 %) had a positive laboratory result, and n = 7748 (52.9 %) had only a clinical diagnosis of COVID-19. Compared with clinically diagnosed cases, those with laboratory-confirmed COVID were similar in age and sex, but differed by race, ethnicity, and insurance status. Laboratory-confirmed cases were more likely to receive certain COVID-19 therapies including hydroxychloroquine, anti-IL6 agents and antivirals (p < 0.001). Those with laboratory-confirmed COVID-19 had lower rates of most complications such as myocardial infarction, but higher overall mortality (p < 0.001)., Conclusion: We observed a two-fold difference in the number of patients hospitalized with COVID-19 depending on whether the case definition required laboratory confirmation. Variations in case definitions also led to differences in cohort characteristics, treatments, and outcomes., Competing Interests: Declaration of Competing Interest RJT and SP are employees of Cerner Corporation, and completed this work as part of their employment with Cerner. AMN and EDP have received consulting fees for research consulting from Cerner Corporation, outside of the scope of this work. The authors declare they have no other competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)