Maternal victimization and neglected offspring: Child marriage, IPV and depression symptoms among Salar Muslim women.

BACKGROUND: Child neglect is prevalent in western rural China, yet there is limited research among ethnic minority communities. The Salar, a Turkic-Muslim ethnic minority residing primarily in western China, also face this specific problem. The group is deeply influenced by ethnicity, Islam and Chinese Confucianism, which in turn makes women vulnerable to child marriage and IPV. These victimizations, coupled with various life stressors, further complicate the challenges of providing adequate care for their children. OBJECTIVE: This study hypothesizes a relationship between child neglect and maternal child marriage, IPV victimization, and depression symptoms. PARTICIPANTS AND SETTING: 201 married Salar women from five villages in Xunhua Salar Autonomous County, China, were randomly selected to participate in the study. METHOD: A probability proportional to size (PPS) sampling approach was used to collect a random representative multi-stage cluster sample in 2022. Random effects Poisson regression models were used to test the hypotheses. RESULTS: The participants reported a 65.6 % rate of child neglect and a 30.8 % rate of IPV in the past year. 37.6 % experienced child marriage. Results revealed significant associations between child neglect and child marriage, IPV, and depression symptoms. A two-way interaction between IPV and depression symptoms was strongly positively associated with child neglect. CONCLUSIONS: This research indicates that Salar Muslim mothers who have experienced child marriage, adulthood victimization, and depression are at a higher risk of neglecting their children. The findings represent a valuable initial step toward researching and addressing the protection needs of women and children from Muslim ethnic minorities in China.

Intergenerational mental health effects of traumatic victimization in Nepal: A 3-D theory study.

BACKGROUND: Intergenerational transmission of trauma is a major focus of international research. Epigenetic, complex-trauma, and intergenerational abuse pattern transmission theoretical explanations all have existing empirical support. Three-D theory argues that in addition to trauma victimization severity, victimization invasiveness and exploitativeness have important independent effects. Moreover, 3-D theory claims that a positive 3-way interaction occurs between trauma victimization invasiveness, exploitativeness, and severity. This study examines the 3-D hypotheses in the context of intergenerational trauma effects on adolescent depression symptoms and suicidal ideation in generation two. Three-D theory may play a particularly important role in intergenerational trauma effects for female victims in the context of conservative South Asian sex role norms. OBJECTIVE: Test for main effects, two-way, and three-way interaction effects of invasiveness, exploitativeness, and severity of traumatic victimization on intergenerational transmission to adolescent depression and suicidal ideation in generation two. Pathways from maternal depression and borderline personality symptoms as well as physical and sexual abuse and neglect of the adolescent child were also tested. PARTICIPANTS: and setting. Participants were a nationally representative, random, multi-stage cluster sample of 1089 Nepali mothers and their 15-17 year old adolescent children. METHODS: Regression models with adjustments for clustering within municipality were used to test the hypotheses. RESULTS: One in four Nepali adolescents had considered suicide; more than half had high depression scores. A three-way interaction effect between maternal trauma invasiveness, exploitativeness, and severity was positively associated with adolescent depression symptoms. A two-way interaction effect between exploitativeness and severity of maternal trauma was positively associated with adolescent suicidal ideation. Maternal depression, BPD symptoms, and very severe physical abuse of the adolescent were also associated with adolescent depression symptoms. CONCLUSION: Further research is needed to measure and investigate invasiveness, exploitativeness, and severity of traumatic victimization as they bear on intergenerational transmission of trauma. Examination of intergenerational transmission of self-concept as a potential vector is recommended.

Clinical and Surgical Applications of Large Language Models: A Systematic Review.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

Physiologic validation of the Compensatory Reserve Metric obtained from pulse oximetry: A step toward advanced medical monitoring on the battlefield.

BACKGROUND: The Compensatory Reserve Metric (CRM) provides a time sensitive indicator of hemodynamic decompensation. However, its in-field utility is limited because of the size and cost-intensive nature of standard vital sign monitors or photoplethysmographic volume-clamp (PPG VC ) devices used to measure arterial waveforms. In this regard, photoplethysmographic measurements obtained from pulse oximetry may serve as a useful, portable alternative. This study aimed to validate CRM values obtained using pulse oximeter (PPG PO ). METHODS: Forty-nine healthy adults (25 females) underwent a graded lower body negative pressure (LBNP) protocol to simulate hemorrhage. Arterial waveforms were sampled using PPG PO and PPG VC . The CRM was calculated using a one-dimensional convolutional neural network. Cardiac output and stroke volume were measured using PPG VC . A brachial artery catheter was used to measure intra-arterial pressure. A three-lead electrocardiogram was used to measure heart rate. Fixed-effect linear mixed models with repeated measures were used to examine the association between CRM values and physiologic variables. Log-rank analyses were used to examine differences in shock determination during LBNP between monitored hemodynamic parameters. RESULTS: The median LBNP stage reached was 70 mm Hg (range, 45-100 mm Hg). Relative to baseline, at tolerance, there was a 47% ± 12% reduction in stroke volume, 64% ± 27% increase in heart rate, and 21% ± 7% reduction in systolic blood pressure ( p < 0.001 for all). Compensatory Reserve Metric values obtained with both PPG PO and PPG VC were associated with changes in heart rate ( p < 0.001), stroke volume ( p < 0.001), and pulse pressure ( p < 0.001). Furthermore, they provided an earlier detection of hemodynamic shock relative to the traditional metrics of shock index ( p < 0.001 for both), systolic blood pressure ( p < 0.001 for both), and heart rate ( p = 0.001 for both). CONCLUSION: The CRM obtained from PPG PO provides a valid, time-sensitized prediction of hemodynamic decompensation, opening the door to provide military medical personnel noninvasive in-field advanced capability for early detection of hemorrhage and imminent onset of shock. LEVEL OF EVIDENCE: Diagnostic Tests or Criteria; Level III.

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Comparing the compensatory reserve metric obtained from invasive arterial measurements and photoplethysmographic volume-clamp during simulated hemorrhage.

PURPOSE: The compensatory reserve metric (CRM) is a novel tool to predict cardiovascular decompensation during hemorrhage. The CRM is traditionally computed using waveforms obtained from photoplethysmographic volume-clamp (PPGVC), yet invasive arterial pressures may be uniquely available. We aimed to examine the level of agreement of CRM values computed from invasive arterial-derived waveforms and values computed from PPGVC-derived waveforms. METHODS: Sixty-nine participants underwent graded lower body negative pressure to simulate hemorrhage. Waveform measurements from a brachial arterial catheter and PPGVC finger-cuff were collected. A PPGVC brachial waveform was reconstructed from the PPGVC finger waveform. Thereafter, CRM values were computed using a deep one-dimensional convolutional neural network for each of the following source waveforms; (1) invasive arterial, (2) PPGVC brachial, and (3) PPGVC finger. Bland-Altman analyses were used to determine the level of agreement between invasive arterial CRM values and PPGVC CRM values, with results presented as the Mean Bias [95% Limits of Agreement]. RESULTS: The mean bias between invasive arterial- and PPGVC brachial CRM values at rest, an applied pressure of -45mmHg, and at tolerance was 6% [-17%, 29%], 1% [-28%, 30%], and 0% [-25%, 25%], respectively. Additionally, the mean bias between invasive arterial- and PPGVC finger CRM values at rest, applied pressure of -45mmHg, and tolerance was 2% [-22%, 26%], 8% [-19%, 35%], and 5% [-15%, 25%], respectively. CONCLUSION: There is generally good agreement between CRM values obtained from invasive arterial waveforms and values obtained from PPGVC waveforms. Invasive arterial waveforms may serve as an alternative for computation of the CRM.

The usefulness of artificial intelligence in breast reconstruction: a systematic review.

BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS: A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS: A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS: In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care: A Scoping Review of Current Clinical Implementations.

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians' perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes.

Stability of One-Step Spray-on Splint for Lower Extremity Fractures During Splinting, MEDEVAC, and Impact.

INTRODUCTION: Military transport can induce whole-body vibrations, and combat almost always involves high impact between lower extremities and the ground. Therefore, robust splinting technology is necessary for lower extremity fractures in these settings. Our team compared a novel one-step spray-on foam splint (FastCast) to the current military standard structured aluminum malleable (SAM) splint. MATERIALS AND METHODS: Ten cadaveric specimens were subjected to complete tibia/fibula osteotomy. Specimens were fitted with custom accelerometer and gyroscope sensors superior and inferior to the fracture line. Each specimen underwent fracture and splinting from a standard of care SAM splint and an experimental FastCast spray foam splint in a randomized order. Each specimen was manually transported to an ambulance and then released from a 1 meter height to simulate impact. The custom sensors recorded accelerations and rotations throughout each event. Repeated-measures Friedman tests were used to assess differences between splint method within each event and between sensors within each splint method. RESULTS: During splinting, overall summation of change and difference of change between sensors for accelerations and rotations were greater for SAM splints than FastCast across all axes (P ≤ 0.03). During transport, the range of acceleration along the linear superior/inferior axis was greater for SAM splint than FastCast (P = 0.02), as was the range of rotation along the transverse plane (P < 0.01). On impact, the summation of change observed was greater for SAM splint than FastCast with respect to acceleration and rotation on the posterior/anterior and superior/inferior axes (P ≤ 0.03), and the cumulative difference between superior and inferior sensors was greater for SAM than FastCast with respect to anterior-axis rotation (P < 0.05). CONCLUSION: FastCast maintains stabilization of fractured lower extremities during transport and impacts to a significantly greater extent than SAM splints. Therefore, FastCast can potentially reduce the risk of fracture complications following physical stressors associated with combat and extraction.

Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review.

INTRODUCTION: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms' use in clinical practice. METHODS: We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords "machine learning," "opioid," and "prediction." The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients. RESULTS: Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation. CONCLUSIONS: ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.

Neglect, online invasive exploitation, and childhood sexual abuse in Hong Kong: Breaking the links.

BACKGROUND: Childhood sexual abuse (CSA) is a form of maltreatment that involves a child in sexual activity that she or he cannot fully comprehend or is unable to give informed consent to. The empirical link between child neglect and contact child sexual abuse is well established but little research examines mediators that explain this link. OBJECTIVE: This study tests online risk behaviors and unwanted sexual experiences online as sequential mediators of the neglect - CSA relationship. PARTICIPANT AND SETTING: The study uses a representative cross-sectional sample of 1097 Hong Kong adolescents. METHODS: Preacher and Hayes' (2008) non-parametric bootstrap approach was used to test three mediation hypotheses. RESULTS: Baseline logistic regression models showed neglected children had 11.2 times higher odds of reporting contact CSA (p < .001). Similarly, neglect was associated with 3.5 times higher odds of more online risk behavior (p < .001), which in turn was associated with 2.7 times higher odds of more online invasive exploitation (p < .001). Online invasive exploitation was associated with 2.7 times higher odds of reporting offline contact CSA (p < .001). The study found online risk behaviors to be a significant mediator of the relationship between neglect and online invasive exploitation (unwanted online sexual experiences). Online invasive exploitation, in turn, mediated the relationship between online risk behaviors and offline contact CSA. CONCLUSION: The findings highlight the importance of intervening against neglect as it appears to play a vital role in the etiology of contact CSA in Hong Kong.

Towards a Lightweight Classifier to Detect Hypovolemic Shock.

Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual's ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual's compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models' coefficients and minimal processor cycles to complete the computations.

Identification of Sleep Patterns via Clustering of Hypnodensities.

Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms to hypnodensities graphs generated by a pre-trained neural network. In a population of 100 subjects we identify two stable clusters whose characteristics we visualize graphically and through estimates of total sleep time. We also find that the hypnodensity representation of the sleep stages produces more robust clustering results than the same methods applied to traditional hypnograms.

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Verification and Validation of Lower Body Negative Pressure as a Non-Invasive Bioengineering Tool for Testing Technologies for Monitoring Human Hemorrhage.

Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans. As an experimental tool, evidence is presented that LBNP is credible, repeatable, and validated as an analog for the study of human hemorrhage physiology compared to actual blood loss. The LBNP tool can promote the testing and development of advanced monitoring algorithms and evaluating wearable sensors with the goal of improving clinical outcomes during use in emergency medical settings.

Observed practices of design engineers.

While there are numerous studies documenting the skills and abilities of experienced designers and engineers, research is needed to document the specific practices or behaviors of design engineers, a subset of creative engineers who solve complex problems. To document observed practices of design engineers, twelve experienced engineers were asked to describe an expert design engineer, someone who always has the solution when others do not. Using inductive thematic analysis, nine observed practices with 30 subtopics were identified from 186 data points. The observed practices of design engineers include being collaborative, confident, creative, independent, intuitive, inquisitive, motivated, systematic, and versatile. Eight additional data points document varying observations of design engineers' interest in mentoring or management. While participants spoke with reverence about the design engineers, some observed practices could have a negative connotation, such as being egotistical, conservative to a fault, and not good at public speaking. One realization from this paper is that studies generally report admirable practices to replicate, when potentially negative practices can help engineering educators to better prepare students for industry. Lastly, this article provides engineering educators with a mapping between the observed practices of design engineers and the graduate attributes used in accrediting Canadian engineering programs.

Childhood emotional abuse and alcohol use disorders in a national Nepali women sample: The mediating role of borderline personality traits.

While many studies have found an association between childhood emotional abuse and alcohol use disorders (AUD) during adulthood, underlying psychological mechanisms linking the two remain inadequately understood. Drawing on the developmental psychopathology perspective, this study examined the relationship between childhood emotional abuse and AUD during adulthood with a national sample of women in Nepal (N = 1,100, M age = 37.73), focusing on the mediating role of borderline personality traits. Mediation analyses were performed using the Karlson-Holm-Breen (KHB) method and bootstrapping confidence intervals. Results indicated that Nepali women's borderline personality traits significantly mediated the relationship between childhood emotional abuse and AUD. Hence, emotional abuse in childhood increases the risk for AUD during adulthood for Nepali women by increasing the risk of borderline personality traits. Findings underscore the necessity of continued emphasis on developing and implementing early interventions for childhood emotional abuse and therapeutic interventions for borderline personality traits in reducing AUD among vulnerable women in Nepal.

Benefits and Challenges of Remote Patient Monitoring as Perceived by Health Care Practitioners: A Systematic Review.

BACKGROUND: Remote patient monitoring (RPM), or telemonitoring, offers ways for health care practitioners to gather real-time information on the physiological conditions of patients. As telemedicine, and thus telemonitoring, is becoming increasingly relevant in today's society, understanding the practitioners' opinions is crucial. This systematic review evaluates the perspectives and experiences of health care practitioners with telemonitoring technologies. METHODS: A database search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the selection of articles measuring health care practitioners' perspectives and experiences with RPM technologies published between 2017 and 2021. Only articles written in English were included. No statistical analysis was performed and thus this is a qualitative review. RESULTS: A total of 1605 studies were identified after the initial search. After applying the inclusion and exclusion criteria of this review's authors, 13 articles were included in this review. In all, 2351 practitioners' perspectives and experience utilizing RPM technology in a variety of medical specialties were evaluated through close- and open-ended surveys. Recurring themes emerged for both the benefits and challenges. Common benefits included continuous monitoring of patients to provide prompt care, improvement of patient self-care, efficient communication, increased patient confidence, visualization of health trends, and greater patient education. Challenges comprised increased workload, higher patient anxiety, data inaccuracy, disorienting technology, financial issues, and privacy concerns. CONCLUSION: Health care practitioners generally believe that RPM is feasible for application. Additionally, there is a consensus that telemonitoring strategies will become increasingly relevant. However, there are still drawbacks to the technology that need to be considered.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.