Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments.

This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (ß = 0.504, CR = 5.064, p < 0.05) and self-concept (ß = 0.860, CR = 5.968, p < 0.05) about mHealth technologies. The findings of the structural equation model (SEM) showed that the model is acceptable (χ2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people's access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion.

Commercially Available Mobile Apps With Family Behavioral Goal Setting and Tracking for Parents: Review and Quality Evaluation.

BACKGROUND: Goal setting and tracking are well established behavior change techniques. Little is known about the extent to which commercially available mobile apps are designed to guide parents in using these strategies, their evidence base, and their quality. OBJECTIVE: This study aims to review commercially available apps that target parents in relation to setting and tracking behavioral goals for their children. The objectives were to classify the apps' general characteristics, features, evidence base, and target behaviors and assess app quality overall and separately for apps that target health-related behaviors (HRBs) and apps without a health-related behavior (WHRB). METHODS: Apps were identified using keyword searches in the Apple App Store and Google Play in the United States. Apps were included if their primary purpose was to assist with setting goals, tracking goals, tracking behaviors, or giving feedback pertaining to goals for children by parents. App characteristics and common features were documented and summarized. Two reviewers assessed app quality using the Mobile App Rating Scale (MARS). Descriptive statistics summarized the MARS total score, 4 quality subscales, and 6 app-specific items that reflect the perceived impact of the app on goal setting and tracking, overall and with subgroup analysis for HRB and WHRB apps. RESULTS: Of the 21 apps identified, 16 (76%) met the review criteria. Overall, 9 apps defined and targeted the following HRBs: nutrition and mealtime (6/16, 38%), physical activity and screen time (5/16, 31%), sleep (7/16, 44%), and personal hygiene (6/16, 38%). Three apps targeted specific age groups (2 apps were for children aged 6-13 years and 1 app was for children aged ≥4 years). None of the apps provided tailored assessments or guidance for goal setting. None of the apps indicated that they were intended for the involvement of a health professional or had been tested for efficacy. The MARS total score indicated moderate app quality overall (mean 3.42, SD 0.49) and ranged from 2.5 to 4.2 out of 5 points. The Habitz app ranked highest on the MARS total score among HRB apps (score=4.2), whereas Thumsters ranked highest (score=3.9) among the WHRB apps. Subgroup analysis revealed a pattern of higher quality ratings in the HRB group than the WHRB group, including the mean MARS total score (mean 3.67, SD 0.34 vs mean 3.09, SD 0.46; P=.02); the engagement and information subscales; and the app-specific items about perceived impact on knowledge, attitudes, and behavior change. CONCLUSIONS: Several high-quality commercially available apps target parents to facilitate goal setting and tracking for child behavior change related to both health and nonhealth behaviors. However, the apps lack evidence of efficacy. Future research should address this gap, particularly targeting parents of young children, and consider individually tailored guided goal setting and involvement of health professionals.

An overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity.

The artificial intelligence (AI) tool ChatGPT, which is based on a large language model (LLM), is gaining popularity in academic institutions, notably in the medical field. This article provides a brief overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity. It provides a list of AI generative tools, common use of AI generative tools for medical writing, and provides a list of AI generative text detection tools. It also provides recommendations for policymakers, information professionals, and medical faculty for the constructive use of AI generative tools and related technology. It also highlights the role of health sciences librarians and educators in protecting students from generating text through ChatGPT in their academic work.

A risk identification model for detection of patients at risk of antidepressant discontinuation.

Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources. Methods: We analyzed 891 patient narratives from the online healthcare forum, "askapatient.com," utilizing content analysis to create PsyRisk-a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation. Results: From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants. Conclusion: The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at risk of prematurely discontinuing their antidepressant treatment.

The prevalence of low health literacy in undergraduate students in Pakistan.

This Regular Feature is based on a PhD study assessing the level of health literacy among university students in Pakistan. A cross-sectional survey was carried out using the validated European Health Literacy Survey (HLS-EU-Q) and non-parametric tests used to analyse data with the aim of determining the influence of personal determinants on health literacy skills. The findings of the study concluded that the population had a low health literacy level with limited skills in accessing, understanding, appraising and applying information for health care. Gender, age, and native languages, all had a statistically significant influence on health literacy skills. Practical implications are presented for the role of university libraries in supporting the development of health literacy in their undergraduate student populations are presented, including the need for the provision of health information in native languages.

Integrating Social and Family Support as a Measure of Health Outcomes: Validity Implications from the Integrated Model of Health Literacy.

(1) Background: Health literacy (HL) is one of the key determinants of health and healthcare outcomes. The objectives of this study are to measure and validate Sørensen et al.'s integrated model of health literacy (IMHL) in a developing country's youth population, as well as to assess the impact of family affluence and social and family support on healthcare domains. (2) Methods: A cross-sectional survey was carried out of undergraduate university students in 19 public and private sector universities in Pakistan during June-August 2022. A nine-factor measurement model was tested using confirmatory factor analysis (CFA), and structural equation modeling (SEM) based on the 56 valid items obtained from three different validated scales, such as the family affluence scale (FAS-II), the multidimensional scale of perceived social support (MSPSS), and the European Health Literacy Questionnaire (the HLS-EU-Q). (3) Results: The data were collected from 1590 participants with a mean age of 21.16 (±2.027) years. The model fit indices indicate that the model partially fitted the data: χ2 = 4.435, df = 1448, p = 0.000, RMSEA = 0.048, TLI = 0.906, CFI = 0.912, IFI = 0.912, GFI = 0.872, NFI = 0.889, RFI = 0.882, PGFI = 0.791. The structural equation model showed acceptable goodness of fit indices, indicating a significant direct influence of social and family support on healthcare and disease prevention. (4) Conclusions: Social and family support are the most influential factors, with regard to HL dimensions, in improving healthcare, disease prevention, and health promotion in low-income settings and among non-English-speaking communities.

The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications.

The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains patients' expression of effectiveness and adverse drug events associated with psychiatric medications. The PsyTAR was generated in four phases. In the first phase, a sample of 891 drugs reviews posted by patients on an online healthcare forum, "askapatient.com", was collected for four psychiatric drugs: Zoloft, Lexapro, Cymbalta, and Effexor XR. For each drug review, patient demographic information, duration of treatment, and satisfaction with the drugs were reported. In the second phase, sentence classification, drug reviews were split to 6009 sentences, and each sentence was labeled for the presence of Adverse Drug Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF), and Others (not applicable). In the third phases, entities including ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792 mentions) were identified and extracted from the sentences. In the four phases, all the identified entities were mapped to the corresponding UMLS Metathesaurus concepts (916) and SNOMED CT concepts (755). In this phase, qualifiers representing severity and persistency of ADRs, WDs, SSIs, and DIs (e.g., mild, short term) were identified. All sentences and identified entities were linked to the original post using IDs (e.g., Zoloft.1, Effexor.29, Cymbalta.31). The PsyTAR dataset can be accessed via Online Supplement #1 under the CC BY 4.0 Data license. The updated versions of the dataset would also be accessible in https://sites.google.com/view/pharmacovigilanceinpsychiatry/home.

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30].

Empowering the Aging with Mobile Health: A mHealth Framework for Supporting Sustainable Healthy Lifestyle Behavior.

Healthcare providers are shifting to a value-based model that acknowledges the importance of a healthy lifestyle for managing chronic disease and mental health. This approach empowers patients to adopt and/or sustain healthy lifestyle choices through the use of innovative technologies-providing beneficial ways of delivering health literacy, self-monitoring, and patient-provider collaboration. Such pathways have the potential to enable healthy lifestyle management for a growing U.S. cohort-the "baby boomer" generation (BBG)-who are at risk for developing heart disease, stroke, arthritis, high cholesterol, and diabetes, etc. In this paper, we argue for a new mHealthy lifestyle management (MLM) model that uses mobile health technology as a means to engage BBG consumers in ways that establish their role in self-care and decision-making, as well as patient-provider collaboration that can significantly impact sustainable healthy lifestyle behaviors. By merging the domains of health informatics and human factors psychology, MLM addresses the complex challenges associated with patient-provider collaborative work, while offering a healthcare framework to BBGs in their quest to self-manage a physical and/or mental healthy lifestyle. A MLM use-case highlights the challenges and solutions for team-based clinical counseling. Finally, recommendations for future MLM tools are outlined that support patient access to personal health eTools, information, and services.

Impact of document consolidation on healthcare providers' perceived workload and information reconciliation tasks: a mixed methods study.

Background: Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods: Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results: While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion: Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.

Simulated Clinical Encounters Using Patient-Operated mHealth: Experimental Study to Investigate Patient-Provider Communication.

BACKGROUND: This study investigates patient-centered mobile health (mHealth) technology in terms of the secondary user experience (UX). Specifically, it examines how personal mobile technology, under patient control, can be used to improve patient-provider communication about the patient's health care during their first visit to a provider. Common ground, a theory about language use, is used as the theoretical basis to examine interactions. A novel concept of this study is that it is one of the first empirical studies to explore the relative meaningfulness of a secondary UX for specific health care tasks. OBJECTIVE: The objective of this study was to investigate the extent that patient-operated mHealth technology can be designed to improve the communication between the patient and provider during an initial face-to-face encounter. METHODS: The experimental study was conducted in 2 large Midwestern cities from February 2016 to May 2016. A custom-designed smartphone app prototype was used as the study treatment. The experimental design was posttest-only control group and included video-recorded simulated face-to-face clinical encounters in which an actor role-played a patient. Experienced clinicians consisting of doctors (n=4) and nurses (n=8) were the study participants. A thematic analysis of qualitative data was performed. Quantitative data collected from time on task measurements were analyzed using descriptive statistics. RESULTS: Three themes that represent how grounding manifested during the encounter, what it meant for communication during the encounter, and how it influenced the provider's perception of the patient emerged from the qualitative analysis. The descriptive statistics were important for inferring evidence of efficiency and effectiveness of communication for providers. Overall, encounter and task times averaged slightly faster in almost every instance for the treatment group than that in the control group. Common ground clearly was better in the treatment group, indicating that the idea of designing for the secondary UX to improve provider outcomes has merit. CONCLUSIONS: Combining the notions of common ground, human-computer interaction design, and smartphone technology resulted in a prototype that improved the efficiency and effectiveness of face-to-face collaboration for secondary users. The experimental study is one of the first studies to demonstrate that an investment in the secondary UX for high payoff tasks has value but that not all secondary UXs are meaningful for design. This observation is useful for prioritizing how resources should be applied when considering the secondary UX.

Identifying the Underlying Factors Associated With Patients' Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews.

BACKGROUND: Nonadherence to antidepressants is a major obstacle to deriving antidepressants' therapeutic benefits, resulting in significant burdens on the individuals and the health care system. Several studies have shown that nonadherence is weakly associated with personal and clinical variables but strongly associated with patients' beliefs and attitudes toward medications. Patients' drug review posts in online health care communities might provide a significant insight into patients' attitude toward antidepressants and could be used to address the challenges of self-report methods such as patients' recruitment. OBJECTIVE: The aim of this study was to use patient-generated data to identify factors affecting the patient's attitude toward 4 antidepressants drugs (sertraline [Zoloft], escitalopram [Lexapro], duloxetine [Cymbalta], and venlafaxine [Effexor XR]), which in turn, is a strong determinant of treatment nonadherence. We hypothesized that clinical variables (drug effectiveness; adverse drug reactions, ADRs; perceived distress from ADRs, ADR-PD; and duration of treatment) and personal variables (age, gender, and patients' knowledge about medications) are associated with patients' attitude toward antidepressants, and experience of ADRs and drug ineffectiveness are strongly associated with negative attitude. METHODS: We used both qualitative and quantitative methods to analyze the dataset. Patients' drug reviews were randomly selected from a health care forum called askapatient. The Framework method was used to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. Then, 4 annotators coded the drug reviews at the sentence level using the analytical framework. After managing missing values, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude. RESULTS: A total of 892 reviews posted between February 2001 and September 2016 were analyzed. Most of the patients were females (680/892, 76.2%) and aged less than 40 years (540/892, 60.5%). Patient attitude was significantly (P<.001) associated with experience of ADRs, ADR-PD, drug effectiveness, perceived lack of knowledge, experience of withdrawal, and duration of usage, whereas oth age (F4,874=0.72, P=.58) and gender (χ24=2.7, P=.21) were not found to be associated with patient attitudes. Moreover, modeling the relationship between variables and attitudes showed that drug effectiveness and perceived distress from adverse drug reactions were the 2 most significant factors affecting patients' attitude toward antidepressants. CONCLUSIONS: Patients' self-report experiences of medications in online health care communities can provide a direct insight into the underlying factors associated with patients' perceptions and attitudes toward antidepressants. However, it cannot be used as a replacement for self-report methods because of the lack of information for some of the variables, colloquial language, and the unstructured format of the reports.

Reconciling disparate information in continuity of care documents: Piloting a system to consolidate structured clinical documents.

BACKGROUND: Due to the nature of information generation in health care, clinical documents contain duplicate and sometimes conflicting information. Recent implementation of Health Information Exchange (HIE) mechanisms in which clinical summary documents are exchanged among disparate health care organizations can proliferate duplicate and conflicting information. MATERIALS AND METHODS: To reduce information overload, a system to automatically consolidate information across multiple clinical summary documents was developed for an HIE network. The system receives any number of Continuity of Care Documents (CCDs) and outputs a single, consolidated record. To test the system, a randomly sampled corpus of 522 CCDs representing 50 unique patients was extracted from a large HIE network. The automated methods were compared to manual consolidation of information for three key sections of the CCD: problems, allergies, and medications. RESULTS: Manual consolidation of 11,631 entries was completed in approximately 150h. The same data were automatically consolidated in 3.3min. The system successfully consolidated 99.1% of problems, 87.0% of allergies, and 91.7% of medications. Almost all of the inaccuracies were caused by issues involving the use of standardized terminologies within the documents to represent individual information entries. CONCLUSION: This study represents a novel, tested tool for de-duplication and consolidation of CDA documents, which is a major step toward improving information access and the interoperability among information systems. While more work is necessary, automated systems like the one evaluated in this study will be necessary to meet the informatics needs of providers and health systems in the future.

Consumer Health Informatics: Empowering Healthy-Living-Seekers Through mHealth.

People are at risk from noncommunicable diseases (NCD) and poor health habits, with interventions like medications and surgery carrying further risk of adverse effects. This paper addresses ways people are increasingly moving to healthy living medicine (HLM) to mitigate such health threats. HLM-seekers increasingly leverage mobile technologies that enable control of personal health information, collaboration with clinicians/other agents to establish healthy living practices. For example, outcomes from consumer health informatics research include empowering users to take charge of their health through active participation in decision-making about healthcare delivery. Because the success of health technology depends on its alignment/integration with a person's sociotechnical system, we introduce SEIPS 2.0 as a useful conceptual model and analytic tool. SEIPS 2.0 approaches human work (i.e., life's effortful activities) within the complexity of the design and implementation of mHealth technologies and their potential to emerge as consumer-facing NLM products that support NCDs like diabetes.

Better managing technology-mediated interruptions in the ICU: Examining the role of patient information for improving text message notifications.

Previous research has identified the need for managing wanted and unwanted interruptions from technology- mediated notifications (TMN) in the intensive care units (ICUs). Current solutions are focused on mobile, asynchronous and context-aware mechanisms that consider a minimal number of factors (location and activity of the receiver). These factors are insufficient for a receiver to effectively decide on whether or not to interrupt their ongoing activities to immediately respond to a TMN. We propose a mobile device solution, known as "patient- enhanced notifications " that presents a preview of TMN with additional patient information. A study comprising of user evaluations and interview sessions helped ascertain that patient vital signs coupled with the actual text message assisted receiving ICU providers in deciding on when to respond to the TMN. We conclude that patient- enhanced notifications has the potential to help ICU clinicians better manage interruptions generated from mobile devices.

Supporting Clinical Cognition: A Human-Centered Approach to a Novel ICU Information Visualization Dashboard.

Advances in intensive care unit bedside displays/interfaces and electronic medical record (EMR) technology have not adequately addressed the topic of visual clarity of patient data/information to further reduce cognitive load during clinical decision-making. We responded to these challenges with a human-centered approach to designing and testing a decision-support tool: MIVA 2.0 (Medical Information Visualization Assistant, v.2). Envisioned as an EMR visualization dashboard to support rapid analysis of real-time clinical data-trends, our primary goal originated from a clinical requirement to reduce cognitive overload. In the study, a convenience sample of 12 participants were recruited, in which quantitative and qualitative measures were used to compare MIVA 2.0 with ICU paper medical-charts, using time-on-task, post-test questionnaires, and interviews. Findings demonstrated a significant difference in speed and accuracy with the use of MIVA 2.0. Qualitative outcomes concurred, with participants acknowledging the potential impact of MIVA 2.0 for reducing cognitive load and enabling more accurate and quicker decision-making.