Predicting the Information Need for Domestic Violence Survivors Based on the Fine-Tuned Large Language Model.

Women with domestic violence experiences often refuse to seek help face-to-face due to embarrassment. They begin to share their emotions and seek help from online health communities. Understanding and responding to these posts can be crucial in providing timely support to the victims. We proposed a fine-tuned large language model (LLM) capable of accurately predicting the informational need based on the content of postings. We fine-tuned the LAMMA2-7B-chat model based on the guidance of identifying the information need and a dataset comprising 273 posts from Reddit, which are manually annotated by domain experts. Furthermore, we evaluated the performance of our model using a random sample of 15 posts, and 66.6% were accurately predicted. The results demonstrate that our model can rapidly capture the information needs expressed in the posts, enabling healthcare providers to provide timely and useful support based on our predictions.

HELPeR: Interface Design Decision and Evaluation.

Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

Utilizing Digital Twin to Create Personas Representing Ovarian Cancer Patients and Their Families.

OvCa patients and caregivers perceived challenges in online health information seeking. The HELPeR recommendation system utilized digital twins to create personas reflecting real-world OvCa patients and caregivers. The aim of this study was to describe the creation of digital twins and demonstrate their use cases in the study. Digital twins of OvCa patients and caregivers were created by triangulating multiple sources, including online cancer forums, direct interviews with patients and caregivers, domain expert input, and clinical notes. 10 personas were created for both OvCa patients and caregivers who had a variety of cancer trajectories and information interests. These digital twins present a potential solution for training artificial intelligence models at the initial phase when there is a scarcity of user information.

Towards Building an E-Librarian System: Exploring Recommendation System Preferences Among Ovarian Cancer Patients and Caregivers.

Ovarian cancer (OvCa) patients encounter complex treatment decisions, and often have difficulties in searching and integrating online health information to guide their treatment decision-making. The objective of this study was to explore the preference of online health information among OvCa patients and caregivers, by exploring their preferred content, format, and function features for the design of a personalized recommender system. This study used qualitative research methods to collect data through in-depth interviews with 18 OvCa patients and 2 caregivers. A total of (N=20) face-to-face interviews were conducted, and subsequently analyzed by audio recordings, verbatim transcription, and theory-driven approach with thematic analysis. A total of 5 themes were identified for content-related design, 4 themes identified for system function and one theme identified for frequency format. The results of this study inform the preference and therefore OvCa specific features can be tailor-made in a recommendation system.

Using Social Media to Understand Primary Discussions in Gastrointestinal Cancers: Machine Learning Approach.

BACKGROUND: The incidence and mortality rates of gastrointestinal (GI) cancers are high in the United States as well as worldwide. The widespread use of social media provides unique opportunities to facilitate the dissemination of information, especially in the context of health. OBJECTIVE: We aim to characterize the public's primary discussions, including perceptions, concerns, and interests toward GI cancers, from prevention, diagnosis, and treatment to survivorship care through the social media platform Twitter, using tweets posted by Twitter users. METHODS: We analyzed 87 860 Twitter posts related to GI cancers. We used machine learning with natural language processing to identify salient topics and themes in the collected tweets. RESULTS: The most common themes across all GI cancer types included cancer risk prevention and awareness outreach programs, risk factors including lifestyles (primarily diet), and cancer survivorship-related discussions (primarily GI symptoms and quality of life). GI symptom-related tweets were prevalent in patients with colorectal and stomach cancers, whereas themes of newer clinical trials, end-of-life trials, palliative care trials, and disease prognosis were common in tweets related to liver/biliary and pancreatic cancers. CONCLUSIONS: Our research emphasizes the importance of individualized approaches in managing GI cancers, considering lifestyle and diet, the need for comprehensive survivorship care, raising awareness, delivering information, and improving targeted interventions related to GI cancers. IMPLICATIONS FOR PRACTICE: Our study suggests utilizing Twitter data to better understand the real-world interest and concerns about GI cancers among the public, which can guide future patient-centered research in this field.

Harnessing Health Information Technology in Domestic Violence in the United States: A Scoping Review.

Objectives: The following scoping review aims to identify and map the existing evidence for HIT interventions among women with DV experiences in the United States. And provide guidance for future research, and facilitate clinical and technical applications for healthcare professionals. Methods: Five databases, PubMed, EBSCOhost CINAHL, Ovid APA PsycINFO, Scopus and Google Scholar, were searched from date of inception to May 2023. Reviewers extracted classification of the intervention, descriptive details, and intervention outcomes, including physical safety, psychological, and technical outcomes, based on representations in the included studies. Results: A total of 24 studies were included, identifying seven web-based interventions and four types of abuse. A total of five studies reported safety outcomes related to physical health. Three studies reported depression, anxiety, and post-traumatic stress disorder as psychological health outcomes. The effectiveness of technology interventions was assessed in eight studies. Conclusion: Domestic violence is a major public health issue, and research has demonstrated the tremendous potential of health information technology, the use of which can support individuals, families, and communities of domestic violence survivors.

Identifying oncology caregivers' pretreatment educational and emotional needs to inform future virtual reality educational interventions.

PURPOSE: To identify oncology caregivers' unmet educational needs for the development of a virtual reality experience. METHODS: A qualitative descriptive methodology was used; data were collected via online surveys. RESULTS: Eighteen participants said their educational experiences were overwhelming and emotionally exhausting. They suggested a need to deliver educational information through different modalities and provide more clinician-based resources and support. CONCLUSION: This study identified opportunities to complement traditional pretreatment education tailored to the caregivers' needs and experiences, such as specific procedural information and emotional management while being a caregiver. Creating virtual reality experiences exclusively for oncology caregivers is a novel nurse-led approach that is currently not in existence.

Racial/ethnic disparities in sleep health among adolescents in South Korea: The role of substance use behaviours.

AIM: To examine the relationship between racial/ethnic disparities and substance use behaviours (alcohol and tobacco use) and their impact on the sleep health of South Korean adolescents. DESIGN: Secondary analysis of cross-sectional study data from the 2021 Korea Youth Risk Behaviour Web-based Survey dataset. METHODS: Given that Korean society has historically linked its racial/ethnic identity to a shared bloodline, we categorized 2644 adolescents from the Korea Youth Risk Behaviour Web-based Survey based on their racial/ethnic status, determined by their parents' birthplaces. Using multiple linear regression, we investigated whether the impact of racial/ethnic disparities on sleep health (sleep duration, debt, and timing) varies depending on substance use behaviours (alcohol and tobacco use) after controlling for age, sex, household economic status, depressed mood, suicidal ideation, perceived excessive stress, and anxiety level. RESULTS: Despite no statistical differences in sleep health and the prevalence of substance use between racial/ethnic groups, racial/ethnic minority adolescents experienced greater sleep debt than racial/ethnic majority adolescents when consuming alcohol. Moreover, racial/ethnic minority adolescents were more likely to report psychosocial distress and had lower parental education level. CONCLUSION: Racial/ethnic minority adolescents were more vulnerable to the detrimental effects of alcohol use on sleep health compared to racial/ethnic majority adolescents. This heightened vulnerability may be attributed to the more pronounced psychosocial challenges and the lower socioeconomic status of parents in the racial/ethnic minority group. IMPACT: Racial/ethnic disparities are concerning in South Korea, particularly since the negative effects of substance use on sleep health are intensified among racial/ethnic minority adolescents. Nurses and other healthcare providers should recognize the importance of addressing the social disadvantages linked to racial/ethnic disparities. Beyond just advocating for the cessation of substance use, it is crucial to address these underlying issues to reduce sleep disparities among South Korean adolescents. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Women with ovarian cancer's information seeking and avoidance behaviors: an interview study.

Objectives: Despite the importance of using information for ovarian cancer (OvCa) disease management and decision-making, some women with OvCa do not actively seek out information. The purpose of this study is to investigate factors that influence information seeking behaviors and information avoidance behaviors and information resources among women with OvCa and their caregivers. Materials and methods: We conducted in-depth interviews with OvCa patients or caregivers of OvCa (n = 20) and employed deductive and inductive coding methodologies for analysis. Results: Our analysis revealed 5 emerging themes associated with active information seeking behavior, 5 themes of passive information acquisition, and 4 themes of information avoidance behavior. Additionally, we identified participants' preferred information sources for OvCa management, such as health organization or government operated resources and web-based social groups. Discussion: To enhance information access, strategies should be developed to motivate people with OvCa to seek rather than avoid information. The study emphasizes the significance of promoting patient-provider communication and leveraging strong social support networks for effective information acquisition. Conclusion: Our findings provide valuable implications for clinical practice and policymaking, emphasizing the need to improve access to information for individuals with OvCa. By addressing the identified factors influencing information seeking behaviors, healthcare professionals and policymakers can better support patients and caregivers in their information-seeking journey, ultimately enhancing disease management and decision-making outcomes.

Dietary Carotene Intake and Suicidal Ideation in Korean Females: Analysis of Data From the Korea National Health and Nutrition Examination Survey (2012, 2013, and 2015).

OBJECTIVE: The suicide rate in Korea was the highest among countries in the Organisation for Economic Co-operation and Development in 2019. In a previous study, higher intake of vegetables and fruits was associated with a lower risk of suicidal ideation, and carotene-rich fruits and vegetables lowered the risk of depression. This study aimed to examine the direct relationship between carotene intake and suicidal ideation, adjusting for the effect on depression. METHODS: This study used data from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted in 2012, 2013, and 2015. Carotene intake was assessed through a food intake frequency survey with a 24-hour recall. Suicidal ideation and depression were assessed using the mental health section of the KNHANES. We applied logistic regression to assess the relationship between carotene intake and suicidal ideation, adjusting for potential confounders. RESULTS: A total of 5,480 females aged 19-64 years were included in this study. Carotene intake was significantly lower in the suicidal ideation group (3,034.5±1,756.4 µg/day) than in the nonsuicidal ideation group (3,225.4±1,795.1 µg/day) (p=0.015). We found a significant inverse association between carotene intake and the risk of suicidal ideation after adjusting for potential confounders (odds ratio=0.934, 95% confidence interval=0.873-0.999). CONCLUSION: These results suggest that carotene intake may be inversely associated with the risk of suicidal ideation. Our findings may inform the development of new nutritional interventions to prevent increases in the risk of suicide worldwide.

Association Between Depressed Mood Changes and Physical Activity Among Adolescents Post COVID-19 Pandemic.

Objectives: Following the coronavirus disease 2019 (COVID-19) pandemic, adolescents have experienced decreased physical activity and a decline in mental health. This study analyzed the association between changes in depressed mood after the COVID-19 pandemic and physical activity among adolescents. Methods: The analysis was based on the results of the 17th Youth Health Behavior Online Survey conducted in 2021, which included 54848 middle and high school students in South Korea. Information on physical activity included low-intensity physical activity lasting >60 min/day, high-intensity physical activity, and strength training exercises. A logistic regression analysis was performed to evaluate the association between physical activity and changes in depression after the COVID-19 pandemic. Results: After adjusting for sociodemographic characteristics and previous depression, adolescents who performed strength training exercises more than once per week had a 0.95-fold lower risk (odds ratio [OR]=0.948, 95% confidence interval [CI]=0.905-0.994, p= 0.027) of increasing depression after the COVID-19 pandemic, while the risk of decreasing depression increased by 1.22-fold (OR=1.215, 95% CI=1.131-1.305, p<0.001). The results were not significant for low-intensity physical activity for >60 min/day and high-intensity physical activity. Conclusion: Strength-training exercises are significantly associated with the prevention of depression among adolescents following the COVID-19 pandemic.

Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.

BACKGROUND: Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. OBJECTIVE: The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. METHODS: We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ≥18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. RESULTS: Overall, 250 postings were included, and 200 (80%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. CONCLUSIONS: OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively.

Factors Influencing the Health Care Utilization Among People With Depression and/or Anxiety Symptoms.

BACKGROUND: This analysis aimed to examine the factors predictive of service utilization among patients with anxiety and/or depression. Quick and appropriate treatment for anxiety and depression can reduce disease burden and improve social functioning. Currently, less than half of the population with comorbid anxiety and depression receives the recommended treatment. AIMS: This analysis aims to identify factors predictive of utilizing mental health treatment for those with anxiety and/or depression by analyzing intrinsic, patient-centered factors. METHOD: This study is a cross-sectional cohort analysis using National Health Interview Survey (NHIS) 2019 data. The sample size is 7,156 adults aged 18 to 64 with family incomes ≤100% of the federal poverty level. We used multivariate logistic regression analysis to identify factors predictive of care utilization in this population. Variables of interest include scores on Patient Health Questionnaire-8 (PHQ-8) and Generalized Anxiety Disorder-7 (GAD-7), service utilization, level of social functioning, having a usual source for care, and previous mental health care utilization. Additional covariates were age, gender, race, country of origin, education, marital status, and insurance coverage. RESULTS: Twenty-one percent of respondents reported using mental health services. Factors predictive of care utilization were older age, female gender, limited social functioning, having a usual source of care, and insurance coverage. CONCLUSION: There are significant barriers to receiving quick and appropriate care for anxiety and/or depression. Strategies should focus on reducing barriers for young adults, men, and the uninsured/underinsured. Strategies for integrating mental health services into primary care could increase the percentage of people with anxiety and/or depression who receive services.

Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models.

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time, the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

Spatiotemporal Patterns of Suicide in Korea From 2009 to 2019 Using Geographic Information Systems.

Objective: When analyzing factors related to suicide, it is necessary to consider the regional characteristics of the areas where individuals live in addition to individual factors. This study aimed to investigate the spatiotemporal association between suicide rates and geographic features and the patterns of this association for all administrative areas in South Korea from 2009 to 2019. Methods: The data used in this study were obtained from the National Statistical Office of the Korean Statistical Information Service. For suicide rates, age-standardized mortality index data per 100 000 people were used. All administrative districts from 2009 to 2019 were divided into 229 regions. Emerging hotspot analysis was used for a 3-dimensional analysis to simultaneously evaluate temporal and spatial clusters. Results: In the 229 regions, there were 27 (11.8%) hotspots and 60 (26.2%) cold spots. Hotspot pattern analysis found 2 (0.9%) new spots, 1 (0.4%) persistent spot, 23 (10.0%) sporadic spots, and 1 (0.4%) oscillating spot. Conclusion: This study found geographic differences in the spatiotemporal patterns of suicide rates in South Korea. The utilization of national resources for suicide prevention should be selectively and intensively prioritized in 3 areas that exhibit unique spatiotemporal patterns.

Harnessing Machine Learning in Tackling Domestic Violence-An Integrative Review.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. METHODS: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. RESULTS: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. CONCLUSIONS: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Digital Health Equity: Addressing Power, Usability, and Trust to Strengthen Health Systems.

BACKGROUND: Without specific attention to health equity considerations in design, implementation, and evaluation, the rapid expansion of digital health approaches threatens to exacerbate rather than ameliorate existing health disparities. METHODS: We explored known factors that increase digital health inequity to contextualize the need for equity-centered informatics. This work used a narrative review method to summarize issues about inequities in digital health and to discuss future directions for researchers and clinicians. We searched literature using a combination of relevant keywords (e.g., "digital health", "health equity", etc.) using PubMed and Google Scholar. RESULTS: We have highlighted strategies for addressing medical marginalization in informatics according to vectors of power such as race and ethnicity, gender identity and modality, sexuality, disability, housing status, citizenship status, and criminalization status. CONCLUSIONS: We have emphasized collaboration with user and patient groups to define priorities, ensure accessibility and localization, and consider risks in development and utilization of digital health tools. Additionally, we encourage consideration of potential pitfalls in adopting these diversity, equity, and inclusion (DEI)-related strategies.

Knowledge Acquisition and Social Support in Online Health Communities: Analysis of an Online Ovarian Cancer Community.

BACKGROUND: Patients and caregivers widely use online health communities (OHCs) to acquire knowledge from peers. Questions posed in OHCs reflect participants' learning objectives and differ in their level of cognitive complexity. However, little is known about the topics and levels of participants' learning objectives and the corresponding support they receive from members of OHCs. OBJECTIVE: This study aimed to investigate the knowledge acquisition of patients and caregivers in an OHC. Specifically, we investigated the distribution and topics of posts with learning objectives at different cognitive complexity levels, the type and amount of social support provided to meet users' learning objectives at different cognitive complexity levels, and the influence of social support on the change in learning objectives. METHODS: We collected 10 years of discussion threads from one of the most active ovarian cancer (OvCa) OHCs. A mixed methods approach was used, including qualitative content analysis and quantitative statistical analysis. Initial posts with questions were manually classified into 1 of the 3 learning objectives with increasing cognitive complexity levels, from low to high, based on the Anderson and Krathwohl taxonomy: understand, analyze, and evaluate. Manual content analysis and automatic classification models were used to identify the types of social support in the comments, including emotional support and 5 types of informational support: advice, referral, act, personal experience, and opinion. RESULTS: The original data set contained 909 initial posts and 14,816 comments, and the final data set for the analysis contained 560 posts with questions and 3998 comments. Our results showed that patients with OvCa and their caregivers mainly used OHCs to acquire knowledge for low- to medium-level learning objectives. Of the questions, 82.3% (461/560) were either understand- or analyze-level questions, in which users were seeking to learn basic facts and medical concepts or draw connections among different situations and conditions. Only 17.7% (99/560) of the questions were at the evaluate level, in which users asked other OHC members to help them make decisions or judgments. Notably, OvCa treatment was the most popular topic of interest among all the questions, regardless of the level of learning objectives. Regarding the social support received for different levels of learning objectives, significant differences were found in the advice (F2437.84=9.69; P<.001), opinion (F2418.18=11.56; P<.001), and emotional support (F2395.88=3.24; P=.01), as determined by one-way ANOVA, whereby questions at the evaluate level were more likely to receive advice, opinion, and emotional support than questions at the lower levels. Additionally, receiving social support tends to drive users to increase the cognitive complexity of the learning objective in the next post. CONCLUSIONS: Our study establishes that OHCs are promising resources for acquiring knowledge of OvCa. Our findings have implications for designing better OHCs that serve the growing OvCa community.

A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning.

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual's whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.

An mHealth-Based Health Management Information System Among Health Workers in Volta and Eastern Regions of Ghana: Pre-Post Comparison Analysis.

BACKGROUND: Despite the increasing attention to electronic health management information systems (HMISs) in global health, most African countries still depend on inefficient paper-based systems. Good Neighbors International and Evaluate 4 Health have recently supported the Ghana Health Service on the rollout of a mobile health-based HMIS called the e-Tracker system in 2 regions in Ghana. The e-Tracker is an Android-based tracker capture app that electronically manages maternal and child health (MCH) data. The Ghana Health Service has implemented this new system in Community Health Planning and Services in the 2 regions (Volta and Eastern). OBJECTIVE: This study aims to evaluate changes in health workers' capacity and behavior after using the e-Tracker to deliver MCH services. Specifically, the study assesses the changes in knowledge, attitude, and practice (KAP) of the health workers toward the e-Tracker system by comparing the pre- and postsurvey results. METHODS: The KAP of frontline health workers was measured through self-administered surveys before and after using the e-Tracker system to assess their capacity and behavioral change toward the system. A total of 1124 health workers from the Volta and Eastern regions responded to the pre-post surveys. This study conducted the McNemar chi-square test and Wilcoxon signed-rank test for a pre-post comparison analysis. In addition, random-effects ordered logistic regression analysis and random-effects panel analysis were conducted to identify factors associated with KAP level. RESULTS: The pre-post comparison analysis showed significant improvement in health workers' capacity, with higher knowledge and practice levels after using the e-Tracker system. As for knowledge, there was a 9.9%-point increase (from 559/1109, 50.41% to 669/1109, 60.32%) in the proportion of the respondents who were able to generate basic statistics on the number of children born in a random month within 30 minutes. In the practice section, the percentage of respondents who had scheduled clientencounters increased from 91.41% (968/1059) to 97.83% (1036/1059). By contrast, responses to the attitude (acceptability) became less favorable after experiencing the actual system. For instance, 48.53% (544/1121) initially expressed their preferences for an electronic system; however, the proportion decreased to 33.45% (375/1121) after the intervention. Random-effects ordered logistic regression showed that days of overwork were significantly associated with health workers' attitudes toward the e-Tracker system. CONCLUSIONS: This study provides empirical evidence that the e-Tracker system is conducive to enhancing capacity in MCH data management for providing necessary MCH services. However, the change in attitude implies that the users appear to feel less comfortable using the new system. As Ghana plans to scale up the electronic HMIS system using the e-Tracker to the national level, strategies to enhance health workers' attitudes are necessary to sustain this new system.