Evaluating the network adequacy of vision care services for children in Arizona: A cross sectional study

Background

Vision challenges are among the most prevalent disabling conditions in childhood, affecting up to 28% of school-age children. These issues can impact the development, learning, and literacy skills of affected children. While vision problems are correctable with timely diagnosis and treatment, insufficient networks can impede children's access to comprehensive, and high-quality care.

Objective

The study aims to determine where pediatric vision care network adequacy exists in the state of Arizona and where there are gaps in receiving vision care for children.

Methods

This cross-sectional study assessed the adequacy of pediatric vision care networks in Arizona through a "secret shopper" phone survey. Calls were made to practices that accept Arizona's Medicaid program, Arizona Health Care Cost Containment System (AHCCCS) and/or commercial insurance. Providers were contacted following a standardized script to schedule routine appointments on behalf of 10 and 3-year-old patients enrolled in either Medicaid or commercial health insurance plans. The study examined various components of children's access to vision care services, including the reliability of provider directory information, time until the next available appointment, bilingual service offerings, ages served, region of practice and types of care available.

Results

A total of 556 practices in Arizona were evaluated through simulations as patients on AHCCCS, and 510 practices were assessed through simulations as patients with commercial health insurance plans. The average wait time for the next available appointment was 13 days for both insurance types. Alarmingly, up to 74% of vision care practices in Arizona do not serve children covered by AHCCCS. Furthermore, only 41% provide services to children 5 years and younger.

Conclusions

Our findings underscore the need to improve access to vision care services for children in Arizona, especially racial/ethnic minorities, low-income groups, and rural residents.

Decision fusion in healthcare and medicine: a narrative review

OBJECTIVE

To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels.

BACKGROUND

The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.

METHODS

We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.

CONCLUSIONS

Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Consumer Perspectives on Maternal and Infant Health Apps: Qualitative Content Analysis

BACKGROUND

Despite the popularity of maternal and infant health mobile apps, ongoing consumer engagement and sustained app use remain barriers. Few studies have examined user experiences or perceived benefits of maternal and infant health app use from consumer perspectives.

OBJECTIVE

This study aims to assess users' self-reported experiences with maternal and infant health apps, perceived benefits, and general feedback by analyzing publicly available user reviews on two popular app stores-Apple App Store and Google Play Store.

METHODS

We conducted a qualitative assessment of publicly available user reviews (N=2422) sampled from 75 maternal and infant health apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. The reviews were coded and analyzed using a general inductive qualitative content analysis approach.

RESULTS

The three major themes included the following: app functionality, where users discussed app features and functions; technical aspects, where users talked about technology-based aspects of an app; and app content, where users specifically focused on the app content and the information it provides. The six minor themes included the following: patterns of use, where users highlighted the frequency and type of use; social support, where users talked about receiving social support from friends, family and community of other users; app cost, where users talked about the cost of an app within the context of being cost-effective or a potential waste of money; app comparisons, where users compared one app with others available in app stores; assistance in health care, where users specifically highlighted the role of an app in offering clinical assistance; and customer care support, where users specifically talked about their interaction with the app customer care support team.

CONCLUSIONS

Users generally tend to value apps that are of low cost and preferably free, with high-quality content, superior features, enhanced technical aspects, and user-friendly interfaces. Users also find app developer responsiveness to be integral, as it offers them an opportunity to engage in the app development and delivery process. These findings may be beneficial for app developers in designing better apps, as no best practice guidelines currently exist for the app environment.

Content analysis of behavior change techniques in maternal and infant health apps

Maternal and infant health (MIH) mobile applications (apps) are increasingly popular and frequently used for health education and decision making. Interventions grounded in theory-based behavior change techniques (BCTs) are shown to be effective in promoting healthy behavior changes. MIH apps have the potential to be useful tools, yet the extent to which they incorporate BCTs is still unknown. The objective of this study was to assess the presence of BCTs in popular MIH apps available in the Apple App and Google Play stores. Twenty-nine popular MIH apps were coded for the presence of 16 BCTs using the mHealth app taxonomy. Popular MIH apps whose purpose was to provide health education or decision-making support to pregnant women or parents/caregivers of infants were included in the final sample. On an average, the reviewed apps included seven BCTs (range 2-16). Techniques such as personalization, review of general or specific goals, macro tailoring, self-monitoring of goals, and health behavior linkages were most frequently present. No differences in the presence of BCTs between paid and free apps were observed. Popular MIH apps typically included only a minority of BCTs found to be useful for health promotion. However, apps developed by healthcare developers incorporated a higher number of BCTs within the app content. Therefore, app developers and policymakers may consider strategies to increase health expert involvement in app design and content delivery.

Advantages and Challenges of Information Fusion Technique for Big Data Analysis: Proposed Framework

Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique. Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines. Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval. Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique.

Innovations in U.S. Health Care Delivery to Reduce Disparities in Maternal Mortality Among African American and American Indian/Alaskan Native Women

Despite spending more on health care than any other country, the United States has the worst maternal mortality rate among all developed nations. African American and American Indian/Alaskan Native women have the worst outcomes by race, representing a stark health disparity within the country. Contributing factors disproportionately experienced by these minority populations include challenges of access to consistent and high-quality prenatal care, prevalence of underlying conditions, toxic stress due to systemic racism, and unconscious bias in health care. While many of these factors lie upstream in the lives of women, and seemingly beyond the scope of the clinical walls, the downstream health care delivery system can serve as a vital part of the solution via innovative practices, community-based collaborations, and by serving as advocates for the communities served. Such alignments between clinicians, community leaders, policymakers, and patients that extend beyond the health system can serve as the missing piece needed within the clinic to reverse the trajectory of maternal mortality for American women, especially those from traditionally underserved populations.

Factors Related to User Ratings and User Downloads of Mobile Apps for Maternal and Infant Health: Cross-Sectional Study

BACKGROUND

Mobile health apps related to maternal and infant health (MIH) are prevalent and frequently used. Some of these apps are extremely popular and have been downloaded over 5 million times. However, the understanding of user behavior and user adoption of these apps based on consumer preferences for different app features and categories is limited.

OBJECTIVE

This study aimed to examine the relationship between MIH app characteristics and users' perceived satisfaction and intent to use.

METHODS

The associations between app characteristics, ratings, and downloads were assessed in a sample of MIH apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. Multivariable linear regression was used to assess the relationship between app characteristics and user ratings, and ordinal logistic regression was used to assess the relationship between app characteristics and user downloads.

RESULTS

The analyses of user ratings and downloads included 421 and 213 apps, respectively. The average user rating was 3.79 out of 5. Compared with the Apple App Store, the Google Play Store was associated with high user ratings (beta=.33; P=.005). Apps with higher standardized user ratings (beta=.80; P<.001), in-app purchases (beta=1.12; P=.002), and in-app advertisements (beta=.64; P=.02) were more frequently downloaded. Having a health care organization developer as part of the development team was neither associated with user ratings (beta=-.20; P=.06) nor downloads (beta=-.14; P=.63).

CONCLUSIONS

A majority of MIH apps are developed by non-health care organizations, which could raise concern about the accuracy and trustworthiness of in-app information. These findings could benefit app developers in designing better apps and could help inform marketing and development strategies. Further work is needed to evaluate the clinical accuracy of information provided within the apps.