Mobile Apps as Audience-Centered Health Communication Platforms

Toward Tailored and Targeted Communication for the Promotion of Firearm Safety: A Qualitative Study With Firearm Retailers

Firearm injury is a major yet understudied public health issue in the U.S. This qualitative study explored firearm retailers' perspectives to inform messaging and communication approaches to promote firearm safety among the gun owning population. Semi-structured interviews were conducted with 17 retailers at a single gun shop in Texas. Thematic analysis identified key themes related to (1) audience segmentation, (2) appropriate use of language, and (3) trusted messengers and modalities for the communication of firearm safety information. This formative work provides practical insights to optimize public health messaging in this arena and ultimately reduce firearm injuries. Overall, this study provides valuable insights to guide the development and implementation of evidence-based, social marketing efforts aiming to promote firearm safety across various gun-owning audiences.

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital

Background

Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission.

Methods

First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories.

Results

The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model.

Conclusions

Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

Systematic review on the cost and cost-effectiveness of mHealth interventions supporting women during pregnancy

OBJECTIVES

The increased integration of digital health into maternity care-alongside growing use of, and access to, personal digital technology among pregnant women-warrants an investigation of the cost-effectiveness of mHealth interventions used by women during pregnancy and the methodological quality of the cost-effectiveness studies.

METHODS

A systematic search was conducted to identify peer-reviewed studies published in the last ten years (2011-2021) reporting on the costs or cost-effectiveness of mHealth interventions used by women during pregnancy. Available data related to program costs, total incremental costs and incremental cost-effectiveness ratios (ICERs) were reported in 2020 United States Dollars. The quality of cost-effectiveness studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS).

FINDINGS

Nine articles reporting on eight studies met the inclusion criteria. Direct intervention costs ranged from $7.04 to $86 per woman, total program costs ranged from $241,341 to $331,136 and total incremental costs ranged from -$21.16 to $1.12 million per woman. The following ICERs were reported: $2168 per DALY averted, $203.44 per woman ceasing smoking, and $3475 per QALY gained. The full economic evaluation studies (n = 4) were moderate to high in quality and all reported the mHealth intervention as cost-effective. Other studies (n = 4) were low to moderate in quality and reported low costs or cost savings associated with the implementation of the mHealth intervention.

CONCLUSIONS FOR PRACTICE

Preliminary evidence suggests mHealth interventions may be cost-effective and "low-cost" but more evidence is needed to ascertain the cost-effectiveness of mHealth interventions regarding positive maternal and child health outcomes and longer-term health service utilisation.

Which communication technology is effective for promoting reproductive health? Television, radio, and mobile phones in sub-Saharan Africa

The use of radio and television as means to spread reproductive health awareness in Sub-Saharan Africa has been extensive, and its impacts significant. More recently, other means of communication, such as mobile phones, have received the attention of researchers and policy makers as health communication tools. However, evidence on which of the two types of communication (i.e. passive communication from TV/radio, or active communication through phones) is more effective in fostering better reproductive health choices is sparse. This study aims to identify the potential influence of TV or radio ownership as opposed to cell phone ownership on contraceptive use and access to maternal healthcare. Cross-sectional, individual analysis from eleven high-maternal mortality Sub-Saharan African countries is conducted. A total of 78,000 women in union are included in the analysis. Results indicate that ownership of TV or radio is more weakly correlated to better outcomes than mobile phone ownership is. Results are stronger for lower educated women and robust across all levels of wealth. Interestingly, the study also finds that decision-making power is a relevant mediator of cell phone ownership on contraceptive use, but not on maternal healthcare access. A key takeaway from the study is that, while the role of television and radio appears to have diminished in recent years, mobile phones have become a key tool for empowerment and behavioural change among Sub-Saharan African women. Health communication policies should be designed to take into account the now prominent role of mobile phones in affecting health behaviours.