A Systematic Review of Network Studies Based on Administrative Health Data

Gender Communication Differences on a Pediatric Provider Listserv

BACKGROUND AND OBJECTIVES

Gender-based communication differences are described in educational online communities, but have not been rigorously evaluated in medical online communities. Understanding gender differences in communication may provide insight into gender disparities in the medical profession. Our objective was to describe gender differences in post frequency, content, and language styles on the American Academy of Pediatrics Section on Hospital Medicine (SOHM) listserv.

METHODS

Posts were obtained from publicly available SOHM listserv archives. The first month of every quarter of 2019 and 2020 were reviewed. Two reviewers assigned a post topic (clinical, research, etc) and format (question vs statement) to all deidentified original posts (K = 1.0 topic, 0.89 format). Six trained reviewers assigned language styles (intraclass coefficient = 0.73, indicating good agreement).

RESULTS

We analyzed 1592 posts: 287 original posts and 1305 responses. Frequency: Women authored 50% of posts. The 9 most frequent posters (7 men, 2 women) accounted for 19.5% of posts. Content: Men's posts had more words than women's (132.51 vs 112.3, P ≤ .01). Men were more likely to post about health policy and research (P < .001). Men were more likely to post statements compared with women (39% vs 21%, P < .001). Style: Men's posts were more likely to be coded adversarial (12.3% vs 5.5%, P < .001) authoritative (12.2% vs 6.5%, P < .001) or self-amplifying (6.5% vs 3.6%, P < .001).

CONCLUSIONS

Women contribute disproportionately fewer posts to the American Academy of Pediatrics SOHM listserv compared with their percentage in the subspecialty. We noted significant gender differences in language style and content, which may impact career development and online community inclusion.

Implications of interhospital patient transfers for emergency medical services transportation systems in the Netherlands: a retrospective study

OBJECTIVES

Interhospital patient transfers have become routine. Known drivers are access to specialty care and non-clinical reasons, such as limited capacity. While emergency medical services (EMS) providers act as main patient transfer operators, the impact of interhospital transfers on EMS service demand and fleet management remains understudied. This study aims to identify patterns in regional interhospital patient transfer volumes and their spatial distribution, and to discuss their potential implications for EMS service demand and fleet management.

DESIGN

A retrospective study was performed analysing EMS transport data from the province of Drenthe in the Netherlands between 2013 and 2019 and public hospital listings. Yearly volume changes in urgent and planned interhospital transfers were quantified. Further network analysis, including geomapping, was used to study how transfer volumes and their spatial distribution relate to hospital specialisation, and servicing multihospital systems. Organisational data were considered for relating transfer patterns to fleet changes.

SETTING

EMS in the province of Drenthe, the Netherlands, 492 167 inhabitants.

PARTICIPANTS

Analyses are based on routinely collected patient data from EMS records, entailing all 248 114 transports (137 168 patients) of the Drenthe EMS provider (2013-2019). From these interhospital transports were selected (24 311 transports).

RESULTS

Interhospital transfers represented a considerable (9.8%) and increasing share of transports (from 8.6% in 2013 to 11.3% in 2019). Most transfers were related to multihospital systems (47.3%, 11 509 transports), resulting in a considerable growth of planned EMS transports (from 2093 in 2013 to 3511 in 2019). Geomapping suggests increasing transfer distances and diminishing resource efficiencies due to lacking follow-up rides. Organisational data clarify how EMS fleets were adjusted by expanding resources and reorganising fleet operation.

CONCLUSIONS

Emerging interhospital network transfers play an important role in EMS service demand. Increased interhospital transport volumes and geographical spread require a redesign of current EMS fleets and management along regional lines.

The impact of public leadership on collaborative administration and public health delivery

BACKGROUND

This research depicts the linkage of public leadership on public health delivery (PHD) and collaborative administration. The research is also focused to examine the effect of public leadership on public health delivery through the intervening variable of collaborative administration by using both social information processing theory and collaboration theory.

METHODS

This research is based on quantitative method. Data was collected from 464 public hospital administration in the context of Pakistan. This study evaluated data using SPSS, AMOS, and PROCESS Macro.

RESULTS

Public leadership has a positive profound effect on public health delivery and collaborative administration, and that collaborative administration significantly promotes public health delivery. The outcomes also exposed that public leadership has substantial influence on public health delivery through intervening collaborative administration.

CONCLUSIONS

Whilst public leadership demonstrated positive outcomes on public health delivery and collaborative administration, there is a need for more rigor studies on collaborative governance leadership, collaborative ethics and collaborative norms in the public health service.

The Effect of Interdependences of Referral Behaviors on the Quality of Ambulatory Care: Evidence from Taiwan

Purpose

The purpose of this study is to investigate the effect of interdependences of healthcare providers’ referral behaviors on the quality of ambulatory care. The significance of this study is to address the concern regarding the low quality of ambulatory care due to the lack of a compulsory referral system under Taiwan’s National Health Insurance system.

Methods

We applied the dynamic connectedness network analysis to estimate the total connectedness index of the referral behavior network, which was separated into the horizontal and vertical referral behavior components in order to measure the interdependences of horizontal and vertical referral behaviors across hospitals and local clinics, respectively.

Results

Our results suggest that the interdependences of referral behaviors increase the quality of ambulatory care. The harmful effect on the quality of ambulatory care from the interdependences of horizontal referral behaviors within the local clinics sector is more significant than that from the interdependences of horizontal referral behaviors within the hospital sector, and the negative effect on the overall and chronic composite measures of avoidable hospital admissions from the interdependences of vertical behaviors associated with local clinics is more substantial than that from the interdependences of vertical behaviors within the hospital sector.

Conclusion

These results not only highlight the significance of care collaboration between local clinics and hospitals to restrain avoidable hospital admissions of chronic diseases for a better overall quality of ambulatory care, but they also suggest that the surveillance system established for the quality of ambulatory care under the global budget payment scheme for the local clinics sector should target ambulatory care for patients with acute conditions.

A weighted patient network-based framework for predicting chronic diseases using graph neural networks

Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.