Using cloud-based, open-source technology to evaluate, improve, and rapidly disseminate community-based intervention data

H.O.P.E. grows: An academic-public health partnership to reimagine public health services and increase mental health access among socially vulnerable populations

OBJECTIVE

To illustrate the process of developing and sustaining an academic-public health partnership for behavioral health integration through an expansion of the Aligning Systems for Health (ASfH) framework.

STUDY SETTING

Practice-informed primary data (2017-2023) from the Holistic Opportunity Program for Everyone (HOPE) Initiative based in Charlotte, NC.

STUDY DESIGN

The unit of analysis in this descriptive case study is inter-organizational, specifically focusing on an academic-public health relationship. We illustrate the partnership process across the ASfH four core areas, including key challenges and insights.

DATA COLLECTION

Utilized a Critical Moments Reflection methodology and review of HOPE program data.

PRINCIPAL FINDINGS

(1) Formal partnership structures and processes are essential to monitoring the four ASfH core components for on-going system alignment. (2) Aligning systems for health principally involves two ecologies: (i) the health program and (ii) the partnership. The vitality and sustainability of both ecologies require continuous attention and resource investment. (3) Relationships rest at the heart of aligning systems. (4) With comparative advantages in research methods, the academic sector is especially poised to collaborate with healthcare systems and human service organizations to study, develop, implement, and scale evidence-based health interventions.

CONCLUSIONS

The academic sector shares overlapping purposes with the public health, healthcare, and social services sectors while providing complementary value. It is a critical sectoral partner in advancing population health and health equity.

Data analytics in a clinical setting: Applications to understanding breathing patterns and their relevance to neonatal disease

In this review, we focus on the use of contemporary linear and non-linear data analytics as well as machine learning/artificial intelligence algorithms to inform treatment of pediatric patients. We specifically focus on methods used to quantify changes in breathing that can lead to increased risk for apnea of prematurity, retinopathy of prematurity (ROP), necrotizing enterocolitis (NEC) and provide a list of potentially useful algorithms that comprise a suite of software tools to enhance prediction of outcome. Next, we provide a brief overview of machine learning/artificial intelligence methods and applications within the sphere of perinatal care. Finally, we provide an overview of the infrastructure needed to use these tools in a clinical setting for real-time data acquisition, data synchrony, data storage and access, and bedside data visualization to assist in clinical decision making and support the medical informatics mission. Our goal is to provide an overview and inspire other investigators to adopt these tools for their own research and optimization of perinatal patient care.

Security challenges and solutions using healthcare cloud computing

Cloud computing is among the most beneficial solutions to digital problems. Security is one of the focal issues in cloud computing technology, and this study aims at investigating security issues of cloud computing and their probable solutions. A systematic review was performed using Scopus, Pubmed, Science Direct, and Web of Science databases. Once the title and abstract were evaluated, the quality of studies was assessed in order to choose the most relevant according to exclusion and inclusion criteria. Then, the full texts of studies selected were read thoroughly to extract the necessary results. According to the review, data security, availability, and integrity, as well as information confidentiality and network security, were the major challenges in cloud security. Further, data encryption, authentication, and classification, besides application programming interfaces (API), were security solutions to cloud infrastructure. Data encryption could be applied to store and retrieve data from the cloud in order to provide secure communication. Besides, several central challenges, which make the cloud security engineering process problematic, have been considered in this study.