The Impact of Big Data on Chronic Disease Management

Predictive analytics and early intervention in healthcare social work: a scoping review

This scoping review investigates the untapped potential of predictive analytics in healthcare social work, specifically targeting early intervention frameworks. Despite the escalating attention predictive analytics has garnered across multiple disciplines, its tailored application in social work remains notably sparse. This study endeavors to fill this lacuna by meticulously reviewing the extant literature and delineating the prospective advantages and inherent constraints of integrating predictive analytics into healthcare social work. The outcomes of this inquiry enrich the prevailing dialogue on the utility of predictive analytics in healthcare, offering indispensable perspectives for practitioners and policymakers in the social work domain.

Exploring perceived challenges of self-management in low-income older people with hypertension: A qualitative study

BACKGROUND

Hypertension is a public health problem globally. Understanding the perceived challenges of low-income older people populations with chronic disease is an obstacle the world is facing today.

AIM

To explore perceived challenges of self-management in low-income older people with hypertension.

METHODS

Data were collected in three communities from September 2019 to October 2019 by semi-structured interviews. Interviews were audio-taped by digital voice recorder and analysed according to Colaizzi's seven steps.

RESULTS

Participants demonstrated perceived challenges concerning hypertension self-management. Six themes were identified: hypertension belief bias, family dysfunction, deep-rooted habit, elder self-neglect, medical informatization and supportive health policy. Each theme was identified with several subthemes.

CONCLUSIONS

Findings implied that most of the low-income older people lacked self-management behaviours. Future research is needed to address perceived challenges related to self-management behaviour for patients with hypertension worldwide.

Big data as a value generator in decision support systems: a literature review

Purpose

This paper aims to analyze how decision support systems manage Big data to obtain value.

Design/methodology/approach

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

Findings

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

Originality/value

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

A call for better understanding of social media in surveillance and management of noncommunicable diseases

Using social media for health purposes has attracted much attention over the past decade. Given the challenges of population ageing and changes in national health profile and disease patterns following the epidemiologic transition, researchers and policy-makers should pay attention to the potential of social media in chronic disease surveillance, management and support. This commentary overviews the evidence base for this inquiry and outlines the key challenges to research laying ahead. The authors provide concrete suggestions and recommendations for developing a research agenda to guide future investigation and action on this topic.

Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach

BACKGROUND

The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data.

OBJECTIVE

This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses.

METHODS

The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute's Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate.

RESULTS

The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases.

CONCLUSIONS

The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.

Information and communication technology for increasing healthy ageing in people with non-communicable diseases: identifying challenges and further areas for development

Information and communication technology (ICT) within healthcare covers a range of technologies that aim to improve disease management or help modify health behaviors. We discuss clinical practice and system-related ICT challenges in Europe in relation to healthy ageing in people with non-communicable diseases (NCD). Although ICT use within healthcare is increasing, several challenges remain, including: (i) variations in ICT use within Europe; (ii) under-use of electronic health records; (iii) frequent use of single domain outcomes; (iv) shortage of clinical trials on current technologies; (v) lack of involvement of patients in ICT development; (vii) need to develop and adapt ICTs for people with cognitive or sensory impairment; and (viii) need to use longitudinal big data better. Close collaboration between key stakeholders (academia, biopharmaceutical and technology industries, healthcare, policy makers, patients, and caregivers) should foster both technological innovation and innovative models to facilitate more cost-effective approaches, ultimately leading to increased healthy ageing.

Representing Nursing Data With Fast Healthcare Interoperability Resources: Early Lessons Learned With a Use Case Scenario on Home-Based Pressure Ulcer Care

Healthcare communities are rapidly embracing Health Level 7's Fast Healthcare Interoperability Resources standard as the next-generation messaging protocol to facilitate data interoperability. Implementation-friendly formats for data representation and compliance to widely adopted industry standards are among the strengths of Fast Healthcare Interoperability Resources that are accelerating its wide adoption. Research confirms the advantages of Fast Healthcare Interoperability Resources in increasing data interoperability in mortality reporting, genetic test sharing, and patient-generated data. However, few studies have investigated the application of Fast Healthcare Interoperability Resources in nursing-specific domains. In this study, a Fast Healthcare Interoperability Resources document was generated for a use case scenario in a home-based, pressure ulcer care setting. Study goals were to describe the step-by-step process of generating a Fast Healthcare Interoperability Resources artifact and to inform nursing communities about the advantages and challenges in representing nursing data with Fast Healthcare Interoperability Resources. Overall, Fast Healthcare Interoperability Resources effectively represented the majority of the data included in the use case scenario. A few challenges that could potentially cause information loss were noted such as the lack of standardized concept codes for value encoding and the difficulty directly connecting an observation to a related condition. Continuous evaluations in diverse nursing domains are needed in order to gain a more thorough insight on potential challenges that Fast Healthcare Interoperability Resources holds in representing nursing data.

Research on the application of health management model based on the perspective of mobile health

The aim of the present study was to explore the application and its effect of mobile medical treatment to chronic disease health management in physical examination population, and to provide references for comprehensive intervention and management of chronic diseases.From January to December 2016, 300 medical examiners in a general hospital health management center were randomly divided into health management group (155 cases) and control group (145 cases). The control group completed routine physical examination and health-risk assessment and provided corresponding reports, repeated annual physical examination and health-risks assessment. In addition to the routine physical examination and health-risk assessment, the health management group reminded the examiners to pay attention to their lifestyle and dietary habits by moving online and offline dynamic health interventions and provide targeted guidance for high-risk population such as diabetes, obesity, hypertension, etc. A review was made after 2 years. The clinical indexes and chronic disease behavior of patients before and after management were compared, and the effect was evaluated by statistical analysis.After management, all the clinical indexes were significantly improved, and the patients' dietary structure, bad living habits, psychologic state, and other chronic disease behaviors were obviously improved. The proportion of patients with high risk of hypertension, diabetes, and obesity in health management group was significantly lower than that before intervention and control group (P < .05).Using mobile network online, offline dynamic health intervention model can reduce the risk of common chronic diseases in health management objects, this health management model of chronic disease is worth popularizing.

Retrospective Toxicological Profiling of Radium-223 Dichloride for the Treatment of Bone Metastases in Prostate Cancer Using Adverse Event Data

Background and Objective: Radium-223 dichloride (Xofigo^®) is a calcium mimetic agent approved for the treatment of castration-resistant prostate cancer patients with symptomatic bone metastases and no known visceral metastatic disease. This targeted, α-particle-emitting therapy has demonstrated significant survival benefit accompanied by a favorable safety profile. Nevertheless, recent evidence suggests that its combined use with abiraterone and prednisone/prednisolone may be associated with increased risk of death and fractures. While the precise pathophysiologic mechanisms of these events are not yet clear, collecting evidence from more clinical trials and translational studies is necessary. The aim of our present study is to assess whether accessible sources of patient outcome data can help gain additional clinical insights to radium-223 dichloride’s safety profile. Materials and Methods: We performed a retrospective analysis of cases extracted from the FDA Adverse Event Reporting System and characterized side effect occurrence by using reporting ratios. Results: A total of ~1500 prostate cancer patients treated with radium-223 dichloride was identified, and side effects reported with the use of radium-223 dichloride alone or in combination with other therapeutic agents were extracted. Our analysis demonstrates that radium-223 dichloride may often come with hematological-related reactions, and that, when administered together with other drugs, its safety profile may differ. Conclusions: While more prospective studies are needed to fully characterize the toxicological profile of radium-223 dichloride, the present work constitutes perhaps the first effort to examine its safety when administered alone and in combination with other agents based on computational evidence from public real-world post marketing data.