Digital Health Data Quality Issues: Systematic Review.

BACKGROUND: The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. OBJECTIVE: The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. RESULTS: The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. CONCLUSIONS: The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Correlative Study of Bone-related Biochemicals and Endocrine Changes in Perimenopausal Women in Enugu, Nigeria.

BACKGROUND AND OBJECTIVE: Perimenopause is a physiological occurrence in women, and is characterized by endocrine and biochemical changes. During perimenopause phase, many derangements or abnormal health conditions start developing as a result of hormonal changes. These derangements in health conditions and biochemical changes lead to higher incidence of metabolic syndrome (MetS) occurrence with or without bone involvement. There is a scarcity of information on MetS in Enugu, Southern Nigeria and there is no available data on the correlation of selected bone-related biochemicals with endocrine parameters and MetS in perimenopausal women from the region. MATERIAL AND METHODS: We consecutively sampled 200 apparently healthy women, and categorized them into 120 perimenopausal women (age (!) = 50years) and a second group of 80 women in premenopause (age (!) = 35years). Measurement of anthropometric indices like blood pressure, height, weight and waist circumference were taken. Fasting blood samples were collected for the estimation of endocrine parameters (estradiol (E2), follicle stimulating hormone (FSH), and luteinizing hormone (LH)) using enzyme linked immunosorbent assay (ELISA) technique. The lipid profile, fasting plasma glucose (FPG), uric acid, inorganic phosphate, calcium and alkaline phosphatase levels were determined using standard biochemical methods. The evaluation of MetS was carried out in the women using the three different criteria: World Health Organization (WHO), National Cholesterol Education Program- Adult Treatment Panel 111 (NCEP-ATP 111) and International Diabetes Federation (IDF). For statistical analysis, Student's t-test, Pearson correlation and Chi-square were used to compare categorical and continuous variables. RESULTS: Calcium was predominantly high in the three criteria (p<0.05). LH and FSH showed a positive correlation with FPG while E2 was negatively associated with FPG. Similarly, LH showed a positive association with inorganic phosphate while E2 was negatively associated with alkaline phosphatase (p<0.05). CONCLUSION: Perimenopausal women are at higher risk of developing osteoporosis than premenopausal women. This emphasizes the need for timely diagnosis of osteoporosis in perimenopausal women.