From subconscious to conscious to artificial intelligence: A focus on electronic health records

Effect Evaluation of Artificial Intelligence-Based Electronic Health PDCA Nursing Model in the Treatment of Mycoplasma Pneumonia in Children

The PDCA cycle, also known as Deming's cycle, mainly includes four stages: planning, implementation, inspection, and processing. As a kind of atypical pneumonia with fever and cough, mycoplasma pneumonia harms the health of many children. The purpose of this study is to investigate the anti-inflammatory and antimycoplasma effects and safety of artificial intelligence e-health PDCA nursing mode on pediatric MPP, to investigate its clinical efficacy, to observe the changes of serum cytokines (IL-10, IL-2, IL-4, IFN-γ), and to explore the mechanism of action and possible targets for the treatment of MPP, to provide a new basis for clinical treatment of MPP. The experimental results show that in the experimental group using PDCA nursing mode, the total satisfaction is 97.22%, higher than the control group of 94.44%; in the experimental group, the hospital stay and symptom disappearance time were significantly shortened by four hours. The satisfaction of nursing staff was significantly increased in statistical significance (P < 0.05). Therefore, in a statistical sense, the artificial intelligence e-health PDCA nursing mode can significantly improve the clinical symptoms of MPP children with wind-heat stagnation of lung syndrome and phlegm-heat closure of lung syndrome, improve the treatment effect of childhood mycoplasma pneumonia epidemic, shorten the time of hospitalization and symptom disappeared, and play a great auxiliary role in the treatment of childhood mycoplasma pneumonia.

Data Equity to Advance Health and Health Equity in Low- and Middle-Income Countries: A Scoping Review

Objective

To assess a common hypothesis that data serve as a mechanism to improve health and health equity in low-and middle-income countries (LMICs), we conducted a synthesis of the evidence about the linkage between data capabilities in LMICs and health outcomes.

Methods

We searched and reviewed peer-reviewed and grey literature published in the past decade that focused on at least one aspect of health data or health equity or provided insights on the relationship between data use and improved health outcomes, decision-making, or both. We supplemented this with expert interviews and convenience-sampled literature.

Results

Of the 50 included articles, 33 discussed data collection, with 23 stating that poor accuracy, reliability, and completeness hindered data-informed decision-making. Of 27 articles discussing data access, 18 described how lack of interoperability between data systems hampered governments’ and other organizations’ ability to leverage the full value of data available. Of 19 articles discussing data use, 13 discussed how data were not getting to those doing work on the ground. Although key informants postulated a virtuous cycle between data and improved health outcomes, evidence did not support this connection.

Conclusions

Findings indicate better data might improve health service delivery. However, more work is needed to examine whether improvements in data yield improvements in health outcomes in LMICs. Our conceptual framework of data equity for health and health equity developed through this scoping review helps identify the key components along which to assess improvements in LMICs’ data capabilities.