Primary health information standard system based on semantic interoperability

Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management

In order to study the clustering algorithm based on density grid, the performance evaluation index system of hospital economic management under the application of electronic health management system is constructed. Firstly, this work designs the basic architecture of electronic health management system, classifies and screens the process of index system of electronic health management system, compares the clustering algorithm based on density grid with the simple clustering algorithm based on density or grid, and then applies it to the performance evaluation index system of hospital economic management. According to the principle of Mitchell scoring method, the expert questionnaire of hospital economic management performance evaluation index system was designed, and Delphi method was used to evaluate the candidate indexes from the three dimensions of right, legitimacy, and urgency. The results show that, compared with simple network clustering algorithm and density clustering algorithm, the clustering algorithm based on density network produces higher purity (94% VS 73% VS 67%) and lower entropy (0.9 VS 1.4 VS 1.54), which effectively saves memory consumption, and the difference is statistically significant (P < 0.05). The core indicators with scores above 4.5 in both dimensions include budget revenue implementation rate, budget expenditure implementation rate, implementation rate of special financial appropriation, asset-liability ratio, hospitalization income cost rate, medical insurance settlement rate, average cost of discharged patients, and drug proportion. The coefficient of variation of the first grade index is between 0.05 and 0.14 and that of the second grade index is between 0.05 and 0.15. Clustering algorithm based on density network has higher purity and lower entropy, which can effectively save memory consumption. The performance evaluation index system of hospital economic management finally determines 6 first-level indexes: budget management, financial fund management, cost management, medical expense management, medical efficiency, medical quality, and 25 second-level indexes.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic

BACKGROUND

Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system.

MATERIALS AND METHODS

First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats.

RESULTS

A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined.

CONCLUSION

The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.

Qualitative and Quantitative Analysis of Definitions of e-Health and m-Health

Objectives

Skills to employ nursing informatics to promote the health of individuals is of such importance that it is considered a core competence. Although investments are made to increase the use of e-health, there is no full understanding of the usability of e-health for healthcare. This paper presents a current picture of how e-health and m-health are defined and used as well as the effects their usage may have on the intended target group.

Methods

Peer-reviewed open-access papers and grey literature that define e-health and m-health from PubMed, SpringerLink, and Google.com were randomized. A mixed method design with an inductive approach was employed. Open-source software were used for analysis.

Results

The overview includes 30 definitions of e-health and m-health, respectively. The definitions were thematised into 14 narrative themes. The results of the study, and primarily a three-level model, provide an understanding of how different types of e-health and m-health can be put into practice, and the effects or consequences of using them, which may be either positive or negative.

Conclusions

Mobility and flexibility is important for both m-health and e-health. Five keywords that characterize the definitions of e-health and m-health are "health", "mobile", "use", "information", and "technology". E-health or m-health cannot replace human actors because e-health and m-health consist of social and material interactions. Using e-health and m-health is, thus, about developing healthcare without compromising native relics.

Correction to: Primary health information standard system based on semantic interoperability

After publication of this supplement article [1], it was brought to our attention that the first and correspondence authors' affiliation information was incorrectly spelt. The original spelling was written as 'Sothern'. However, the correct spelling should be 'Southern'.