Pahlevanynejad Shahrbanoo, Danaei Navid, Safdari Reza
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Department of Health Information Technology, Sorkheh School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran.
Digit Health. 2022 Oct 11;8:20552076221127776. doi: 10.1177/20552076221127776. eCollection 2022 Jan-Dec.
Low birth weight is the most important condition of neonatal community health and the main cause of neonates' mortality. Identifying the indexes associated with this condition, and factors to prevent, and managing related data can help reduce the birth of premature infants to reduce the mortality rate due to this condition. The goal of present study was to design, implement and evaluate an innovative intelligence information management system for premature infants.
The present study was a multidisciplinary research that was done in 2019 to 2021 in four integrated phases in Iran. The first phase aimed to compare the current status of registration systems of premature infants through a systematic review and semi-structured interviews by using the Delphi model Then the minimum data set was determined and was designed a proposed model based on it. In the second phase, the structure and how the user interacts with the system were determined, and, using Microsoft Visio software, Unified Modeling Language diagrams were drawn to define the logical relationship of data. In the third phase, the system was developed, and finally in the last phase, in three methods, users' views on the usability of the system were evaluated.
The findings of this study included 233 essential data elements that were placed in two main groups of essential data, and the system was approved by end users for 87.73% consent and 67.19% satisfaction for SUMI (Software Usability Measurement Inventory) and 7.97 of 9 in QUIS questionnaire.
This research's results can be beneficial and functional such as a complete sample for design and development of other systems concerned to health systems.
低出生体重是新生儿群体健康的最重要状况，也是新生儿死亡的主要原因。识别与此状况相关的指标、预防因素并管理相关数据，有助于减少早产儿的出生，降低因该状况导致的死亡率。本研究的目的是设计、实施和评估一种针对早产儿的创新智能信息管理系统。
本研究是一项多学科研究，于2019年至2021年在伊朗分四个综合阶段进行。第一阶段旨在通过系统评价和半结构化访谈，采用德尔菲模型比较早产儿登记系统的现状，然后确定最小数据集，并据此设计一个提议模型。第二阶段，确定系统结构以及用户与系统的交互方式，并使用Microsoft Visio软件绘制统一建模语言图来定义数据的逻辑关系。第三阶段，开发系统，最后在最后阶段，通过三种方法评估用户对系统可用性的看法。
本研究的结果包括233个基本数据元素，这些元素被归入两个基本数据的主要组中，该系统获得了最终用户的认可，在软件可用性测量量表（SUMI）上的同意率为87.73%，满意度为67.19%，在QUIS问卷中得分为9分中的7.97分。
本研究的结果可能是有益且实用的，例如为健康系统相关的其他系统的设计和开发提供一个完整的样本。
