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Design, implementation, and evaluation of an innovative intelligence information management system for premature
Shahrbanoo Pahlevanynejad1,2, Navid Danaei3, Reza Safdari1
1Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Insights
This study developed an innovative information management system to improve care for premature infants. The system, designed with user input, aims to reduce low birth weight and associated neonatal mortality rates.
Area of Science:
- Neonatal Health
- Health Informatics
- System Development
Background:
- Low birth weight is a critical factor in neonatal mortality.
- Effective data management is crucial for preventing premature births and reducing mortality.
- Existing registration systems for premature infants require improvement.
Purpose of the Study:
- To design, implement, and evaluate an innovative intelligence information management system for premature infants.
- To identify key data elements and factors for preventing low birth weight.
- To enhance the management of data related to premature infants.
Main Methods:
- A multidisciplinary, four-phase research approach was employed from 2019 to 2021 in Iran.
- Phase 1 involved a systematic review, Delphi model, and semi-structured interviews to define a minimum data set.
- Phases 2 and 3 focused on system design using UML diagrams and subsequent development, followed by usability evaluation in Phase 4.
Main Results:
- Identified 233 essential data elements for the system.
- Achieved 87.73% end-user consent and 67.19% satisfaction via the Software Usability Measurement Inventory (SUMI).
- Received a high score of 7.97 out of 9 on the QUIS questionnaire for usability.
Conclusions:
- The developed system demonstrates high usability and user acceptance.
- This innovative system can serve as a model for designing and developing other health information systems.
- The findings contribute to improving the management of premature infant data and potentially reducing mortality.
Introduction:
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.
Material And Method:
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.
Results:
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.
Conclusion:
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.
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