Secondary use of hospital information system data for safe bedside radiography in terms of patient factors

Sasaki, Makoto; Uto, Yoshihiro; Yoshida, Takuma; Iwaanakuchi, Takashi; Muranaga, Fuminori; Saigo, Yasumasa; Kumamoto, Ichiro

doi:10.1177/1833358317749162

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…These included a shortage of registers; incompetence in staff members’ handling of data; omission of some indicators in DHIS2 and frequent changes in some indicators and forms in DHIS2. The literature has suggested that these challenges are not new in Africa (Ojo, 2018; Sasaki et al, 2018; Smith et al, 2007) and may be linked to the fact that development of HIS in Africa has encountered serious problems due to substantial disparities in access to resources, including human resources. These resource insufficiencies in the health sector, coupled with the burden of disease, poor indicators and frail and unsuitable health systems are exacerbated by extreme poverty (Ssekitooleko, 2017).…”

Section: Discussionmentioning

confidence: 99%

The need for health information management professionals in Malawi health facilities

Chima¹,

Mkwinda²,

Kumwenda³

2023

HIM J

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Background: Health information management (HIM) is at the core of health organisations, providing essential information. In Malawi, there is a substantial deficit of qualified personnel, specifically health information managers, who can properly manage health information in electronic and paper-based formats. The nation has no higher education institution offering an academic programme in HIM. Objective: To investigate the need for HIM professionals in Malawi government health facilities, to determine the kinds of data managed by data users; competencies of HIM workers and challenges associated with the current HIM system. Method: A cross-sectional research design was adopted, with a qualitative approach to gather data from data users and key informants, using two focused interview guides. Data were collected from 13 participants from 6 government health facilities representing the primary, secondary and tertiary healthcare delivery levels. Data were analysed thematically. Results: Data users handled a diverse range of data, the majority having moderate skills in HIM. Both data users and key informants reported experiencing challenges in dealing with the existing HIM system. Findings also revealed key challenges associated with the absence, or inadequacy, of a well-trained HIM professional workforce in Malawi health facilities. Conclusion: Introducing a training programme in HIM would improve data management in health facilities in Malawi. Well-managed data would improve the delivery of health care services.

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Section: Discussionmentioning

confidence: 99%

The need for health information management professionals in Malawi health facilities

Chima¹,

Mkwinda²,

Kumwenda³

2023

HIM J

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“…Among medical professionals, nurses have the closest daily contact with patients, and are most likely to notice and record changes in the conditions of patient. Nurses carefully monitor patients, maintain records on their current conditions, and predict possible future changes in their conditions (Imai & Takase, 2017; Sasaki et al, 2019). Their records are a valuable source of information to other medical staff.…”

Section: Nursing Observation Recordsmentioning

confidence: 99%

Supporting the Early Detection of Disease Onset and Change Using Document Vector Analysis of Nursing Observation Records

et al. 2021

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Nursing records are an account of patient condition and treatment during their hospital stay. In this study, we developed a system that can automatically analyze nursing records to predict the occurrence of diseases and incidents (e.g., falls). Text vectorization was performed for nursing records and compared with past case data on aspiration pneumonia, to develop an onset prediction system. Nursing records for a patient group that developed aspiration pneumonia during hospitalization and a non-onset control group were randomly assigned to definitive diagnostic (for learning), preliminary survey, and test datasets. Data from the preliminary survey were used to adjust parameters and influencing factors. The final verification used the test data and revealed the highest compatibility to predict the onset of aspiration pneumonia (sensitivity = 90.9%, specificity = 60.3%) with the parameter values of size = 80 (number of dimensions of the sentence vector), window = 13 (number of words before and after the learned word), and min_count = 2 (threshold of wordcount for word to be included). This method represents the foundation for a discovery/warning system using machine-based automated monitoring to predict the onset of diseases and prevent adverse incidents such as falls.

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Secondary use of hospital information system data for safe bedside radiography in terms of patient factors

Cited by 2 publications

References 22 publications

The need for health information management professionals in Malawi health facilities

The need for health information management professionals in Malawi health facilities

Supporting the Early Detection of Disease Onset and Change Using Document Vector Analysis of Nursing Observation Records

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