Electronic Health Records: Benefits and Challenges for Data Quality

Saiod, Abdul Kader; Greunen, Darelle van; Veldsman, Alida

doi:10.1007/978-3-319-58280-1_6

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…The results obtained in modeling depend largely on the quality of the data [30] . The first task of our ACODAT is to detect and correct possible errors to perform the next tasks in the best way.…”

Section: Methodsmentioning

confidence: 99%

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An autonomous cycle of data analysis tasks for the clinical management of dengue

Hoyos

Aguilar²,

Toro³

2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

“…In general, health data have some very common particularities, such as low quality [30] . In the case of dengue data, there are many errors for different reasons.…”

Section: Methodsmentioning

confidence: 99%

An autonomous cycle of data analysis tasks for the clinical management of dengue

Hoyos

Aguilar²,

Toro³

2022

Heliyon

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“…Evans (2016) states that, As EHRs evolved, they became a digital tool containing the information collected from all the clinicians involved in a patient's care and could be potentially accessed by all authorized clinicians to provide care to that patient, going beyond standard clinical data collected in clinical settings. And according to Saiod et al (2017), EHRs are real-time, patient-centred records that make information available instantly and securely to authorised users. While an EHR does contain the medical and treatment histories of patients, an EHR system is built to go beyond standard clinical data collected in a provider's office and can include a broader view of a patient's care.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Effect of Health Records Digitalization on Healthcare Facility Operational Efficiency

Ng’andu,

Haabazoka

2024

OJBM

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Historically, public health spending has consistently accounted for a substantial portion of government expenditure. In line with the 2022 national budget allocation analysis conducted by the Policy Monitoring and Research Centre, it was revealed that over 13 billion kwacha had been allocated to health services. As society continued to age and exerted a growing strain on healthcare systems, healthcare stakeholders actively sought solutions to address pressing concerns, including cost management, data quality, and overall efficiency. The study hypothesized that digitizing medical health records would improve overall health care facility operational efficiency by improving access to patient information, streamlining workflows, reducing documentation errors, and improving communication among healthcare stakeholders. Furthermore, the study aimed to identify potential implementation challenges and barriers, providing valuable insights for healthcare institutions looking to adopt or optimize their EHR systems. The research design used a survey-based approach with a sample size of 150 respondents from ten public health care facilities in Lusaka using an EHR such as SmartCare or paper-based records. The respondents included healthcare professionals, health facility administrators, and data entry clerk personnel. The primary data collection instrument used was a structured questionnaire that targeted key facility operational efficiency indicators such as time efficiency, workflow optimization, documentation accuracy, information accessibility and cost efficiency. The collected data was analyzed using SPSS and Microsoft Excel software. Descriptive statistics were used to summarize and interpret survey responses, while inferential statistics were used to establish relationships and correlations between variables. The study's findings were presented in tables, graphs, and charts, allowing for a thorough understanding of the findings. The results collected and analyzed highlight the significance of addressing these issues to guarantee the success-

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“…The implementation of EHR systems can result in improved patient safety by reducing medical errors in hospitals. The overarching challenges of the eHealth in CC is to develop a system which provides a single, centralised and homogeneous interface for users to efficiently integrate data from diverse heterogeneous sources [22,23]. The EHRs flow process has several factors that influence the Data Quality (DQ) obtained from such data at the later stage.…”

Section: Challenges Of Cloud Integration In Ehealth Datamentioning

confidence: 99%

Cloud Integration for eHealth Data

Saiod

Greunen

Kalpa Publications in Computing

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There is the new promise of Cloud Integration (CI) for outsourcing Electronic Health (eHealth) data, computation and processes that provide real-time data exchange of various application systems and repositories. CI technology combines eHealth data in cloud environments, which are operated by multiple devices over the network and Internet. In addition, many Healthcare Organisations (HCOs) are migrating their systems to a cloud platform to reduce their business cost. Although CI provides attractive and cost-effective opportunities, it is essential to remember that Cloud Computing (CC) paradigms are still under development and not mature. One of the main challenges in CI is that all data management, security, availability, maintenance and domain control is done via a third-party service provider. Therefore, HCOs have no control over these matters. Trust of a third-party service provider is one of the essential factors for CI and this adds a new dimension to the opportunity. As a result of the scalability of applications and the lower costs, CI is viewed as the preferred infrastructure for most eHealth systems, but without comprising the privacy of data. In this paper, we present a novel CI approach using the Hybrid Integration Method (HIM) combined with the promising features of Fuzzy-Ontology. The aim is to strengthen the management of eHealth business and IT environments. The key outcome of this paper is to present a new technique to merge data and to identify conflicting data entries. A series of experiments were performed to prove that the proposed HIM is effective and accurate for eHealth data integration.

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Electronic Health Records: Benefits and Challenges for Data Quality

Cited by 7 publications

References 29 publications

An autonomous cycle of data analysis tasks for the clinical management of dengue

An autonomous cycle of data analysis tasks for the clinical management of dengue

A Study of the Effect of Health Records Digitalization on Healthcare Facility Operational Efficiency

Cloud Integration for eHealth Data

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