Electronic health records post-implementation challenges in selected hospitals: A qualitative study in the Central Region of southern Ghana

Implementation of a Laboratory Information Management System (LIMS) for microbiology in Timor-Leste: challenges, mitigation strategies, and end-user experiences

BACKGROUND

Effective diagnostic capacity is crucial for clinical decision-making, with up to 70% of decisions in high-resource settings based on laboratory test results. However, in low- and middle-income countries (LMIC) access to diagnostic services is often limited due to the absence of Laboratory Information Management Systems (LIMS). LIMS streamline laboratory operations by automating sample handling, analysis, and reporting, leading to improved quality and faster results. Despite these benefits, sustainably implementing LIMS in LMIC is challenging due to high costs, inadequate infrastructure, and limited technical expertise.

METHODS

This study evaluated the implementation of a customised microbiology LIMS at the National Health Laboratory (NHL) in Timor-Leste. The LIMS was deployed in November 2020, with an accompanying online results portal introduced in early 2021. The implementation was assessed via a checklist based on key challenges and requirements for LIMS in LMIC, alongside a post-implementation survey of scientists and clinicians.

RESULTS

The assessment revealed significant improvements in laboratory processes, including enhanced sample throughput, data management, and result reporting. The LIMS reduced transcription errors and standardised reporting of antimicrobial susceptibility testing (AST), improving data quality and accessibility. However, challenges such as unreliable internet connectivity and the need for ongoing funding and technical support persist. The user satisfaction survey, with responses from 19 laboratory scientists and 15 clinicians, revealed positive feedback on workflow improvements and result accessibility, although concerns about internet speed, sustainability, and the need for further training were noted.

CONCLUSION

This study highlights the importance of careful planning, customisation, and stakeholder engagement in LIMS implementation in LMIC. The success in Timor-Leste demonstrates the potential for improved laboratory quality and patient outcomes, but also underscores the need for ongoing investment in infrastructure, technical expertise, and sustainability planning.

Effectiveness of EHR systems on decision support in primary healthcare: a technology acceptance model 3 perspective

PURPOSE

This study aims to evaluate healthcare practitioners' perceptions of electronic health record (EHR) systems and their effectiveness in supporting clinical decision-making in Tanzanian Primary Healthcare (PHC) facilities.

DESIGN/METHODOLOGY/APPROACH

A mixed-methods approach was employed, combining quantitative data from structured questionnaires and qualitative insights from open-ended responses. The study was conducted in the Dodoma region of Tanzania, focusing on a diverse representation of PHC facilities, including district hospitals, health centres and dispensaries. Data were analysed using multiple linear regression for quantitative data, and thematic analysis was applied to qualitative responses.

FINDINGS

The results revealed that while EHR systems are widely used in Tanzanian PHC facilities, their impact on clinical decision-making remains limited. Only a moderate portion of practitioners perceived EHR systems as effective in decision support, and frequent system use was negatively correlated with user satisfaction. Challenges such as inadequate training and support, system crashes, slow performance and poor usability and integration into clinical workflows were significant barriers to effectively utilising EHR systems.

ORIGINALITY/VALUE

This study contributes to the limited literature on EHR system implementation in low-resource settings, specifically Tanzania, by focusing on decision-support features within EHR systems. The findings offer valuable insights for healthcare policymakers, system designers and practitioners to optimise EHR implementation and improve healthcare outcomes in resource-constrained environments.

Challenges and successes in implementing an integrated electronic patient record (HIVE) at the Manchester University National Health Service Foundation Trust, England: 1000+ legacy systems, 10 hospitals, one electronic patient record

BACKGROUND

The Manchester University National Health Service (NHS) Foundation Trust (MFT) is one of the largest NHS Trusts in England. Historically, the Trust has had very mixed clinical record keeping, including over 1000 individual information systems. None of these health information technology systems had the full functionality of an integrated electronic patient record (EPR). MFT evolved to its current size and complexity with a vision to improve patient care in Greater Manchester by adopting a Trust-wide EPR. The EPR "Go Live" occurred in September 2022.

AIM

To describe the process of EPR integration as it reflected and impacted upon MFT's health information management (HIM) teams.

METHOD

MFT worked through a 2-year readiness program of work. This included technical readiness, software development and migration planning. Migration of data from the approximately 1000 systems was a major undertaking, during which access to the clinical history and ongoing operational reporting needed to be maintained. Pre-implementation requirements were outlined, a change management program was implemented, and the overall implementation was managed to tight timelines.

DISCUSSION

"Go Live" was achieved for the EPIC EPR product (HIVE) within MFT. Legacy systems are still in the process of being decommissioned and staff are transacting within HIVE. Significant changes in processes and reporting continue to be made, despite some challenges.

CONCLUSION

The Trust delivered the single largest EPIC European "Go live." Lessons learnt continue to be identified. The impact of what the EPR means for the HIM function is described.

Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs)

The spread of machine learning models, coupled with by the growing adoption of electronic health records (EHRs), has opened the door for developing clinical decision support systems. However, despite the great promise of machine learning for healthcare in low-middle-income countries (LMICs), many data-specific limitations, such as the small size and irregular sampling, hinder the progress in such applications. Recently, deep generative models have been proposed to generate realistic-looking synthetic data, including EHRs, by learning the underlying data distribution without compromising patient privacy. In this study, we first use a deep generative model to generate synthetic data based on a small dataset (364 patients) from a LMIC setting. Next, we use synthetic data to build models that predict the onset of hospital-acquired infections based on minimal information collected at patient ICU admission. The performance of the diagnostic model trained on the synthetic data outperformed models trained on the original and oversampled data using techniques such as SMOTE. We also experiment with varying the size of the synthetic data and observe the impact on the performance and interpretability of the models. Our results show the promise of using deep generative models in enabling healthcare data owners to develop and validate models that serve their needs and applications, despite limitations in dataset size.