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  1. Mohd Nor NA, Taib NA, Saad M, Zaini HS, Ahmad Z, Ahmad Y, et al.
    BMC Bioinformatics, 2019 Feb 04;19(Suppl 13):402.
    PMID: 30717675 DOI: 10.1186/s12859-018-2406-9
    BACKGROUND: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.

    RESULTS: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.

    CONCLUSION: The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.

  2. Tan WM, Ng WL, Ganggayah MD, Hoe VCW, Rahmat K, Zaini HS, et al.
    Health Informatics J, 2023;29(3):14604582231203763.
    PMID: 37740904 DOI: 10.1177/14604582231203763
    Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstructured breast radiology reports into structured formats using natural language processing (NLP) algorithm. This study used 327 de-identified breast radiology reports from the anonymous institute. The radiologist identified the significant data elements to be extracted. Our NLP algorithm achieved 97% and 94.9% accuracy in training and testing data, respectively. Henceforth, the structured information was used to build the predictive model for predicting the value of the BIRADS category. The model based on random forest generated the highest accuracy of 92%. Our study not only fulfilled the demands of clinicians by enhancing communication between medical personnel, but it also demonstrated the usefulness of mineable structured data in yielding significant insights.
  3. Tun Firzara AM, Teo CH, Teh SY, Su JY, Mohd Zaini HS, Suhaimi A, et al.
    Fam Pract, 2023 Dec 22;40(5-6):742-752.
    PMID: 37237425 DOI: 10.1093/fampra/cmad044
    BACKGROUND: Low back pain (LBP) is a common reason for primary care consultation; yet doctors often find managing it challenging. An electronic decision support system for LBP (DeSSBack) was developed based on an evidence-based risk stratification tool to improve the management of patients with LBP in a Malaysian primary care setting. This pilot study aimed to assess the feasibility, acceptability, and preliminary effectiveness of DeSSBack for the conduct of a future definitive trial.

    METHODS: A pilot cluster randomized controlled trial (cRCT) with qualitative interviews was conducted. Each primary care doctor was considered a cluster and randomized to either the control (usual practice) or intervention (DeSSBack) group. Patient outcomes including Roland-Morris Disability Questionnaire (RMDQ), Hospital Anxiety and Depression Scale, and a 10-point pain rating scale were measured at baseline and 2-month postintervention. The doctors in the intervention group were interviewed to explore feasibility and acceptability of using DeSSBack.

    RESULTS: Thirty-six patients with nonspecific LBP participated in this study (intervention n = 23; control n = 13). Fidelity was poor among patients but good among doctors. The RMDQ and anxiety score had medium effect sizes of 0.718 and 0.480, respectively. The effect sizes for pain score (0.070) and depression score were small (0.087). There was appreciable acceptability and satisfaction with use of DeSSBack, as it was helpful in facilitating thorough and standardized management, providing appropriate treatment plans based on risk stratification, improving consultation time, empowering patient-centred care, and easy to use.

    CONCLUSIONS: A future cRCT to evaluate the effectiveness of DeSSBack is feasible to be conducted in a primary care setting with minor modifications. DeSSBack was found useful by doctors and can be improved to enhance efficiency.

    TRIAL REGISTRATION: The protocol of the cluster randomized controlled trial was registered at ClinicalTrials.gov (NCT04959669).

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