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  1. Moissinac K, Merican H, Sukumar M, Azmi S, Raja Lope A, Tajuddin AJ, et al.
    Trop Doct, 2004 Jul;34(3):191.
    PMID: 15267066
    Matched MeSH terms: Patient Discharge/statistics & numerical data*
  2. Abdul Rahman N', Nurumal MS, Awang MS, Mohd Shah ANS
    Australas Emerg Care, 2020 Dec;23(4):240-246.
    PMID: 32713770 DOI: 10.1016/j.auec.2020.06.005
    INTRODUCTION: Emergency departments (EDs) routinely provide discharge instructions due to a large number of patients with mild traumatic brain injury (mTBI) being discharged home directly from ED. This study aims to evaluate the quality of available mTBI discharge instructions provided by EDs of Malaysia government hospitals.

    METHODS: All 132 EDs were requested for a copy of written discharge instruction given to the patients. The mTBI discharge instructions were evaluated using the Patient Education Materials Assessment-Printable Tool (PEMAT-P) for understandability and actionability. Readability was measured using an online readability tool of Malay text. The content was compared against the discharge instructions recommended by established guidelines.

    RESULTS: 49 articles were eligible for the study. 26 of the articles met the criteria of understandability, and 3 met the criteria for actionability. The average readability level met the ability of average adult. Most of the discharge instructions focused on emergency symptoms, and none contained post-concussion features.

    CONCLUSION: Majority of the discharge instructions provided were appropriate for average people to read but difficult to understand and act upon. Important information was neglected in most discharge instructions. Thus, revision and future development of mTBI discharge instruction should consider health literacy demand and cognitive ability to process such information.

    Matched MeSH terms: Patient Discharge/statistics & numerical data
  3. Woon YL, Lee KY, Mohd Anuar SFZ, Goh PP, Lim TO
    BMC Health Serv Res, 2018 04 20;18(1):292.
    PMID: 29678172 DOI: 10.1186/s12913-018-3104-z
    BACKGROUND: Hospitalization due to dengue illness is an important measure of dengue morbidity. However, limited studies are based on administrative database because the validity of the diagnosis codes is unknown. We validated the International Classification of Diseases, 10th revision (ICD) diagnosis coding for dengue infections in the Malaysian Ministry of Health's (MOH) hospital discharge database.

    METHODS: This validation study involves retrospective review of available hospital discharge records and hand-search medical records for years 2010 and 2013. We randomly selected 3219 hospital discharge records coded with dengue and non-dengue infections as their discharge diagnoses from the national hospital discharge database. We then randomly sampled 216 and 144 records for patients with and without codes for dengue respectively, in keeping with their relative frequency in the MOH database, for chart review. The ICD codes for dengue were validated against lab-based diagnostic standard (NS1 or IgM).

    RESULTS: The ICD-10-CM codes for dengue had a sensitivity of 94%, modest specificity of 83%, positive predictive value of 87% and negative predictive value 92%. These results were stable between 2010 and 2013. However, its specificity decreased substantially when patients manifested with bleeding or low platelet count.

    CONCLUSION: The diagnostic performance of the ICD codes for dengue in the MOH's hospital discharge database is adequate for use in health services research on dengue.

    Matched MeSH terms: Patient Discharge/statistics & numerical data*
  4. Yusof M, Sahroni MN
    Int J Health Care Qual Assur, 2018 Oct 08;31(8):1014-1029.
    PMID: 30415623 DOI: 10.1108/IJHCQA-07-2017-0125
    PURPOSE: The purpose of this paper is to present a review of health information system (HIS)-induced errors and its management. This paper concludes that the occurrence of errors is inevitable but it can be minimised with preventive measures. The review of classifications can be used to evaluate medical errors related to HISs using a socio-technical approach. The evaluation could provide an understanding of errors as a learning process in managing medical errors.

    DESIGN/METHODOLOGY/APPROACH: A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.

    FINDINGS: Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.

    RESEARCH LIMITATIONS/IMPLICATIONS: Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.

    PRACTICAL IMPLICATIONS: This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.

    ORIGINALITY/VALUE: Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.

    Matched MeSH terms: Patient Discharge/statistics & numerical data
  5. Hassan Y, Aziz NA, Al-Jabi SW, Looi I, Zyoud SH
    J Cardiovasc Pharmacol Ther, 2010 Sep;15(3):274-81.
    PMID: 20624923 DOI: 10.1177/1074248410373751
    Angiotensin-converting enzyme inhibitors (ACEIs) have shown promising results in decreasing the incidence and the severity of ischemic stroke in populations at risk and in improving ischemic stroke outcomes.
    Matched MeSH terms: Patient Discharge/statistics & numerical data
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