Displaying publications 1 - 20 of 61 in total

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  1. Ahmad Shahabuddin F, Wah KY, Buji RI, Zulkafli NS, Lee SW, Soon HL, et al.
    BJPsych Int, 2020 May;17(2):43-44.
    PMID: 32558818 DOI: 10.1192/bji.2019.29
    We used medical record abstraction to conduct research in a psychiatric hospital with paper-based medical records. The challenges we encountered included: the difficulty in retrieving files; the extensive effort and time needed to extract clinical information; the lack of a standardised documentation system of medical records; and the need for advanced computer literacy. To promote future research using electronic medical records, potential solutions include creating a registry of all patients receiving treatment, as well as equipping busy clinicians with computer skills.
    Matched MeSH terms: Electronic Health Records
  2. Ahmadi H, Nilashi M, Ibrahim O, Raisian K
    Curr Health Sci J, 2016 03 29;42(1):82-93.
    PMID: 30568817 DOI: 10.12865/CHSJ.42.01.12
    As Electronic Medical Records (EMRs) have a great possibility for rising physician's performance in their daily work which improves quality, safety and efficiency in healthcare, they are implemented throughout the world (Boonstra and Broekhuis, 2010). In physician practices the rate of EMRs adoption has been slow and restricted (around 25%) according to Endsley, Baker, Kershner, and Curtin (2005) in spite of the cost savings through lower administrative costs and medical errors related with EMRs systems. The core objective of this research is to identify, categorize, and analyse meso-level factors introduced by Lau et al, 2012, perceived by physicians to the adoption of EMRs in order to give more knowledge in primary care setting. Finding was extracted through questionnaire which distributed to 350 physicians in primary cares in Malaysia to assess their perception towards EMRs adoption. The findings showed that Physicians had positive perception towards some features related to technology adoption success and emphasized EMRs had helpful impact in their office. The fuzzy TOPSIS physician EMRs adoption model in meso-level developed and its factors and sub-factors discussed in this study which provide making sense of EMRs adoption. The related factors based on meso-level perspective prioritized and ranked by using the fuzzy TOPSIS. The purpose of ranking using these approaches is to inspect which factors are more imperative in EMRs adoption among primary care physicians. The result of performing fuzzy TOPSIS is as a novelty method to identify the critical factors which assist healthcare organizations to inspire their users in accepting of new technology.
    Matched MeSH terms: Electronic Health Records
  3. Ainon RN, Bulgiba AM, Lahsasna A
    J Med Syst, 2012 Apr;36(2):463-73.
    PMID: 20703704 DOI: 10.1007/s10916-010-9491-2
    This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.
    Matched MeSH terms: Electronic Health Records*
  4. Alanazi HO, Zaidan AA, Zaidan BB, Kiah ML, Al-Bakri SH
    J Med Syst, 2015 Jan;39(1):165.
    PMID: 25481568 DOI: 10.1007/s10916-014-0165-3
    This study has two objectives. First, it aims to develop a system with a highly secured approach to transmitting electronic medical records (EMRs), and second, it aims to identify entities that transmit private patient information without permission. The NTRU and the Advanced Encryption Standard (AES) cryptosystems are secured encryption methods. The AES is a tested technology that has already been utilized in several systems to secure sensitive data. The United States government has been using AES since June 2003 to protect sensitive and essential information. Meanwhile, NTRU protects sensitive data against attacks through the use of quantum computers, which can break the RSA cryptosystem and elliptic curve cryptography algorithms. A hybrid of AES and NTRU is developed in this work to improve EMR security. The proposed hybrid cryptography technique is implemented to secure the data transmission process of EMRs. The proposed security solution can provide protection for over 40 years and is resistant to quantum computers. Moreover, the technique provides the necessary evidence required by law to identify disclosure or misuse of patient records. The proposed solution can effectively secure EMR transmission and protect patient rights. It also identifies the source responsible for disclosing confidential patient records. The proposed hybrid technique for securing data managed by institutional websites must be improved in the future.
    Matched MeSH terms: Electronic Health Records/organization & administration*
  5. Alhammad A, Yusof MM, Jambari DI
    Expert Rev Med Devices, 2024 Mar;21(3):217-229.
    PMID: 38318674 DOI: 10.1080/17434440.2024.2315024
    INTRODUCTION: Medical device (MD)-integrated (I) electronic medical record (EMR) (MDI-EMR) poses cyber threats that undermine patient safety, and thus, they require effective control mechanisms. We reviewed the related literature, including existing EMR and MD risk assessment approaches, to identify MDI-EMR comprehensive evaluation dimensions and measures.

    AREAS COVERED: We searched multiple databases, including PubMed, Web of Knowledge, Scopus, ACM, Embase, IEEE and Ingenta. We explored various evaluation aspects of MD and EMR to gain a better understanding of their complex integration. We reviewed numerous risk management and assessment frameworks related to MD and EMR security aspects and mitigation controls and then identified their common evaluation aspects. Our review indicated that previous evaluation frameworks assessed MD and EMR independently. To address this gap, we proposed an evaluation framework based on the sociotechnical dimensions of health information systems and risk assessment approaches for MDs to evaluate MDI-EMR integratively.

    EXPERT OPINION: The emergence of MDI-EMR cyber threats requires appropriate evaluation tools to ensure the safe development and application of MDI-EMR. Consequently, our proposed framework will continue to evolve through subsequent validations and refinements. This process aims to establish its applicability in informing stakeholders of the safety level and assessing its effectiveness in mitigating risks for future improvements.

    Matched MeSH terms: Electronic Health Records*
  6. Ali A, Ali H, Saeed A, Ahmed Khan A, Tin TT, Assam M, et al.
    Sensors (Basel), 2023 Sep 07;23(18).
    PMID: 37765797 DOI: 10.3390/s23187740
    The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.
    Matched MeSH terms: Electronic Health Records
  7. Ali A, Al-Rimy BAS, Tin TT, Altamimi SN, Qasem SN, Saeed F
    Sensors (Basel), 2023 Aug 28;23(17).
    PMID: 37687931 DOI: 10.3390/s23177476
    Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain's immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.
    Matched MeSH terms: Electronic Health Records
  8. Alsyouf A, Ishak AK, Lutfi A, Alhazmi FN, Al-Okaily M
    Int J Environ Res Public Health, 2022 Sep 05;19(17).
    PMID: 36078837 DOI: 10.3390/ijerph191711125
    This study examines nurses' Continuance Intention (CI) to use electronic health records (EHRs) through a combination of three conceptual frameworks: the Unified Theory of Acceptance and Use of Technology (UTAUT), the theory of expectation-confirmation (ECT), and the Five-Factor Model (FFM). A model is developed to examine and predict the determinants of nurses' CI to use EHRs, including top management support (TMS) and the FFM's five personality domains. Data were collected from a survey of 497 nurses, which were analyzed using partial least squares. No significant relationship was found between TMS and CI. The study revealed that performance expectancy significantly mediated the influences of two different hypotheses of two predictors: agreeableness and openness to testing CI. A significant moderating impact of conscientiousness was found on the relationship between performance expectancy and CI and the relationship between social influence and CI. The findings of this study indicated that rigorous attention to the personality of individual nurses and substantial TMS could improve nurses' CI to use EHRs. A literature gap was filled concerning the mediating effects of performance expectancy on the FFM-CI relationship, and the moderation effects of Conscientiousness on UTAUT constructs and CI are another addition to the literature. The results are expected to assist government agencies, health policymakers, and health institutions all over the globe in their attempts to understand the post-adoption use of EHRs.
    Matched MeSH terms: Electronic Health Records*
  9. Bervell B, Al-Samarraie H
    Soc Sci Med, 2019 07;232:1-16.
    PMID: 31035241 DOI: 10.1016/j.socscimed.2019.04.024
    This study distinguished between the application of e-health and m-health technologies in sub-Saharan African (SSA) countries based on the dimensions of use, targeted diseases or health conditions, locations of use, and beneficiaries (types of patients or health workers) in a country specific context. It further characterized the main opportunities and challenges associated with these dimensions across the sub-region. A systematic review of the literature was conducted on 66 published peer reviewed articles. The review followed the scientific process of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines of identification, selection, assessment, synthesis and interpretation of findings. The results of the study showed that m-health was prevalent in usage for promoting information for treatment and prevention of diseases as well as serving as an effective technology for reminders towards adherence. For e-health, the uniqueness lay in data acquisition and patients' records management; diagnosis; training and recruitment. While m-health was never used for monitoring or training and recruitment, e-health on the other hand could not serve the purpose of reminders or for reporting cases from the field. Both technologies were however useful for adherence, diagnosis, disease control mechanisms, information provision, and decision-making/referrals. HIV/AIDS, malaria, and maternal (postnatal and antenatal) healthcare were important in both m-health and e-health interventions mostly concentrated in the rural settings of South Africa and Kenya. ICT infrastructure, trained personnel, illiteracy, lack of multilingual text and voice messages were major challenges hindering the effective usage of both m-health and e-health technologies.
    Matched MeSH terms: Electronic Health Records/organization & administration*
  10. Bulgiba, A.M.
    JUMMEC, 2006;9(1):39-43.
    MyJurnal
    The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. Simple neural networks (simple perceptrons) were built and trained using a subset of 470 records with and without pre-processing using principal components analysis (PCA). These were subsequently tested on another subset of 417 records. Average sensitivity of 80.75% (95% CI 79.54%, 81.96%), specificity of 41.64% (95% CI 40.13%, 43.15%), PPV of 46.73% (95% CI 45.20%, 48.26%) and NPV of 77.39% (95% CI 76.11%, 78.67%) were achieved with the simple perceptron. When PCA pre-processing was used, the perceptrons had a sensitivity of 1.43% (95% CI 1.06%, 1.80%), specificity of 98.32% (95% CI 97.92%, 98.72%), PPV of 32.95% (95% CI 31.51%, 34.39%) and NPV of 61.33% (95% CI 59.84%, 62.82%). These results show that it is possible for a simple neural network to have respectable sensitivity and specificity levels for angina.
    Matched MeSH terms: Electronic Health Records
  11. Chet LS, Hamid SAA, Bachok N, Chidambaram SK, Adnan WNAW
    Saudi J Med Med Sci, 2021 04 29;9(2):135-144.
    PMID: 34084104 DOI: 10.4103/sjmms.sjmms_72_20
    Background: Antiretroviral therapy (ART) has transformed the management of human immunodeficiency virus (HIV) infection and significantly improved survival rates, but there is lack of such survival data from Malaysia.

    Objective: The objective was to determine the survival rates and prognostic factors of survival in HIV-infected adults treated with ART in Malaysia.

    Materials and Methods: This retrospective cohort study considered all HIV-positive adult patients registered in Sungai Buloh Hospital, a major referral center in Malaysia, between January 1, 2007 and December 31, 2016. Then, patients were selected through a systematic sampling method. Demographic, clinical, and treatment data were extracted from electronic medical records. Person-years at risk and incidence of mortality rate per 100 person-years were calculated. The Kaplan-Meier survival curve and log-rank test were used to compare the overall survival rates. Cox proportional hazards regression was applied to determine the prognostic factors for survival.

    Results: A total of 339 patients were included. The estimated overall survival rates were 93.8%, 90.4%, 84.9%, and 72.8% at 1, 3, 5, and 10 years, respectively, from ART initiation. The results of multiple Cox proportional hazard regression indicated that anemic patients were at a 3.76 times higher risk of mortality (95% confidence interval [CI]: 1.97-7.18; P < 0.001). The hazard risk was 2.09 times higher for HIV patients co-infected with tuberculosis (95% CI: 1.10, 3.96; P = 0.024).

    Conclusion: The overall survival rates among HIV-infected adults in this study are higher than that from low-income countries but lower than that from high-income countries. Low baseline hemoglobin levels of <11 g/dL and tuberculosis co-infection were strong prognostic factors for survival.

    Matched MeSH terms: Electronic Health Records
  12. Choon SE, Wright AK, Griffiths CEM, Tey KE, Wong KW, Lee YW, et al.
    Br J Dermatol, 2022 Nov;187(5):713-721.
    PMID: 35830199 DOI: 10.1111/bjd.21768
    BACKGROUND: There are no population-based epidemiological data on psoriasis in Southeast Asia, including Malaysia.

    OBJECTIVES: To determine the incidence and prevalence of psoriasis over 11 years in multiethnic Johor Bahru, Malaysia.

    METHODS: A population-based cohort study was made using the Teleprimary Care database between January 2010 and December 2020. Cases of psoriasis, identified by ICD-10 diagnostic codes, were validated by dermatologists. Annual prevalence and incidence were estimated and stratified by age, sex and ethnicity.

    RESULTS: We identified 3932 people with dermatologist-confirmed psoriasis, including 1830 incident cases, among 1 164 724 Malaysians, yielding an 11-year prevalence of 0·34% [95% confidence interval (CI) 0·33-0·35] and incidence of 34·2 per 100 000 person-years (95% CI 32·6-35·8). Rates were higher in Indian patients; the prevalences were 0·54% (0·50-0·58) in Indian, 0·38% (0·36-0·40) in Chinese and 0·29% (0·28-0·30) in Malay patients, and the respective incidences per 100 000 person-years were 52·5 (47·3-57·7), 38·0 (34·1-41·8) and 30·0 (28·2-31·8). Rates were higher in males; the prevalence was 0·39% (0·37-0·41) in males and 0·29% (0·27-0·30) in females, and the respective incidences per 100 000 person-years were 40·7 (38·2-43·2) and 28·3 (26·4-30·3). Between 2010 and 2020, annual psoriasis prevalence and incidence increased steadily from 0·27% to 0·51% and from 27·8 to 60·9 per 100 000 person-years, respectively. Annual rates were consistently higher in male and Indian patients. Overall, psoriasis was significantly more common in males than females [odds ratio (OR) 1·37, 95% CI 1·29-1·46] and in Indian and Chinese patients vs. Malay (OR 1·85, 1·71-2·01 and OR 1·30, 1·20-1·41, respectively). Prevalence increased with age, with the highest rates in the groups aged 50-59 and 60-69 years at 0·67% and 0·66%, respectively. A modest bimodal trend in age of psoriasis onset was observed, with first and second peaks at 20-29 and 50-59 years. Disease onset was significantly earlier in females than males [mean (SD) 36·8 (17·3) vs. 42·0 (17·2) years, P 

    Matched MeSH terms: Electronic Health Records*
  13. Choon SE, Wright AK, Griffiths CEM, Wong KW, Tey KE, Lim YT, et al.
    Br J Dermatol, 2023 Sep 15;189(4):410-418.
    PMID: 37162007 DOI: 10.1093/bjd/ljad158
    BACKGROUND: There is limited understanding of the epidemiology of generalized pustular psoriasis (GPP) internationally, with no population-based estimates of GPP in South East Asia.

    OBJECTIVES: To determine the incidence and prevalence of GPP in the Malaysian population and characterize its flares and trigger factors.

    METHODS: We conducted a population-based cohort study using the Teleprimary Care database between January 2010 and December 2020. We identified 230 dermatologist-confirmed GPP cases using International Classification of Diseases, 10th revision, diagnostic codes. Annual prevalence and incidence rates were stratified by age, sex and ethnicity. We compared data regarding flares and trigger factors for patients with GPP who had associated psoriasis vulgaris (PV) with those who did not have associated PV.

    RESULTS: The prevalence of GPP was 198 per million (267 women, 127 men) and incidence was 27.2 per million person-years [95% confidence interval (CI) 22.8-31.6]; 35.3 (28.4-42.2) per million person-years for women and 18.3 (13.1-23.5) per million person-years for men. Rates were higher in Chinese individuals [prevalence 271 per million; incidence 41.6 per million person-years (28.9-54.3)] than in the Malay population [prevalence 186; incidence 24.6 (19.4-29.7)] or the Indian ethnic group [prevalence 179; incidence 25.0 (13.8-36.3)]. Annual prevalence was consistently higher in women than in men and highest among the Chinese population, followed by the Indian and Malay populations. Overall, 67% of patients with GPP had associated PV. The prevalence and incidence of GPP without PV were lower than GPP with PV at 66 vs. 132 per million and 19.3 (95% CI 15.6-23.0) vs. 8.0 (95% CI 5.6-10.3) per million person-years, respectively. The mean age at GPP onset was 42.7 years (SD 18.4). A bimodal trend in the age of GPP onset was observed, with first and second peaks at age 20-29 years and age 50-59 years, respectively. Disease onset was significantly earlier in patients with GPP without PV than in those with PV [mean age 37.5 years (SD 20.7) vs. 44.9 years (SD 17.0), P = 0.026]. Flares occurred more frequently in patients without PV than in those with PV [mean number of flares per patient per year was 1.35 (SD 0.77) vs. 1.25 (SD 0.58), P = 0.039]. Common triggers of flares in patients with GPP who did not have PV were infections, pregnancy, menstruation and stress, whereas withdrawal of therapy, particularly systemic corticosteroids, was a more frequent trigger in patients with GPP who also had PV.

    CONCLUSIONS: Our findings contribute to the global mapping of GPP, which will help inform the management of this rare condition.

    Matched MeSH terms: Electronic Health Records
  14. El-Hassan O, Sharif A, Al Redha M, Blair I
    PMID: 29295053
    In the United Arab Emirates (UAE), health services have developed greatly in the past 40 years. To ensure they continue to meet the needs of the population, innovation and change are required including investment in a strong e-Health infrastructure with a single transferrable electronic patient record. In this paper, using the Emirate of Dubai as a case study, we report on the Middle East Electronic Medical Record Adoption Model (EMRAM). Between 2011-2016, the number of participating hospitals has increased from 23 to 33. Currently, while 20/33 of hospitals are at Stage 2 or less, 10/33 have reached Stage 5. Also Dubai's median EMRAM score in 2016 (2.5) was higher than the scores reported from Australia (2.2), New Zealand (2.3), Malaysia (0.06), the Philippines (0.06) and Thailand (0.5). EMRAM has allowed the tracking of the progress being made by healthcare facilities in Dubai towards upgrading their information technology infrastructure and the introduction of electronic medical records.
    Matched MeSH terms: Electronic Health Records*
  15. ElAbd R, AlTarrah D, AlYouha S, Bastaki H, Almazeedi S, Al-Haddad M, et al.
    Front Med (Lausanne), 2021;8:600385.
    PMID: 33748156 DOI: 10.3389/fmed.2021.600385
    Introduction: Corona Virus disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic. The aim of this study was to investigate the impact of being on an Angiotensin-Converting Enzyme Inhibitors (ACEI) and/or Angiotensin Receptor Blockers (ARB) on hospital admission, on the following COVID-19 outcomes: disease severity, ICU admission, and mortality. Methods: The charts of all patients consecutively diagnosed with COVID-19 from the 24th of February to the 16th of June of the year 2020 in Jaber Al-Ahmed Al-Sabah hospital in Kuwait were checked. All related patient information and clinical data was retrieved from the hospitals electronic medical record system. The primary outcome was COVID-19 disease severity defined as the need for Intensive Care Unit (ICU) admission. Secondary outcome was mortality. Results: A total of 4,019 COVID-19 patients were included, of which 325 patients (8.1%) used ACEI/ARB, users of ACEI/ARB were found to be significantly older (54.4 vs. 40.5 years). ACEI/ARB users were found to have more co-morbidities; diabetes (45.8 vs. 14.8%) and hypertension (92.9 vs. 13.0%). ACEI/ARB use was found to be significantly associated with greater risk of ICU admission in the unadjusted analysis [OR, 1.51 (95% CI: 1.04-2.19), p = 0.028]. After adjustment for age, gender, nationality, coronary artery disease, diabetes and hypertension, ICU admission was found to be inversely associated with ACEI use [OR, 0.57 (95% CI: 0.34-0.88), p = 0.01] and inversely associated with mortality [OR, 0.56 (95% CI: 0.33-0.95), p = 0.032]. Conclusion: The current evidence in the literature supports continuation of ACEI/ARB medications for patients with co-morbidities that acquire COVID-19 infection. Although, the protective effects of such medications on COVID-19 disease severity and mortality remain unclear, the findings of the present study support the use of ACEI/ARB medication.
    Matched MeSH terms: Electronic Health Records
  16. Esther Omolara A, Jantan A, Abiodun OI, Arshad H, Dada KV, Emmanuel E
    Health Informatics J, 2020 09;26(3):2083-2104.
    PMID: 31957538 DOI: 10.1177/1460458219894479
    Advancements in electronic health record system allow patients to store and selectively share their medical records as needed with doctors. However, privacy concerns represent one of the major threats facing the electronic health record system. For instance, a cybercriminal may use a brute-force attack to authenticate into a patient's account to steal the patient's personal, medical or genetic details. This threat is amplified given that an individual's genetic content is connected to their family, thus leading to security risks for their family members as well. Several cases of patient's data theft have been reported where cybercriminals authenticated into the patient's account, stole the patient's medical data and assumed the identity of the patients. In some cases, the stolen data were used to access the patient's accounts on other platforms and in other cases, to make fraudulent health insurance claims. Several measures have been suggested to address the security issues in electronic health record systems. Nevertheless, we emphasize that current measures proffer security in the short-term. This work studies the feasibility of using a decoy-based system named HoneyDetails in the security of the electronic health record system. HoneyDetails will serve fictitious medical data to the adversary during his hacking attempt to steal the patient's data. However, the adversary will remain oblivious to the deceit due to the realistic structure of the data. Our findings indicate that the proposed system may serve as a potential measure for safeguarding against patient's information theft.
    Matched MeSH terms: Electronic Health Records
  17. Flenady V, Wojcieszek AM, Fjeldheim I, Friberg IK, Nankabirwa V, Jani JV, et al.
    BMC Pregnancy Childbirth, 2016 Sep 30;16(1):293.
    PMID: 27716088
    BACKGROUND: Electronic health registries - eRegistries - can systematically collect relevant information at the point of care for reproductive, maternal, newborn and child health (RMNCH). However, a suite of process and outcome indicators is needed for RMNCH to monitor care and to ensure comparability between settings. Here we report on the assessment of current global indicators and the development of a suite of indicators for the WHO Essential Interventions for use at various levels of health care systems nationally and globally.

    METHODS: Currently available indicators from both household and facility surveys were collated through publicly available global databases and respective survey instruments. We then developed a suite of potential indicators and associated data points for the 45 WHO Essential Interventions spanning preconception to newborn care. Four types of performance indicators were identified (where applicable): process (i.e. coverage) and outcome (i.e. impact) indicators for both screening and treatment/prevention. Indicators were evaluated by an international expert panel against the eRegistries indicator evaluation criteria and further refined based on feedback by the eRegistries technical team.

    RESULTS: Of the 45 WHO Essential Interventions, only 16 were addressed in any of the household survey data available. A set of 216 potential indicators was developed. These indicators were generally evaluated favourably by the panel, but difficulties in data ascertainment, including for outcome measures of cause-specific morbidity and mortality, were frequently reported as barriers to the feasibility of indicators. Indicators were refined based on feedback, culminating in the final list of 193 total unique indicators: 93 for preconception and antenatal care; 53 for childbirth and postpartum care; and 47 for newborn and small and ill baby care.

    CONCLUSIONS: Large gaps exist in the availability of information currently collected to support the implementation of the WHO Essential Interventions. The development of this suite of indicators can be used to support the implementation of eRegistries and other data platforms, to ensure that data are utilised to support evidence-based practice, facilitate measurement and accountability, and improve maternal and child health outcomes.

    Matched MeSH terms: Electronic Health Records/statistics & numerical data*
  18. Ghaibeh AA, Kasem A, Ng XJ, Nair HLK, Hirose J, Thiruchelvam V
    Stud Health Technol Inform, 2018;247:386-390.
    PMID: 29677988
    The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.
    Matched MeSH terms: Electronic Health Records
  19. Gibson BA, Ghosh D, Morano JP, Altice FL
    Health Place, 2014 Jul;28:153-66.
    PMID: 24853039 DOI: 10.1016/j.healthplace.2014.04.008
    We mapped mobile medical clinic (MMC) clients for spatial distribution of their self-reported locations and travel behaviors to better understand health-seeking and utilization patterns of medically vulnerable populations in Connecticut. Contrary to distance decay literature, we found that a small but significant proportion of clients was traveling substantial distances to receive repeat care at the MMC. Of 8404 total clients, 90.2% lived within 5 miles of a MMC site, yet mean utilization was highest (5.3 visits per client) among those living 11-20 miles of MMCs, primarily for those with substance use disorders. Of clients making >20 visits, 15.0% traveled >10 miles, suggesting that a significant minority of clients traveled to MMC sites because of their need-specific healthcare services, which are not only free but available at an acceptable and accommodating environment. The findings of this study contribute to the important research on healthcare utilization among vulnerable population by focusing on broader dimensions of accessibility in a setting where both mobile and fixed healthcare services coexist.
    Matched MeSH terms: Electronic Health Records
  20. Goh CC, Koh KH, Goh S, Koh Y, Tan NC
    Malays Fam Physician, 2018;13(2):10-18.
    PMID: 30302178
    Introduction: Achieving optimal glycated hemoglobin (HbA1c), blood pressure (BP), and LDL-Cholesterol (LDL-C) in patients mitigates macro- and micro-vascular complications, which is the key treatment goal in managing type 2 diabetes mellitus (T2DM). This study aimed to determine the proportion of patients in an urban community with T2DM and the above modifiable conditions attaining triple vascular treatment goals based on current practice guidelines.

    Methods: A questionnaire was distributed to adult Asian patients with dyslipidemia at two primary care clinics (polyclinics) in northeastern Singapore. The demographic and clinical data for this sub-population with both T2DM and dyslipidemia were collated with laboratory and treatment information retrieved from their electronic health records. The combined data was then analyzed to determine the proportion of patients who attained triple treatment goals, and logistic regression analysis was used to identify factors associated with this outcome.

    Results: 665 eligible patients [60.5% female, 30.5% Chinese, 35% Malays, and 34.4% Indians] with a mean age of 60.6 years were recruited. Of these patients, 71% achieved LDL-C ≤2.6 mmol/L, 70.4% had BP

    Matched MeSH terms: Electronic Health Records
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