Displaying publications 1 - 20 of 26 in total

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  1. Olufisayo O, Mohd Yusof M, Ezat Wan Puteh S
    Stud Health Technol Inform, 2018;255:112-116.
    PMID: 30306918
    Despite the widespread use of clinical decision support systems with its alert function, there has been an increase in medical errors, adverse events as well as issues regarding patient safety, quality and efficiency. The appropriateness of CDSS must be properly evaluated by ensuring that CDSS provides clinicians with useful information at the point of care. Inefficient clinical workflow affects clinical processes; hence, it is necessary to identify processes in the healthcare system that affect provider's workflow. The Lean method was used to eliminate waste (non-value added) activities that affect the appropriate use of CDSS. Ohno's seven waste model was used to categorize waste in the context of healthcare and information technology.
    Matched MeSH terms: Decision Support Systems, Clinical*
  2. 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).

    Matched MeSH terms: Decision Support Systems, Clinical*
  3. Cheah YN, Abidi SS
    PMID: 11187672
    The abundance and transient nature to healthcare knowledge has rendered it difficult to acquire with traditional knowledge acquisition methods. In this paper, we propose a Knowledge Management approach, through the use of scenarios, as a mean to acquire and represent tacit healthcare knowledge. This proposition is based on the premise that tacit knowledge is best manifested in atypical situations. We also provide an overview of the representational scheme and novel acquisition mechanism of scenarios.
    Matched MeSH terms: Decision Support Systems, Clinical*
  4. Olakotan OO, Yusof MM
    J Eval Clin Pract, 2021 Aug;27(4):868-876.
    PMID: 33009698 DOI: 10.1111/jep.13488
    RATIONALE, AIMS, AND OBJECTIVES: Clinical decision support (CDS) generates excessive alerts that disrupt the workflow of clinicians. Therefore, inefficient clinical processes that contribute to the misfit between CDS alert and workflow must be evaluated. This study evaluates the appropriateness of CDS alerts in supporting clinical workflow from a socio-technical perspective.

    METHOD: A qualitative case study evaluation was conducted at a 620-bed public teaching hospital in Malaysia using interview, observation, and document analysis to investigate the features and functions of alert appropriateness and workflow-related issues in cardiological and dermatological settings. The current state map for medication prescribing process was also modelled to identify problems pertinent to CDS alert appropriateness.

    RESULTS: The main findings showed that CDS was not well designed to fit into a clinician's workflow due to influencing factors such as technology (usability, alert content, and alert timing), human (training, perception, knowledge, and skills), organizational (rules and regulations, privacy, and security), and processes (documenting patient information, overriding default option, waste, and delay) impeding the use of CDS with its alert function. We illustrated how alert affect workflow in clinical processes using a Lean tool known as value stream mapping. This study also proposes how CDS alerts should be integrated into clinical workflows to optimize their potential to enhance patient safety.

    CONCLUSION: The design and implementation of CDS alerts should be aligned with and incorporate socio-technical factors. Process improvement methods such as Lean can be used to enhance the appropriateness of CDS alerts by identifying inefficient clinical processes that impede the fit of these alerts into clinical workflow.

    Matched MeSH terms: Decision Support Systems, Clinical*
  5. Olakotan OO, Mohd Yusof M
    Health Informatics J, 2021 4 16;27(2):14604582211007536.
    PMID: 33853395 DOI: 10.1177/14604582211007536
    A CDSS generates a high number of inappropriate alerts that interrupt the clinical workflow. As a result, clinicians silence, disable, or ignore alerts, thereby undermining patient safety. Therefore, the effectiveness and appropriateness of CDSS alerts need to be evaluated. A systematic review was carried out to identify the factors that affect CDSS alert appropriateness in supporting clinical workflow. Seven electronic databases (PubMed, Scopus, ACM, Science Direct, IEEE, Ovid Medline, and Ebscohost) were searched for English language articles published between 1997 and 2018. Seventy six papers met the inclusion criteria, of which 26, 24, 15, and 11 papers are retrospective cohort, qualitative, quantitative, and mixed-method studies, respectively. The review highlights various factors influencing the appropriateness and efficiencies of CDSS alerts. These factors are categorized into technology, human, organization, and process aspects using a combination of approaches, including socio-technical framework, five rights of CDSS, and Lean. Most CDSS alerts were not properly designed based on human factor methods and principles, explaining high alert overrides in clinical practices. The identified factors and recommendations from the review may offer valuable insights into how CDSS alerts can be designed appropriately to support clinical workflow.
    Matched MeSH terms: Decision Support Systems, Clinical*
  6. Olakotan O, Mohd Yusof M, Ezat Wan Puteh S
    Stud Health Technol Inform, 2020 Jun 16;270:906-910.
    PMID: 32570513 DOI: 10.3233/SHTI200293
    Clinical decision support systems (CDSSs) provides vital information for managing patients by advising clinicians through an alert or reminders about adverse events and medication errors. Clinicians receive a high number of alerts, resulting in alert override and workflow disruptions. A systematic review was carried out to identify factors affecting CDSS alert appropriateness in supporting clinical workflows using a recently introduced framework. The review findings identified several influencing factors of CDSS alert appropriateness including: technology (usability, alert presentation, workload and data entry), human (training, knowledge and skills, attitude and behavior), organization (rules and regulation, privacy and security) and process (waste, delay, tuning and optimization). The findings can be used to guide the design of CDSS alert and minimise potential safety hazards associated with CDSS use.
    Matched MeSH terms: Decision Support Systems, Clinical*
  7. Cheah YN, Abidi SS
    PMID: 11187669
    The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.
    Matched MeSH terms: Decision Support Systems, Clinical*
  8. Fletcher E, Burns A, Wiering B, Lavu D, Shephard E, Hamilton W, et al.
    BMC Prim Care, 2023 Jan 20;24(1):23.
    PMID: 36670354 DOI: 10.1186/s12875-023-01973-2
    BACKGROUND: Electronic clinical decision support tools (eCDS) are increasingly available to assist General Practitioners (GP) with the diagnosis and management of a range of health conditions. It is unclear whether the use of eCDS tools has an impact on GP workload. This scoping review aimed to identify the available evidence on the use of eCDS tools by health professionals in general practice in relation to their impact on workload and workflow.

    METHODS: A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed.

    RESULTS: The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue".

    CONCLUSIONS: The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.

    Matched MeSH terms: Decision Support Systems, Clinical*
  9. Reza AW, Eswaran C
    J Med Syst, 2011 Feb;35(1):17-24.
    PMID: 20703589 DOI: 10.1007/s10916-009-9337-y
    The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.
    Matched MeSH terms: Decision Support Systems, Clinical*
  10. Faisal T, Ibrahim F, Taib MN
    PMID: 19163874 DOI: 10.1109/IEMBS.2008.4650371
    This study presents a new approach to determine the significant prognosis factors in dengue patients utilizing the self-organizing map (SOM). SOM was used to visualize and determine the significant factors that can differentiate between the dengue patients and the healthy subjects. Bioimpedance analysis (BIA) parameters and symptoms/signs obtained from the 210 dengue patients during their hospitalization were used in this study. Database comprised of 329 sample (210 dengue patients and 119 healthy subjects) were used in the study. Accordingly, two maps were constructed. A total of 35 predictors (17 BIA parameters, 18 symptoms/signs) were investigated on the day of defervescence of fever. The first map was constructed based on BIA parameters while the second map utilized the symptoms and signs. The visualized results indicated that, the significant BIA prognosis factors for differentiating the dengue patients from the healthy subjects are reactance, intracellular water, ratio of the extracellular water and intracellular water, and ratio of the extracellular mass and body cell mass.
    Matched MeSH terms: Decision Support Systems, Clinical*
  11. Sambasivan M, Esmaeilzadeh P, Kumar N, Nezakati H
    PMID: 23216866 DOI: 10.1186/1472-6947-12-142
    Computer-based clinical decision support systems (CDSS) are regarded as a key element to enhance decision-making in a healthcare environment to improve the quality of medical care delivery. The concern of having new CDSS unused is still one of the biggest issues in developing countries for the developers and implementers of clinical IT systems. The main objectives of this study are to determine whether (1) the physician's perceived professional autonomy, (2) involvement in the decision to implement CDSS and (3) the belief that CDSS will improve job performance increase the intention to adopt CDSS. Four hypotheses were formulated and tested.
    Matched MeSH terms: Decision Support Systems, Clinical*
  12. Ravindran S, Jambek AB, Muthusamy H, Neoh SC
    Comput Math Methods Med, 2015;2015:283532.
    PMID: 25793009 DOI: 10.1155/2015/283532
    A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.
    Matched MeSH terms: Decision Support Systems, Clinical*
  13. Ahmadi H, Gholamzadeh M, Shahmoradi L, Nilashi M, Rashvand P
    Comput Methods Programs Biomed, 2018 Jul;161:145-172.
    PMID: 29852957 DOI: 10.1016/j.cmpb.2018.04.013
    BACKGROUND AND OBJECTIVE: Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.

    METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.

    RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.

    CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.

    Matched MeSH terms: Decision Support Systems, Clinical*
  14. Abidi SS, Manickam S
    PMID: 11187645
    Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.
    Matched MeSH terms: Decision Support Systems, Clinical
  15. Abidi SS, Goh A, Yusoff Z
    Stud Health Technol Inform, 1998;52 Pt 2:1282-6.
    PMID: 10384666
    The practice of medicine, with its wide range of environmental conditions and complex dependencies, has long been used as a test bed for various advanced technologies. Telemedicine, as conceptualised within the Multimedia Super Corridor (MSC) context, is seen as the application of several relatively mature technologiesartificial intelligence (AI), multimedia communication and information systems (IS) amongst othersso as to benefit a large cross-section of the Malaysian population. We will discuss in general terms the Malaysian vision on the comprehensive MSC telemedicine solution, its functionality and associated operational conditions. In particular, this paper focuses on the conceptualisation of one key telemedical component i.e. the Lifetime Health Plan (LHP) system, which is eventually intended to be a distributed multi-module application for the periodic monitoring and generation of health-care advisories for upwards of 20 million Malaysians.
    Matched MeSH terms: Decision Support Systems, Clinical
  16. Teng CL
    MyJurnal
    In the developing world, clinical knowledge management in primary care has a long way to go. Clinical decision support systems, despite its promise to revolutionise healthcare, is slow in its implementation due to the lack of financial investment in information technology. Point-of-care resources, such as comprehensive electronic textbooks delivered via the web or mobile devices, have yet to be fully utilised by the healthcare organisation or individual clinicians. Increasing amount of applicable knowledge of good quality (e.g. clinical practice guidelines and other pre-appraised resources) are now available via the internet. The policy makers and clinicians need to be more informed about the potential benefits and
    limitations of these new tools and resources and make the necessary budgetary provision and learn how best to harness them for patient care.
    Matched MeSH terms: Decision Support Systems, Clinical
  17. Olakotan OO, Yusof MM
    J Biomed Inform, 2020 06;106:103453.
    PMID: 32417444 DOI: 10.1016/j.jbi.2020.103453
    The overwhelming number of medication alerts generated by clinical decision support systems (CDSS) has led to inappropriate alert overrides, which may lead to unintended patient harm. This review highlights the factors affecting the alert appropriateness of CDSS and barriers to the fit of CDSS alert with clinical workflow. A literature review was conducted to identify features and functions pertinent to CDSS alert appropriateness using the five rights of CDSS. Moreover, a process improvement method, namely, Lean, was used as a tool to optimise clinical workflows, and the appropriate design for CDSS alert using a human automation interaction (HAI) model was recommended. Evaluating the appropriateness of CDSS alert and its impact on workflow provided insights into how alerts can be designed and triggered effectively to support clinical workflow. The application of Lean methods and tools to analyse alert efficiencies in supporting workflow in this study provides an in-depth understanding of alert-workflow fit problems and their root cause, which is required for improving CDSS design. The application of the HAI model is recommended in the design of CDSS alerts to support various levels and stages of alert automations, namely, information acquisition and analysis, decision action and action implementation.
    Matched MeSH terms: Decision Support Systems, Clinical
  18. Faisal T, Taib MN, Ibrahim F
    J Med Syst, 2012 Apr;36(2):661-76.
    PMID: 20703665 DOI: 10.1007/s10916-010-9532-x
    With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models' parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75% prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7% prediction accuracy were achieved by using Levenberg-Marquardt algorithm.
    Matched MeSH terms: Decision Support Systems, Clinical/organization & administration*
  19. Nguyen QT, Naguib RN, Abd Ghani MK, Bali RK, Lee IM
    Int J Electron Healthc, 2008;4(2):184-207.
    PMID: 18676343
    This paper presents an overview of the healthcare systems in Southeast Asia, with a focus on the healthcare informatics development and deployment in seven countries, namely, Singapore, Cambodia, Malaysia, Thailand, Laos, the Philippines and Vietnam. Brief geographic and demographic information is provided for each country, followed by a historical review of the national strategies for healthcare informatics development. An analysis of the state-of-the-art healthcare infrastructure is also given, along with a critical appraisal of national healthcare provisions.
    Matched MeSH terms: Decision Support Systems, Clinical/organization & administration
  20. Lim CP, Leong JH, Kuan MM
    IEEE Trans Pattern Anal Mach Intell, 2005 Apr;27(4):648-53.
    PMID: 15794170
    A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.
    Matched MeSH terms: Decision Support Systems, Clinical*
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