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).
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.
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.
Objective: To examine the effects of a quality improvement intervention comprising information and communications technology and contact with nonphysician personnel on the care and cardiometabolic risk factors of patients with type 2 diabetes in 8 Asia-Pacific countries.
Design, Setting, and Participants: This 12-month multinational open-label randomized clinical trial was conducted from June 28, 2012, to April 28, 2016, at 50 primary care or hospital-based diabetes centers in 8 Asia-Pacific countries (India, Indonesia, Malaysia, the Philippines, Singapore, Taiwan, Thailand, and Vietnam). Six countries were low and middle income, and 2 countries were high income. The study was conducted in 2 phases; phase 1 enrolled 7537 participants, and phase 2 enrolled 13 297 participants. Participants in both phases were randomized on a 1:1 ratio to intervention or control groups. Data were analyzed by intention to treat and per protocol from July 3, 2019, to July 21, 2020.
Interventions: In both phases, the intervention group received 3 care components: a nurse-led Joint Asia Diabetes Evaluation (JADE) technology-guided structured evaluation, automated personalized reports to encourage patient empowerment, and 2 or more telephone or face-to-face contacts by nurses to increase patient engagement. In phase 1, the control group received the JADE technology-guided structured evaluation and automated personalized reports. In phase 2, the control group received the JADE technology-guided structured evaluation only.
Main Outcomes and Measures: The primary outcome was the incidence of a composite of diabetes-associated end points, including cardiovascular disease, chronic kidney disease, visual impairment or eye surgery, lower extremity amputation or foot ulcers requiring hospitalization, all-site cancers, and death. The secondary outcomes were the attainment of 2 or more primary diabetes-associated targets (glycated hemoglobin A1c <7.0%, blood pressure <130/80 mm Hg, and low-density lipoprotein cholesterol <100 mg/dL) and/or 2 or more key performance indices (reduction in glycated hemoglobin A1c≥0.5%, reduction in systolic blood pressure ≥5 mm Hg, reduction in low-density lipoprotein cholesterol ≥19 mg/dL, and reduction in body weight ≥3.0%).
Results: A total of 20 834 patients with type 2 diabetes were randomized in phases 1 and 2. In phase 1, 7537 participants (mean [SD] age, 60.0 [11.3] years; 3914 men [51.9%]; 4855 patients [64.4%] from low- and middle-income countries) were randomized, with 3732 patients allocated to the intervention group and 3805 patients allocated to the control group. In phase 2, 13 297 participants (mean [SD] age, 54.0 [11.1] years; 7754 men [58.3%]; 13 297 patients [100%] from low- and middle-income countries) were randomized, with 6645 patients allocated to the intervention group and 6652 patients allocated to the control group. In phase 1, compared with the control group, the intervention group had a similar risk of experiencing any of the primary outcomes (odds ratio [OR], 0.94; 95% CI, 0.74-1.21) but had an increased likelihood of attaining 2 or more primary targets (OR, 1.34; 95% CI, 1.21-1.49) and 2 or more key performance indices (OR, 1.18; 95% CI, 1.04-1.34). In phase 2, the intervention group also had a similar risk of experiencing any of the primary outcomes (OR, 1.02; 95% CI, 0.83-1.25) and had a greater likelihood of attaining 2 or more primary targets (OR, 1.25; 95% CI, 1.14-1.37) and 2 or more key performance indices (OR, 1.50; 95% CI, 1.33-1.68) compared with the control group. For attainment of 2 or more primary targets, larger effects were observed among patients in low- and middle-income countries (OR, 1.50; 95% CI, 1.29-1.74) compared with high-income countries (OR, 1.20; 95% CI, 1.03-1.39) (P = .04).
Conclusions and Relevance: In this 12-month clinical trial, the use of information and communications technology and nurses to empower and engage patients did not change the number of clinical events but did reduce cardiometabolic risk factors among patients with type 2 diabetes, especially those in low- and middle-income countries in the Asia-Pacific region.
Trial Registration: ClinicalTrials.gov Identifier: NCT01631084.
OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.
METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.
RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.
DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.
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.
METHODS: A call for papers was announced on the website of Methods of Information in Medicine in April 2016 with submission deadline in September 2016. A peer review process was established to select the papers for the focus theme, managed by two guest editors.
RESULTS: Three papers were selected to be included in the focus theme. Topics range from contributions to patient care through implementation of clinical decision support functionality in clinical registries; analysing similar-purposed acute coronary syndrome registries of two countries and their registry-to-SNOMED CT maps; and data extraction for speciality population registries from electronic health record data rather than manual abstraction.
CONCLUSIONS: The focus theme gives insight into new developments related to disease registration. This applies to technical challenges such as data linkage and data as well as data structure abstraction, but also the utilisation for clinical decision making.
METHOD: This study is a retrospective review of 116 pediatric patients presenting with sore throat. Group A comprised patients before the implementation of the McIsaac rule and Group B comprised patients after the implementation.
RESULTS: Unnecessary throat swab cultures were reduced by 40% (P = 0.003). Redundant antibiotic prescriptions were reduced by 26.5% (P = 0.003) and the overall use of antibiotics was reduced by 22.1% (P = 0.003). The pediatricians' compliance rate to McIsaac rule criteria was 45% before implementation of the McIsaac rule, but improved to 67.9% (P = 0.0005) after implementation.
DISCUSSION: The McIsaac rule is an effective tool for the management of sore throat in children in Malaysia.