ORCID Profile
0000-0002-0614-123X
Current Organisations
Taipei Medical University
,
University of New South Wales
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Publisher: BMJ
Date: 04-2021
DOI: 10.1136/BMJHCI-2020-100291
Abstract: To conduct systematic review and meta-analysis of interventional studies to investigate the impact of diabetes self-management education and support (DSMES) apps on adherence in patients with type 2 diabetes mellitus (T2D). PubMed, Embase, CENTRAL, Web of Science, Scopus and ProQuest were searched, in addition to references of identified articles and similar reviews. Experimental studies, reported in English, assessing DSMES app intervention’s impact on adherence and clinical outcomes of patients with T2D compared with usual care were included. Study bias was assessed using Cochrane Risk of Bias V.2.0 tool. Analysis plan involved narrative synthesis, moderator and meta-analysis. Six randomised controlled trials were included, involving 696 participants (average age 57.6 years, SD 10.59). Mobile apps were mostly used for imputing clinical data, dietary intake or physical activity, and transmitting information to the provider. At 3 months, DSMES apps proved effective in improving medication adherence (standardized mean difference (SMD)=0.393, 95% CI 0.17 to 0.61), glycated haemoglobin (HbA1c) (mean difference (MD)=−0.314, 95% CI −0.477 to –0.151) and Body Mass Index (BMI) (MD=−0.28, 95% CI −0.545 to –0.015). All pooled estimates had low heterogeneity ( I 2 0%). Four studies had moderate risk of bias while one each was judged to be low and high risks, respectively. DSMES apps had significant small to moderate effects on medication adherence, HbA1c and BMI of patients with T2D compared with usual care. Apps were described as reliable, easy to use and convenient, though participants were required to be phone literate. Evidence comes from feasibility trials with generally moderate risk of bias. Larger trials with longer follow-up periods using theory-based interventions are required to improve current evidence.
Publisher: BMJ
Date: 08-2022
Publisher: JMIR Publications Inc.
Date: 18-08-2020
Abstract: n this paper we propose the idea that Artificial intelligence (AI) is ushering in a new era of “Earlier Medicine,” which is a predictive approach for disease prevention based on AI modeling and big data. The flourishing health care technological landscape is showing great potential—from diagnosis and prescription automation to the early detection of disease through efficient and cost-effective patient data screening tools that benefit from the predictive capabilities of AI. Monitoring the trajectories of both in- and outpatients has proven to be a task AI can perform to a reliable degree. Predictions can be a significant advantage to health care if they are accurate, prompt, and can be personalized and acted upon efficiently. This is where AI plays a crucial role in “Earlier Medicine” implementation.
Publisher: BMJ
Date: 09-2022
Publisher: JMIR Publications Inc.
Date: 11-08-2020
DOI: 10.2196/17211
Abstract: In this paper we propose the idea that Artificial intelligence (AI) is ushering in a new era of “Earlier Medicine,” which is a predictive approach for disease prevention based on AI modeling and big data. The flourishing health care technological landscape is showing great potential—from diagnosis and prescription automation to the early detection of disease through efficient and cost-effective patient data screening tools that benefit from the predictive capabilities of AI. Monitoring the trajectories of both in- and outpatients has proven to be a task AI can perform to a reliable degree. Predictions can be a significant advantage to health care if they are accurate, prompt, and can be personalized and acted upon efficiently. This is where AI plays a crucial role in “Earlier Medicine” implementation.
Publisher: Public Library of Science (PLoS)
Date: 28-07-2021
DOI: 10.1371/JOURNAL.PNTD.0008824
Abstract: Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.
Publisher: JMIR Publications Inc.
Date: 26-11-2019
Abstract: n this paper we propose the idea that Artificial intelligence (AI) is ushering in a new era of “Earlier Medicine,” which is a predictive approach for disease prevention based on AI modeling and big data. The flourishing health care technological landscape is showing great potential—from diagnosis and prescription automation to the early detection of disease through efficient and cost-effective patient data screening tools that benefit from the predictive capabilities of AI. Monitoring the trajectories of both in- and outpatients has proven to be a task AI can perform to a reliable degree. Predictions can be a significant advantage to health care if they are accurate, prompt, and can be personalized and acted upon efficiently. This is where AI plays a crucial role in “Earlier Medicine” implementation.
Publisher: JMIR Publications Inc.
Date: 26-08-2020
DOI: 10.2196/23645
Publisher: Public Library of Science (PLoS)
Date: 11-2021
DOI: 10.1371/JOURNAL.PMED.1003829
Abstract: The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45) followed by Quebec, 27 (18 to 43) Alberta, 23 (9 to 38) UK, 12 (7 to 20) and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.
Location: Pakistan
No related grants have been discovered for Usman Iqbal.