A Systematic Review of Decision-Support Tools for Regenerative Medicine

2018 ◽  
Author(s):  
Ching Lam ◽  
Edward Meinert ◽  
Abrar Alturkistani ◽  
Alison Carter ◽  
Jeffery M. Karp ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Regenerative Medicine ◽  
Decision Support Tools ◽  
Support Tools

scholarly journals Decision Support Tools for Regenerative Medicine: Systematic Review

10.2196/12448 ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. e12448 ◽  
Author(s):  
Ching Lam ◽  
Edward Meinert ◽  
Abrar Alturkistani ◽  
Alison R Carter ◽  
Jeffrey Karp ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Regenerative Medicine ◽  
Decision Support Tools ◽  
Support Tools

scholarly journals Decision support tools to improve cancer diagnostic decision making in primary care: a systematic review

2019 ◽  
Vol 69 (689) ◽  
pp. e809-e818 ◽  
Author(s):  
Sophie Chima ◽  
Jeanette C Reece ◽  
Kristi Milley ◽  
Shakira Milton ◽  
Jennifer G McIntosh ◽  
...  
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Decision Making ◽  
Decision Support ◽  
Cost Effectiveness ◽  
Cancer Diagnosis ◽  
Decision Support Tools ◽  
Diagnostic Decision ◽  
Support Tools ◽  
Diagnostic Decision Making

BackgroundThe diagnosis of cancer in primary care is complex and challenging. Electronic clinical decision support tools (eCDSTs) have been proposed as an approach to improve GP decision making, but no systematic review has examined their role in cancer diagnosis.AimTo investigate whether eCDSTs improve diagnostic decision making for cancer in primary care and to determine which elements influence successful implementation.Design and settingA systematic review of relevant studies conducted worldwide and published in English between 1 January 1998 and 31 December 2018.MethodPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched, and a consultation of reference lists and citation tracking was carried out. Exclusion criteria included the absence of eCDSTs used in asymptomatic populations, and studies that did not involve support delivered to the GP. The most relevant Joanna Briggs Institute Critical Appraisal Checklists were applied according to study design of the included paper.ResultsOf the nine studies included, three showed improvements in decision making for cancer diagnosis, three demonstrated positive effects on secondary clinical or health service outcomes such as prescribing, quality of referrals, or cost-effectiveness, and one study found a reduction in time to cancer diagnosis. Barriers to implementation included trust, the compatibility of eCDST recommendations with the GP’s role as a gatekeeper, and impact on workflow.ConclusioneCDSTs have the capacity to improve decision making for a cancer diagnosis, but the optimal mode of delivery remains unclear. Although such tools could assist GPs in the future, further well-designed trials of all eCDSTs are needed to determine their cost-effectiveness and the most appropriate implementation methods.

scholarly journals Decision Support Tools for Regenerative Medicine: Systematic Review (Preprint)

2018 ◽  
Author(s):  
Ching Lam ◽  
Edward Meinert ◽  
Abrar Alturkistani ◽  
Alison R. Carter ◽  
Jeffrey Karp ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Gene Therapy ◽  
Decision Support ◽  
Regenerative Medicine ◽  
Product Development ◽  
Cost Effective ◽  
Decision Support Tools ◽  
Cell Therapies ◽  
Allogeneic Cell Therapy ◽  
Support Tools

BACKGROUND Decisional tools have demonstrated their importance in informing manufacturing and commercial decisions in the monoclonal antibody domain. Recent approved therapies in regenerative medicine have shown great clinical benefits to patients. OBJECTIVE The objective of this review was to investigate what decisional tools are available and what issues and gaps have been raised for their use in regenerative medicine. METHODS We systematically searched MEDLINE to identify articles on decision support tools relevant to tissue engineering, and cell and gene therapy, with the aim of identifying gaps for future decisional tool development. We included published studies in English including a description of decisional tools in regenerative medicines. We extracted data using a predesigned Excel table and assessed the data both quantitatively and qualitatively. RESULTS We identified 9 articles addressing key decisions in manufacturing and product development challenges in cell therapies. The decision objectives, parameters, assumptions, and solution methods were analyzed in detail. We found that all decisional tools focused on cell therapies, and 6 of the 9 reviews focused on allogeneic cell therapy products. We identified no available tools on tissue-engineering and gene therapy products. These studies addressed key decisions in manufacturing and product development challenges in cell therapies, such as choice of technology, through modeling. CONCLUSIONS Our review identified a limited number of decisional tools. While the monoclonal antibodies and biologics decisional tool domain has been well developed and has shown great importance in driving more cost-effective manufacturing processes and better investment decisions, there is a lot to be learned in the regenerative medicine domain. There is ample space for expansion, especially with regard to autologous cell therapies, tissue engineering, and gene therapies. To consider the problem more comprehensively, the full needle-to-needle process should be modeled and evaluated.

scholarly journals Can evidence-based decision support tools transform antibiotic management? A systematic review and meta-analyses

2020 ◽  
Vol 75 (5) ◽  
pp. 1099-1111 ◽  
Author(s):  
Mah Laka ◽  
Adriana Milazzo ◽  
Tracy Merlin
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Methodological Quality ◽  
Length Of Hospital Stay ◽  
Clinical Decision Support Systems ◽  
Antibiotic Prescribing ◽  
Decision Support Tools ◽  
Support Tools ◽  
Antibiotic Management ◽  
Meta Analyses

Abstract Objectives To assess the effectiveness of clinical decision support systems (CDSSs) at reducing unnecessary and suboptimal antibiotic prescribing within different healthcare settings. Methods A systematic review of published studies was undertaken with seven databases from database inception to November 2018. A protocol was developed using the PRISMA-P checklist and study selection criteria were determined prior to performing the search. Critical appraisal of studies was undertaken using relevant tools. Meta-analyses were performed using a random-effects model to determine whether CDSS use affected optimal antibiotic management. Results Fifty-seven studies were identified that reported on CDSS effectiveness. Most were non-randomized studies with low methodological quality. However, randomized controlled trials of moderate methodological quality were available and assessed separately. The meta-analyses indicated that appropriate antibiotic therapy was twice as likely to occur following the implementation of CDSSs (OR 2.28, 95% CI 1.82–2.86, k = 20). The use of CDSSs was also associated with a relative decrease (18%) in mortality (OR 0.82, 95% CI 0.73–0.91, k = 18). CDSS implementation also decreased the overall volume of antibiotic use, length of hospital stay, duration and cost of therapy. The magnitude of the effect did vary by study design, but the direction of the effect was consistent in favouring CDSSs. Conclusions Decision support tools can be effective to improve antibiotic prescribing, although there is limited evidence available on use in primary care. Our findings suggest that a focus on system requirements and implementation processes would improve CDSS uptake and provide more definitive benefits for antibiotic stewardship.

Shared Decision-Support Tools in Hospital Emergency Departments: A Systematic Review

2019 ◽  
Vol 45 (4) ◽  
pp. 386-393 ◽  
Author(s):  
Valle Coronado-Vázquez ◽  
Juan Gómez-Salgado ◽  
Javier Cerezo-Espinosa de los Monteros ◽  
Miren Arantzazu García-Colinas
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Emergency Departments ◽  
Decision Support Tools ◽  
Shared Decision ◽  
Hospital Emergency ◽  
Support Tools ◽  
Hospital Emergency Departments

scholarly journals Use of Decision Support Tools to Empower Pregnant Women: Systematic Review (Preprint)

2020 ◽  
Author(s):  
Elin Ngo ◽  
Maria Bich-Thuy Truong ◽  
Hedvig Nordeng
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Pregnant Women ◽  
Management Tool ◽  
Control Group ◽  
Decision Support Tools ◽  
Patient Centered ◽  
Review Management ◽  
Support Tools ◽  
Health Related

BACKGROUND Women face many health-related decisions during pregnancy. Digitalization, new technology, and a greater focus on empowering patients have driven the development of patient-centered decision support tools. OBJECTIVE This systematic review provides an overview of studies investigating the effect of patient-centered decision support tools for pregnant women. METHODS We searched 5 online databases, MEDLINE, EMBASE, Web of Science, PsycINFO, and Scopus, from inception to December 1, 2019. Two independent researchers screened titles, abstracts, and full-texts against the inclusion criteria. All studies investigating the effect of patient-centered decision support tools for health-related issues among pregnant women were included. Study characteristics and results were extracted using the review management tool Rayyan and analyzed according to topic, type of decision support tools, control group, outcome measurements, and results. RESULTS The 25 eligible studies covered a range of health topics, including prenatal screening (n=10), gestational diabetes and weight gain (n=7), lifestyle (n=3), blood pressure and preeclampsia (n=2), depression (n=1), asthma (n=1), and psychological well-being (n=1). In general, the use of decision support tools increased women's knowledge, and recording symptoms enhanced satisfaction with maternity care. CONCLUSIONS The opportunities created by digitalization and technology should be used to develop innovative patient-centered decision support tools tailored to support pregnant women. Effect on clinical outcomes should be documented.

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