Dataset Information

Highlighting Clinical Metagenomics for Enhanced Diagnostic Decision-making: A Step Towards Wider Implementation.

ABSTRACT: Clinical metagenomics (CMg) is the discipline that refers to the sequencing of all nucleic acid material present within a clinical specimen with the intent to recover clinically relevant microbial information. From a diagnostic perspective, next-generation sequencing (NGS) offers the ability to rapidly identify putative pathogens and predict their antimicrobial resistance profiles to optimize targeted treatment regimens. Since the introduction of metagenomics nearly a decade ago, numerous reports have described successful applications in an increasing variety of biological specimens, such as respiratory secretions, cerebrospinal fluid, stool, blood and tissue. Considerable advancements in sequencing and computational technologies in recent years have made CMg a promising tool in clinical microbiology laboratories. Moreover, costs per sample and turnaround time from specimen receipt to clinical management continue to decrease, making the prospect of CMg more feasible. Many difficulties, however, are associated with CMg and warrant further improvements such as the informatics infrastructure and analytical pipelines. Thus, the current review focuses on comprehensively assessing applications of CMg for diagnostic and subtyping purposes.

SUBMITTER: Forbes JD

PROVIDER: S-EPMC6050174 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Highlighting Clinical Metagenomics for Enhanced Diagnostic Decision-making: A Step Towards Wider Implementation.

Forbes Jessica D JD Knox Natalie C NC Peterson Christy-Lynn CL Reimer Aleisha R AR

Computational and structural biotechnology journal 20180227

Clinical metagenomics (CMg) is the discipline that refers to the sequencing of all nucleic acid material present within a clinical specimen with the intent to recover clinically relevant microbial information. From a diagnostic perspective, next-generation sequencing (NGS) offers the ability to rapidly identify putative pathogens and predict their antimicrobial resistance profiles to optimize targeted treatment regimens. Since the introduction of metagenomics nearly a decade ago, numerous report ...[more]

PMID: 30026887

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Project description:Background. Although effective interventions for shared decision making (SDM) exist, there is a lack of uptake of these tools into clinical practice. "Nudges," which draw on behavioral economics and target automatic thinking processes, are used by policy makers to influence population-level behavior change. Nudges have not been applied in the context of SDM interventions but have potential to influence clinician motivation, a primary barrier to long-term adoption of SDM tools. Objective. Describe, evaluate, and propose recommendations for the use of a behavioral economics framework (MINDSPACE) on clinician motivation and behavior during implementation of a validated decision aid (DA) for left ventricular assist device at nine hospitals. Methods. Qualitative thematic analysis of process notes from stakeholder meetings during the first 6 months of implementation to identify examples of how the MINDSPACE framework was operationalized. Quantitative implementation progress was evaluated using the RE-AIM framework. Results. MINDSPACE components were translated into concrete approaches that leveraged influential stakeholders, fostered ownership over the DA and positive emotional associations, spread desirable norms across sites, and situated the DA within established default processes. DA reach to eligible patients increased from 9.8% in the first month of implementation to 70.0% in the sixth month. Larger gains in reach were observed following meetings using MINDSPACE approaches. Limitations. The MINDSPACE framework does not capture all possible influences on behavior and responses to nudges may differ across populations. Conclusions. Behavioral economics can be applied to implementation science to foster uptake of SDM tools by increasing clinician motivation. Our recommendations can help other researchers effectively apply these approaches in real-world settings when there are often limited incentives and opportunities to change organizational- or structural-level factors.

Project description:BACKGROUND:There is little information about the functions and behavior change techniques (BCTs) needed to implement shared decision making (SDM) in clinical practice. To guide future implementation initiatives, we sought to develop a BCT taxonomy for SDM implementation interventions. METHODS:This study is a secondary analysis of a 2018 Cochrane review on interventions for increasing the use of shared decision making by healthcare professionals. We examined all 87 studies included in the review. We extracted relevant information on each study intervention into a spreadsheet. Coders had undergone a training workshop on intervention functions and online training on BCT Taxonomy version 1 (BCTTv1). We performed functions and BCTs coding trials, and identified coding rules. We used Michie's guide for designing behavior change interventions to code the functions and BCTs used in the interventions. Coders met to compare coding and discrepancies were discussed until consensus was reached. Data was analyzed using simple descriptive statistics. RESULTS:Overall, 7 functions, 24 combinations of functions and 32 BCTs were used in the 87 SDM implementation interventions. The mean of functions per intervention was 2.5 and the mean of BCTs per intervention was 3.7. The functions Coercion and Restriction were not found. The most common function was Education (73 studies). Three combinations of functions were most common (e.g: Education + Persuasion, used in 10 studies). The functions associated with more effective SDM implementation interventions were Modeling and Training. The most effective combination of functions was Education + Training + Modeling + Enablement. The most commonly used BCT was Instruction on how to perform the behavior (43 studies). BCTs associated with more effective SDM implementation interventions were: Instruction on how to perform the behavior, Demonstration of the behavior, Feedback on behavior, Pharmacological support, Material reward, and Biofeedback. Twenty-five BCTs were associated with less effective SDM implementation interventions. Four new BCTs were identified: General information to support the behavior, Tailoring, Exercises to conceptually prepare for the behavior, and Experience sharing and learning. CONCLUSIONS:We established a BCT taxonomy specific to the field of SDM to guide future SDM implementation interventions. Four new BCTs should be added to BCTTv1.

Dataset Information

Highlighting Clinical Metagenomics for Enhanced Diagnostic Decision-making: A Step Towards Wider Implementation.

Publications

Highlighting Clinical Metagenomics for Enhanced Diagnostic Decision-making: A Step Towards Wider Implementation.

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