Dataset Information

Quality Tolerance Limits: Framework for Successful Implementation in Clinical Development.

ABSTRACT: The International Council for Harmonisation (ICH) E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf . Accessed 5 Dec 2019) introduced Quality Tolerance Limits (QTLs) to the industry, and in doing so, modernized quality control for clinical trials. QTLs provide measured feedback on clinical trial parameters previously only used by statistical and clinical functions to track trial progress toward endpoints. Elevating these measures as part of the Quality Management System (QMS) provides greater visibility across clinical trial functions and the enterprise as well as to measures that are important indicators of the state of participant protection and reliability of trial results. In support of this new requirement, TransCelerate developed a framework to guide industry sponsors and their agents in implementing QTLs. This QTL Framework is intended to aid industry's ability to improve the quality of clinical research through the implementation of QTLs in a way that helps protect trial participants and reliability of trial results while meeting Health Authority (HA) expectations. The framework is intended to maximize efficiency and minimize confusion in the implementation of QTLs. The framework includes proposed approaches for implementation of QTLs for a clinical trial as defined in Section 5.0.4 and 5.0.7 of ICH E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf . Accessed 5 Dec 2019) and considerations for setting thresholds.

SUBMITTER: Bhagat R

PROVIDER: S-EPMC7864825 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Quality Tolerance Limits: Framework for Successful Implementation in Clinical Development.

Bhagat Ruma R Bojarski Lukasz L Chevalier Soazig S Görtz Dagmar R DR Le Meignen Stéphanie S Makowski Marcin M Nadolny Patrick P Pillwein Marion M Suprin Melissa M Turri Sabine S

Therapeutic innovation & regulatory science 20200903 2

The International Council for Harmonisation (ICH) E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf . Accessed 5 Dec 2019) introduced Quality Tolerance Limits (QTLs) to the industry, and in doing so, modernized quality control for clinical trials. QTLs provide measured feedback on clinical trial parameters previously ...[more]

PMID: 32885397

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Project description:BackgroundThe health care quality improvement movement is a complex enterprise. Implementing clinical quality initiatives requires attitude and behaviour change on the part of clinicians, but this has proven to be difficult. In an attempt to solve this kind of behavioural challenge, the theoretical domains framework (TDF) has been developed. The TDF consists of 14 domains from psychological and organisational theory said to influence behaviour change. No systematic research has been conducted into the ways in which clinical quality initiatives map on to the domains of the framework. We therefore conducted a qualitative mapping experiment to determine to what extent, and in what ways, the TDF is relevant to the implementation of clinical quality interventions.MethodsWe conducted a thematic synthesis of the qualitative literature exploring clinicians' perceptions of various clinical quality interventions. We analysed and synthesised 50 studies in total, in five domains of clinical quality interventions: clinical quality interventions in general, structural interventions, audit-type interventions, interventions aimed at making practice more evidence-based, and risk management interventions. Data were analysed thematically, followed by synthesis of these themes into categories and concepts, which were then mapped to the domains of the TDF.ResultsOur results suggest that the TDF is highly relevant to the implementation of clinical quality interventions. It can be used to map most, if not all, of the attitudinal and behavioural barriers and facilitators of uptake of clinical quality interventions. Each of these 14 domains appeared to be relevant to many different types of clinical quality interventions. One possible additional domain might relate to perceived trustworthiness of those instituting clinical quality interventions.ConclusionsThe TDF can be usefully applied to a wide range of clinical quality interventions. Because all 14 of the domains emerged as relevant, and we did not identify any obvious differences between different kinds of clinical quality interventions, our findings support an initially broad approach to identifying barriers and facilitators, followed by a "drilling down" to what is most contextually salient. In future, it may be possible to establish a model of clinical quality policy implementation using the TDF.

Project description:BackgroundRelatively little guidance exists on how to use virtual implementation facilitation to successfully implement evidence-based practices and innovations into clinical programs. Yet virtual methods are increasingly common. They have potentially wider reach, emergent public health situations necessitate their use, and restrictions on resources can make them more attractive. We therefore outline a set of principles for virtual external implementation facilitation and a series of recommendations based on extensive experience successfully using virtual external implementation facilitation in a national program.Model and recommendationsSuccess in virtual external implementation facilitation may be achieved by facilitators applying three overarching principles: pilot everything, incorporate a model, and prioritize metacognition. Five practical principles also help: plan in advance, communicate in real time, build relationships, engage participants, and construct a virtual room for participants. We present eight concrete suggestions for enacting the practical principles: (1) assign key facilitation roles to facilitation team members to ensure the program runs smoothly; (2) create small cohorts of participants so they can have meaningful interactions; (3) provide clarity and structure for all participant interactions; (4) structure program content to ensure key points are described, reinforced, and practiced; (5) use visuals to supplement audio content; (6) build activities into the agenda that enable participants to immediately apply knowledge at their own sites, separate from the virtual experience; (7) create backup plans whenever possible; and (8) engage all participants in the program. These principles represent a novel conceptualization of virtual external implementation facilitation, giving structure to a process that has been, to date, inadequately described. The associated actions are demonstrably useful in supporting the principles and offer teams interested in virtual external implementation facilitation concrete methods by which to ensure success. Our examples stem from experiences in healthcare. But the principles can, in theory, be applied to virtual external implementation facilitation regardless of setting, as they and the associated actions are not setting specific.

Project description:The PARiHS framework (Promoting Action on Research Implementation in Health Services) has proved to be a useful practical and conceptual heuristic for many researchers and practitioners in framing their research or knowledge translation endeavours. However, as a conceptual framework it still remains untested and therefore its contribution to the overall development and testing of theory in the field of implementation science is largely unquantified.This being the case, the paper provides an integrated summary of our conceptual and theoretical thinking so far and introduces a typology (derived from social policy analysis) used to distinguish between the terms conceptual framework, theory and model - important definitional and conceptual issues in trying to refine theoretical and methodological approaches to knowledge translation. Secondly, the paper describes the next phase of our work, in particular concentrating on the conceptual thinking and mapping that has led to the generation of the hypothesis that the PARiHS framework is best utilised as a two-stage process: as a preliminary (diagnostic and evaluative) measure of the elements and sub-elements of evidence (E) and context (C), and then using the aggregated data from these measures to determine the most appropriate facilitation method. The exact nature of the intervention is thus determined by the specific actors in the specific context at a specific time and place. In the process of refining this next phase of our work, we have had to consider the wider issues around the use of theories to inform and shape our research activity; the ongoing challenges of developing robust and sensitive measures; facilitation as an intervention for getting research into practice; and finally to note how the current debates around evidence into practice are adopting wider notions that fit innovations more generally.The paper concludes by suggesting that the future direction of the work on the PARiHS framework is to develop a two-stage diagnostic and evaluative approach, where the intervention is shaped and moulded by the information gathered about the specific situation and from participating stakeholders. In order to expedite the generation of new evidence and testing of emerging theories, we suggest the formation of an international research implementation science collaborative that can systematically collect and analyse experiences of using and testing the PARiHS framework and similar conceptual and theoretical approaches. We also recommend further refinement of the definitions around conceptual framework, theory, and model, suggesting a wider discussion that embraces multiple epistemological and ontological perspectives.

Project description:ObjectiveTo systematically survey existing definitions, concepts, and criteria of clinical research quality, both developed by stakeholder groups as well as in the medical literature. This study serves as a first step in the development of a comprehensive framework for the quality of clinical research.Study design and settingWe systematically and in duplicate searched definitions, concepts and criteria of clinical research quality on websites of stakeholders in clinical research until no further insights emerged and in MEDLINE up to February 2015. Stakeholders included governmental bodies, regulatory agencies, the pharmaceutical industry, academic and commercial contract research organizations, initiatives, research ethics committees, patient organizations and funding agencies from 13 countries. Data synthesis involved descriptive and qualitative analyses following the Framework Method on definitions, concepts, and criteria of clinical research quality. Descriptive codes were applied and grouped into clusters to identify common and stakeholder-specific quality themes.ResultsStakeholder concepts on how to assure quality throughout study conduct or articles on quality assessment tools were common, generally with no a priori definition of the term quality itself. We identified a total of 20 explicit definitions of clinical research quality including varying quality dimensions and focusing on different stages in the clinical research process. Encountered quality dimensions include ethical conduct, patient safety/rights/priorities, internal validity, precision of results, generalizability or external validity, scientific and societal relevance, transparency and accessibility of information, research infrastructure and sustainability. None of the definitions appeared to be comprehensive either in terms of quality dimensions, research stages, or stakeholder perspectives.ConclusionClinical research quality is often discussed but rarely defined. A framework defining clinical research quality across stakeholders' individual perspectives is desirable to facilitate discussion, assessment, and improvement of quality at all stages of clinical research.

Project description:BackgroundClinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence-enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice.ObjectiveThe purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence-enabled clinical decision support system evaluation framework.MethodsAn artificial intelligence-enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents.ResultsThe Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension.ConclusionsUser acceptance is the central dimension of artificial intelligence-enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

Dataset Information

Quality Tolerance Limits: Framework for Successful Implementation in Clinical Development.

Publications

Quality Tolerance Limits: Framework for Successful Implementation in Clinical Development.

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