Study protocol: a cluster randomized controlled trial of web-based decision support tools for increasing BRCA1/2 genetic counseling referral in primary care

Background BRCA1 and BRCA2 mutations confer a substantial breast risk of developing breast cancer to those who carry them. For this reason, the United States Preventative Services Task Force (USPSTF) has recommended that all women be screened in the primary care setting for a family history indicative of a mutation, and women with strong family histories of breast or ovarian cancer be referred to genetic counseling. However, few high-risk women are being routinely screened and fewer are referred to genetic counseling. To address this need we have developed two decision support tools that are integrated into clinical care. Method This study is a cluster randomized controlled trial of high-risk patients and their health care providers. Patient-provider dyads will be randomized to receive either standard education that is supplemented with the patient-facing decision aid, RealRisks, and the provider-facing Breast Cancer Risk Navigation Toolbox (BNAV) or standard education alone. We will assess these tools’ effectiveness in promoting genetic counseling uptake and informed and shared decision making about genetic testing. Discussion If found to be effective, these tools can help integrate genomic risk assessment into primary care and, ultimately, help expand access to risk-appropriate breast cancer prevention options to a broader population of high-risk women. Trial registration This trial is retrospectively registered with ClinicalTrials.gov Identifier: NCT03470402 : 20 March 2018

Beyond adoption: A new framework for theorising and evaluating Non-adoption, Abandonment and challenges to Scale-up, Spread and Sustainability (NASSS) of health and care technologies

© 2017 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background: Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. Objective: Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. Methods: The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs—video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing—using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. Results: The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs.Peer reviewe

IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study

BACKGROUND: Factors of IT adoption have largely been discussed in the literature. However, existing frameworks (such as TAM or TTF) are failing to include one important aspect, the interaction between user and task. METHOD: Based on a literature study and a case study, we developed the FITT framework to help analyse the socio-organisational-technical factors that influence IT adoption in a health care setting. RESULTS: Our FITT framework ("Fit between Individuals, Task and Technology") is based on the idea that IT adoption in a clinical environment depends on the fit between the attributes of the individual users (e.g. computer anxiety, motivation), attributes of the technology (e.g. usability, functionality, performance), and attributes of the clinical tasks and processes (e.g. organisation, task complexity). We used this framework in the retrospective analysis of a three-year case study, describing the adoption of a nursing documentation system in various departments in a German University Hospital. We will show how the FITT framework helped analyzing the process of IT adoption during an IT implementation: we were able to describe every found IT adoption problem with regard to the three fit dimensions, and any intervention on the fit can be described with regard to the three objects of the FITT framework (individual, task, technology). We also derive facilitators and barriers to IT adoption of clinical information systems. CONCLUSION: This work should support a better understanding of the reasons for IT adoption failures and therefore enable better prepared and more successful IT introduction projects. We will discuss, however, that from a more epistemological point of view, it may be difficult or even impossible to analyse the complex and interacting factors that predict success or failure of IT projects in a socio-technical environment

Using text messages to support recovering substance misusers

YesBackground: The use of digital technology in health and social care is developing rapidly. It is promoted in UK policy and research which suggests varied results surrounding its implementation and outcomes. Introduction: This article aimed to test the implementation and outcomes of a short messaging service sent to a dedicated phone. The target cohort were drug treatment clients in two sites in Northern England. Materials and methods: Through staff focus groups and interviews with a small cohort of clients, the implementation and perceptions of the system were examined. Results: Nineteen participants were recruited to site 1 (15 male, 4 female, average age=37.7 years) and 12 participants were recruited to site 2 (9 male, 3 female, average age=40.3 years). One outcome that was of interest was well-being in treatment which, in this study, was described as an overall sense of feeling better rather than just focusing on the rehabilitation aspect of the programme. Other outcomes included: the successful completion of treatment and any relapse or associated reported drug use. Discussion: The system shows some evidence of its ‘social actor’ role; however, its implementation was hindered by staff citing that it called for increased resources. For future implementation the use of client’s own phones may be considered which may help to embed the system more fully in recovery planning and targeting clients at a different treatment stage. Conclusions: Despite some indications of positive results for clients and a perception that the system may have value as an addition to existing clinical interventions, more evaluation is required to determine whether this system can be implemented in a drug treatment setting

Evidence in the learning organization

<p>Abstract</p> <p>Background</p> <p>Organizational leaders in business and medicine have been experiencing a similar dilemma: how to ensure that their organizational members are adopting work innovations in a timely fashion. Organizational leaders in healthcare have attempted to resolve this dilemma by offering specific solutions, such as evidence-based medicine (EBM), but organizations are still not systematically adopting evidence-based practice innovations as rapidly as expected by policy-makers (the knowing-doing gap problem). Some business leaders have adopted a systems-based perspective, called the learning organization (LO), to address a similar dilemma. Three years ago, the Society of General Internal Medicine's Evidence-based Medicine Task Force began an inquiry to integrate the EBM and LO concepts into one model to address the knowing-doing gap problem.</p> <p>Methods</p> <p>During the model development process, the authors searched several databases for relevant LO frameworks and their related concepts by using a broad search strategy. To identify the key LO frameworks and consolidate them into one model, the authors used consensus-based decision-making and a narrative thematic synthesis guided by several qualitative criteria. The authors subjected the model to external, independent review and improved upon its design with this feedback.</p> <p>Results</p> <p>The authors found seven LO frameworks particularly relevant to evidence-based practice innovations in organizations. The authors describe their interpretations of these frameworks for healthcare organizations, the process they used to integrate the LO frameworks with EBM principles, and the resulting Evidence in the Learning Organization (ELO) model. They also provide a health organization scenario to illustrate ELO concepts in application.</p> <p>Conclusion</p> <p>The authors intend, by sharing the LO frameworks and the ELO model, to help organizations identify their capacities to learn and share knowledge about evidence-based practice innovations. The ELO model will need further validation and improvement through its use in organizational settings and applied health services research.</p

Design and methods for a randomized clinical trial treating comorbid obesity and major depressive disorder

<p>Abstract</p> <p>Background</p> <p>Obesity is often comorbid with depression and individuals with this comorbidity fare worse in behavioral weight loss treatment. Treating depression directly prior to behavioral weight loss treatment might bolster weight loss outcomes in this population, but this has not yet been tested in a randomized clinical trial.</p> <p>Methods and design</p> <p>This randomized clinical trial will examine whether behavior therapy for depression administered prior to standard weight loss treatment produces greater weight loss than standard weight loss treatment alone. Obese women with major depressive disorder (N = 174) will be recruited from primary care clinics and the community and randomly assigned to one of the two treatment conditions. Treatment will last 2 years, and will include a 6-month intensive treatment phase followed by an 18-month maintenance phase. Follow-up assessment will occur at 6-months and 1- and 2 years following randomization. The primary outcome is weight loss. The study was designed to provide 90% power for detecting a weight change difference between conditions of 3.1 kg (standard deviation of 5.5 kg) at 1-year assuming a 25% rate of loss to follow-up. Secondary outcomes include depression, physical activity, dietary intake, psychosocial variables and cardiovascular risk factors. Potential mediators (e.g., adherence, depression, physical activity and caloric intake) of the intervention effect on weight change will also be examined.</p> <p>Discussion</p> <p>Treating depression before administering intensive health behavior interventions could potentially boost the impact on both mental and physical health outcomes.</p> <p>Trial registration</p> <p>NCT00572520</p

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview

Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care