Participatory methods to support team science development for predictive analytics in health.
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ABSTRACT:
SUBMITTER: Arevian AC
PROVIDER: S-EPMC6199545 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This perspective provides a rationale for redesigning and a framework for expanding the graduate health science analytics and biomedical doctoral program curricula. It responds to digital revolution pressures, ubiquitous proliferation of big biomedical data, substantial recent advances in scientific technologies, and rapid progress in health analytics. Specifically, the paper presents a set of common prerequisites, a proposal for core computational and data analytic curriculum, and a list of expected outcome competencies for graduates of doctoral health science and biomedical programs. The manuscript emphasizes the necessity for coordinated efforts of all stakeholders, including trainees, educators, academic institutions, funding agencies, and policy makers. Concrete recommendations are presented of how to ensure graduates with terminal health science analytics and biomedical degrees are trained and able to continuously self-learn, effectively communicate across disciplines, and promote adaptation and change to counteract the relentless pace of automation and the law of diminishing returns.
| S-EPMC7031195 | biostudies-literature
Project description:The trajectory of aging is profoundly impacted by the physical and social environmental contexts in which we live. While "top-down" policy activities can have potentially wide impacts on such contexts, they often take time, resources, and political will, and therefore can be less accessible to underserved communities. This article describes a "bottom-up", resident-engaged method to advance local environmental and policy change, called Our Voice, that can complement policy-level strategies for improving the health, function, and well-being of older adults. Using the World Health Organization's age-friendly cities global strategy, we describe the Our Voice citizen science program of research that has specifically targeted older adults as environmental change agents to improve their own health and well-being as well as that of their communities. Results from 14 Our Voice studies that have occurred across five continents demonstrate that older adults can learn to use mobile technology to systematically capture and collectively analyze their own data. They can then successfully build consensus around high-priority issues that can be realistically changed and work effectively with local stakeholders to enact meaningful environmental and policy changes that can help to promote healthy aging. The article ends with recommended next steps for growing the resident-engaged citizen science field to advance the health and welfare of all older adults.
| S-EPMC7084614 | biostudies-literature
Project description:BACKGROUND:When health service providers (HSP) plan to develop electronic health (eHealth) resources for health service users (HSU), the latter's involvement is essential. Typically, however, HSP, HSU, and technology developers engaged to produce the resources lack expertise in participatory design methodologies suited to the eHealth context. Furthermore, it can be difficult to identify an established method to use, or determine how to work stepwise through any particular process. OBJECTIVE:We sought to summarize the evidence about participatory methods and frameworks used to engage HSU in the development of eHealth resources from the beginning of the design process. METHODS:We searched for studies reporting participatory processes in initial development of eHealth resources from 2006 to 2016 in 9 bibliographic databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Emcare, Cochrane Library, Web of Science, ACM Guide to Computing Literature, and IEEE Xplore. From 15,117 records initially screened on title and abstract for relevance to eHealth and early participatory design, 603 studies were assessed for eligibility on full text. The remaining 90 studies were rated by 2 reviewers using the Mixed Methods Appraisal Tool Version 2011 (Pluye et al; MMAT) and analyzed with respect to health area, purpose, technology type, and country of study. The 30 studies scoring 90% or higher on MMAT were included in a detailed qualitative synthesis. RESULTS:Of the 90 MMAT-rated studies, the highest reported (1) health areas were cancer and mental disorders, (2) eHealth technologies were websites and mobile apps, (3) targeted populations were youth and women, and (4) countries of study were the United States, the United Kingdom, and the Netherlands. Of the top 30 studies the highest reported participatory frameworks were User-Centered Design, Participatory Action Research Framework, and the Center for eHealth Research and Disease Management (CeHRes) Roadmap, and the highest reported model underpinning development and engagement was Social Cognitive Theory. Of the 30 studies, 4 reported on all the 5 stages of the CeHRes Roadmap. CONCLUSIONS:The top 30 studies yielded 24 participatory frameworks. Many studies referred to using participatory design methods without reference to a framework. The application of a structured framework such as the CeHRes Roadmap and a model such as Social Cognitive Theory creates a foundation for a well-designed eHealth initiative that ensures clarity and enables replication across participatory design projects. The framework and model need to be clearly articulated and address issues that include resource availability, responsiveness to change, and the criteria for good practice. This review creates an information resource for future eHealth developers, to guide the design of their eHealth resource with a framework that can support further evaluation and development. TRIAL REGISTRATION:PROSPERO CRD42017053838; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=53838.
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Project description:Natural scientists from Climate Central and social scientists from Carnegie Mellon University collaborated to develop science communications aimed at presenting personalized coastal flood risk information to the public. We encountered four main challenges: agreeing on goals; balancing complexity and simplicity; relying on data, not intuition; and negotiating external pressures. Each challenge demanded its own approach. We navigated agreement on goals through intensive internal communication early on in the project. We balanced complexity and simplicity through evaluation of communication materials for user understanding and scientific content. Early user test results that overturned some of our intuitions strengthened our commitment to testing communication elements whenever possible. Finally, we did our best to negotiate external pressures through regular internal communication and willingness to compromise.
| S-EPMC4183174 | biostudies-other
Project description:BackgroundAntimicrobial resistance poses a threat to patient safety worldwide. To stop antimicrobial resistance, Antimicrobial Stewardship Programs (ASPs; programs for optimizing antimicrobial use), need to be implemented. Within these programs, nurses are important actors, as they put antimicrobial treatment into effect. To optimally support nurses in ASPs, they should have access to information that supports them in their preparation, administration and monitoring tasks. In addition, it should help them to detect possible risks or adverse events associated with antimicrobial therapy. In this formative study, we investigate how nurses' can be supported in ASPs by means of an eHealth intervention that targets their information needs.MethodsWe applied a participatory development approach that involves iterative cycles in which health care workers, mostly nurses, participate. Focus groups, observations, prototype evaluations (via a card sort task and a scenario-based information searching task) and interviews are done with stakeholders (nurses, managers, pharmacist, and microbiologist) on two pulmonary wards of a 1000-bed teaching hospital.ResultsTo perform the complex antimicrobial-related tasks well, nurses need to consult various information sources on a myriad of occasions. In addition, the current information infrastructure is unsupportive of ASP-related tasks, mainly because information is not structured to match nurse tasks, is hard to find, out of date, and insufficiently supportive of awareness. Based our findings, we created a concept for a nurse information application. We attuned the application's functionality, content, and structure to nurse work practice and tasks.ConclusionsBy applying a participatory development approach, we showed that task support is a basic need for nurses. Participatory development proved useful regarding several aspects. First, it allows for combining bottom-up needs (nurses') and top-down legislations (medical protocols). Second, it enabled us to fragmentise and analyse tasks and to reduce and translate extensive information into task-oriented content. Third, this facilitated a tailored application to support awareness and enhance patient safety. Finally, the involvement of stakeholders created commitment and ownership, and helped to weigh needs from multiple perspectives.
| S-EPMC4074392 | biostudies-literature
Project description:There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For algorithms based on "black box" machine learning methods, software for algorithm implementation is a must. Hiding algorithms for commercial exploitation is unethical, because there is no possibility to assess whether algorithms work as advertised or to monitor when and how algorithms are updated. Journals and funders should demand maximal transparency for publications on predictive algorithms, and clinical guidelines should only recommend publicly available algorithms.
| S-EPMC6857503 | biostudies-literature
Project description:BackgroundThe solution to the growing problem of rural residents lacking health care access may be found in the use of telemedicine and mobile health (mHealth). Using mHealth or telemedicine allows patients from rural or remote areas to have better access to health care.ObjectiveThe objective of this study was to understand factors influencing the choice of communication medium for receiving care, through the analysis of mHealth versus telemedicine encounters with a virtual urgent clinic.MethodsWe conducted a postdeployment evaluation of a new virtual health care service, Virtual Urgent Clinic, which uses mHealth and telemedicine modalities to provide patient care. We used a multinomial logistic model to test the significance and predictive power of a set of features in determining patients' preferred method of telecare encounters-a nominal outcome variable of two levels (mHealth and telemedicine).ResultsPostdeployment, 1403 encounters were recorded, of which 1228 (87.53%) were completed with mHealth and 175 (12.47%) were telemedicine encounters. Patients' sex (P=.004) and setting (P<.001) were the most predictive determinants of their preferred method of telecare delivery, with significantly small P values of less than .01. Pearson chi-square test returned a strong indication of dependency between chief concern and encounter mediums, with an extremely small P<.001. Of the 169 mHealth patients who responded to the survey, 154 (91.1%) were satisfied by their encounter, compared with 31 of 35 (89%) telemedicine patients.ConclusionsWe studied factors influencing patients' choice of communication medium, either mHealth or telemedicine, for a virtual care clinic. Sex and geographic location, as well as their chief concern, were strong predictors of patients' choice of communication medium for their urgent care needs. This study suggests providing the option of mHealth or telemedicine to patients, and suggesting which medium would be a better fit for the patient based on their characteristics.
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Project description:We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.
| S-EPMC8459828 | biostudies-literature
Project description:ObjectiveTo use the experience from a health services research evaluation to provide guidance in team development for mixed methods research.MethodsThe Research Initiative Valuing Eldercare (THRIVE) team was organized by the Robert Wood Johnson Foundation to evaluate The Green House nursing home culture change program. This article describes the development of the research team and provides insights into how funders might engage with mixed methods research teams to maximize the value of the team.ResultsLike many mixed methods collaborations, the THRIVE team consisted of researchers from diverse disciplines, embracing diverse methodologies, and operating under a framework of nonhierarchical, shared leadership that required new collaborations, engagement, and commitment in the context of finite resources. Strategies to overcome these potential obstacles and achieve success included implementation of a Coordinating Center, dedicated time for planning and collaborating across researchers and methodologies, funded support for in-person meetings, and creative optimization of resources.ConclusionsChallenges are inevitably present in the formation and operation of effective mixed methods research teams. However, funders and research teams can implement strategies to promote success.
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Project description:PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.
| S-EPMC7125275 | biostudies-literature