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What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project.

ABSTRACT: BACKGROUND:To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS:The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS:Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS:By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.

SUBMITTER: Fico G

PROVIDER: S-EPMC6697904 | biostudies-other | 2019 Aug

REPOSITORIES: biostudies-other

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What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project.

Fico Giuseppe G Hernanzez Liss L Cancela Jorge J Dagliati Arianna A Sacchi Lucia L Martinez-Millana Antonio A Posada Jorge J Manero Lidia L Verdú Jose J Facchinetti Andrea A Ottaviano Manuel M Zarkogianni Konstantia K Nikita Konstantina K Groop Leif L Gabriel-Sanchez Rafael R Chiovato Luca L Traver Vicente V Merino-Torres Juan Francisco JF Cobelli Claudio C Bellazzi Riccardo R Arredondo Maria Teresa MT

BMC medical informatics and decision making 20190816 1

<h4>Background</h4>To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models.<h4>Methods</h4>The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of mul ...[more]

PMID: 31419982

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Project description:BackgroundIn this study, we sought to assess healthcare professionals' acceptance of and satisfaction with a shared decision making (SDM) educational workshop, its impact on their intention to use SDM, and their perceived facilitators and barriers to the implementation of SDM in clinical settings in Iran.MethodsWe conducted an observational quantitative study that involved measurements before, during, and immediately after the educational intervention at stake. We invited healthcare professionals affiliated with Tabriz University of Medical Sciences, East Azerbaijan, Iran, to attend a half-day workshop on SDM in December 2016. Decisions about prenatal screening and knee replacement surgery was used as clinical vignettes. We provided a patient decision aid on prenatal screening that complied with the International Patient Decision Aids Standards and used illustrate videos. Participants completed a sociodemographic questionnaire and a questionnaire to assess their familiarity with SDM, a questionnaire based on theoretical domains framework to assess their intention to implement SDM, a questionnaire about their perceived facilitators and barriers of implementing SDM in their clinical practice, continuous professional development reaction questionnaire, and workshop evaluation. Quantitative data was analyzed descriptively and with multiple linear regression.ResultsAmong the 60 healthcare professionals invited, 41 participated (68%). Twenty-three were female (57%), 18 were specialized in family and emergency medicine, or community and preventive medicine (43%), nine were surgeons (22%), and 14 (35%) were other types of specialists. Participants' mean age was 37.51 ± 8.64 years with 8.09 ± 7.8 years of clinical experience. Prior to the workshop, their familiarity with SDM was 3.10 ± 2.82 out of 9. After the workshop, their belief that practicing SDM would be beneficial and useful (beliefs about consequences) (beta = 0.67, 95% CI 0.27, 1.06) and beliefs about capability of using SDM (beta = 0.32, 95% CI -0.08, 0.72) had the strongest influence on their intention of practicing SDM. Participants perceived the main facilitator and barrier to perform SDM were training and high patient load, respectively.ConclusionsParticipants thought the workshop was a good way to learn SDM and that they would be able to use what they had learned in their clinical practice. Future studies need to study the level of intention of participants in longer term and evaluate the impact of cultural differences on practicing SDM and its implementation in both western and non-western countries.

Project description:BACKGROUND:Clinical trials management can be studied using project management theory. The CYCLE pilot randomized controlled trial (RCT) was conducted to determine the feasibility of a future rehabilitation trial of early in-bed cycling in the intensive care unit (ICU). In-bed cycling is a novel intervention, not typically available in ICUs. Implementation of this intervention requires personnel with specialized clinical expertise caring for critically ill patients and use of the in-bed cycle. Our objective was to describe the implementation and conduct of our pilot RCT using a project management approach. METHODS:We retrospectively reviewed activities, timelines, and personnel involved in the trial. We organized activities into four project management phases: initiation, planning, execution, and monitoring and controlling. Data sources included Methods Centre documents used for trial coordination and conduct, and the trial data set. We report descriptive statistics as counts and proportions and also medians and quartiles, and we summarize the lessons learned. RESULTS:Seven ICUs in Canada participated in the trial. Time from research ethics board and contracts submission to first enrolment was a median (first quartile, third quartile) of 185 (146, 209) and 162 (114, 181) days, respectively. We trained 128 personnel on the CYCLE pilot RCT protocol, and 80 (63%) completed trial-related activities. Four sites required additional training after start-up due to staff turnover and leaves of absence. Over 15?months, we screened 864 patients: 256 were eligible and 66 were enrolled. Despite an 85% consent rate, 74% (190/256) of eligible patients were not randomized, largely (80% [152/190]) due to physiotherapist availability. Thirteen percent of recruitment weeks were lost due to physiotherapist staffing shortages. We highlight five key lessons learned: (1) prepare and anticipate site needs; (2) communicate regularly; (3) proactively analyse and act on process measure data; (4) develop contingency plans; (5) express appreciation to participating sites. CONCLUSIONS:Our analysis highlights the scope of relevant activities, rigorous training and monitoring, number and types of required personnel, and time required to conduct a multicentre ICU rehabilitation intervention trial. Our lessons learned can help others interested in implementing complex intervention trials, such as rehabilitation. TRIAL REGISTRATION:ClinicalTrials.gov, NCT02377830 . Registered prospectively on 4 March 2015.

Project description:Simulation-based research has grown substantially over the past two decades; however, relatively few published simulation studies are multicenter in nature. Multicenter research confers many distinct advantages over single-center studies, including larger sample sizes for more generalizable findings, sharing resources amongst collaborative sites, and promoting networking. Well-executed multicenter studies are more likely to improve provider performance and/or have a positive impact on patient outcomes. In this manuscript, we offer a step-by-step guide to conducting multicenter, simulation-based research based upon our collective experience with the International Network for Simulation-based Pediatric Innovation, Research and Education (INSPIRE). Like multicenter clinical research, simulation-based multicenter research can be divided into four distinct phases. Each phase has specific differences when applied to simulation research: (1) Planning phase, to define the research question, systematically review the literature, identify outcome measures, and conduct pilot studies to ensure feasibility and estimate power; (2) Project Development phase, when the primary investigator identifies collaborators, develops the protocol and research operations manual, prepares grant applications, obtains ethical approval and executes subsite contracts, registers the study in a clinical trial registry, forms a manuscript oversight committee, and conducts feasibility testing and data validation at each site; (3) Study Execution phase, involving recruitment and enrollment of subjects, clear communication and decision-making, quality assurance measures and data abstraction, validation, and analysis; and (4) Dissemination phase, where the research team shares results via conference presentations, publications, traditional media, social media, and implements strategies for translating results to practice. With this manuscript, we provide a guide to conducting quantitative multicenter research with a focus on simulation-specific issues.

Dataset Information

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project.

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What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project.

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