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SCALE-UP II: protocol for a pragmatic randomised trial examining population health management interventions to increase the uptake of at-home COVID-19 testing in community health centres

Por: Del Fiol · G. · Orleans · B. · Kuzmenko · T. V. · Chipman · J. · Greene · T. · Martinez · A. · Wirth · J. · Meads · R. · Kaphingst · K. K. · Gibson · B. · Kawamoto · K. · King · A. J. · Siaperas · T. · Hughes · S. · Pruhs · A. · Pariera Dinkins · C. · Lam · C. Y. · Pierce · J. H. · Benson
Introduction

SCALE-UP II aims to investigate the effectiveness of population health management interventions using text messaging (TM), chatbots and patient navigation (PN) in increasing the uptake of at-home COVID-19 testing among patients in historically marginalised communities, specifically, those receiving care at community health centres (CHCs).

Methods and analysis

The trial is a multisite, randomised pragmatic clinical trial. Eligible patients are >18 years old with a primary care visit in the last 3 years at one of the participating CHCs. Demographic data will be obtained from CHC electronic health records. Patients will be randomised to one of two factorial designs based on smartphone ownership. Patients who self-report replying to a text message that they have a smartphone will be randomised in a 2x2x2 factorial fashion to receive (1) chatbot or TM; (2) PN (yes or no); and (3) repeated offers to interact with the interventions every 10 or 30 days. Participants who do not self-report as having a smartphone will be randomised in a 2x2 factorial fashion to receive (1) TM with or without PN; and (2) repeated offers every 10 or 30 days. The interventions will be sent in English or Spanish, with an option to request at-home COVID-19 test kits. The primary outcome is the proportion of participants using at-home COVID-19 tests during a 90-day follow-up. The study will evaluate the main effects and interactions among interventions, implementation outcomes and predictors and moderators of study outcomes. Statistical analyses will include logistic regression, stratified subgroup analyses and adjustment for stratification factors.

Ethics and dissemination

The protocol was approved by the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with National Institutes of Health data sharing policies. Results will be disseminated through study partners and peer-reviewed publications.

Trial registration number

ClinicalTrials.gov: NCT05533918 and NCT05533359.

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study

Por: Joyce · D. W. · Kormilitzin · A. · Hamer-Hunt · J. · McKee · K. R. · Tomasev · N.
Introduction

For artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering marginalised communities to take an active role in constructing research agendas and outputs. Given the unmet needs of the LGBTQI+ (Lesbian, Gay, Bisexual, Transgender, Queer and Intersex) community in mental healthcare, there is a pressing need for participatory research to include a range of diverse queer perspectives on issues of data collection and use (in routine clinical care as well as for research) as well as AI design. Here we propose a protocol for a Delphi consensus process for the development of PARticipatory Queer AI Research for Mental Health (PARQAIR-MH) practices, aimed at informing digital health practices and policy.

Methods and analysis

The development of PARQAIR-MH is comprised of four stages. In stage 1, a review of recent literature and fact-finding consultation with stakeholder organisations will be conducted to define a terms-of-reference for stage 2, the Delphi process. Our Delphi process consists of three rounds, where the first two rounds will iterate and identify items to be included in the final Delphi survey for consensus ratings. Stage 3 consists of consensus meetings to review and aggregate the Delphi survey responses, leading to stage 4 where we will produce a reusable toolkit to facilitate participatory development of future bespoke LGBTQI+–adapted data collection, harmonisation, and use for data-driven AI applications specifically in mental healthcare settings.

Ethics and dissemination

PARQAIR-MH aims to deliver a toolkit that will help to ensure that the specific needs of LGBTQI+ communities are accounted for in mental health applications of data-driven technologies. The study is expected to run from June 2024 through January 2025, with the final outputs delivered in mid-2025. Participants in the Delphi process will be recruited by snowball and opportunistic sampling via professional networks and social media (but not by direct approach to healthcare service users, patients, specific clinical services, or via clinicians’ caseloads). Participants will not be required to share personal narratives and experiences of healthcare or treatment for any condition. Before agreeing to participate, people will be given information about the issues considered to be in-scope for the Delphi (eg, developing best practices and methods for collecting and harmonising sensitive characteristics data; developing guidelines for data use/reuse) alongside specific risks of unintended harm from participating that can be reasonably anticipated. Outputs will be made available in open-access peer-reviewed publications, blogs, social media, and on a dedicated project website for future reuse.

Qualitative study of challenges with recruitment of hospitals into a cluster controlled trial of clinical decision support in Australia

Por: Baysari · M. T. · Van Dort · B. A. · Stanceski · K. · Hargreaves · A. · Zheng · W. Y. · Moran · M. · Day · R. O. · Li · L. · Westbrook · J. · Hilmer · S. N.
Objective

To identify barriers to hospital participation in controlled cluster trials of clinical decision support (CDS) and potential strategies for addressing barriers.

Design

Qualitative descriptive design comprising semistructured interviews.

Setting

Five hospitals in New South Wales and one hospital in Queensland, Australia.

Participants

Senior hospital staff, including department directors, chief information officers and those working in health informatics teams.

Results

20 senior hospital staff took part. Barriers to hospital-level recruitment primarily related to perceptions of risk associated with not implementing CDS as a control site. Perceived risks included reductions in patient safety, reputational risk and increased likelihood that benefits would not be achieved following electronic medical record (EMR) implementation without CDS alerts in place. Senior staff recommended clear communication of trial information to all relevant stakeholders as a key strategy for boosting hospital-level participation in trials.

Conclusion

Hospital participation in controlled cluster trials of CDS is hindered by perceptions that adopting an EMR without CDS is risky for both patients and organisations. The improvements in safety expected to follow CDS implementation makes it challenging and counterintuitive for hospitals to implement EMR without incorporating CDS alerts for the purposes of a research trial. To counteract these barriers, clear communication regarding the evidence base and rationale for a controlled trial is needed.

Students perceptions and experiences of an online well-being programme: a phenomenological study protocol

Por: Escuadra · C. J. · Chiong Maya · A. · Nava · J. B. P. · Vergara · J. A. · Bea · T. C. · Javier · A. M. · Karamihan · F. · Padilla · D. P. · Reyes · A. J. · Samonte · J. · Serrano · S. I.
Background

The pandemic has ensued challenges across all sections of the human population such as livelihood and educational changes, which involve the abrupt shift to online learning, immensely affecting the students’ well-being. Negative health consequences of e-learning among students stem from the increased demand for new technological skills, productivity, information overload and restriction of students to spend time with their peers.

Objective

To explore the experiences of the students from the University of Santo Tomas—College of Rehabilitation Sciences (UST-CRS) who participated in the online well-being programme.

Methodology

A phenomenological design will be used to determine the participants’ perceptions and experiences. Purposive sampling will be used to recruit 8–10 undergraduate students from UST-CRS ages 18–22 years, who participated in the well-being programme, and completed the study’s quantitative counterpart. Semistructured, in-depth questions will be used to conduct a focus group discussion. The transcripts will be analysed using thematic analysis via the NVivo V.12 software.

Ethics and dissemination

The study protocol is approved by the UST-CRS Ethical Review Committee (Protocol Number: SI-2022–034 (V.4)). It will be implemented in accordance with the Declaration of Helsinki and the National Ethical Guidelines for Health and Health-Related Research, and Data Privacy Act. Findings will be published in accredited journals and presented in related scientific fora.

Registration ID

PHRR230214-005419; Philippine Health Research Registry.

Barriers and facilitators to use of digital health tools by healthcare practitioners and their patients, before and during the COVID-19 pandemic: a multimethods study

Por: Turnbull · S. L. · Dack · C. · Lei · J. · Aksu · I. · Grant · S. · Lasseter · G. · Silarova · B. · Ainsworth · B.
Objectives

To explore how healthcare practitioners (HCPs) made decisions about the implementation of digital health technologies (DHTs) in their clinical practice before and during the COVID-19 pandemic.

Design

A multimethods study, comprising semistructured interviews conducted prior to the COVID-19 pandemic, supplemented with an online survey that was conducted during the pandemic with a different sample, to ensure the qualitative findings remained relevant within the rapidly changing healthcare context. Participants were recruited through HCP networks, snowballing and social media. Data were analysed thematically.

Setting

Phone interviews and online survey.

Participants

HCPs represented a range of professions from primary and secondary care across England, with varied socioeconomic deprivation.

Results

24 HCPs were interviewed, and 16 HCPs responded to the survey. In the interviews, HCPs described three levels where decisions were made, which determined who would have access to what DHTs: health organisation, HCP and patient levels. These decisions resulted in the unequal implementation of DHTs across health services, created barriers for HCPs using DHTs in their practice and influenced HCPs’ decisions on which patients to supply DHTs with. In the survey, HCPs described being provided support to overcome some of the barriers at the organisation and HCP level during the pandemic. However, they cited similar concerns to pre-pandemic about barriers patients faced using DHTs (eg, digital literacy). In the absence of centralised guidance on how to manage these barriers, health services made their own decisions about how to adapt their services for those who struggled with DHTs.

Conclusions

Decision-making at the health organisation, HCP and patient levels influences inequalities in access to DHTs for HCPs and patients. The mobilisation of centralised information and resources during the pandemic can be viewed as good practice for reducing barriers to use of DHTs for HCPs. However, attention must also be paid to reducing barriers to accessing DHTs for patients.

Assessing the performance of the family folder system for collecting community-based health information in Tigray Region, North Ethiopia: a capture-recapture study

Por: Derbew · A. A. · Debeb · H. G. · Kinsman · J. · Myleus · A. · Byass · P.
Objectives

To assess completeness and accuracy of the family folder in terms of capturing community-level health data.

Study design

A capture–recapture method was applied in six randomly selected districts of Tigray Region, Ethiopia.

Participants

Child health data, abstracted from randomly selected 24 073 family folders from 99 health posts, were compared with similar data recaptured through household survey and routine health information made by these health posts.

Primary and secondary outcome measures

Completeness and accuracy of the family folder data; and coverage selected child health indicators, respectively.

Results

Demographic data captured by the family folders and household survey were highly concordant, concordance correlation for total population, women 15–49 years age and under 5-year child were 0.97 (95% CI 0.94 to 0.99, p

Conclusion

The family folder system comprises a promising development. However, operational issues concerning the seamless capture and recording of events and merging community and facility data at the health centre level need improvement.

Quantifying Parkinsons disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol

Por: Ymeri · G. · Salvi · D. · Olsson · C. M. · Wassenburg · M. V. · Tsanas · A. · Svenningsson · P.
Introduction

The clinical assessment of Parkinson’s disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients’ homes.

Aim

To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

Methods and analysis

In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck’s Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson’s Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson’s Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

Ethics and dissemination

The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

External validation of the QCovid 2 and 3 risk prediction algorithms for risk of COVID-19 hospitalisation and mortality in adults: a national cohort study in Scotland

Por: Kerr · S. · Millington · T. · Rudan · I. · McCowan · C. · Tibble · H. · Jeffrey · K. · Fagbamigbe · A. F. · Simpson · C. R. · Robertson · C. · Hippisley-Cox · J. · Sheikh · A.
Objective

The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland.

Methods

We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021.

Results

Our validation dataset comprised 465 058 individuals, aged 19–100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell’s C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death.

Conclusions

We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated.

Exploring mobility data for enhancing HIV care engagement in Black/African American and Hispanic/Latinx individuals: a longitudinal observational study protocol

Por: Hassani · M. · De Haro · C. · Flores · L. · Emish · M. · Kim · S. · Kelani · Z. · Ugarte · D. A. · Hightow-Weidman · L. · Castel · A. · Li · X. · Theall · K. P. · Young · S.
Introduction

Increasing engagement in HIV care among people living with HIV, especially those from Black/African American and Hispanic/Latinx communities, is an urgent need. Mobility data that measure individuals’ movements over time in combination with sociostructural data (eg, crime, census) can potentially identify barriers and facilitators to HIV care engagement and can enhance public health surveillance and inform interventions.

Methods and analysis

The proposed work is a longitudinal observational cohort study aiming to enrol 400 Black/African American and Hispanic/Latinx individuals living with HIV in areas of the USA with high prevalence rates of HIV. Each participant will be asked to share at least 14 consecutive days of mobility data per month through the study app for 1 year and complete surveys at five time points (baseline, 3, 6, 9 and 12 months). The study app will collect Global Positioning System (GPS) data. These GPS data will be merged with other data sets containing information related to HIV care facilities, other healthcare, business and service locations, and sociostructural data. Machine learning and deep learning models will be used for data analysis to identify contextual predictors of HIV care engagement. The study includes interviews with stakeholders to evaluate the implementation and ethical concerns of using mobility data to increase engagement in HIV care. We seek to study the relationship between mobility patterns and HIV care engagement.

Ethics and dissemination

Ethical approval has been obtained from the Institutional Review Board of the University of California, Irvine (#20205923). Collected data will be deidentified and securely stored. Dissemination of findings will be done through presentations, posters and research papers while collaborating with other research teams.

Exploring challenges and recommendations for verbal autopsy implementation in low-/middle-income countries: a cross-sectional study of Iringa Region--Tanzania

Por: Tunga · M. · Lungo · J. H. · Chambua · J. · Kateule · R. · Lyatuu · I.
Background

Verbal autopsy (VA) plays a vital role in providing cause-of-death information in places where such information is not available. Many low-/middle-income countries (LMICs) including Tanzania are still struggling to yield quality and adequate cause-of-death data for Civil Registration and Vital Statistics (CRVS).

Objective

To highlight challenges and recommendations for VA implementation to support LMICs yield quality and adequate mortality statistics for informed decisions on healthcare interventions.

Design

Cross-sectional study.

Study setting

Iringa region in Tanzania.

Participants

41 people including 33 community health workers, 1 VA national coordinator, 5 national task force members, 1 VA regional coordinator and 1 member of the VA data management team.

Results

The perceived challenges of key informants include a weak death notification system, lengthy VA questionnaire, poor data quality and inconsistent responses, lack of clarity in the inclusion criteria, poor commitment to roles and responsibilities, poor coordination, poor financial mechanism and no or delayed feedback to VA implementers. Based on these findings, we recommend the following strategies for effective adaptation and use of VAs: (1) reinforce or implement legislative procedures towards the legal requirement for death notification. (2) Engage key stakeholders in the overall implementation of VAs. (3) Build capacity for data collection, monitoring, processing and use of VA data. (4) Improve the VA questionnaire and quality control mechanism for optimal use in data collection. (5) Create sustainable financing mechanisms and institutionalisation of VA implementation. (6) Integrating VA Implementation in CRVS.

Conclusion

Effective VA implementation demands through planning, stakeholder engagement, upskilling of local experts and fair compensation for interviewers. Such coordinated endeavours will overcome systemic, technical and behavioural challenges hindering VA’s successful implementation.

Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives

Por: Fazakarley · C. A. · Breen · M. · Leeson · P. · Thompson · B. · Williamson · V.
Objectives

Artificial intelligence (AI) is a rapidly developing field in healthcare, with tools being developed across various specialties to support healthcare professionals and reduce workloads. It is important to understand the experiences of professionals working in healthcare to ensure that future AI tools are acceptable and effectively implemented. The aim of this study was to gain an in-depth understanding of the experiences and perceptions of UK healthcare workers and other key stakeholders about the use of AI in the National Health Service (NHS).

Design

A qualitative study using semistructured interviews conducted remotely via MS Teams. Thematic analysis was carried out.

Setting

NHS and UK higher education institutes.

Participants

Thirteen participants were recruited, including clinical and non-clinical participants working for the NHS and researchers working to develop AI tools for healthcare settings.

Results

Four core themes were identified: positive perceptions of AI; potential barriers to using AI in healthcare; concerns regarding AI use and steps needed to ensure the acceptability of future AI tools. Overall, we found that those working in healthcare were generally open to the use of AI and expected it to have many benefits for patients and facilitate access to care. However, concerns were raised regarding the security of patient data, the potential for misdiagnosis and that AI could increase the burden on already strained healthcare staff.

Conclusion

This study found that healthcare staff are willing to engage with AI research and incorporate AI tools into care pathways. Going forward, the NHS and AI developers will need to collaborate closely to ensure that future tools are suitable for their intended use and do not negatively impact workloads or patient trust. Future AI studies should continue to incorporate the views of key stakeholders to improve tool acceptability.

Trial registration number

NCT05028179; ISRCTN15113915; IRAS ref: 293515.

Large language model-based information extraction from free-text radiology reports: a scoping review protocol

Por: Reichenpfader · D. · Müller · H. · Denecke · K.
Introduction

Radiological imaging is one of the most frequently performed diagnostic tests worldwide. The free-text contained in radiology reports is currently only rarely used for secondary use purposes, including research and predictive analysis. However, this data might be made available by means of information extraction (IE), based on natural language processing (NLP). Recently, a new approach to NLP, large language models (LLMs), has gained momentum and continues to improve performance of IE-related tasks. The objective of this scoping review is to show the state of research regarding IE from free-text radiology reports based on LLMs, to investigate applied methods and to guide future research by showing open challenges and limitations of current approaches. To our knowledge, no systematic or scoping review of IE from radiology reports based on LLMs has been published. Existing publications are outdated and do not comprise LLM-based methods.

Methods and analysis

This protocol is designed based on the JBI Manual for Evidence Synthesis, chapter 11.2: ‘Development of a scoping review protocol’. Inclusion criteria and a search strategy comprising four databases (PubMed, IEEE Xplore, Web of Science Core Collection and ACM Digital Library) are defined. Furthermore, we describe the screening process, data charting, analysis and presentation of extracted data.

Ethics and dissemination

This protocol describes the methodology of a scoping literature review and does not comprise research on or with humans, animals or their data. Therefore, no ethical approval is required. After the publication of this protocol and the conduct of the review, its results are going to be published in an open access journal dedicated to biomedical informatics/digital health.

Healthcare professionals intention to adopt mobile phone-based SMS and its predictors for adherence support and care of TB patients in a resource-limited setting: a structural equation modelling analysis

Por: Walle · A. D. · Hunde · M. K. · Demsash · A. W.
Objective

To assess healthcare providers’ intentions and the associated factors to use mobile phone-based short message service (SMS) to support adherence and care of tuberculosis (TB) patients in the Oromia region of southwest Ethiopia.

Study design

An institutional-based cross-sectional study was conducted from October to November 2022.

Study setting

The study was conducted in public hospitals which include Mettu Karl referral hospital, Dembi Hospital, Bedelle Hospital, Darimu Hospital and Chora Hospital in Ilu Aba Bor and Buno Bedelle zones.

Participants

A total of 625 (54.9% male and 45.1% female) health professionals participated in the study. The study participants were selected using a simple random sampling technique. All health professionals permanently working in Ilu Aba Bor and Buno Bedelle zone hospitals were included in this study. However, health professionals with less than 6 months of experience and those who were not present during the data collection period were excluded from this study.

Outcome measure

The intention to use mobile phone-based SMS to support TB patients.

Results

Healthcare professionals’ intention to use mobile SMS was 54.4%. Effort expectancy had a significant direct effect on attitude (β=0.162, p

Conclusions

Overall, intention to use of mobile-based SMS was high. Effort expectancy, attitude and facilitating conditions were significant factors that determined healthcare professionals’ behavioural intention to use mobile phone SMS. Effort expectancy had a more significant prediction power than others. As a result, system forms that are easily interactive and applicable should be implemented to improve capacity building and support the adherence and care of TB patients.

Clinical impacts of an integrated electronic health record-based smoking cessation intervention during hospitalisation

Por: Banerjee · S. · Alabaster · A. · Adams · A. S. · Fogelberg · R. · Patel · N. · Young-Wolff · K.
Objective

To assess the effects of an electronic health record (EHR) intervention that prompts the clinician to prescribe nicotine replacement therapy (NRT) at hospital admission and discharge in a large integrated health system.

Design

Retrospective cohort study using interrupted time series (ITS) analysis leveraging EHR data generated before and after implementation of the 2015 EHR-based intervention.

Setting

Kaiser Permanente Northern California, a large integrated health system with 4.2 million members.

Participants

Current smokers aged ≥18 hospitalised for any reason.

Exposure

EHR-based clinical decision supports that prompted the clinician to order NRT on hospital admission (implemented February 2015) and discharge (implemented September 2015).

Main outcomes and measures

Primary outcomes included the monthly percentage of admitted smokers with NRT orders during admission and at discharge. A secondary outcome assessed patient quit rates within 30 days of hospital discharge as reported during discharge follow-up outpatient visits.

Results

The percentage of admissions with NRT orders increased from 29.9% in the year preceding the intervention to 78.1% in the year following (41.8% change, 95% CI 38.6% to 44.9%) after implementation of the admission hard-stop intervention compared with the baseline trend (ITS estimate). The percentage of discharges with NRT orders increased acutely at the time of both interventions (admission intervention ITS estimate 15.5%, 95% CI 11% to 20%; discharge intervention ITS estimate 13.4%, 95% CI 9.1% to 17.7%). Following the implementation of the discharge intervention, there was a small increase in patient-reported quit rates (ITS estimate 5.0%, 95% CI 2.2% to 7.8%).

Conclusions

An EHR-based clinical decision-making support embedded into admission and discharge documentation was associated with an increase in NRT prescriptions and improvement in quit rates. Similar systemic EHR interventions can help improve smoking cessation efforts after hospitalisation.

Infographic summaries for clinical practice guidelines: results from user testing of the BMJ Rapid Recommendations in primary care

Por: Van Bostraeten · P. · Aertgeerts · B. · Bekkering · G. E. · Delvaux · N. · Dijckmans · C. · Ostyn · E. · Soontjens · W. · Matthysen · W. · Haers · A. · Vanheeswyck · M. · Vandekendelaere · A. · Van der Auwera · N. · Schenk · N. · Stahl-Timmins · W. · Agoritsas · T. · Vermandere · M.
Objectives

Infographics have the potential to enhance knowledge translation and implementation of clinical practice guidelines at the point of care. They can provide a synoptic view of recommendations, their rationale and supporting evidence. They should be understandable and easy to use. Little evaluation of these infographics regarding user experience has taken place. We explored general practitioners’ experiences with five selected BMJ Rapid Recommendation infographics suited for primary care.

Methods

An iterative, qualitative user testing design was applied on two consecutive groups of 10 general practitioners for five selected infographics. The physicians used the infographics before clinical encounters and we performed hybrid think-aloud interviews afterwards. 20 interviews were analysed using the Qualitative Analysis Guide of Leuven.

Results

Many clinicians reported that the infographics were simple and rewarding to use, time-efficient and easy to understand. They were perceived as innovative and their knowledge basis as trustworthy and supportive for decision-making. The interactive, expandable format was preferred over a static version as general practitioners focused mainly on the core message. Rapid access through the electronic health record was highly desirable. The main issues were about the use of complex scales and terminology. Understanding terminology related to evidence appraisal as well as the interpretation of statistics and unfamiliar scales remained difficult, despite the infographics.

Conclusions

General practitioners perceive infographics as useful tools for guideline translation and implementation in primary care. They offer information in an enjoyable and user friendly format and are used mainly for rapid, tailored and just in time information retrieval. We recommend future infographic producers to provide information as concise as possible, carefully define the core message and explore ways to enhance the understandability of statistics and difficult concepts related to evidence appraisal.

Trial registration number

MP011977.

How digital health translational research is prioritised: a qualitative stakeholder-driven approach to decision support evaluation

Por: Bamgboje-Ayodele · A. · McPhail · S. M. · Brain · D. · Taggart · R. · Burger · M. · Bruce · L. · Holtby · C. · Pradhan · M. · Simpson · M. · Shaw · T. J. · Baysari · M. T.
Objectives

Digital health is now routinely being applied in clinical care, and with a variety of clinician-facing systems available, healthcare organisations are increasingly required to make decisions about technology implementation and evaluation. However, few studies have examined how digital health research is prioritised, particularly research focused on clinician-facing decision support systems. This study aimed to identify criteria for prioritising digital health research, examine how these differ from criteria for prioritising traditional health research and determine priority decision support use cases for a collaborative implementation research programme.

Methods

Drawing on an interpretive listening model for priority setting and a stakeholder-driven approach, our prioritisation process involved stakeholder identification, eliciting decision support use case priorities from stakeholders, generating initial use case priorities and finalising preferred use cases based on consultations. In this qualitative study, online focus group session(s) were held with stakeholders, audiorecorded, transcribed and analysed thematically.

Results

Fifteen participants attended the online priority setting sessions. Criteria for prioritising digital health research fell into three themes, namely: public health benefit, health system-level factors and research process and feasibility. We identified criteria unique to digital health research as the availability of suitable governance frameworks, candidate technology’s alignment with other technologies in use,and the possibility of data-driven insights from health technology data. The final selected use cases were remote monitoring of patients with pulmonary conditions, sepsis detection and automated breast screening.

Conclusion

The criteria for determining digital health research priority areas are more nuanced than that of traditional health condition focused research and can neither be viewed solely through a clinical lens nor technological lens. As digital health research relies heavily on health technology implementation, digital health prioritisation criteria comprised enablers of successful technology implementation. Our prioritisation process could be applied to other settings and collaborative projects where research institutions partner with healthcare delivery organisations.

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