Experiences and Views of Young People and Health Care Professionals of Using Social Media to Self-Manage Type 1 Diabetes Mellitus: Thematic Synthesis of Qualitative Studies.

BACKGROUND: Social media have shown the potential to support type 1 diabetes self-management by providing informational, emotional, and peer-to-peer support. However, the perceptions of young people and health care professionals' (HCPs) toward the use of social media for type 1 diabetes self-management have not been systematically reviewed. OBJECTIVE: The aim of this study is to explore and summarize the experiences and views of young people with type 1 diabetes and their HCPs on using social media for self-management across qualitative findings. METHODS: We searched MEDLINE, Embase, PsycINFO, and CINAHL from 2012 to 2023 using Medical Subject Heading terms and text words related to type 1 diabetes and social media. We screened and selected the studies according to the inclusion and exclusion criteria. We quality appraised and characterized the included studies and conducted a thematic synthesis. RESULTS: We included 11 studies in our synthesis. A total of 9 of them were qualitative and 2 were mixed methods studies. Ten focused on young people with type 1 diabetes and 1 on HCPs. All used content analysis and were of moderate to high quality. Thirteen descriptive themes were yielded by our thematic synthesis, contributing to five analytic themes: (1) differences in how young people interact with social media, (2) characteristics of social media platforms that influence their use and uptake for type 1 diabetes self-management, (3) social media as a source of information, (4) impact on young people's coping and emotional well-being, and (5) impact on support from and relationships with HCPs and services. CONCLUSIONS: The synthesis suggests that we should consider leveraging social media's peer support capabilities to augment the traditional services for young people with type 1 diabetes. However, the patients may have privacy concerns about HCPs' involvement in their online activities. This warrants an update of existing guidelines to help young people use social media safely for self-managing their diabetes.

The development and validation of a needs assessment tool for use with YOUng adult survivors of a CentrAl Nervous system tumor (YOU-CAN).

Background: Adolescent and young adult (AYA) survivors of a central nervous system (CNS) tumor represent a vulnerable group who can experience: social isolation, low rates of employment, and achieving independence can be compromised, leading to poorer quality of life compared with survivors of other cancer types. The aim of this study is to develop and evaluate the validity of a needs assessment tool (NAT) for AYA survivors of a CNS tumor. Methods: Items generated using data from 29 qualitative studies and cognitive interviews (n = 8) produced NAT V1.1 (49 items). 128 of 316 eligible participants attending neuro-oncology clinics at 4 NHS sites between June 2022 and March 2023 completed the NAT V1.1 to allow for item reduction and refinement and to evaluate reliability and validity. A pilot study (n = 6) using YOU-CAN in routine follow-up concluded the study. Results: Hierarchical analysis and Rasch analysis identified 18- and 15-items for removal, respectively. YOU-CAN, comprised of the remaining 16 items, demonstrates excellent test-retest reliability (intra-class correlation coefficient, 0.901, n = 40) and sufficient correlation with the European Quality of Life questionnaire and Supportive Care Needs Survey (Pearson r = 0.433 and 0.590, respectively). Pilot testing showed YOU-CAN triggered discussions of unmet needs in consultations and highlighted the importance of multidisciplinary support. Conclusions: YOU-CAN is a valid and reliable instrument containing items related to concerns about physical and emotional health; family and relationships; self-acceptance; and independence. Future efforts should examine YOU-CAN's feasibility, and develop guidance for managing unmet needs. Routine use of YOU-CAN may improve the identification of otherwise undiscussed unmet needs and opportunities to deliver personalized support.

Reliability, validity, and responsiveness of a smartphone-based manikin to support pain self-reporting.

Introduction: Many people worldwide suffer from chronic pain. Improving our knowledge on chronic pain prevalence and management requires methods to collect pain self-reports in large populations. Smartphone-based tools could aid data collection by allowing people to use their own device, but the measurement properties of such tools are largely unknown. Objectives: To assess the reliability, validity, and responsiveness of a smartphone-based manikin to support pain self-reporting. Methods: We recruited people with fibromyalgia, rheumatoid arthritis, and/or osteoarthritis and access to a smartphone and the internet. Data collection included the Global Pain Scale at baseline and follow-up, and 30 daily pain drawings completed on a 2-dimensional, gender-neutral manikin. After deriving participants' pain extent from their manikin drawings, we evaluated convergent and discriminative validity, test-retest reliability, and responsiveness and assessed findings against internationally agreed criteria for good measurement properties. Results: We recruited 131 people; 104 were included in the full sample, submitting 2185 unique pain drawings. Manikin-derived pain extent had excellent test-retest reliability (intraclass correlation coefficient, 0.94), moderate convergent validity (ρ, 0.46), and an ability to distinguish fibromyalgia and osteoarthritis from rheumatoid arthritis (F statistics, 30.41 and 14.36, respectively; P < 0.001). Responsiveness was poor (ρ, 0.2; P, 0.06) and did not meet the respective criterion for good measurement properties. Conclusion: Our findings suggest that smartphone-based manikins can be a reliable and valid method for pain self-reporting, but that further research is warranted to explore, enhance, and confirm the ability of such manikins to detect a change in pain over time.

Users' views on the use of a smartwatch app to collect daily symptom data in individuals with multiple long-term conditions (Multimorbidity): A qualitative study.

Introduction: Long-term conditions are a major burden on health systems. One way to facilitate more research and better clinical care among patients with long-term conditions is to collect accurate data on their daily symptoms (patient-generated health data) using wearable technologies. Whilst evidence is growing for the use of wearable technologies in single conditions, there is less evidence of the utility of frequent symptom tracking in those who have more than one condition. Aims: To explore patient views of the acceptability of collecting daily patient-generated health data for three months using a smartwatch app. Methods: Watch Your Steps was a longitudinal study which recruited 53 patients to track over 20 symptoms per day for a 90-day period using a study app on smartwatches. Semi-structured interviews were conducted with a sub-sample of 20 participants to explore their experience of engaging with the app. Results: In a population of older people with multimorbidity, patients were willing and able to engage with a patient-generated health data app on a smartwatch. It was suggested that to maintain engagement over a longer period, more 'real-time' feedback from the app should be available. Participants did not seem to consider the management of more than one condition to be a factor in either engagement or use of the app, but the presence of severe or chronic pain was at times a barrier. Conclusion: This study has provided preliminary evidence that multimorbidity was not a major barrier to engagement with patient-generated health data via a smartwatch symptom tracking app.

Measurement Properties of Patient-Reported Outcome Measures for Adolescent and Young Adult Survivors of a Central Nervous System Tumor: A Systematic Review.

Purpose: To identify and evaluate patient-reported outcome measures (PROMs) for assessing survivorship-related concepts for adolescent and young adult (AYA) survivors of central nervous system (CNS) tumors. Methods: We searched five electronic databases. Two researchers independently screened all titles for inclusion and used consensus-based standards for the selection of health measurement instruments (COSMIN) guidance to grade the quality of evidence for each measurement property. Results: Four studies met eligibility criteria: single-item pain thermometer; single-item fatigue thermometer; 37-item pediatric functional assessment of cancer therapy-brain tumor survivors, measuring quality of life; and 12-item Perceived Barriers Scale to assess barriers to employment. The Perceived Barrier Scale showed high-quality evidence for internal consistency and moderate quality evidence for construct and structural validity. Evidence for the measurement properties of the other PROMs was low-to-moderate quality. Conclusion: We found one PROM with sufficient evidence for good measurement properties to support its use. This warrants development and evaluation of further PROMs to inform ongoing supportive care for this population. Implications for Cancer Survivors: The Perceived Barriers Scale is sufficiently validated and could be considered to guide support for AYA survivors of CNS tumors to achieve their employment goals.

Experiences and Unmet Needs of Adolescent and Young Adult Survivors of a Brain Tumor (Aged 15-39 Years): A Systematic Review and Meta-ethnography.

BACKGROUND: Brain tumors account for 15% of all adolescent and young adult cancers, and survivors are at risk of ongoing late effects that can severely impact their ability to reach independence. Despite follow-up initiatives advocating a personalized approach, survivors continue to experience ongoing sequelae. A better understanding of the survivorship experience is required to ensure services are able to deliver personalized support. OBJECTIVE: The aim of this systematic search and meta-ethnography was to identify and synthesize qualitative studies to better understand the experiences, perspectives, and needs of adolescent and young adult brain tumor survivors. METHODS: Five databases were searched using predefined criteria, studies were independently screened by two researchers, and those meeting inclusion criteria were synthesized. RESULTS: Twenty-seven studies were synthesized, generating 2 main themes, each with subthemes: (1) individual factors impacting resilience, namely, positive coping styles, managing emotions, and family functioning, and (2) cancer-related factors that challenge the individual, namely, living with societal expectations and barriers to coping. CONCLUSION: The conceptual framework illustrates the challenges and resilience of survivors along the continuum from adolescence to adulthood, reflecting the needs of this age group in 1 model, despite it being a time of rapid growth. The lack of awareness of potential physical and emotional late effects challenges individual resilience, which is further challenged when significant milestones cannot be reached. IMPLICATIONS FOR PRACTICE: There is a role for follow-up services to identify and address unmet needs, provide better information to equip survivors to manage late effects, and support families, particularly those who underwent more intensive treatment.

Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice.

PURPOSE: This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice. METHODS: We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions. RESULTS: Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population. CONCLUSION: Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.

Virtual reality and augmented reality smartphone applications for upskilling care home workers in hand hygiene: a realist multi-site feasibility, usability, acceptability, and efficacy study.

OBJECTIVES: To assess the feasibility and implementation, usability, acceptability and efficacy of virtual reality (VR), and augmented reality (AR) smartphone applications for upskilling care home workers in hand hygiene and to explore underlying learning mechanisms. MATERIALS AND METHODS: Care homes in Northwest England were recruited. We took a mixed-methods and pre-test and post-test approach by analyzing uptake and completion rates of AR, immersive VR or non-immersive VR training, validated and bespoke questionnaires, observations, videos, and interviews. Quantitative data were analyzed descriptively. Qualitative data were analyzed using a combined inductive and deductive approach. RESULTS: Forty-eight care staff completed AR training (n = 19), immersive VR training (n = 21), or non-immersive VR training (n = 8). The immersive VR and AR training had good usability with System Usability Scale scores of 84.40 and 77.89 (of 100), respectively. They had high acceptability, with 95% of staff supporting further use. The non-immersive VR training had borderline poor usability, scoring 67.19 and only 63% would support further use. There was minimal improved knowledge, with an average of 6% increase to the knowledge questionnaire. Average hand hygiene technique scores increased from 4.77 (of 11) to 7.23 after the training. Repeated practice, task realism, feedback and reminding, and interactivity were important learning mechanisms triggered by AR/VR. Feasibility and implementation considerations included managerial support, physical space, providing support, screen size, lagging Internet, and fitting the headset. CONCLUSIONS: AR and immersive VR apps are feasible, usable, and acceptable for delivering training. Future work should explore whether they are more effective than previous training and ensure equity in training opportunities.

Feasibility and acceptability to use a smartphone-based manikin for daily longitudinal self-reporting of chronic pain.

Background: As management of chronic pain continues to be suboptimal, there is a need for tools that support frequent, longitudinal pain self-reporting to improve our understanding of pain. This study aimed to assess the feasibility and acceptability of daily pain self-reporting using a smartphone-based pain manikin. Methods: For this prospective feasibility study, we recruited adults with lived experience of painful musculoskeletal condition. They were asked to complete daily pain self-reports via an app for 30 days. We assessed feasibility by calculating pain report completion levels, and investigated differences in completion levels between subgroups. We assessed acceptability via an end-of-study questionnaire, which we analysed descriptively. Results: Of the 104 participants, the majority were female (n = 87; 84%), aged 45-64 (n = 59; 57%), and of white ethnic background (n = 89; 86%). The mean completion levels was 21 (± 7.7) pain self-reports. People who were not working (odds ratio (OR) = 1.84; 95% confidence interval (CI), 1.52-2.23) were more likely, and people living in less deprived areas (OR = 0.77; 95% CI, 0.62-0.97) and of non-white ethnicity (OR = 0.45; 95% CI, 0.36-0.57) were less likely to complete pain self-reports than their employed, more deprived and white counterparts, respectively. Of the 96 participants completing the end-of-study questionnaire, almost all participants agreed that it was easy to complete a pain drawing (n = 89; 93%). Conclusion: It is feasible and acceptable to self-report pain using a smartphone-based manikin over a month. For its wider adoption for pain self-reporting, the feasibility and acceptability should be further explored among people with diverse socio-economic and ethnic backgrounds.

Exploring the Cross-cultural Acceptability of Digital Tools for Pain Self-reporting: Qualitative Study.

BACKGROUND: Culture and ethnicity influence how people communicate about their pain. This makes it challenging to develop pain self-report tools that are acceptable across ethnic groups. OBJECTIVE: We aimed to inform the development of cross-culturally acceptable digital pain self-report tools by better understanding the similarities and differences between ethnic groups in pain experiences and self-reporting needs. METHODS: Three web-based workshops consisting of a focus group and a user requirement exercise with people who self-identified as being of Black African (n=6), South Asian (n=10), or White British (n=7) ethnicity were conducted. RESULTS: Across ethnic groups, participants shared similar lived experiences and challenges in communicating their pain to health care professionals. However, there were differences in beliefs about the causes of pain, attitudes toward pain medication, and experiences of how stigma and gender norms influenced pain-reporting behavior. Despite these differences, they agreed on important aspects for pain self-report, but participants from non-White backgrounds had additional language requirements such as culturally appropriate pain terminologies to reduce self-reporting barriers. CONCLUSIONS: To improve the cross-cultural acceptability and equity of digital pain self-report tools, future developments should address the differences among ethnic groups on pain perceptions and beliefs, factors influencing pain reporting behavior, and language requirements.

Charting a Course for Smartphones and Wearables to Transform Population Health Research.

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people's lives for the better.

Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review.

OBJECTIVE: Outpatient no-shows have important implications for costs and the quality of care. Predictive models of no-shows could be used to target intervention delivery to reduce no-shows. We reviewed the effectiveness of predictive model-based interventions on outpatient no-shows, intervention costs, acceptability, and equity. MATERIALS AND METHODS: Rapid systematic review of randomized controlled trials (RCTs) and non-RCTs. We searched Medline, Cochrane CENTRAL, Embase, IEEE Xplore, and Clinical Trial Registries on March 30, 2022 (updated on July 8, 2022). Two reviewers extracted outcome data and assessed the risk of bias using ROB 2, ROBINS-I, and confidence in the evidence using GRADE. We calculated risk ratios (RRs) for the relationship between the intervention and no-show rates (primary outcome), compared with usual appointment scheduling. Meta-analysis was not possible due to heterogeneity. RESULTS: We included 7 RCTs and 1 non-RCT, in dermatology (n = 2), outpatient primary care (n = 2), endoscopy, oncology, mental health, pneumology, and an magnetic resonance imaging clinic. There was high certainty evidence that predictive model-based text message reminders reduced no-shows (1 RCT, median RR 0.91, interquartile range [IQR] 0.90, 0.92). There was moderate certainty evidence that predictive model-based phone call reminders (3 RCTs, median RR 0.61, IQR 0.49, 0.68) and patient navigators reduced no-shows (1 RCT, RR 0.55, 95% confidence interval 0.46, 0.67). The effect of predictive model-based overbooking was uncertain. Limited information was reported on cost-effectiveness, acceptability, and equity. DISCUSSION AND CONCLUSIONS: Predictive modeling plus text message reminders, phone call reminders, and patient navigator calls are probably effective at reducing no-shows. Further research is needed on the comparative effectiveness of predictive model-based interventions addressed to patients at high risk of no-shows versus nontargeted interventions addressed to all patients.

Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review.

BACKGROUND: There is growing interest in the collection and use of patient-reported outcome measures (PROMs) to support clinical decision making in patients with non-small cell lung cancer (NSCLC). However, an overview of research into the prognostic value of PROMs is currently lacking. AIM: To explore to what extent, how, and how robustly the value of PROMs for prognostic prediction has been investigated in adults diagnosed with NSCLC. METHODS: We systematically searched Medline, Embase, CINAHL Plus and Scopus for English-language articles published from 2011 to 2021 that report prognostic factor study, prognostic model development or validation study. Example data charting forms from the Cochrane Prognosis Methods Group guided our data charting on study characteristics, PROMs as predictors, predicted outcomes, and statistical methods. Two reviewers independently charted the data and critically appraised studies using the QUality In Prognosis Studies (QUIPS) tool for prognostic factor studies, and the risk of bias assessment section of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) for prognostic model studies. RESULTS: Our search yielded 2,769 unique titles of which we included 31 studies, reporting the results of 33 unique analyses and models. Out of the 17 PROMs used for prediction, the EORTC QLQ-C30 was most frequently used (16/33); 12/33 analyses used PROM subdomain scores instead of the overall scores. PROMs data was mostly collected at baseline (24/33) and predominantly used to predict survival (32/33) but seldom other clinical outcomes (1/33). Almost all prognostic factor studies (26/27) had moderate to high risk of bias and all four prognostic model development studies had high risk of bias. CONCLUSION: There is an emerging body of research into the value of PROMs as a prognostic factor for survival in people with NSCLC but the methodological quality of this research is poor with significant bias. This warrants more robust studies into the prognostic value of PROMs, in particular for predicting outcomes other than survival. This will enable further development of PROM-based prediction models to support clinical decision making in NSCLC.

Understanding How the Design and Implementation of Online Consultations Affect Primary Care Quality: Systematic Review of Evidence With Recommendations for Designers, Providers, and Researchers.

BACKGROUND: Online consultations (OCs) allow patients to contact their care providers on the web. Worldwide, OCs have been rolled out in primary care rapidly owing to policy initiatives and COVID-19. There is a lack of evidence regarding how OC design and implementation influence care quality. OBJECTIVE: We aimed to synthesize research on the impacts of OCs on primary care quality, and how these are influenced by system design and implementation. METHODS: We searched databases from January 2010 to February 2022. We included quantitative and qualitative studies of real-world OC use in primary care. Quantitative data were transformed into qualitative themes. We used thematic synthesis informed by the Institute of Medicine domains of health care quality, and framework analysis informed by the nonadoption, abandonment, scale-up, spread, and sustainability framework. Strength of evidence was judged using the GRADE-CERQual approach. RESULTS: We synthesized 63 studies from 9 countries covering 31 OC systems, 14 (22%) of which used artificial intelligence; 41% (26/63) of studies were published from 2020 onward, and 17% (11/63) were published after the COVID-19 pandemic. There was no quantitative evidence for negative impacts of OCs on patient safety, and qualitative studies suggested varied perceptions of their safety. Some participants believed OCs improved safety, particularly when patients could describe their queries using free text. Staff workload decreased when sufficient resources were allocated to implement OCs and patients used them for simple problems or could describe their queries using free text. Staff workload increased when OCs were not integrated with other software or organizational workflows and patients used them for complex queries. OC systems that required patients to describe their queries using multiple-choice questionnaires increased workload for patients and staff. Health costs decreased when patients used OCs for simple queries and increased when patients used them for complex queries. Patients using OCs were more likely to be female, younger, and native speakers, with higher socioeconomic status. OCs increased primary care access for patients with mental health conditions, verbal communication difficulties, and barriers to attending in-person appointments. Access also increased by providing a timely response to patients' queries. Patient satisfaction increased when using OCs owing to better primary care access, although it decreased when using multiple-choice questionnaire formats. CONCLUSIONS: This is the first theoretically informed synthesis of research on OCs in primary care and includes studies conducted during the COVID-19 pandemic. It contributes new knowledge that, in addition to having positive impacts on care quality such as increased access, OCs also have negative impacts such as increased workload. Negative impacts can be mitigated through appropriate OC system design (eg, free text format), incorporation of advanced technologies (eg, artificial intelligence), and integration into technical infrastructure (eg, software) and organizational workflows (eg, timely responses). TRIAL REGISTRATION: PROSPERO CRD42020191802; https://tinyurl.com/2p84ezjy.

Adoption of Digital Pain Manikins for Research Data Collection: A Systematic Review.

Chronic pain is common and disabling. Researchers need robust methods to collect pain data in large populations to enhance knowledge on pain prevalence, causes and treatment. Digital pain manikins address this by enabling self-reporting of location-specific pain. However, it is unknown to what extent pain studies adopted digital manikins for data collection. Therefore, we systematically searched the literature. We included 17 studies. Most were published after 2017, collected pain data cross-sectionally in ≥50 participants, and reported pain distribution and pain extent as manikin-derived summary metrics. Across the studies, 13 unique manikins were used, of which four had been evaluated. Our review shows that adoption of digital pain manikins in research settings has been slow. Harnessing the digital nature of manikins, enabling use of personal devices, and assessing and improving the reliability, validity and responsiveness of digital manikins will expedite their adoption as digital data collection tools for pain research.

KidneyCloud: A Clinically-Codesigned Solution to Support Kidney Services with Assessing Patients for Transplantation.

There is a need for IT systems that support the complex needs of data management in kidney transplantation. The KidneyCloud project aims to inform a transplant-specific digital solution by exploring patient pathways and data journeys. This paper reports on the early prototyping of the KidneyCloud clinician interface using an iterative codesign methodology. User workshops identified that for making clinical decisions and adding patients to the national waiting list transplant teams relied heavily on manual processes to access data across systems and organisations. Based on the requirements gathered, a prototype interface was designed to provide a unified view on the available patient data, which aligned with clinical workflows. Interactive prototype screens allowed users to gain hands-on experience and provide rich real-time feedback. This informed the necessary functionalities of the interface, but also helped us understand the capabilities required of the back-end solution.

The evaluation of digital transformation in renal transplantation in the United Kingdom: A national interview study.

BACKGROUND: Care pathways in renal transplantation involve multi-speciality coordination and administration of clinical data across organisational boundaries. The potential for information technology (IT) to support the service through data management, communication and national registration has been reported. However, no previous national-level evaluation on the current state of digital transformation has been undertaken. METHODS: We undertook structured phone interviews with transplant coordinators at all 23 transplant centres in the United Kingdom (UK). The interview covered topics including clinical workflows during transplant referral and current data management processes. Based on established frameworks, we elicited IT capabilities, readiness and infrastructure to evaluate the digital maturity at each centre. We analysed interview data thematically and synthesised results across centres to identify common challenges and inform recommendations. RESULTS: Transplant coordinators across centres reported similar care pathways with patients being referred by to transplant centres from regional renal referral centres. Analysis revealed that a key challenge revolved around the inability to access data across organisational boundaries. This resulted in dependence on postal or electronic mail to transfer clinical data, such as blood results or medication lists. Most centres had a hospital-wide electronic medical record, which was unable to meet the specific workflow requirements of transplantation. This was primarily because implemented solutions were unable to provide a single unified view of transplant-related data. As a result, transplant coordinators reported several workarounds to manage clinical data, such as paper folders, Excel© sheets and scanned files on hospital shared drives. CONCLUSIONS: Existing front-line IT solutions and wider infrastructure did not support the requirements of care pathways in renal transplantation. Digital transformation should focus on the need to surface patient data across organisational boundaries and provide specific views of data that complement the clinical workflow. This study highlights how regional interoperability remains a major priority to support multi-centre specialist services.

Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study.

BACKGROUND: Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. OBJECTIVE: To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. METHODS: An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. RESULTS: Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. CONCLUSIONS: Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation.

Using patient-reported data from a smartphone app to capture and characterize real-time patient-reported flares in rheumatoid arthritis.

Objective: We aimed to explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with RA. Methods: We used data from the Remote Monitoring of RA study, in which patients tracked their daily symptoms and weekly flares on an app. We summarized the number of self-reported flare weeks. For each week preceding a flare question, we calculated three summary features for daily symptoms: mean, variability and slope. Mixed effects logistic regression models quantified associations between flare weeks and symptom summary features. Pain was used as an example symptom for multivariate modelling. Results: Twenty patients tracked their symptoms for a median of 81 days (interquartile range 80, 82). Fifteen of 20 participants reported at least one flare week, adding up to 54 flare weeks out of 198 participant weeks in total. Univariate mixed effects models showed that higher mean and steeper upward slopes in symptom scores in the week preceding the flare increased the likelihood of flare occurrence, but the association with variability was less strong. Multivariate modelling showed that for pain, mean scores and variability were associated with higher odds of flare, with odds ratios 1.83 (95% CI, 1.15, 2.97) and 3.12 (95% CI, 1.07, 9.13), respectively. Conclusion: Our study suggests that patient-reported flares are common and are associated with higher daily RA symptom scores in the preceding week. Enabling patients to collect daily symptom data on their smartphones might, ultimately, facilitate prediction and more timely management of imminent flares.