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BackgroundAdvancements in wearable sensors have caused a resurgence in their use, particularly because their miniaturization offers ambulatory advantages while performing continuous vital sign monitoring. Digital alerts can be generated following early recognition of clinical deterioration through breaches of set parameter thresholds, permitting earlier intervention. However, a systematic real-world evaluation of these alerting systems has yet to be conducted, and their efficacy remains unknown. ObjectiveThe aim of this study is to implement wearable sensors and digital alerting systems in acute general wards to evaluate the resultant clinical outcomes. MethodsParticipants on acute general wards will be screened and recruited into a trial with a pre-post implementation design. In the preimplementation phase, the SensiumVitals monitoring system, which continuously measures temperature, heart, and respiratory rates, will be used for monitoring alongside usual care. In the postimplementation phase, alerts will be generated from the SensiumVitals system when pre-established thresholds for vital parameters have been crossed, requiring acknowledgement from health care staff; subsequent clinical outcomes will be analyzed. ResultsEnrolment is currently underway, having started in September 2017, and is anticipated to end shortly. Data analysis is expected to be completed in 2021. ConclusionsThis study will offer insight into the implementation of digital health technologies within a health care trust and aims to describe the effectiveness of wearable sensors for ambulatory continuous monitoring and digital alerts on clinical outcomes in acute general ward settings. Trial RegistrationClinicalTrials.gov NCT04638738; https://clinicaltrials.gov/ct2/show/NCT04638738. International Registered Report Identifier (IRRID)DERR1-10.2196/26240

BackgroundSharing personal health information positively impacts quality of care across several domains, and particularly, safety and patient-centeredness. Patients may identify and flag up inconsistencies in their electronic health records (EHRs), leading to improved information quality and patient safety. However, in order to identify potential errors, patients need to be able to understand the information contained in their EHRs. ObjectiveThe aim of this study was to assess patients’ perceptions of their ability to understand the information contained in their EHRs and to analyze the main barriers to their understanding. Additionally, the main types of patient-reported errors were characterized. MethodsA cross-sectional web-based survey was undertaken between March 2017 and September 2017. A total of 682 registered users of the Care Information Exchange, a patient portal, with at least one access during the time of the study were invited to complete the survey containing both structured (multiple choice) and unstructured (free text) questions. The survey contained questions on patients’ perceived ability to understand their EHR information and therefore, to identify errors. Free-text questions allowed respondents to expand on the reasoning for their structured responses and provide more detail about their perceptions of EHRs and identifying errors within them. Qualitative data were systematically reviewed by 2 independent researchers using the framework analysis method in order to identify emerging themes. ResultsA total of 210 responses were obtained. The majority of the responses (123/210, 58.6%) reported understanding of the information. The main barriers identified were information-related (medical terminology and knowledge and interpretation of test results) and technology-related (user-friendliness of the portal, information display). Inconsistencies relating to incomplete and incorrect information were reported in 12.4% (26/210) of the responses. ConclusionsWhile the majority of the responses affirmed the understanding of the information contained within the EHRs, both technology and information-based barriers persist. There is a potential to improve the system design to better support opportunities for patients to identify errors. This is with the aim of improving the accuracy, quality, and timeliness of the information held in the EHRs and a mechanism to further engage patients in their health care.

BackgroundEvidence suggests that health care data sharing may strengthen care coordination, improve quality and safety, and reduce costs. However, to achieve efficient and meaningful adoption of health care data-sharing initiatives, it is necessary to engage all stakeholders, from health care professionals to patients. Although previous work has assessed health care professionals’ perceptions of data sharing, perspectives of the general public and particularly of seldom heard groups have yet to be fully assessed. ObjectiveThis study aims to explore the views of the public, particularly their hopes and concerns, around health care data sharing. MethodsAn original, immersive public engagement interactive experience was developed—The Can of Worms installation—in which participants were prompted to reflect about data sharing through listening to individual stories around health care data sharing. A multidisciplinary team with expertise in research, public involvement, and human-centered design developed this concept. The installation took place in three separate events between November 2018 and November 2019. A combination of convenience and snowball sampling was used in this study. Participants were asked to fill self-administered feedback cards and to describe their hopes and fears about the meaningful use of data in health care. The transcripts were compiled verbatim and systematically reviewed by four independent reviewers using the thematic analysis method to identify emerging themes. ResultsOur approach exemplifies the potential of using interdisciplinary expertise in research, public involvement, and human-centered design to tell stories, collect perspectives, and spark conversations around complex topics in participatory digital medicine. A total of 352 qualitative feedback cards were collected, each reflecting participants’ hopes and fears for health care data sharing. Thematic analyses identified six themes under hopes: enablement of personal access and ownership, increased interoperability and collaboration, generation of evidence for better and safer care, improved timeliness and efficiency, delivery of more personalized care, and equality. The five main fears identified included inadequate security and exploitation, data inaccuracy, distrust, discrimination and inequality, and less patient-centered care. ConclusionsThis study sheds new light on the main hopes and fears of the public regarding health care data sharing. Importantly, our results highlight novel concerns from the public, particularly in terms of the impact on health disparities, both at international and local levels, and on delivering patient-centered care. Incorporating the knowledge generated and focusing on co-designing solutions to tackle these concerns is critical to engage the public as active contributors and to fully leverage the potential of health care data use.

BackgroundThe diagnosis and management of sepsis remain a global health care challenge. Digital technologies have the potential to improve sepsis care. ObjectiveThe aim of this paper was to systematically review the evidence on the impact of digital alerting systems on sepsis related outcomes. MethodsThe following databases were searched for studies published from April 1964 to February 12, 2019, with no language restriction: EMBASE, MEDLINE, HMIC, PsycINFO, and Cochrane. All full-text reports of studies identified as potentially eligible after title and abstract reviews were obtained for further review. The search was limited to adult inpatients. Relevant articles were hand searched for other studies. Only studies with clear pre- and postalerting phases were included. Primary outcomes were hospital length of stay (LOS) and intensive care LOS, whereas secondary outcomes were time to antibiotics and mortality. Studies based solely on intensive care, case reports, narrative reviews, editorials, and commentaries were excluded. All other trial designs were included. A qualitative assessment and meta-analysis were performed. ResultsThis review identified 72 full-text articles. From these, 16 studies met the inclusion criteria and were included in the final analysis. Of these, 8 studies reviewed hospital LOS, 12 reviewed mortality outcomes, 5 studies explored time to antibiotics, and 5 studies investigated intensive care unit (ICU) LOS. Both quantitative and qualitative assessments of the studies were performed. There was evidence of a significant benefit of digital alerting in hospital LOS, which reduced by 1.31 days (P=.014), and ICU LOS, which reduced by 0.766 days (P=.007). There was no significant association between digital alerts and mortality (mean decrease 11.4%; P=.77) or time to antibiotics (mean decrease 126 min; P=.13). ConclusionsThis review highlights that digital alerts can considerably reduce hospital and ICU stay for patients with sepsis. Further studies including randomized controlled trials are necessary to confirm these findings and identify the choice of alerting system according to the patient status and pathological cohort.

BackgroundMedication adherence is an expensive and damaging problem for patients and health care providers. Patients adhere to only 50% of drugs prescribed for chronic diseases in developed nations. Digital health has paved the way for innovative smartphone solutions to tackle this challenge. However, despite numerous apps available claiming to improve adherence, a thorough review of adherence apps has not been carried out to date. ObjectiveThe aims of this study were to (1) review medication adherence apps available in app repositories in terms of their evidence base, medical professional involvement in development, and strategies used to facilitate behavior change and improve adherence and (2) provide a system of classification for these apps. MethodsIn April 2015, relevant medication adherence apps were identified by searching the Apple App Store and the Google Play Store using a combination of relevant search terms. Data extracted included app store source, app price, documentation of health care professional (HCP) involvement during app development, and evidence base for each respective app. Free apps were downloaded to explore the strategies used to promote medication adherence. Testing involved a standardized medication regimen of three reminders over a 4-hour period. Nonadherence features designed to enhance user experience were also documented. ResultsThe app repository search identified a total of 5881 apps. Of these, 805 fulfilled the inclusion criteria initially and were tested. Furthermore, 681 apps were further analyzed for data extraction. Of these, 420 apps were free for testing, 58 were inaccessible and 203 required payment. Of the 420 free apps, 57 apps were developed with HCP involvement and an evidence base was identified in only 4 apps. Of the paid apps, 9 apps had HCP involvement, 1 app had a documented evidence base, and 1 app had both. In addition, 18 inaccessible apps were produced with HCP involvement, whereas 2 apps had a documented evidence base. The 420 free apps were further analyzed to identify strategies used to improve medication adherence. This identified three broad categories of adherence strategies, reminder, behavioral, and educational. A total of 250 apps utilized a single method, 149 apps used two methods, and only 22 apps utilized all three methods. ConclusionsTo our knowledge, this is the first study to systematically review all available medication adherence apps on the two largest app repositories. The results demonstrate a concerning lack of HCP involvement in app development and evidence base of effectiveness. More collaboration is required between relevant stakeholders to ensure development of high quality and relevant adherence apps with well-powered and robust clinical trials investigating the effectiveness of these interventions. A sound evidence base will encourage the adoption of effective adherence apps, and thus improve patient welfare in the process.

BackgroundPatients in health systems across the world can now choose between different health care providers. Patients are increasingly using websites and apps to compare the quality of health care services available in order to make a choice of provider. In keeping with many patient-facing platforms, most services currently providing comparative information on different providers do not take account of end-user requirements or the available evidence base. ObjectiveTo investigate what factors were considered most important when choosing nonemergency secondary health care providers in the United Kingdom with the purpose of translating these insights into a ratings platform delivered through a consumer mHealth app. MethodsA mixed methods approach was used to identify key indicators incorporating a literature review to identify and categorize existing quality indicators, a questionnaire survey to formulate a ranked list of performance indicators, and focus groups to explore rationales behind the rankings. Findings from qualitative and quantitative methodologies were mapped onto each other under the four categories identified by the literature review. ResultsQuality indicators were divided into four categories. Hospital access was the least important category. The mean differences between the other three categories hospital statistics, hospital staff, and hospital facilities, were not statistically significant. Staff competence was the most important indicator in the hospital staff category; cleanliness and up-to-date facilities were equally important in hospital facilities; ease of travel to the hospital was found to be most important in hospital access. All quality indicators within the hospital statistics category were equally important. Focus groups elaborated that users find it difficult to judge staff competence despite its importance. ConclusionsA mixed methods approach is presented, which supported a patient-centered development and evaluation of a hospital ratings mobile app. Where possible, mHealth developers should use systematic research methods in order to more closely meet the needs of the end user and add credibility to their platform.

BackgroundThere are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. ObjectiveWe attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care. MethodsWe applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. ResultsThere was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P

BackgroundPatients are increasingly rating their family physicians on the Internet in the same way as they might rate a hotel on TripAdvisor or a seller on eBay, despite physicians’ concerns about this process. ObjectiveThis study aims to examine the usage of NHS Choices, a government website that encourages patients to rate the quality of family practices in England, and associations between web-based patient ratings and conventional measures of patient experience and clinical quality in primary care. MethodsWe obtained all (16,952) ratings of family practices posted on NHS Choices between October 2009 and December 2010. We examined associations between patient ratings and family practice and population characteristics. Associations between ratings and survey measures of patient experience and clinical outcomes were examined. Results61% of the 8089 family practices in England were rated, and 69% of ratings would recommend their family practice. Practices serving younger, less deprived, and more densely populated areas were more likely to be rated. There were moderate associations with survey measures of patient experience (Spearman ρ 0.37−0.48, P