BackgroundeHealth has an enormous potential to improve healthcare cost, effectiveness, and quality of care. However, there seems to be a gap between the foreseen benefits of research and clinical reality.ObjectiveOur objective was to systematically review the factors influencing the outcome of eHealth interventions in terms of success and failure.MethodsWe searched the PubMed database for original peer-reviewed studies on implemented eHealth tools that reported on the factors for the success or failure, or both, of the intervention. We conducted the systematic review by following the patient, intervention, comparison, and outcome framework, with 2 of the authors independently reviewing the abstract and full text of the articles. We collected data using standardized forms that reflected the categorization model used in the qualitative analysis of the outcomes reported in the included articles.ResultsAmong the 903 identified articles, a total of 221 studies complied with the inclusion criteria. The studies were heterogeneous by country, type of eHealth intervention, method of implementation, and reporting perspectives. The article frequency analysis did not show a significant discrepancy between the number of reports on failure (392/844, 46.5%) and on success (452/844, 53.6%). The qualitative analysis identified 27 categories that represented the factors for success or failure of eHealth interventions. A quantitative analysis of the results revealed the category quality of healthcare (n=55) as the most mentioned as contributing to the success of eHealth interventions, and the category costs (n=42) as the most mentioned as contributing to failure. For the category with the highest unique article frequency, workflow (n=51), we conducted a full-text review. The analysis of the 23 articles that met the inclusion criteria identified 6 barriers related to workflow: workload (n=12), role definition (n=7), undermining of face-to-face communication (n=6), workflow disruption (n=6), alignment with clinical processes (n=2), and staff turnover (n=1).ConclusionsThe reviewed literature suggested that, to increase the likelihood of success of eHealth interventions, future research must ensure a positive impact in the quality of care, with particular attention given to improved diagnosis, clinical management, and patient-centered care. There is a critical need to perform in-depth studies of the workflow(s) that the intervention will support and to perceive the clinical processes involved.

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BackgroundOver the last two decades, the number of studies on electronic symptom reporting has increased greatly. However, the field is very heterogeneous: the choices of patient groups, health service innovations, and research targets seem to involve a broad range of foci. To move the field forward, it is necessary to build on work that has been done and direct further research to the areas holding most promise. Therefore, we conducted a comprehensive review of randomized controlled trials (RCTs) focusing on electronic communication between patient and provider to improve health care service quality, presented in two parts. Part 2 investigates the methodological quality and effects of the RCTs, and demonstrates some promising benefits of electronic symptom reporting.ObjectiveTo give a comprehensive overview of the most mature part of this emerging field regarding (1) patient groups, (2) health service innovations, and (3) research targets relevant to electronic symptom reporting.MethodsWe searched Medline, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, and IEEE Xplore for original studies presented in English-language articles published from 1990 to November 2011. Inclusion criteria were RCTs of interventions where patients or parents reported health information electronically to the health care system for health care purposes and were given feedback.ResultsOf 642 records identified, we included 32 articles representing 29 studies. The included articles were published from 2002, with 24 published during the last 5 years. The following five patient groups were represented: respiratory and lung diseases (12 studies), cancer (6), psychiatry (6), cardiovascular (3), and diabetes (1). In addition to these, 1 study had a mix of three groups. All included studies, except 1, focused on long-term conditions. We identified four categories of health service innovations: consultation support (7 studies), monitoring with clinician support (12), self-management with clinician support (9), and therapy (1). Most of the research (21/29, 72%) was conducted within four combinations: consultation support innovation in the cancer group (5/29, 17%), monitoring innovation in the respiratory and lung diseases group (8/29, 28%), and self-management innovations in psychiatry (4/29, 14%) and in the respiratory and lung diseases group (4/29, 14%). Research targets in the consultation support studies focused on increased patient centeredness, while monitoring and self-management mainly aimed at documenting health benefits. All except 1 study aiming for reduced health care costs were in the monitoring group.ConclusionRCT-based research on electronic symptom reporting has developed enormously since 2002. Research including additional patient groups or new combinations of patient groups with the four identified health service innovations can be expected in the near future. We suggest that developing a generic model (not diagnosis specific) for electronic patient symptom reporting for long-term conditions may benefit the field.

BackgroundWe conducted in two parts a systematic review of randomized controlled trials (RCTs) on electronic symptom reporting between patients and providers to improve health care service quality. Part 1 reviewed the typology of patient groups, health service innovations, and research targets. Four innovation categories were identified: consultation support, monitoring with clinician support, self-management with clinician support, and therapy.ObjectiveTo assess the methodological quality of the RCTs, and summarize effects and benefits from the methodologically best studies.MethodsWe searched Medline, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, and IEEE Xplore for original studies presented in English-language articles between 1990 and November 2011. Risk of bias and feasibility were judged according to the Cochrane recommendation, and theoretical evidence and preclinical testing were evaluated according to the Framework for Design and Evaluation of Complex Interventions to Improve Health. Three authors assessed the risk of bias and two authors extracted the effect data independently. Disagreement regarding bias assessment, extraction, and interpretation of results were resolved by consensus discussions.ResultsOf 642 records identified, we included 32 articles representing 29 studies. No articles fulfilled all quality requirements. All interventions were feasible to implement in a real-life setting, and theoretical evidence was provided for almost all studies. However, preclinical testing was reported in only a third of the articles. We judged three-quarters of the articles to have low risk for random sequence allocation and approximately half of the articles to have low risk for the following biases: allocation concealment, incomplete outcome data, and selective reporting. Slightly more than one fifth of the articles were judged as low risk for blinding of outcome assessment. Only 1 article had low risk of bias for blinding of participants and personnel. We excluded 12 articles showing high risk or unclear risk for both selective reporting and blinding of outcome assessment from the effect assessment. The authors’ hypothesis was confirmed for 13 (65%) of the 20 remaining articles. Articles on self-management support were of higher quality, allowing us to assess effects in a larger proportion of studies. All except one self-management interventions were equally effective to or better than the control option. The self-management articles document substantial benefits for patients, and partly also for health professionals and the health care system.ConclusionElectronic symptom reporting between patients and providers is an exciting area of development for health services. However, the research generally is of low quality. The field would benefit from increased focus on methods for conducting and reporting RCTs. It appears particularly important to improve blinding of outcome assessment and to precisely define primary outcomes to avoid selective reporting. Supporting self-management seems to b...

The costs of telemedicine screening for diabetic retinopathy were examined in a trial conducted in northern Norway, involving the University Hospital of Tromsø (UHT) and the primary care centre in Alta, approximately 400 km away. In Alta, specially trained nurses examined 42 diabetic patients using a digital camera to obtain images of the retina. The images were then sent by email to an eye specialist at the UHT. A cost-minimization analysis showed that at low workloads, for example 20 patients per annum, telemedicine was more expensive than conventional examination: NKr8555 versus NKr428 per patient. However, at higher workloads, telemedicine was cheaper. For example, at 200 patients per annum, telemedicine cost NKr971 and conventional examination cost NKr1440 per patient. The break-even point occurred at a patient workload of 110 per annum. Given that there are some 250 diabetic patients in Alta, telemedicine screening is the cheaper service for the public sector.

In a pilot project, telemedicine was used to conduct retinal examinations of diabetic patients in the Alta municipality of Norway. All health-care workers who were involved in the project were interviewed. The ophthalmologists found that the grading of the level of retinopathy was quicker with digital images than with slit-lamp examinations. Fifty patients with type II diabetes were invited to attend a telemedicine check-up and 42 did so. Patients were asked to complete a questionnaire after the telemedicine examination and we received 32 replies (a 76% response rate), of which 12 were from men and 20 from women. The patients expressed a high degree of satisfaction with the telemedicine examination. The results of the evaluation also clearly showed that trust between health personnel was of major importance in engendering positive attitudes. Confidence is the basis of good collaboration between the various professions in the health-care sector, between health-care levels and between patients and treatment providers - in terms not only of individuals' confidence but also of routines, procedures and the system as a whole.

Background Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

BackgroundReducing maternal mortality, neonatal mortality and under 5-year mortality are important targets addressed by the United Nations' Sustainable Development Goals. Despite studies reported an improvement in maternal and child health indicators, the progress achieved is not uniform across regions. Due to the increasing availability of mobile phones in low and middle-income countries, mHealth could impact considerably on reducing maternal and child mortality and maximizing women's access to quality care, from the antenatal stage to the post-natal period.MethodsA systematic literature review of mHealth interventions aimed at reducing maternal and child mortality in Sub-Saharan Africa and Southern Asia. Primary outcomes were maternal mortality, neonatal mortality, and under-five mortality. Secondary outcomes were skilled birth attendance, antenatal care (ANC) and post-natal care (PNC) attendance, and vaccination/immunization coverage. We searched for articles published from January 2010 to December 2020 in Embase, Medline and Web of Science. Quantitative comparative studies were included. The protocol was developed according to the PRISMA Checklist and published in PROSPERO [CRD42019109434]. The Quality Assessment Tool for Quantitative Studies was used to assess the quality of the eligible studies.Results23 studies were included in the review, 16 undertaken in Sub-Saharan Africa and 7 in Southern Asia. Most studies used SMS or voice message reminders for education purposes. Only two studies reported outcomes on neonatal mortality, with positive results. None of the studies reported results on maternal mortality or under-five mortality. Outcomes on skilled birth attendance, ANC attendance, PNC attendance, and vaccination coverage were reported in six, six, five, and eleven studies, respectively. Most of these studies showed a positive impact of mHealth interventions on the secondary outcomes.ConclusionSimple mHealth educational interventions based on SMS and voice message reminders are effective at supporting behavior change of pregnant women and training of health workers, thus improving ANC and PNC attendance, vaccination coverage and skilled birth attendance. Higher quality studies addressing the role of mHealth in reducing maternal and child mortality in resource-limited settings are needed, especially in Southern Asia.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019109434, identifier CRD42019109434.