Teledermatology, originating in 1995, has been one of the first telemedicine services to see the light of day. Two decades of teledermatology research is summarized in this review. A literature search was conducted in PubMed. Search terms included “teledermatology,” “teledermoscopy,” “tele wound care,” “telederm*,” “(dermatology OR dermoscopy OR wound care OR skin) AND (telemedicine OR ehealth or mhealth OR telecare OR teledermatology OR teledermoscopy).” Inclusion criteria were (i) Dutch or English written papers and (ii) publication year from 2011 to present or (iii) (systematic) reviews with publication year before 2011. One hundred fourteen publications and 14 (systematic) reviews were included for full text reading. Focus of this review is on the following outcomes: (i) actors (primary, secondary, tertiary), (ii) purposes (consultation, triage, follow-up, education) and subspecialties (tele-wound care, burn care, teledermoscopy (teledermatoscopy), teledermatopathology, and mobile teledermatology), (iii) delivery modalities and technologies (store and forward, real-time interactive, and hybrid modalities using web-based systems, email, mobile phones, tablets, or videoconferencing equipment), (iv) business models, (v) integration of teledermatology into national healthcare systems, (vi) preconditions and requirements for implementation (security, ethical issues, responsibility, reimbursement, user satisfaction, technique, and technology standards), and (vii) added value.To conclude, teledermatology is an efficient and effective healthcare service compared to in-person care. Teledermatology reduces patients’ travel time and waiting time, avoids (unnecessary) dermatologic visits, and improves access of care to underserved patients.

Retrospectively collected information on lifestyle during pregnancy, birth, and early childhood behavior is sometimes biased, which limits its value in estimating the contribution of early-life adversity to health in later life.

SummaryObjectives: To analyze the attitude of physicians towards alerting in CPOE systems in different hospitals in different countries, addressing various organizational and technical settings and the view of physicians not currently using a CPOE.Methods: A cross-sectional quantitative and qualitative questionnaire survey. We invited 2,600 physicians in eleven hospitals from nine countries to participate. Eight of the hospitals had different CPOE systems in use, and three of the participating hospitals were not using a CPOE system.Results: 1,018 physicians participated. The general attitude of the physicians towards CPOE alerting is positive and is found to be mostly independent of the country, the specific organizational settings in the hospitals and their personal experience with CPOE systems. Both quantitative and qualitative results show that the majority of the physicians, both CPOE-users and non-users, appreciate the benefits of alerting in CPOE systems on medication safety. However, alerting should be better adapted to the clinical context and make use of more sophisticated ways to present alert information. The vast majority of physicians agree that additional information regarding interactions is useful on demand. Around half of the respondents see possible alert overload as a major problem; in this regard, physicians in hospitals with sophisticated alerting strategies show partly better attitude scores.Conclusions: Our results indicate that the way alerting information is presented to the physicians may play a role in their general attitude towards alerting, and that hospitals with a sophisticated alerting strategy with less interruptive alerts tend towards more positive attitudes. This aspect needs to be further investigated in future studies.

For clinically referred children with Autism Spectrum Disorder (ASD) or Attention Deficit/Hyperactivity Disorder (ADHD) several early indicators have been described. However, knowledge is lacking on early markers of less severe variants of ASD and ADHD from the general population. The aim of the present study is to identify early indicators of high risk groups for ASD and ADHD problems based on routine data from community pediatric services between infancy and age four. Data are from 1,816 participants who take part in Tracking Adolescents’ Individual Lives Survey (TRAILS), a longitudinal study. Information on early developmental factors was extracted from charts of routine Preventive Child Healthcare (PCH) visits. To assess ASD and ADHD problems, respectively, we used the Children’s Social Behavior Questionnaire (CSBQ) and the Child Behavior Checklist (CBCL), filled out by parents three times between the ages of 11 and 17. Note that these are parent ratings and not diagnostic instruments performed by trained clinicians. Male gender, low birth weight, low level of education of the mother, social, behavioral, language, psychomotor and eating problems significantly predicted ASD problems (odds ratios (OR) between 1.34 and 2.41). ADHD problems were also predicted by male gender and low level of education of the mother and by maternal smoking during pregnancy, good gross motor skills in first year, early attention and hyperactivity problems, and absence of parent-reported positive behavior (ORs between 1.36 and 1.74). Routine data on early childhood from PCH services are predictive for ASD and ADHD problems in adolescents in the general population. The PCH services are a useful setting to identify high risk groups, and to monitor them subsequently.

Trajectories of emotional and behavioral problems during adolescence are rather stable and can be predicted by a parsimonious set of data from early well-child assessments.

Large relative increases in weight from ages 2 to 7 years are associated with adolescent adiposity and MS. This is more pronounced in adolescents whose mothers smoked during pregnancy.

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While the amount of patient-centered eHealth interventions increases, many questions remain as to whether and to what extent human factors mediate their use and impact. Future research should adopt a formal theory-driven approach towards human factors when investigating those factors' influence on the effectiveness of these interventions. Insights could then be used to better tailor the content and design of eHealth solutions according to patient user profiles, so as to enhance eHealth interventions impact on patient behavior, self-efficacy, and health-related outcomes.