Background The number of online services in health care is increasing rapidly in developed countries. Users are expected to take a more skilled and active role in taking care of their health and prevention of ill health. This induces risks that users (especially those who need the services the most) will drop out of digital services, resulting in a digital divide or exclusion. To ensure wide and equal use of online services, all users must experience them as beneficial. Objective This study aimed to examine associations of (1) demographics (age, gender, and degree of urbanization), (2) self-rated health, (3) socioeconomic position (education, experienced financial hardship, labor market position, and living alone), (4) social participation (voting, satisfaction with relationships, and keeping in touch with friends and family members), and (5) access, skills, and extent of use of information and communication technologies (ICT) with perceived benefits of online health care and social welfare services. Associations were examined separately for perceived health, economic, and collaboration benefits. Methods We used a large random sample representative of the Finnish population including 4495 (56.77% women) respondents aged between 20 and 97 years. Analyses of covariance were used to examine the associations of independent variables with perceived benefits. Results Access to online services, ICT skills, and extent of use were associated with all examined benefits of online services. ICT skills seemed to be the most important factor. Poor self-rated health was also consistently associated with lower levels of perceived benefits. Similarly, those who were keeping in touch with their friends and relatives at least once a week perceived online services more often beneficial in all the examined dimensions. Those who had experienced financial hardship perceived fewer health and economic benefits than others. Those who were satisfied with their relationships reported higher levels of health and collaboration benefits compared with their counterparts. Also age, education, and degree of urbanization had some statistically significant associations with benefits but they seemed to be at least partly explained by differences in access, skills, and extent of use of online services. Conclusions According to our results, providing health care services online has the potential to reinforce existing social and health inequalities. Our findings suggest that access to online services, skills to use them, and extent of use play crucial roles in perceiving them as beneficial. Moreover, there is a risk of digital exclusion among those who are socioeconomically disadvantaged, in poor health, or socially isolated. In times when health and social services are increasingly offered online, this digital divide may predispose people with high needs for services to exclusion from them.

Background In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. Objective This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. Methods We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (ρ=0.82-0.84), and Brazil (ρ=0.77-0.83) and moderate correlations with those occurring in Norway (ρ=0.32-0.35) and Finland (ρ=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. Conclusions Common cold–related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them.

Background In the abnormal circumstances caused by the COVID-19 pandemic, patient portals have supported patient empowerment and engagement by providing patients with access to their health care documents and medical information. However, the potential benefits of patient portals cannot be utilized unless the patients accept and use the services. Disparities in the use of patient portals may exacerbate the already existing inequalities in health care access and health outcomes, possibly increasing the digital inequality in societies. Objective The aim of this study is to examine the factors associated with nonuse of and dissatisfaction with the Finnish nationwide patient portal My Kanta Pages among the users of health care services during the COVID-19 outbreak. Several factors related to sociodemographic characteristics, health, and the use of health care services; experiences of guidance concerning electronic services; and digital skills and attitudes were evaluated. Methods A national population survey was sent using stratified sampling to 13,200 Finnish residents who had reached the age of 20 years. Data were collected from September 2020 to February 2021 during the COVID-19 pandemic. Respondents who had used health care services and the internet for transactions or for searching for information in the past 12 months were included in the analyses. Bivariate logistic regression analyses were used to examine the adjusted associations of respondent characteristics with the nonuse of My Kanta Pages and dissatisfaction with the service. The inverse probability weighting (IPW) method was applied in all statistical analyses to correct for bias. Results In total, 3919 (64.9%) of 6034 respondents were included in the study. Most respondents (3330/3919, 85.0%) used My Kanta Pages, and 2841 (85.3%) of them were satisfied. Nonusers (589/3919, 15%) were a minority among all respondents, and only 489 (14.7%) of the 3330 users were dissatisfied with the service. Especially patients without a long-term illness (odds ratio [OR] 2.14, 95% CI 1.48-3.10), those who were not referred to electronic health care services by a professional (OR 2.51, 95% CI 1.70-3.71), and those in need of guidance using online social and health care services (OR 2.26, 95% CI 1.41-3.65) were more likely nonusers of the patient portal. Perceptions of poor health (OR 2.10, 95% CI 1.51-2.93) and security concerns (OR 1.87, 95% CI 1.33-2.62) were associated with dissatisfaction with the service. Conclusions Patients without long-term illnesses, those not referred to electronic health care services, and those in need of guidance on the use of online social and health care services seemed to be more likely nonusers of the Finnish nationwide patient portal. Moreover, poor health and security concerns appeared to be associated with dissatisfaction with the service. Interventions to promote referral to electronic health care services by professionals are needed. Attention should be targeted to information security of the service and promotion of the public’s confidence in the protection of their confidential data.

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Background The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks. Objective The aim of this study was (1) to assess whether citizens’ and professionals’ searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and (2) to test our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model). Methods We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 2020 (49 weeks). Two major medical internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician’s Database (PD), a database widely used among HCPs. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs. Results We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database. Conclusions Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases.

The number of online services in health care is increasing rapidly in developed countries. Users are expected to take a more skilled and active role in taking care of their health and prevention of ill health. This induces risks that users (especially those who need the services the most) will drop out of digital services, resulting in a digital divide or exclusion. To ensure wide and equal use of online services, all users must experience them as beneficial.

Background To address the current COVID-19 and any future pandemic, we need robust, real-time, and population-scale collection and analysis of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into the progression of viral spread, and helps to predict the needs and timing of professional health care. Objective The objective of this study was to use a Conformité Européenne (CE)-marked medical online symptom checker service, Omaolo, and validate the data against the national demand for COVID-19–related care to predict the pandemic progression in Finland. Methods Our data comprised real-time Omaolo COVID-19 symptom checker responses (414,477 in total) and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare from March 16 to June 15, 2020 (the first wave of the pandemic in Finland). The symptom checker responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having COVID-19, and provides graded recommendations for further actions. We trained linear regression and extreme gradient boosting (XGBoost) models together with F-score and mutual information feature preselectors to predict the admissions once a week, 1 week in advance. Results Our models reached a mean absolute percentage error between 24.2% and 36.4% in predicting the national daily patient admissions. The best result was achieved by combining both Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the feature preselector. Conclusions Accurate short-term predictions of COVID-19 patient admissions can be made, and both symptom check questionnaires and daily admissions data contribute to the accuracy of the predictions. Thus, symptom checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic.

BACKGROUND In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. OBJECTIVE This study aims to assess whether web-based searches on <i>common cold</i> would correlate with and help to predict asthma hospitalizations. METHODS We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on <i>common cold</i> were retrieved from Google Trends (GT) using the <i>pseudo-influenza syndrome</i> topic and local language search terms for <i>common cold</i> for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. RESULTS In time series analyses, GT data on <i>common cold</i> displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (<i>ρ</i>=0.82-0.84), and Brazil (<i>ρ</i>=0.77-0.83) and moderate correlations with those occurring in Norway (<i>ρ</i>=0.32-0.35) and Finland (<i>ρ</i>=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. CONCLUSIONS Common cold–related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them. CLINICALTRIAL