This systematic review was based on a pre-established protocol and was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.24 In Figure 1, we illustrated the analytic framework of this meta-narrative review. It was hypothesized that websites disseminating high-quality health information may improve knowledge transfer, skills, and attitudes for patients/public regarding their healthcare. Consequently, that enhancement may yield benefits to both the patients/public and the healthcare systems (e.g., improve health outcomes, reduce costs, improve quality of life).
Literature Search and Study Selection
We conducted a comprehensive search of the following databases: EMBASE, EBM Reviews–Cochrane Central Register of Controlled Trials, EBM Reviews–Cochrane Database of Systematic Reviews, Ovid MEDLINE® Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE®, CINAHL, LISA, Scopus, Web of Science, and ERIC from database inception to November 2017. We searched all study designs and conducted reference mining of relevant publications to identify additional literature. Gray literature was also searched through all of the following sources: conference abstracts, dissertations, AHRQ, Health Canada, the first 100 entries of Google Scholar, and OpenGrey. A health sciences librarian with consultation with the PI developed and executed the search strategy (Appendix in the Electronic Supplementary Material).
Eligibility Criteria
We included all types of study designs that systematically evaluated websites’ quality with validated and non-validated scales. These websites needed to fulfill the following characteristics: provide information on any health condition, have the general public as the target population, and are published in English. We excluded studies targeting healthcare workers, professionals, or medical students and studies focused on validation of quality tools or studies of quality assessment of printed materials. We also excluded editorials, letters, and abstracts. We did not restrict studies to any specific region and included publications from the last 10 years (2008 to 2017).
Independent reviewers screened the titles, abstracts, and then full text in duplicate to select eligible references. Discrepancies among reviewers were resolved through discussions and consensus.
Data Extraction and Methodological Quality Assessment
We developed a data extraction form which was first pilot tested by all of the reviewers. For eligible references, data extracted included the following: author, year, journal, study design, search engines, health conditions, type of organization, quality scales, quality scores, and definition of quality.
For classifying affiliations of websites, we used the following method: (1) websites with “.gov” domains were classified as government, (2) websites with “.org” domains and foundations, support groups, or societies were classified as non-profit, (3) websites with “.edu” domain or affiliated with university, hospitals, clinics, or professional medical organization were classified as academic/hospitals/professional medical, (4) websites with news portals were classified as media, (5) websites that did not disclose affiliation, had commercial contents, or had affiliation to a private holder were classified as private/commercial, and the rest were classified as other.
For classifying websites by health conditions, we combined all health conditions into 10 different categories: (1) anesthesiology; (2) ear, nose, and throat (ENT); (3) gynecology and obstetrics; (4) internal medicine; (5) neurology/neurosurgery; (6) oncology; (7) orthopedic surgery; (8) psychiatry; (9) surgery; (10) pediatric; and (11) other.
For classifying websites’ quality, we developed the following criteria by consulting the quality scales identified in this systematic review (Table 1):
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> 80%: excellent
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66–79%: very good
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45–65%: good
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< 44%: poor
Table 1 List of Quality Scales with Minimum and Maximum Scores For the methodological quality appraisal, we modified the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies to fit the goal of this study.25 Data extraction and quality assessment were completed by independent reviewers and audited by a third reviewer for completeness and accuracy.
Data Synthesis and Analysis
Descriptive statistics were used to synthesize data. Using Stata 14 (StataCorp LP., College Station, TX), we calculated medians and interquartile ranges (IQR) for the percentiles of total scores for each scale. Data was presented using graphs and tables.
We stratified quality by type of organization and by health conditions using the two most commonly applied tools:
The initial intent of DISCERN was to evaluate written information on treatment choices for one specific health condition by expert users and health information producers. The tool consists of 15 key questions and an overall quality scoring option. Each key question represents a separate quality criterion, and the questions are organized into three sections: reliability (questions 1–8), specific information about treatment choices (questions 9–15), and overall quality rating (question 16). To facilitate analysis, we used 3 different sets of total scores: 5, 75, and 80, and analyzed and reported data based on the percentile of these total scores.26,27,28
The HON Code is a code of ethics developed for site managers to follow for disseminating quality, objective, and transparent medical information on the Internet.29, 30 The Code consists of eight quality criteria, each with a definition: (1) authoritative, (2) complementarity, (3) privacy, (4) attribution, (5) justifiability, (6) transparency, (7) financial disclosure, and (8) advertising policy. Organizations are required to obtain certification from the Health On the Net Foundation to use the Code. We analyzed data based on two categories: (1) HON certified and (2) not certified.
For the rest of the quality scales, we have separately analyzed and synthesized quality scores.