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Skip search results from other journals and go to results- 41 Journal of Medical Internet Research
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The AI Reviewer: Evaluating AI’s Role in Citation Screening for Streamlined Systematic Reviews
No personal or patient-level data were used, and no identifiers were included. Formal research ethics board approval was therefore not required.
Among the 121 total citations, the LLMs’ sensitivity (correctly identifying included citations) ranged from 57% to 100%, and specificity (correctly excluding noneligible citations) ranged from 18% to 79%. Chat GPT 3.5 achieved the highest sensitivity (100%) and the highest specificity (79%). Full results are shown in Table 1.
JMIR Form Res 2025;9:e58366
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An empirical analysis based on data from the United States Health Information Trends Survey revealed that OHIS has a positive, relatively large, and statistically significant effect on individual health care demand [21].
J Med Internet Res 2025;27:e66683
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Accordingly, the primary objective of this study is to determine whether the All of Us data are fit for an analysis of women who had a mastectomy.
The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is the data standard used by the All of Us Research Program. The OMOP CDM consists of standardized concepts and relationships, allowing for harmonizing data from different sources.
JMIR Cancer 2025;11:e59298
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Assessing Digital Maturity of Hospitals: Viewpoint Comparing National Approaches in Five Countries
We assigned academic (KC, FJ, LW, TP, and EA) or policy (ML and SM) leads to each of the participating countries, who were responsible for collecting descriptive data. Using the nominal group technique with leads, we cocreated a data collection template table for each country, representing key features and learnings identified through discussions in group meetings.
J Med Internet Res 2025;27:e57858
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Accurate and well-formatted data are key to delivering high-quality health care and fueling medical research [1-3]. All health care actors acquire real-world data, defined as any health care-related information captured from the patient [4]. The volume, velocity, and variety of acquired data, however, raise challenges for data processing systems [5].
J Med Internet Res 2025;27:e68083
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EHR data are generally recorded in 2 forms: structured and unstructured data. Structured data includes clinical codes for documenting clinical events, such as diagnoses, medications, procedures, and measurements. Structured data is particularly suitable for observational research due to its consistent meaning, tabular format, and standardized vocabulary of codes.
J Med Internet Res 2025;27:e66910
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The survey was conducted from August 1 to September 21, 2023, and the collected data were stored in a report. No human participants were involved, and thus institutional review board (IRB) approval was not obtained.
Five healthy city experts performed a qualitative analysis on the collected data through a group deep discussion. The experts were recruited from October 1, 2023, to November 3, 2023.
JMIR Form Res 2025;9:e62935
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This paucity of data is particularly apparent in the evaluation of gender differences in the clinical presentation and medical management of IHD in LMICs [8,9]. A promising direction is the use of electronic health records (EHRs) to analyze patient data to better inform clinical decision-making and assess adherence to IHD treatment guidelines using a gender lens.
JMIR Form Res 2024;8:e42774
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The results from the queries run across all the data partners and are automatically collated and displayed within the Gateway for analysis by the user who initiated the query.
To configure the federated platform, we worked across 22 different data partners to on-board their data. Data partners included academic groups collecting data from a consented research cohort and national Trusted Research Environments (TREs) collecting population-wide, routinely collected data.
J Med Internet Res 2024;26:e50235
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Numerous health care data security breaches have occurred owing to a lack of attitude by the user [8]. Most prominently, digital health data were the victim of phishing, ransomware, and malware [9].
As studies elucidated, the attitudes of health professionals toward digital health data security were prominent factors in determining the consequences of adopting digital technology to secure health care data [3].
Online J Public Health Inform 2024;16:e57764
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