Effects of Electronic Health Record Implementation and Barriers to Adoption and Use: A Scoping Review and Qualitative Analysis of the Content
Open accessAbstract
Despite the great advances in the field of electronic health records (EHRs) over the past 25 years, implementation and adoption challenges persist, and the benefits realized remain below expectations. This scoping review aimed to present current knowledge about the effects of EHR implementation and the barriers to EHR adoption and use. A literature search was conducted in PubMed, Web of Science, IEEE Xplore Digital Library and ACM Digital Library for studies published between January 2005 and May 2020. In total, 7641 studies were identified of which 142 met the criteria and attained the consensus of all researchers on inclusion. Most studies (n = 91) were published between 2017 and 2019 and 81 studies had the United States as the country of origin. Both positive and negative effects of EHR implementation were identified, relating to clinical work, data and information, patient care and economic impact. Resource constraints, poor/insufficient training and technical/educational support for users, as well as poor literacy and skills in technology were the identified barriers to adoption and use that occurred frequently. Although this review did not conduct a quality analysis of the included papers, the lack of uniformity in the use of EHR definitions and detailed contextual information concerning the study settings could be observed.
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AI clinical decision support assists clinicians in making more informed decisions.
“This assists clinicians in making more informed decisions.[13]”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [13]
In Results §3.1, under “Support Learning and Decision-Making” (page not provided), the review reports that clinicians saw EHRs as supporting learning and decision-making. It does not establish that AI clinical decision support, specifically, helps clinicians make more informed decisions. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI systems provide healthcare professionals with real-time clinical decision support, offering evidence-based recommendations for treatment plans, drug interactions, and diagnostic accuracy.
“AI systems provide healthcare professionals with real-time clinical decision support, offering evidence-based recommendations for treatment plans, drug interactions, and diagnostic accuracy.”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [13]
The Results section (3.1, “Support Learning and Decision-Making”; page not available) reports that EHRs can support clinicians’ learning and decision-making. Our copy does not address AI systems providing real-time, evidence-based recommendations for treatment plans, drug interactions, or diagnosis; searches for AI clinical decision support and drug interactions found no passage supporting those specifics. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAI-driven algorithms can analyze vast datasets, including genomic information, to tailor treatment plans to individual patients.
“AI-driven algorithms can analyze vast datasets, including genomic information, to tailor treatment plans to individual patients.”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [11]
The Results section (3.1, page unavailable) discusses EHR support for disease management and clinical decision-making, but not AI-driven analysis of genomic data to tailor treatment plans. Searches for “genomic” and “AI-driven algorithms” found no passage addressing that claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredPrecision medicine allows for more accurate diagnosis and personalized treatment strategies.
“This approach, known as precision medicine, allows for more accurate diagnosis and personalized treatment strategies.[11]”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [11]
The Results (§3.1, page not available in our copy) discuss EHR-supported disease management and person-centered care, not precision medicine. Searches for “precision medicine,” diagnostic accuracy, and personalized treatment found no passage addressing the claimed benefits. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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- Authors
- Tsai, C. H., Eghdam, A., Davoody, N., Wright, G., Flowerday, S., & Koch, S.
- Affiliations
- Karolinska Institutet (SE)
- Rhodes University (ZA)
- Corresponding author
- Koch, Sabine
- Venue
- Life
- Publisher
- MDPI AG
- Published
- 2020-12-04
- Volume
- 10
- Issue
- 12
- Pages
- 327
- ISSN
- 2075-1729 (ISSN-L)
- PMC
- PMC7761950
- OpenAlex
- W3109639294
- Cited by
- 305
- References
- 164
- Citations by year
- 2026: 63 · 2025: 95 · 2024: 68 · 2023: 48 · 2022: 18 · 2021: 13
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Life (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd (2016-07324)
- South African Medical Research Council (2016-07324)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- electronic health records, EHR implementation, clinical work, patient care, economic impact, resource constraints, employee training, technical support, digital literacy, technological competence
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