Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Studies in health technology and informatics
Electronic Health Record System Support for Physician-Patient Communication: National Cross-Sectional Survey on Outpatient Physicians' Experiences
The impact of digital patient engagement platforms (DPEPs) on interactions between physicians and patients has been widely studied from patients' perspectives. However, research into physicians' viewpoints remains scarce. This study aimed to identify electronic health record (EHR) characteristics that support physician-patient collaboration from the perspective of physicians working in outpatient care. A national cross-sectional survey of physicians' experiences with EHRs was conducted in Finland in 2021 where EHR coverage had reached 100% already in 2010. We selected the responses of 1983 physicians working in public sector outpatient clinics (primary and specialized care). Linear regression analysis was used to examine the relationship between eight EHR characteristics and perceived support for physician-patient collaboration and information exchange. Of these, rapid system responsiveness, ease of utilizing patient-provided data, support for adherence to treatment recommendations, assistance in preventing medication errors, and comprehensive and reliable patient data were found to be associated with support. The model accounted for 27% of the variance in the dependent variable. In addition to enhancing care outcomes, clinical decision support and easy access to patient data can aid physicians in interactions with patients. Further investigations should continue to analyze how these identified features could better support collaboration with patients - beyond DPEPs and visible interactions during appointments. Considering the rapid evolution of DPEPs, future studies should also take into account the depth of the DPEP-EHR integration.
24 May 2026
Read appraisal →Journal of medical Internet research
Understanding Patient-Reported Offenses in Electronic Health Records: Cross-Sectional Mixed Methods Survey
BACKGROUND: Patients' access to their electronic health record (EHR) supports their participation and satisfaction with care. Despite the benefits, some patients have been upset after reading their EHR. Additionally, health care professionals are concerned that patients, particularly those with mental health conditions, may be offended, and they have expressed a need for further guidelines on how to write EHRs. Experiences among various patient groups are essential to support the relationship between patients and professionals. However, prior studies have often focused on single patient groups or specific clinical contexts, leaving a limited understanding of differences across multiple patient groups. OBJECTIVE: This study aimed to determine whether certain patient groups are more likely to feel offended while reading their EHRs and which information is perceived as offensive and to provide a comparison across multiple patient groups using a mixed methods approach. METHODS: A cross-sectional survey was conducted via the Finnish national patient portal using a web-based patient survey, adopting a mixed methods approach. The survey included multiple-choice and open-ended questions. The total sample comprised 4681 respondents. The survey respondents were placed into 4 patient groups: those who had received care for mental health, cancer, or other conditions and those who had received no care. Associations between the type of care and patients who felt offended were estimated using multivariate binary logistic regression. Inductive content analysis (n=502) was conducted to identify information perceived as offensive in the EHR. RESULTS: The patients who had received mental health care (166/654, 25.4%) or cancer and mental health care (9/39, 23.1%) were more likely to be offended by information in their EHR compared to the other groups (cancer care: 37/375, 9.9%; other conditions care: 383/3316, 11.6%; no care: 22/206, 10.7%; other conditions care: odds ratio 0.37, 95% CI 0.29-0.46; P<.001; model A). Additionally, female patients, those with bad or very bad health conditions, and patients with bachelor's or master's degrees were significantly more likely to feel offended. Errors, the health care professionals' disrespectful language, and perceived unnecessary information were the most frequently mentioned reasons for being offended. Patients with mental health care reported more often that unnecessary information and professionals' opinions and word choices were experienced as offensive compared to other patients. CONCLUSIONS: This study contributes new knowledge by identifying differences across multiple patient groups. Although a minority of patients felt offended by their EHR, health care professionals should consider that some patients, particularly those who have received mental health care or cancer and mental health care, may be offended by specific information or word choices in their EHRs. To address this, health care professionals should receive education on how to write their notes in a neutral tone and avoid potentially offensive topics. Improving the quality of EHRs could strengthen the relationship between patients and professionals.
15 May 2026
Read appraisal →Journal of medical Internet research
The Real-Time Support Role of Augmented Reality Technology in Shared Decision-Making in Neurosurgery Under the SEGUE Framework: Randomized Controlled Trial
BACKGROUND: Preoperative risk communication is essential for shared decision-making (SDM) in neurosurgery; however, conveying complex neuroanatomy and surgical risks using traditional verbal explanations can limit understanding and contribute to dissatisfaction and medicolegal disputes. Augmented reality (AR) may provide patient-specific, interactive 3D visualization to support these conversations. OBJECTIVE: This study evaluates whether AR-assisted preoperative risk communication improves objective understanding and other SDM-related outcomes, compared with communication supported by a conventional physical anatomical model within a standardized SEGUE-informed protocol. METHODS: A prospective, single-center, randomized controlled trial was conducted with 62 neurosurgery communication recipients (patients when capable; otherwise, a legally authorized representative [LAR]). Patients were stratified by planned surgical approach (frontal, parietal, and occipital) and, within each stratum, were randomized into an experimental AR group and a control physical-model group. The primary outcome was postsession objective understanding, assessed by a multiple-choice knowledge questionnaire. Secondary outcomes were subjective understanding, communication satisfaction, pre-to-post anxiety changes, communication duration, and neurosurgeons' communication skills from video recordings. RESULTS: Of the 67 individuals screened, 62 communication recipients were enrolled and completed all assessments (patients, n=30; LARs, n=32; AR, n=32; and control, n=30). Objective understanding was higher with AR than with the physical model in the prespecified pooled comparison (P=.01). Communication satisfaction was also higher with AR (P<.001). There were no clear between-group differences in subjective understanding (P=.41), anxiety changes (ΔState-Trait Anxiety Inventory [ΔSTAI] Y-1, P=.37; ΔSTAI Y-2, P=.84), in-session face-to-face communication duration (excluding any presession AR technical preparation time; P=.73), or SEGUE scores (P=.60). Exploratory stratified analyses suggested larger comprehension gains with AR in the parietal and occipital approach strata. CONCLUSIONS: In a standardized preoperative SDM conversation, an integrated visualization-support package combining AR, patient-specific modeling, and interactivity improved neurosurgical decision makers' objective understanding and satisfaction without prolonging in-session, face-to-face communication duration. Larger multicenter trials with longer-term outcomes are warranted to confirm effectiveness and to evaluate implementation and cost considerations. TRIAL REGISTRATION: ISRCTN Registry ISRCTN11483487; http://www.isrctn.com/ISRCTN11483487.
20 Apr 2026
Read appraisal →Patient education and counseling
Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication
OBJECTIVE: To examine the influence of the emerging use of generative artificial intelligence (GenAI) within electronic health records and among the public on the patient-centeredness of communication in healthcare. METHOD: In this scoping review, we conducted a systematic search for peer-reviewed studies in PubMed and PsycInfo that empirically examined GenAI involvement in clinical communication. We then mapped study findings onto a well-established framework for patient-centered communication. RESULTS: Our search yielded 67 studies for analysis. Results suggest that integration of GenAI into healthcare communication has the potential to increase clinician efficiency in interacting with patients, to expand channels for patients to obtain information about their healthcare, and to enhance empathy in clinical communication. However, findings also indicate variability in the quality of information produced by GenAI, the potential for GenAI to recast the clinician as a technical supervisor rather than a humanistic care provider, and several issues of equity and privacy raised by engagement with GenAI. CONCLUSION: As GenAI becomes more prevalent in healthcare, rigorous examination of GenAI is needed to ensure that its development and implementation aids rather than hinders patient-centered communication. We conclude with an agenda for further research on GenAI grounded in the PCC framework underlying our review. PRACTICE IMPLICATIONS: Findings from this review highlight the current potential benefits and limitations of GenAI as a third party to clinical communication. Continued efforts toward developing and applying GenAI for effective healthcare communication should focus on protecting patients from potential drawbacks and maximizing nascent benefits for patient-centered communication.
4 Apr 2026
Read appraisal →Am J Obstet Gynecol
Can artificial intelligence improve the readability of patient education information in gynecology?
BACKGROUND: The American Medical Association recommends that patient information be written at a sixth-grade level to increase accessibility. However, most existing patient education materials exceed this threshold, posing challenges to patient comprehension. Artificial intelligence, particularly large language models, presents an opportunity to improve the readability of medical information. Despite the growing integration of artificial intelligence in healthcare, few studies have evaluated the effectiveness of large language models in generating or improving readability of existing patient education materials within gynecology. OBJECTIVE: To assess the readability and effectiveness of patient education materials generated by ChatGPT, Gemini, and CoPilot compared to American College of Obstetricians and Gynecologists and UpToDate.com. Additionally, to determine whether these large language models can successfully adjust the reading level to a sixth-grade standard. STUDY DESIGN: This cross-sectional study analyzed American College of Obstetricians and Gynecologists, UpToDate, and large language model-generated content, evaluating large language models for 2 tasks: 1) independent large language model-generated materials and 2) large language model-enhanced versions reducing existing patient information to sixth-grade level. All materials were assessed for basic textual analysis and readability using 8 readability formulas. Two board-certified obstetrician-gynecologists evaluated blinded patient education materials for accuracy, clarity, and comprehension. Analysis of variance was used to compare textual analysis and readability scores, with Tukey post-hoc tests identifying differences for both original and enhanced materials. An alpha threshold of P<.004 was used to account for multiple comparisons. RESULTS: Large language model-generated materials were significantly shorter (mean word count 407.9 vs 1132.0; P<.001) but had a higher proportion of difficult words (36.7% vs 27.4%; P<.001). American College of Obstetricians and Gynecologists and UpToDate materials averaged ninth-grade and 8.6-grade levels, respectively, while artificial intelligence-generated content reached a 10.6-grade level (P = .008). Although CoPilot and Gemini improved readability when prompted, no large language model successfully reached the sixth-grade benchmark, and ChatGPT increased reading difficulty. CONCLUSION: Large language models generated more concise patient education materials but often introduced more complex vocabulary, ultimately failing to meet recommended health literacy standards. Even when explicitly prompted, no large language model achieved the sixth-grade reading level required for optimal patient comprehension. Without proper oversight, artificial intelligence-generated patient education materials may create the illusion of simplicity while reducing true accessibility. Future efforts should focus on integrating health literacy safeguards into artificial intelligence models before clinical implementation.
1 Dec 2025
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