Patient Information Research Articles

Utilization of Artificial Intelligence in the Creation of Patient Information on Laryngology Topics.

To evaluate and compare the readability and quality of patient information generated by Chat-Generative Pre-Trained Transformer-3.5 (ChatGPT) and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) using validated instruments including Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease, DISCERN, and Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P). ENTHealth.org and ChatGPT-3.5 were queried for patient information on laryngology topics. ChatGPT-3.5 was queried twice for a given topic to evaluate for reliability. This generated three de-identified text documents for each topic: one from AAO-HNS and two from ChatGPT (ChatGPT Output 1, ChatGPT Output 2). Grade level and reading ease were compared between the three sources using a one-way analysis of variance and Tukey's post hoc test. Independent t-tests were used to compare DISCERN and PEMAT understandability and actionability scores between AAO-HNS and ChatGPT Output 1. Material generated from ChatGPT Output 1 and ChatGPT Output 2 were at least two reading grade levels higher than that of material from AAO-HNS (p < 0.001). Regarding reading ease, ChatGPT Output 1 and ChatGPT Output 2 documents had significantly lower mean scores compared to AAO-HNS (p < 0.001). Moreover, ChatGPT Output 1 material on vocal cord paralysis had a lower PEMAT-P understandability compared to that of AAO-HNS material (p > 0.05). Patient information on the ENTHealth.org website for select laryngology topics was, on average, of a lower grade level and higher reading ease compared to that produced by ChatGPT, but interestingly with largely no difference in the quality of information provided. NA Laryngoscope, 2024.

Remote Foot Temperature Monitoring Among Veterans: Large Observational Study of Noncompliance and Its Correlates.

In-home remote foot temperature monitoring (RTM) holds promise as a method to reduce foot ulceration in high-risk patients with diabetes. Few studies have evaluated adherence to this method or evaluated the factors associated with noncompliance. The aims of this study were to estimate noncompliance in patients who were enrolled in RTM nationwide across Department of Veterans Affairs (VA) and to evaluate characteristics associated with noncompliance. We conducted an observational study including 1137 patients in the VA who were enrolled in RTM between January 2019 and June 2021, with follow-up through October 2021. Patient information was obtained from the VA's electronic health record and RTM use was obtained from the company. Noncompliance was defined as using the mat <2 days per week for ≥4 of the 12 months of follow-up. Using a multivariable model, we calculated odds ratios (ORs) and 95% CIs for associations between various factors and noncompliance and compared using Akaike information criterion statistics, a measure of model fit. The sample was predominantly male (n=1125, 98.94%) ; 21.1% (n=230) were Black and 75.7% (n=825) were White. Overall, 37.6% (428/1137) of patients were classified as noncompliant. In the multivariable model, an intermediate area deprivation index was statistically significantly and inversely associated with noncompliance (area deprivation index 50-74 vs 1-24; OR 0.56, 95% CI 0.35-0.89); factors significantly and positively associated with noncompliance included recent history of osteomyelitis (OR 1.44, 95% CI 1.06-1.97), Gagne comorbidity index score ≥4 (vs ≤0; OR 1.81, 95% CI 1.15-2.83), telehealth encounters (28+ vs <6; OR 1.70, 95% CI 1.02-2.84), hemoglobin A1c≥10 (vs <5.7; OR 2.67, 95% CI 1.27-5.58), and current smoking (OR 2.06, 95% CI 1.32-3.20). Based on Akaike information criterion differences, the strongest factors associated with noncompliance were behavioral factors (poor glucose control [as measured by hemoglobin A1c] and smoking), and to a lesser extent, factors such as a recent history of osteomyelitis and an elevated Gagne comorbidity index, indicating a high comorbidity burden. To reduce the risk of ulcer recurrence and amputation, proactively providing additional support for self-monitoring to patients with characteristics identified in this study (poor glucose control, current smoking, high comorbidity burden) may be helpful. Furthermore, research is needed to better understand barriers to use, and whether the addition of design features, reminders, or incentives may reduce noncompliance and the risk of foot ulcers.

An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.

ChatGPT as a Source for Patient Information on Patellofemoral Surgery—A Comparative Study Amongst Laymen, Doctors, and Experts

Introduction: In November 2022, OpenAI launched ChatGPT for public use through a free online platform. ChatGPT is an artificial intelligence (AI) chatbot trained on a broad dataset encompassing a wide range of topics, including medical literature. The usability in the medical field and the quality of AI-generated responses are widely discussed and are the subject of current investigations. Patellofemoral pain is one of the most common conditions among young adults, often prompting patients to seek advice. This study examines the quality of ChatGPT as a source of information regarding patellofemoral conditions and surgery, hypothesizing that there will be differences in the evaluation of responses generated by ChatGPT between populations with different levels of expertise in patellofemoral disorders. Methods: A comparison was conducted between laymen, doctors (non-orthopedic), and experts in patellofemoral disorders based on a list of 12 questions. These questions were divided into descriptive and recommendatory categories, with each category further split into basic and advanced content. Questions were used to prompt ChatGPT in April 2024 using the ChatGPT 4.0 engine, and answers were evaluated using a custom tool inspired by the Ensuring Quality Information for Patients (EQIP) instrument. Evaluations were performed independently by laymen, non-orthopedic doctors, and experts, with the results statistically analyzed using a Mann–Whitney U Test. A p-value of less than 0.05 was considered statistically significant. Results: The study included data from seventeen participants: four experts in patellofemoral disorders, seven non-orthopedic doctors, and six laymen. Experts rated the answers lower on average compared to non-experts. Significant differences were observed in the ratings of descriptive answers with increasing complexity. The average score for experts was 29.3 ± 5.8, whereas non-experts averaged 35.3 ± 5.7. For recommendatory answers, experts also gave lower ratings, particularly for more complex questions. Conclusion: ChatGPT provides good quality answers to questions concerning patellofemoral disorders, although questions with higher complexity were rated lower by patellofemoral experts compared to non-experts. This study emphasizes the potential of ChatGPT as a complementary tool for patient information on patellofemoral disorders, although the quality of the answers fluctuates with the complexity of the questions, which might not be recognized by non-experts. The lack of personalized recommendations and the problem of “AI hallucinations” remain a challenge. Human expertise and judgement, especially from trained healthcare experts, remain irreplaceable.

Online video versus face-to-face patient-surgeon consultation: a systematic review.

Online video consultation (OVC) is increasingly used in patient-surgeon pre-surgical and follow-up consultation but a comprehensive review assessing its benefits and downsides as compared to face-to-face (F2F) consultation is currently lacking. This systematic review evaluated the effectiveness of OVC as compared to F2F consultation. A literature search (Ovid/Medline, Embase, and Clarivate Analytics/Web of Science Core Collection) was conducted including studies comparing efficacy, patient and surgeon satisfaction, and information recall between OVC and F2F patient-surgeon consultation (inception-December 4, 2023). Out of 1021 studies, 14 studies with 13,564 patients met the eligibility criteria, consisting of seven RCTs, three prospective, and four retrospective studies. Various types of surgical consultations were evaluated, including new referrals, routine follow-ups, postoperative follow-ups, and mixed consultations (both pre- and postoperative). None of the randomized trials exclusively compared OVC with F2F consultations in the high-demand preoperative setting, or assessed patient information recall. Efficacy outcomes were reported by seven studies. Among these, three RCTs showed that OVC improved efficacy in terms of waiting time (8.2 vs. 20.7 min, P = 0.01) and total appointment time (24 vs 71 min, P = 0.001, and 21.9 vs. 154.8 min, P = 0.001). Patient satisfaction was reported by 10 studies. Regarding patient satisfaction, one "mixed design" study favoured OVC (92% vs. 63%, P = 0.04), while eight studies reported similar outcomes. This systematic review identified some benefits of OVC such as shorter waiting and total appointment times as compared to F2F patient-surgeon consultation, although the true value of OVC remains unknown due to the limited available evidence. Future pragmatic RCTs are needed, which should include the pre-surgical consultation and assess patient information recall.

Management of inoperable malignant bowel obstruction using the 4-step BOUNCED diet.

Malignant bowel obstruction (MBO) presents with multiple symptoms. The 4-step BOUNCED diet educates patients to self-manage oral intake according to symptoms. It includes clear fluids, thin liquids, purée and soft, sloppy foods, which are low in fibre. This mixed methods single-arm feasibility study aimed to establish if the diet could reduce MBO symptoms in patients with inoperable colorectal and gynaecological malignancies. The secondary objectives were to investigate if it was easily followed, improved quality of life (QOL) and reduced hospital admissions. Patients able to tolerate an oral diet with one or more symptoms (pain, bloating, early satiety, nausea and vomiting) were eligible. Following informed consent, an oncology dietitian took a diet history and determined which step of the diet they needed to follow using a detailed patient information leaflet. Patients remained on the trial for 28 days. Symptom and QOL data were collected on Days 1 and 28 using the Memorial Symptom Assessment Scale and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ-C30) questionnaires. Thirty patients (24 female, 6 male) aged 18-85 years consented from March 2021 to November 2022. Twenty-six participants completed the trial and 25 found the diet very easy or easy to understand. There was a significant reduction in pain from 96% on Day 1 to 63% on Day 28 (p = 0.004). The mean increase of EORTC global health status/QOL was 23.5 points, 95% CI (12.4-32.5) (p ≤ 0.001). There was a significant difference between number of admissions (p = 0.018) and bed days (p = 0.004) in the 28 days prior to consent compared to the trial period. A modified consistency low-fibre diet is easily followed, may reduce symptoms of MBO, admissions to hospital and improve QOL.

Charting a course: Use of Electronic Health Records in skills‐based pharmacy education

AbstractIntroductionCompetent navigation of an electronic health record (EHR) has become a critical skill for health professionals, and pharmacists need to be adept in EHR utilization given the extent of EHR involvement in routine medication management. Limited data exists related to current approaches to teaching EHR skills in pre‐Advanced Pharmacy Practice Experiences (pre‐APPE) pharmacy curricula.ObjectivesThe purpose of this study was to describe activities, utilization, and barriers to implementation of teaching EHRs (tEHRs) into pharmacy curricula in skills‐based courses.MethodsFaculty members of the Big Ten Academic Alliance Skills‐Based Education and Assessment Collaborative (BTAA‐SBEAC) were surveyed. The questionnaire included open‐ended questions related to tEHR type, curriculum placement, and current use of tEHRs, as well as barriers to incorporation of tEHRs in the curriculum. Inductive content analysis was used to evaluate qualitative responses.ResultsA representative from all 10 colleges within the BTAA responded. Most programs focused on EHR teaching in the second and third years of the curriculum. All respondents reported having access to a tEHR for internal case building. The tEHR was used by all institutions to allow students to practice in gathering patient information as the first step in the Pharmacists' Patient Care Process. Other activities commonly performed using the tEHR included patient care documentation, medication order verification, prescription processing, and medication reconciliation. Faculty time and cost were barriers identified for successful tEHR implementation.ConclusionMultiple skills are taught through the utilization of a tEHR among the colleges within the BTAA. These activities vary between institutions, and barriers exist that limit expansion and consistent integration across a skills‐based curriculum. Recognition of EHR utilization as an essential pharmacy skill can allow for development of formalized learning outcomes, leading to better support and integration throughout skills‐based courses.

Analysis of clinicopathological factors associate with the visibility of early gastric cancer in endoscopic examination and usefulness of linked color imaging: A multicenter prospective study.

This study investigated clinicopathological factors associated with the visibility of early gastric cancer and the efficacy of linked color imaging. Patients with early gastric cancer who underwent endoscopic treatment between April 2021 and July 2022 were enrolled. All cases underwent white light imaging and linked color imaging. Three experts evaluated lesion visibility using a visual analog scale. A mean score ≥3 on white light imaging was defined as "good visibility", and <3 as "poor visibility". We extracted patient information and endoscopic and pathological data for the lesion and background mucosa, analyzed factors associated with the visibility of early gastric cancer, and compared visibility between white light imaging and linked color imaging. Ninety-seven lesions were analyzed, with good visibility in 49 and poor visibility in 48. Multivariate analysis revealed small lesion size (odds ratio 1.89) and presence of endoscopic intestinal metaplasia (odds ratio 0.49) as significantly associated with the poor visibility of early gastric cancer. Mean visibility score was significantly higher for linked color imaging (P<0.001). Mean score for linked color imaging was significantly higher in the poor visibility group (P<0.001), but not significantly different in the good visibility group (P = 0.292). Mean score was significantly higher with linked color imaging in cases with endoscopic intestinal metaplasia (P = 0.0496) and lesions <20 mm in diameter (<10 mm, P = 0.002; 10-20 mm, P = 0.004). Lesion size and endoscopic intestinal metaplasia are associated with the visibility of early gastric cancer in white light imaging. Linked color imaging improves visibility of gastric cancer with these factors.

Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation.

Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data.

Implementing an intelligent diagnosis and treatment system for in-hospital cardiac arrest in the Utstein style: a multi-center case study

BackgroundCardiac arrest presents a variety of causes and complexities, making it challenging to develop targeted treatment plans. Often, the original data are either inadequate or lack essential patient information. In this study, we introduce an intelligent system for diagnosing and treating in-hospital cardiac arrest (IHCA), aimed at improving the success rate of cardiopulmonary resuscitation and restoring spontaneous circulation.MethodsTo compensate for insufficient or incomplete data, a hybrid mega trend diffusion method was used to generate virtual samples, enhancing system performance. The core of the system is a modified episodic deep reinforcement learning module, which facilitates the diagnosis and treatment process while improving sample efficiency. Uncertainty analysis was performed using Monte Carlo simulations, and dependencies between different parameters were assessed using regular vine copula. The system's effectiveness was evaluated using ten years of data from Utstein-style IHCA registries across seven hospitals in China's Hebei Province.ResultsThe system demonstrated improved performance compared to other models, particularly in scenarios with inadequate data or missing patient information. The average reward scores in two key stages increased by 2.3–9 and 9.9–23, respectively.ConclusionsThe intelligent diagnosis and treatment effectively addresses IHCA, providing reliable diagnosis and treatment plans in IHCA scenarios. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes even when original data are insufficient or basic patient information is missing.

Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East

Background: Colorectal cancer (CRC) displays a complex pattern of inheritance. It is postulated that much of the missing heritability of CRC is enriched in high-impact rare alleles, which might play a crucial role in the etiology and susceptibility of CRC. Methods: In this study, an exome-wide association analysis was performed in 146 patients with high-risk CRC in the Middle East and 1395 healthy controls. The aim was to identify rare germline variants in coding regions and their splicing sites associated with high-risk CRC in the Middle Eastern population. Results: Rare inactivating variants (RIVs) in APC had the strongest association with high-risk CRC (6/146 in cases vs. 1/1395 in controls, OR = 59.7, p = 5.13 × 10−12), whereas RIVs in RIMS1, an RAS superfamily member, were significantly associated with high-risk CRC (5/146 case vs. 2/1395 controls, OR = 24.7, p = 2.03 × 10−8). Rare damaging variants in 17 genes were associated with high-risk CRC at the exome-wide threshold (p &lt; 2.5 × 10−6). Based on the sequence kernel association test, nonsynonymous variants in six genes (TNXB, TAP2, GPSM3, ADGRG4, TMEM229A, and ANKRD33B) had a significant association with high-risk CRC. RIVs in APC—the most common high-penetrance genetic factor—were associated with patients with high-risk CRC in the Middle East. Individuals who inherited APC RIVs had an approximate 60-fold increased risk of developing CRC and were likely to develop the disease earlier. Conclusions: We identified new potential CRC predisposition variants in other genes that could play a role in CRC inheritance. However, large collaborative studies are needed to confirm the association of these variants with high-risk CRC. These results provide information for counseling patients with high-risk CRC and their families in our population.

The Effect of Mobile Application Follow-Up on Treatment Compliance and Self-Care Management in Patients With Hypertension: Randomized Controlled Trial.

Hypertension (HT) is a global health problem. Although there are effective treatment protocols, patients have difficulty in adapting to regular use of drugs, diet, and lifestyle changes. Mobile apps can be a potential alternative for managing HT and improving self-care behavior. This study aimed to determine the effect of mobile application follow-up on treatment compliance and self-care management in patients with HT. This randomized controlled trial was conducted in November 2021 and March 2022 with 40 experimental and 40 control group patients with HT. Research data were collected using a patient information form, "Hill-Bone Hypertension Treatment Compliance Scale (HBHTTCS)," "Hypertension Self-Care Profile-Motivation Scale (HTSPMS)," a mobile application program (daily blood pressure, pulse, medication, diet, exercise tracking) uploaded to the mobile phones of the patients, and individual motivational messages. The mean total score of the HBHTTCS at Week 6 was 14.57±1.89 and the mean total score of the HTSPMS was 78.15±2.05.There was a statistically significant difference in mean total HTSPMS (p = 0.000) and HBHTTCS (p = 0.000) scores and mean scores on medical (p = 0.002) and nutrition (p = 0.002) subscales of HBHTTCS between the first interview (1st-week follow-up) and the last interview (6th-week follow-up) of patients in the intervention group. This study showed that mobile application follow-up increased motivation levels and treatment compliance in patients with HT and resulted in better self-care. These positive findings indicate importance of integrating mobile applications in the disease management of HT patients. ClinicalTrials.gov ID: NCT05334446, https://clinicaltrials.gov/ct2/show/NCT05334446.

Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease

The World Health Organization declared a state of emergency in 2022 because of monkeypox. This disease has raised international concern as it has spread beyond Africa, where it is endemic. The global community has shown attention and solidarity in combating this disease as its daily increase becomes evident. Various skin symptoms appear in people infected with this disease, which can spread easily, especially in a polluted environment. It is difficult to diagnose monkeypox in its early stages because of its similarity with the symptoms of other diseases such as chicken pox and measles. Recently, computer-aided classification methods such as deep learning and machine learning within artificial intelligence have been employed to detect various diseases, including COVID-19, tumor cells, and Monkeypox, in a short period and with high accuracy. In this study, we propose the CanDark model, an end-to-end deep-learning model that incorporates cancelable biometrics for diagnosing Monkeypox. CanDark stands for cancelable DarkNet-53, which means that DarkNet-53 CNN is utilized for extracting deep features from Monkeypox skin images. Then a cancelable method is applied to these features to protect patient information. Various cancelable techniques have been evaluated, such as bio-hashing, multilayer perceptron (MLP) hashing, index-of-maximum Gaussian random projection-based hashing (IoM-GRP), and index-of-maximum uniformly random permutation-based hashing (IoM-URP). The proposed approach’s performance is evaluated using various assessment issues such as accuracy, specificity, precision, recall, and fscore. Using the IoM-URP, the CanDark model is superior to other state-of-the-art Monkeypox diagnostic techniques. The proposed framework achieved an accuracy of 98.81%, a specificity of 98.73%, a precision of 98.9%, a recall of 97.02%, and fscore of 97.95%.

Informed consent in gynecological oncology: a JAGO/NOGGO survey on real-world practices in daily clinical routine.

Informed consent is a quintessential element of contemporary medicine, reflecting the fundamental right of patients to participate in decision-making regarding their health. Despite its critical importance, there is a lack of data on real-world practices regarding patient informed consent in the context of modern, high-pressure medical environments. Weconducted a multinational multicentric survey from February 24, 2022, to September 14, 2022, investigating the practices and challenges surrounding informed consent in hospitals across Germany, Austria, and Switzerland with the use of a specifically developed questionnaire. Drawing on over 200 responses from gynecologists, the survey shows a critical need for structured training in conducting informed consent discussions with over 80% of participants expressing interest in courses addressing this aspect. Notably, a considerable portion of the physicians (59.9%) reported conducting discussions on procedures they had never personally witnessed. Significant disparities between types of hospitals and professional groups were observed in the frequency of informed consent discussions, with limitations arising from factors such as time constraints, language barriers, and insufficient resources for patient education. Moreover, the psychological burden experienced by physicians after informed-consent discussions underscores the need for systemic changes to alleviate concerns regarding patient safety, legal repercussions, and patient satisfaction. This study serves as a call to action, emphasizing the need of enhancing resources and support for medical professionals to uphold the principles of empathic and comprehensive patient information and shared decision-making. DRKS00028295, 25.07.2024.

Role of Charlson comorbidity index in predicting intensive care unit readmission in patients with aortic aneurysm.

The purpose of this study was to investigate the value of the Charlson comorbidity index (CCI) in predicting intensive care unit (ICU) readmission in aortic aneurysm (AA) patients. Patient information came from the Medical Information Mart for Intensive Care- IV (MIMIC-IV) database. The relationship between CCI and ICU readmission was analyzed by restricted cubic spline, generalized linear regression, trend analysis, and hierarchical analysis. The clinical value of CCI in predicting ICU readmission was analyzed by receiver operating characteristic curve, decision curve analysis, XGBoost regression, and random forest regression. A total of 523 patients with AA were enrolled in the study. Patients with AA who were readmitted to the ICU had higher width of red blood cell distribution width (RDW) and higher CCI. CCI had better performance and clinical net benefit for predicting ICU readmission than RDW. An independent nonlinear relationship was found between CCI and ICU readmission. The trend analysis suggested that the risk of ICU readmission increased with higher CCI scores. The hierarchical analysis showed that their association was mainly found in surgery requirement populations regardless of AA types. Further, CCI was found to have better clinical value in predicting ICU readmission of thoracic aortic aneurysm (TAA) patients undergoing surgery. Age, renal disease, chronic lung disease, and dementia were important components of CCI in predicting ICU readmission of TAA patients undergoing surgery. CCI was independently associated with the ICU readmission of AA patients in a positive relationship and had more favorable prediction performance in TAA patients who underwent surgery.

Patient experiences of cancer genetic testing by non-genetics providers in the surgical setting.

As indications for hereditary cancer genetic testing (GT) for patients with breast cancer (BC) expand, breast surgery teams offer GT to newly diagnosed patients to inform surgical plans. There is, however, limited data on the experiences of patients undergoing cancer GT by non-genetic providers. This study used in-depth interviews with 21 women recently diagnosed with BC at a large academic health system to capture their experiences. Post-positivist codebook thematic analysis was used to identify major themes from the interviews. Participants reported an overall positive experience of this GT process, stating that they prefer GT at an existing appointment shortly after their diagnosis, even though they described the conversation as brief. Many participants indicated thinking about or desiring GT before the offer was made. Interestingly, most participants did not see surgical decision-making as the main reason for GT and were instead motivated by concern for relatives and to have complete information. Interview data indicated areas for improvement in patient-provider communication, and most participants agreed that additional reference information on GT in the form of written or video materials would be helpful. Offering GT at an initial breast surgery appointment is acceptable and desired by patients with a new BC diagnosis and should be considered as a way to increase access to GT for these patients. However, additional information for patients is needed to close gaps in communication and provide a trustworthy reference following a busy medical appointment.

Readability of patient information for cervical cancer in National Cancer Institute-designated comprehensive cancer centers: Addressing cancer-care barriers

Readability of patient information for cervical cancer in National Cancer Institute-designated comprehensive cancer centers: Addressing cancer-care barriers

Exploration of Factors Affecting Pro-social Intention to Disclose Information in Healthcare Settings

This study investigated the relationships of situational factors, motivational assessment–related factors, and situationally induced personal characteristics with pro-social intention to disclose patient information among health and medical students. In total, 210 students from Konyang University participated in the survey, including 116 medical students and 94 other health students. To measure the influence of variables on pro-social intention to disclose information related to health information security, a 27-item questionnaire was used. The reliability of the survey was shown by Cronbach’s α values of 0.859 to 0.917. According to students’ perceptions, higher disease severity was significantly related to higher impacts on the patient, family, and themselves (p&amp;lt;0.01), as well as higher situational empathy (p&amp;lt;0.05). Pro-social intention to disclose showed negative correlations with health information security awareness, information education experience, perceived impact on the self, responsibility to disclose, and personal norms (r=-0.136 to -0.647, p&amp;lt;0.05). Responsibility to disclose and situational empathy explained approximately 44% of the variance in pro-social intention to disclose. Additionally, students who received information security training perceived a significantly higher responsibility to disclose, exhibited higher health information security awareness, and had lower pro-social intention to disclose. This study confirmed the need for information security education for health and medical students, and suggested that pro-social characteristics such as empathy and responsibility need to be carefully addressed in information security education.

The Growing Cybersecurity Crisis in Healthcare: A Call to Action

Electronic healthcare technology is pervasive throughout the globe, and it affords vast opportunities to enhance clinical outcomes, as well as for the transformation of models of care. Concerns are, however, growing, related to healthcare data and device security. Increased connectivity to legacy computer networks brought cybersecurity vulnerabilities for medical devices. Healthcare represents an attractive target for cybercrime because healthcare data is precious. This sector in present times is full of unique cybersecurity challenges, especially for susceptible kinds of patient information at stake. Second, many legacy systems will be prevalent—not adding more to that—with the changing face of cyber threats. Cybersecurity breaches have comprised stealing health information and focused ransomware attacks on hospitals; this could mean as vivid an attack as on implanted medical devices. This only points to the fact that ransomware attacks and other kinds of cyber-attacks against hospitals and other medical facilities are gaining ground; there is every reason to get alarmed and put in place stricter cybersecurity measures. An excellent healthcare cybersecurity strategy, therefore, has to consider access control, intrusion detection systems, encryption techniques, and periodic security testing. Data breaches and cyber-attacks are forcing any healthcare provider to invest in new state-of-the-art technologies related to keeping pace with trends regarding cybersecurity. The dangers that can be caused by cyber-attack include a considerable diminution in patient trust, potential health system collapse, human life threats, etc. On the whole, cybersecurity is strenuously linked with the question of patient safety.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.