Exploring the nexus between the standardized nursing terminologies and the unfinished nursing care phenomenon: An empty systematic review.

PURPOSE: To identify and synthesize evidence regarding the documented relationship between the standardized nursing terminologies and the unfinished nursing care phenomenon. DATA SOURCES: A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Scopus, and Cumulative Index to Nursing and Allied Health Literature Complete databases were last consulted on November 27, 2023. The review included primary quantitative studies that reported an association between recognized standardized nursing terminologies and unfinished nursing care. Two researchers completedtitle and abstract and full-text screening. DATA SYNTHESIS: Our search identified 149 citations. A full-text review of one paper was undertaken. No studies met our inclusion criteria. We report an empty review. CONCLUSIONS: Standardized nursing terminologies and Unfinished Care are two sides of the same coin: despite their potential commonalities, no studies have documented their potential links. Digital systems, such as electronic health records and decision support systems, could foster this linkage. IMPLICATIONS FOR NURSING PRACTICE: This review suggests that linking the conceptual frameworks can promote the diffusion of standardized nursing terminologies in clinical practice and increase accuracy in the measurement of Unfinished Care. This synergy could promote the contribution of nursing knowledge to patient care, nursing visibility, and be beneficial to clinical nurses, managers, and healthcare systems to international level.

Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data.

INTRODUCTION: Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high-quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non-small cell lung cancer (NSCLC) protocols in China, on the risk-benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. METHODS: To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross-dimensional real-world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit-risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. DISCUSSION: This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies.

Influence of Disease-Related Stigma on Patients' Decisions to Upload Medical Reports to the German Electronic Health Record: Randomized Controlled Trial.

BACKGROUND: The rollout of the electronic health record (EHR) represents a central component of the digital transformation of the German health care system. Although the EHR promises more effective, safer, and faster treatment of patients from a systems perspective, the successful implementation of the EHR largely depends on the patient. In a recent survey, 3 out of 4 Germans stated that they intend to use the EHR, whereas other studies show that the intention to use a technology is not a reliable and sufficient predictor of actual use. OBJECTIVE: Controlling for patients' intention to use the EHR, we investigated whether disease-specific risk perceptions related to the time course of the disease and disease-related stigma explain the additional variance in patients' decisions to upload medical reports to the EHR. METHODS: In an online user study, 241 German participants were asked to interact with a randomly assigned medical report that varied systematically in terms of disease-related stigma (high vs low) and disease time course (acute vs chronic) and to decide whether to upload it to the EHR. RESULTS: Disease-related stigma (odds ratio 0.154, P<.001) offset the generally positive relationship between intention to use and the upload decision (odds ratio 2.628, P<.001), whereas the disease time course showed no effect. CONCLUSIONS: Even if patients generally intend to use the EHR, risk perceptions such as those related to diseases associated with social stigma may deter people from uploading related medical reports to the EHR. To ensure the reliable use of this key technology in a digitalized health care system, transparent and easy-to-comprehend information about the safety standards of the EHR are warranted across the board, even for populations that are generally in favor of using the EHR.

How do opt-in versus opt-out settings nudge patients toward electronic health record adoption? An exploratory study of facilitators and barriers in Austria and France.

BACKGROUND: Electronic health records (EHR) are becoming an integral part of the health system in many developed countries, though implementations and settings vary across countries. Some countries have adopted an opt-out policy, in which patients are enrolled in the EHR system following a default nudge, while others have applied an opt-in policy, where patients have to take action to opt into the system. While opt-in systems may exhibit lower levels of active user requests for access, this contrasts with opt-out systems where a notable percentage of users may passively retain access. Thus, our research endeavor aims to explore facilitators and barriers that contribute to explaining EHR usage (i.e., actively accessing the EHR system) in two countries with either an opt-in or opt-out setting, exemplified by France and Austria. METHODS: A qualitative exploratory approach using a semi-structured interview guideline was undertaken in both countries: 1) In Austria, with four homogenously composed group discussions, and 2) in France, with 19 single patient interviews. The data were collected from October 2020 to January 2021. RESULTS: Influencing factors were categorized into twelve subcategories. Patients have similar experiences in both countries with regard to all facilitating categories, for instance, the role of health providers, awareness of EHR and social norms. However, we highlighted important differences between the two systems regarding hurdles impeding EHR usage, namely, a lack of communication as well as transparency or information security about EHR. CONCLUSION: Implementing additional safeguards to enhance privacy protection and supporting patients to improve their digital ability may help to diminish the perception of EHR-induced barriers and improve patients' health and commitment in the long term. PRACTICAL IMPLICATIONS: Understanding the differences and similarities will help to develop practical implications to tackle the problem of low EHR usage rates in the long run. This problem is prevalent in countries with both types of EHR default settings.

Engagement in GI Behavioral Health Is Associated with Reduced Portal Messages, Phone Calls, and ED Visits.

BACKGROUND AND AIMS: Chronic digestive disorders are associated with increased costs for healthcare systems and often require provision of both urgent care and non-face-to-face (non-F2F) care, such as responding to patient messages. Numerous benefits of integrated gastroenterology (GI) behavioral health have been identified; however, it is unclear if integrated care impacts healthcare utilization, including urgent care and non-F2F contact. We sought to investigate the association between patient engagement with GI behavioral health and healthcare utilization. METHODS: We performed a retrospective chart review study of adult patients who were referred for and completed at least one behavioral health appointment between January 1, 2019 and December 21, 2021 in the Gastroenterology and Hepatology department of a large academic medical center. Data on electronic medical record (EMR) messages, phone calls, and Emergency Department utilization were collected 6 months before and 9 months after patient engagement with GI behavioral health. RESULTS: 466 adult patients completed at least one behavioral health visit from 2019 to 2021. Overall, messages, phone calls, and ED visits all decreased significantly from the 6 months before behavioral health treatment to 6 months after (all P values < 0.001). CONCLUSION: Engagement with integrated GI behavioral health is associated with reduced non-F2F care and emergency department utilization in patients with chronic digestive disorders. Increasing access to GI behavioral health may result in reduced provider workload and healthcare system costs.

Genetically guided precision medicine clinical decision support tools: a systematic review.

OBJECTIVES: Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). MATERIALS AND METHODS: A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients. RESULTS: The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy. DISCUSSION: Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes. CONCLUSION: We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

The Effect of Using a Client-Accessible Health Record on Perceived Quality of Care: Interview Study Among Parents and Adolescents.

BACKGROUND: Patient-accessible electronic health records (PAEHRs) are assumed to enhance the quality of care, expressed in terms of safety, effectiveness, timeliness, person centeredness, efficiency, and equity. However, research on the impact of PAEHRs on the perceived quality of care among parents, children, and adolescents is largely lacking. In the Netherlands, a PAEHR (Iuvenelis) was developed for preventive child health care and youth care. Parents and adolescents had access to its full content, could manage appointments, ask questions, and comment on written reports. OBJECTIVE: This study aims to assess whether and how using this PAEHR contributes to perceived quality of care from a client's perspective. METHODS: We chose a qualitative design with a phenomenological approach to explore how parents and adolescents perceived the impact of using a PAEHR on quality of care. In-depth interviews that simultaneously included 1 to 3 people were conducted in 2021. In total, 20 participants were included in the study, representing parents and adolescents, both sexes, different educational levels, different native countries, and all participating municipalities. Within this group, 7 of 13 (54%) parents had not previously been informed about the existence of a client portal. Their expectations of using the client portal, in relation to quality of care, were discussed after a demonstration of the portal. RESULTS: Parents and adolescents perceived that using Iuvenelis contributed to the quality of care because they felt better informed and more involved in the care process than before the introduction of Iuvenelis. Moreover, they experienced more control over their health data, faster and simpler access to their health information, and found it easier to manage appointments or ask questions at their convenience. Parents from a migratory background, among whom 6 of 7 (86%) had not previously been informed about the portal, expected that portal access would enhance their understanding of and control over their care processes. The parents expressed concerns about equity because parents from a migratory background might have less access to the service. Nevertheless, portal usability was regarded as high. Furthermore, both parents and adolescents saw room for improvement in the broader interdisciplinary use of Iuvenelis and the quality of reporting. CONCLUSIONS: Using Iuvenelis can contribute to the client-experienced quality of care, more specifically to perceived person centeredness, timeliness, safety, efficiency, and integration of care. However, some quality aspects, such as equity, still need addressing. In general, client information about the portal needs to be improved, specifically focusing on people in vulnerable circumstances, such as those from migratory backgrounds. In addition, to maximize the potential benefit of using Iuvenelis, stimulating a person-centered attitude among professionals is important. Considering the small number of adolescent participants (n=7), adding quantitative data from a structured survey could strengthen the available evidence.

Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.

Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children's Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system's performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.

Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.

Background: With the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use. Objective: We sought to develop a weak supervision-based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses. Methods: We applied data programming, where labeling heuristics are expressed as code-based labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis. Results: The labeler model derived from the labeling functions showed high accuracy (0.92; F1-score=0.77) on the training set. The classifier trained on the probabilistically labeled notes accurately identified patients with an AF prediagnosis (0.95; F1-score=0.83). The cohort study conducted using the constructed system carried enough statistical power to verify the key findings of the Apple Heart Study, which enrolled a much larger number of participants, where patients who received a prediagnosis tended to be older, male, and White with higher CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes, stroke, vascular disease, age 65-74 years, sex category) scores (P<.001). We also made a novel discovery that patients with a prediagnosis were more likely to use anticoagulants (525/1037, 50.63% vs 5936/16,560, 35.85%) and have an eventual AF diagnosis (305/1037, 29.41% vs 262/16,560, 1.58%). At the index diagnosis, the existence of a prediagnosis did not distinguish patients based on clinical characteristics, but did correlate with anticoagulant prescription (P=.004 for apixaban and P=.01 for rivaroxaban). Conclusions: Our work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites.

Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals. Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples. Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set. Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

A Health Information Technology Protocol to Enhance Colorectal Cancer Screening.

This study addresses barriers to electronic health records-based colorectal cancer screening and follow-up in primary care through the development and implementation of a health information technology protocol.

Genome-first evaluation with exome sequence and clinical data uncovers underdiagnosed genetic disorders in a large healthcare system.

Population-based genomic screening may help diagnose individuals with disease-risk variants. Here, we perform a genome-first evaluation for nine disorders in 29,039 participants with linked exome sequences and electronic health records (EHRs). We identify 614 individuals with 303 pathogenic/likely pathogenic or predicted loss-of-function (P/LP/LoF) variants, yielding 644 observations; 487 observations (76%) lack a corresponding clinical diagnosis in the EHR. Upon further investigation, 75 clinically undiagnosed observations (15%) have evidence of symptomatic untreated disease, including familial hypercholesterolemia (3 of 6 [50%] undiagnosed observations with disease evidence) and breast cancer (23 of 106 [22%]). These genetic findings enable targeted phenotyping that reveals new diagnoses in previously undiagnosed individuals. Disease yield is greater with variants in penetrant genes for which disease is observed in carriers in an independent cohort. The prevalence of P/LP/LoF variants exceeds that of clinical diagnoses, and some clinically undiagnosed carriers are discovered to have disease. These results highlight the potential of population-based genomic screening.

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls.

BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation. OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data. METHODS: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool. RESULTS: The following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. CONCLUSIONS: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care.

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks-outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care-which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling.

Application of a user experience design approach for an EHR-based clinical decision support system.

Objective: We applied a user experience (UX) design approach to clinical decision support (CDS) tool development for the specific use case of pediatric asthma. Our objective was to understand physicians' workflows, decision-making processes, barriers (ie, pain points), and facilitators to increase usability of the tool. Materials and methods: We used a mixed-methods approach with semi-structured interviews and surveys. The coded interviews were synthesized into physician-user journey maps (ie, visualization of a process to accomplish goals) and personas (ie, user types). Interviews were conducted via video. We developed physician journey maps and user personas informed by their goals, systems interactions, and experiences with pediatric asthma management. Results: The physician end-user personas identified were: efficiency, relationship, and learning. Features of a potential asthma CDS tool sought varied by physician practice type and persona. It was important to the physician end-user that the asthma CDS tool demonstrate value by lowering workflow friction (ie, difficulty or obstacles), improving the environment surrounding physicians and patients, and using it as a teaching tool. Customizability versus standardization were important considerations for uptake. Discussion: Different values and motivations of physicians influence their use and interaction with the EHR and CDS tools. These different perspectives can be captured by applying a UX design approach to the development process. For example, with the importance of customizability, one approach may be to build a core module with variations depending on end-user preference. Conclusion: A UX approach can drive design to help understand physician-users and meet their needs; ultimately with the goal of increased uptake.

Quantifying diagnosis and treatment practices of opioid use disorder in primary care practices using chart review data.

BACKGROUND: Opioid misuse is a significant public health crisis. The aim sought to identify potential gaps in opioid care in primary care practices. METHODS: American Academy of Family Physicians (AAFP) offered a monthly online educational series to seven U.S. practices. Practices were asked to complete up to 50 chart reviews for visits during two periods: February-April, 2019, and February-April, 2022. Each chart had to have an ICD-10 diagnosis of opioid misuse, opioid dependence, or opioid use. Chart reviews consisted of 14 questions derived from an American Academy of Addiction Psychiatry (AAAP) Performance in Practice activity, and then, scored based on practices' responses. Descriptive statistics and binary logistic and multinomial regressions were used. RESULTS: Both periods had 173 chart reviews (total: 346) from the six practices. Most chart reviews were for patients with a diagnosis of opioid dependence (2019: 90.2%; 2022: 83.2%). Three questions for assessing OUD treatment behaviors had high levels of documentation across both time periods (>85%): other drug use, treatment readiness, and treatment discussion. DISCUSSION: Results show a gap in the treatment of patients with OUD in primary care across several clinical practice recommendations. CONCLUSIONS: Expanding OUD treatment integration to primary care remains the most promising effort to combat the opioid crisis.

Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review.

The cognitive load theory suggests that completing a task relies on the interplay between sensory input, working memory, and long-term memory. Cognitive overload occurs when the working memory's limited capacity is exceeded due to excessive information processing. In health care, clinicians face increasing cognitive load as the complexity of patient care has risen, leading to potential burnout. Electronic health records (EHRs) have become a common feature in modern health care, offering improved access to data and the ability to provide better patient care. They have been added to the electronic ecosystem alongside emails and other resources, such as guidelines and literature searches. Concerns have arisen in recent years that despite many benefits, the use of EHRs may lead to cognitive overload, which can impact the performance and well-being of clinicians. We aimed to review the impact of EHR use on cognitive load and how it correlates with physician burnout. Additionally, we wanted to identify potential strategies recommended in the literature that could be implemented to decrease the cognitive burden associated with the use of EHRs, with the goal of reducing clinician burnout. Using a comprehensive literature review on the topic, we have explored the link between EHR use, cognitive load, and burnout among health care professionals. We have also noted key factors that can help reduce EHR-related cognitive load, which may help reduce clinician burnout. The research findings suggest that inadequate efforts to present large amounts of clinical data to users in a manner that allows the user to control the cognitive burden in the EHR and the complexity of the user interfaces, thus adding more "work" to tasks, can lead to cognitive overload and burnout; this calls for strategies to mitigate these effects. Several factors, such as the presentation of information in the EHR, the specialty, the health care setting, and the time spent completing documentation and navigating systems, can contribute to this excess cognitive load and result in burnout. Potential strategies to mitigate this might include improving user interfaces, streamlining information, and reducing documentation burden requirements for clinicians. New technologies may facilitate these strategies. The review highlights the importance of addressing cognitive overload as one of the unintended consequences of EHR adoption and potential strategies for mitigation, identifying gaps in the current literature that require further exploration.

Development of a Trusted Third Party at a Large University Hospital: Design and Implementation Study.

Background: Pseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring the direct identification of data to support various research processes while still allowing for advanced processing activities, such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units known as trusted third parties (TTPs). However, pseudonymization can significantly increase the complexity of data management and research workflows, necessitating adequate tool support. Common tasks of TTPs include supporting the secure registration and pseudonymization of patient and sample identities as well as managing consent. Objective: Despite the challenges involved, little has been published about successful architectures and functional tools for implementing TTPs in large university hospitals. The aim of this paper is to fill this research gap by describing the software architecture and tool set developed and deployed as part of a TTP established at Charité - Universitätsmedizin Berlin. Methods: The infrastructure for the TTP was designed to provide a modular structure while keeping maintenance requirements low. Basic functionalities were realized with the free MOSAIC tools. However, supporting common study processes requires implementing workflows that span different basic services, such as patient registration, followed by pseudonym generation and concluded by consent collection. To achieve this, an integration layer was developed to provide a unified Representational state transfer (REST) application programming interface (API) as a basis for more complex workflows. Based on this API, a unified graphical user interface was also implemented, providing an integrated view of information objects and workflows supported by the TTP. The API was implemented using Java and Spring Boot, while the graphical user interface was implemented in PHP and Laravel. Both services use a shared Keycloak instance as a unified management system for roles and rights. Results: By the end of 2022, the TTP has already supported more than 10 research projects since its launch in December 2019. Within these projects, more than 3000 identities were stored, more than 30,000 pseudonyms were generated, and more than 1500 consent forms were submitted. In total, more than 150 people regularly work with the software platform. By implementing the integration layer and the unified user interface, together with comprehensive roles and rights management, the effort for operating the TTP could be significantly reduced, as personnel of the supported research projects can use many functionalities independently. Conclusions: With the architecture and components described, we created a user-friendly and compliant environment for supporting research projects. We believe that the insights into the design and implementation of our TTP can help other institutions to efficiently and effectively set up corresponding structures.

Design and evaluation of a web-based electronic health record for amblyopia.

Introduction: Amblyopia, or lazy eye, is a type of visual impairment in which the eyesight is not complete, even with the use of glasses. For the treatment of this disease, accurate and continuous examinations are needed. Nowadays, patient-centered care, by relying on web-based electronic records for amblyopia, has the potential to reduce treatment costs, increase the quality of care, and improve the safety and effectiveness of treatment. Therefore, the purpose of this study is to design and evaluate an Electronic Health Record (EHR) for patients with amblyopia. Methods: The present study is applied developmental research. Using a Morgan table as a sampling tool, a straightforward random sampling technique selected 150 records from 1,500 records that were free of flaws. The design of the electronic version proceeded in a cascading manner so that after the design of each part, it was presented to the amblyopia experts, and if approved, the next part was designed. To design this EHR, the C# programming language and MySQL database were used. A system evaluation was performed by entering and recording patient information. For this purpose, the standard Questionnaire of User Interaction Satisfaction (QUIS), consisting of 18 questions, was used. Results: According to the amblyopia EHR data elements, the data of physician and patient, examinations, website members, and members' roles were determined. After defining the fields and classes that explain the tables, the EHR was designed. The usability evaluation of the system showed that the mean selection of very good and good options by the users of EHRs was over 90%, indicating the patients' acceptance of web-based EHRs. Conclusion: The design of an EHR for amblyopia is an effective step toward integrating and improving the information management of these patients. It will also enable the storage and retrieval of patients' information to reduce and facilitate the control of amblyopia complications.