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The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113 dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration. [ABSTRACT FROM AUTHOR]

Administering medications to patients with documented drug hypersensitivity reactions (DHR) poses a significant risk for adverse events, ranging from mild reactions to life-threatening incidents. Electronic healthcare systems have revolutionized the modern clinical decision-making process, with built in warnings. However, as these alerts become a routine part of healthcare provider's workflow, alert fatigue becomes a challenge. This study was conducted within the Ministry of National Guard Health Affairs (MNGHA), a government healthcare system in Saudi Arabia. A taskforce of experts was formed to develop an electronic path that would prevent unintentional overrides of severe drug allergy alerts. The system underwent rigorous testing, and monitoring parameters were established. We outline the implementation of a system upgrade designed to trigger an alternative interruption in the computerized physician order entry (CPOE) process, distinct from the regular allergy pop-up alerts. The alternate path is activated upon a CPOE with a drug-to-drug match and a documented severe drug allergy symptom, necessitating co-signature form another prescriber before proceeding. The adopted upgrade is a proactive approach to enhance medication safety in electronic healthcare systems, ensuring that serious allergy-related warnings are not overridden, ultimately enhancing patient safety. Further monitoring will confirm the safety and effectiveness of this measure. This study provides a model for institutions seeking to prevent allergy-related harm within their patient population. [ABSTRACT FROM AUTHOR]

Purpose: Racial and ethnic healthcare disparities require innovative solutions. Patient portals enable online access to health records and clinician communication and are associated with improved health outcomes. Nevertheless, a digital divide in access to such portals persist, especially among people of minoritized race and non-English-speakers. This study assesses the impact of automatic enrollment (autoenrollment) on patient portal activation rates among adult patients at the University of California, San Francisco (UCSF), with a focus on disparities by race, ethnicity, and primary language. Materials and methods: Starting March 2020, autoenrollment offers for patient portals were sent to UCSF adult patients aged 18 or older via text message. Analysis considered patient portal activation before and after the intervention, examining variations by race, ethnicity, and primary language. Descriptive statistics and an interrupted time series analysis were used to assess the intervention's impact. Results: Autoenrollment increased patient portal activation rates among all adult patients and patients of minoritized races saw greater increases in activation rates than White patients. While initially not statistically significant, by the end of the surveillance period, we observed statistically significant increases in activation rates in Latinx (3.5-fold, p = < 0.001), Black (3.2-fold, p = 0.003), and Asian (3.1-fold, p = 0.002) patient populations when compared with White patients. Increased activation rates over time in patients with a preferred language other than English (13-fold) were also statistically significant (p = < 0.001) when compared with the increase in English preferred language patients. Conclusion: An organization-based workflow intervention that provided autoenrollment in patient portals via text message was associated with statistically significant mitigation of racial, ethnic, and language-based disparities in patient portal activation rates. Although promising, the autoenrollment intervention did not eliminate disparities in portal enrollment. More work must be done to close the digital divide in access to healthcare technology. [ABSTRACT FROM AUTHOR]

IT has made significant progress in various fields over the past few years, with many industries transitioning from paper-based to electronic media. However, sharing electronic medical records remains a long-term challenge, particularly when patients are in emergency situations, making it difficult to access and control their medical information. Previous studies have proposed permissioned blockchains with limited participants or mechanisms that allow emergency medical information sharing to pre-designated participants. However, permissioned blockchains require prior participation by medical institutions, and limiting sharing entities restricts the number of potential partners. This means that sharing medical information with local emergency doctors becomes impossible if a patient is unconscious and far away from home, such as when traveling abroad. To tackle this challenge, we propose an emergency access control system for a global electronic medical information system that can be shared using a public blockchain, allowing anyone to participate. Our proposed system assumes that the patient wears a pendant with tamper-proof and biometric authentication capabilities. In the event of unconsciousness, emergency doctors can perform biometrics on behalf of the patient, allowing the family doctor to share health records with the emergency doctor through a secure channel that uses the Diffie-Hellman (DH) key exchange protocol. The pendant's biometric authentication function prevents unauthorized use if it is stolen, and we have tested the blockchain's fee for using the public blockchain, demonstrating that the proposed system is practical. [ABSTRACT FROM AUTHOR]

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance. [ABSTRACT FROM AUTHOR]

The purpose of this scoping review is to identify and evaluate studies that examine the effectiveness and implementation strategies of Electronic Health Record (EHR)-integrated digital technologies aimed at improving medication-related outcomes and promoting health equity among hospitalised adults. Using the Consolidated Framework for Implementation Research (CFIR), the implementation methods and outcomes of the studies were evaluated, as was the assessment of methodological quality and risk of bias. Searches through Medline, Embase, Web of Science, and CINAHL Plus yielded 23 relevant studies from 1,232 abstracts, spanning 11 countries and from 2008 to 2022, with varied research designs. Integrated digital tools such as alert systems, clinical decision support systems, predictive analytics, risk assessment, and real-time screening and surveillance within EHRs demonstrated potential in reducing medication errors, adverse events, and inappropriate medication use, particularly in older patients. Challenges include alert fatigue, clinician acceptance, workflow integration, cost, data integrity, interoperability, and the potential for algorithmic bias, with a call for long-term and ongoing monitoring of patient safety and health equity outcomes. This review, guided by the CFIR framework, highlights the importance of designing health technology based on evidence and user-centred practices. Quality assessments identified eligibility and representativeness issues that affected the reliability and generalisability of the findings. This review also highlights a critical research gap on whether EHR-integrated digital tools can address or worsen health inequities among hospitalised patients. Recognising the growing role of Artificial Intelligence (AI) and Machine Learning (ML), this review calls for further research on its influence on medication management and health equity through integration of EHR and digital technology. [ABSTRACT FROM AUTHOR]

The rapid development of the digital healthcare and the electronic health records (EHR) requires smooth networking infrastructure to access data using Hypertext Transfer Protocol (HTTP)-based applications. The new HTTP/3 standard should provide performance and security improvements over HTTP/2. The goal of our work was to test the performance of HTTP/2 and HTTP/3 in the context of the EHRs. We used 45,000 test FHIR Patient resources downloaded and uploaded using 20, 50, 100 and 200 resources per Bundle, which resulted in 2251, 901, 451 and 226 HTTP GET and POST requests respectively. The first test downloading 20 resources per Bundle showed that HTTP/3 outperformed HTTP/2 in the local (mean request time 16.57 ms ± 7.2 standard deviation [SD]) and in the remote network (71.45 ms ± 43.5 SD) which is almost 3 times faster. In the 50 and 100 resources per Bundle test the HTTP/3 protocol demonstrated again more than two times gain in downloading performance for remote requests with mean request time 91.13 ms ± 34.54 SD and 88.09 ms ± 21.66 SD respectively. Furthermore, HTTP/3 outperformed HTTP/2 in the constructed clinical dataset remote transfer. In the upload tests HTTP/3 showed only a slight gain in performance merely in the remote network. The HTTP/3 protocol is a relatively new development and a major improvement for the worldwide web. This new technology is still missing in the digital health and EHRs. Its use could offer a major performance gain in situations where data is gathered from multiple remote locations. [ABSTRACT FROM AUTHOR]

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation. [ABSTRACT FROM AUTHOR]

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. [ABSTRACT FROM AUTHOR]

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data. [ABSTRACT FROM AUTHOR]

In today's data-driven world, the exponential growth of digital information poses significant challenges in data management. In recent years, the adoption of cloud-based Electronic Health Records (EHR) sharing schemes has yielded numerous advantages like improved accessibility, availability, and enhanced interoperability. However, the centralized nature of cloud storage presents challenges in terms of information storage, privacy protection, and security. Despite several approaches that have been presented to ensure secure deduplication of similar EHRs, the validation of data integrity without a third-party auditor (TPA) remains a persistent task. Because involving a TPA raises concerns about the confidentiality and privacy of crucial healthcare information. To tackle this challenge, a novel cloud storage auditing technique is proposed that incorporates cross-patient block-level deduplication while upholding strong privacy protection, ensuring that EHR is not compromised. Here, we introduced blockchain technology to achieve integrity verification, thus eliminating the need for a TPA by providing a decentralized and transparent mechanism. Additionally, an index for all EHRs has been generated to facilitate block-level duplicate checks and employ a novel strategy to prevent adversaries from acquiring original information saved in the cloud storage. The security of the proposed approach is established against factorization attacks and decrypt exponent attacks. The performance evaluation demonstrates the superior efficiency of the proposed scheme in terms of file authenticator generation, challenge creation, and proof verification to other existing client-side deduplication approaches. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

In 2020, the U.S. Department of Veterans Affairs (VA) expanded an initiative to distribute video-enabled tablets to Veterans with limited virtual care access. We examined patient characteristics associated with adoption and sustained use of video-based primary care among Veterans. We conducted a retrospective cohort study of Veterans who received VA-issued tablets between 3/11/2020-9/10/2020. We used generalized linear models to evaluate the sociodemographic and clinical factors associated with video-based primary care adoption (i.e., likelihood of having a primary care video visit) and sustained use (i.e., rate of video care) in the six months after a Veteran received a VA-issued tablet. Of the 36,077 Veterans who received a tablet, 69% had at least one video-based visit within six months, and 24% had a video-based visit in primary care. Veterans with a history of housing instability or a mental health condition, and those meeting VA enrollment criteria for low-income were significantly less likely to adopt video-based primary care. However, among Veterans who had a video visit in primary care (e.g., those with at least one video visit), older Veterans, and Veterans with a mental health condition had more sustained use (higher rate) than younger Veterans or those without a mental health condition. We found no differences in adoption of video-based primary care by rurality, age, race, ethnicity, or low/moderate disability and high disability priority groups compared to Veterans with no special enrollment category. VA's tablet initiative has supported many Veterans with complex needs in accessing primary care by video. While Veterans with certain social and clinical challenges were less likely to have a video visit, those who adopted video telehealth generally had similar or higher rates of sustained use. These patterns suggest opportunities for tailored interventions that focus on needs specific to initial uptake vs. sustained use of video care. [ABSTRACT FROM AUTHOR]

The development of health information technology available and accessible to professionals is increasing in the last few years. However, a low number of electronic health tools included some kind of information about medication reconciliation. To identify all the electronic medication reconciliation tools aimed at healthcare professionals and summarize their main features, availability, and clinical impact on patient safety. A systematic review of studies that included a description of an electronic medication reconciliation tool (web-based or mobile app) aimed at healthcare professionals was conducted. The review protocol was registered with PROSPERO: registration number CRD42022366662, and followed PRISMA guidelines. The literature search was performed using four healthcare databases: PubMed, EMBASE, Cochrane Library, and Scopus with no language or publication date restrictions. We identified a total of 1227 articles, of which only 12 met the inclusion criteria.Through these articles,12 electronic tools were detected. Viewing and comparing different medication lists and grouping medications into multiple categories were some of the more recurring features of the tools. With respect to the clinical impact on patient safety, a reduction in adverse drug events or medication discrepancies was detected in up to four tools, but no significant differences in emergency room visits or hospital readmissions were found. 12 e-MedRec tools aimed at health professionals have been developed to date but none was designed as a mobile app. The main features that healthcare professionals requested to be included in e-MedRec tools were interoperability, "user-friendly" information, and integration with the ordering process. [ABSTRACT FROM AUTHOR]

Although the Canada federal government has invested over $3.1 billion developing health information technology (HIT), all 10 provinces still have their own separate HIT systems, which are non-interoperable, expensive, and inconsistent. After first reviewing how these systems operate, this paper analyzes the costs and savings of integrating the common billing, lab results, and diagnostic imaging (BLD) functions of these separate systems using free and open-source software and proposes a system for this, HermesAPI. Currently, 8 provincial governments representing over 95% of Canada’s population allow private companies to create their own electronic medical records (EMR) system and integrate with provincial BLD systems. This study found the cost to develop and maintain HermesAPI would be between CAD$610,000 to CAD$740,000, but would prevent CAD$120,000 per company per province in development costs for a total savings of $6.4 million. HermesAPI would lower barriers to entry for the HIT industry to increase competition, improve the quality of HIT products, and ultimately patient care. The proposed open-source approach of the HermesAPI is one option towards building a more interoperable, less expensive, and more consistent HIT system for Canada. [ABSTRACT FROM AUTHOR]

Background: The application of standardized patient summaries would reduce the risk of information overload and related problems for physicians and nurses. Although the International Patient Summary (IPS) standard has been developed, disseminating its applications has challenges, including data conversion of existing systems and development of application matching with common use cases in Japan. This study aimed to develop a patient summary application that summarizes and visualizes patient information accumulated by existing systems. Methods: We converted clinical data from the Standardized Structured Medical Information eXchange version 2 (SS-MIX2) storage at Tohoku University Hospital into the Health Level 7 Fast Healthcare Interoperability Resource (FHIR) repository. Subsequently, we implemented a patient summary web application concerning the IPS and evaluated 12 common use cases of the discharge summary. Results: The FHIR resources of seven of the necessary IPS sections were successfully converted from existing SS-MIX2 data. In the main view of the application we developed, all the minimum necessary patient information was summarized and visualized. All types of mandatory or required sections in the IPS and all structured information items of the discharge summary were displayed. Of the discharge summary, 75% of sections and 61.7% of information items were completely displayed, matching 12 common use cases in Japan. Conclusions: We implemented a patient summary application that summarizes and visualizes patient information accumulated by existing systems and is evaluated in common use cases in Japan. Efficient sharing of the minimum necessary patient information for physicians is expected to reduce information overload, workload, and burnout. [ABSTRACT FROM AUTHOR]

Real-world data sources can facilitate essential understanding of the epidemiological features of anaphylaxis. However, the accuracy of case-identifying definitions based on diagnosis codes for anaphylaxis in healthcare databases remains understudied. We conducted a cross-sectional study analyzing claims data from the largest multi-institutional healthcare system in Taiwan during 2017–2021. We included patients with incident anaphylaxis identified by either ICD-10-CM codes for anaphylaxis (Group 1) or ICD-10-CM codes for severe allergic or drug adverse events and additional modifier codes for acute allergy events (Group 2). We randomly selected 20% of the cases to determine the positive predictive value (PPV) of anaphylaxis case-identifying definitions in Groups 1 and 2 after review of electronic medical records by two physicians. From the original cohort (n = 2,176), we randomly selected 433 patients with either a diagnosis of anaphylaxis (Group 1), or a diagnosis of severe allergic and drug adverse events with additional modifier codes for acute allergy events (Group 2). In Group 1, we judged 135 / 170 patients as true anaphylaxis cases, giving a PPV of 79.4% (95% CI: 73.3–85.5). In Group 2, we judged 47 / 263 patients as true anaphylaxis cases, giving a PPV of 17.9% (95% CI: 13.3–22.5). In conclusion, acceptable PPVs were observed when anaphylaxis cases were identified by ICD-10-CM codes for anaphylaxis, but not by ICD-10-CM codes for severe allergic or drug adverse event with additional modifier codes for acute allergy events. Our multi-institutional findings could serve as a fundamental reference for further studies of anaphylaxis based on real-world healthcare databases. [ABSTRACT FROM AUTHOR]

We investigated the role of both individual-level social needs and community-level social determinants of health (SDOH) in explaining emergency department (ED) utilization rates. We also assessed the potential synergies between the two levels of analysis and their combined effect on patterns of ED visits. We extracted electronic health record (EHR) data between July 2016 and June 2020 for 1,308,598 unique Maryland residents who received care at Johns Hopkins Health System, of which 28,937 (2.2%) patients had at least one documented social need. There was a negative correlation between median household income in a neighborhood with having a social need such as financial resource strain, food insecurity, and residential instability (correlation coefficient: -0.05, -0.01, and − 0.06, p = 0, respectively). In a multilevel model with random effects after adjusting for other factors, living in a more disadvantaged neighborhood was found to be significantly associated with ED utilization statewide and within Baltimore City (OR: 1.005, 95% CI: 1.003–1.007 and 1.020, 95% CI: 1.017–1.022, respectively). However, individual-level social needs appeared to enhance the statewide effect of living in a more disadvantaged neighborhood with the OR for the interaction term between social needs and SDOH being larger, and more positive, than SDOH alone (OR: 1.012, 95% CI: 1.011–1.014). No such moderation was found in Baltimore City. To our knowledge, this study is one of the first attempts by a major academic healthcare system to assess the combined impact of patient-level social needs in association with community-level SDOH on healthcare utilization and can serve as a baseline for future studies using EHR data linked to population-level data to assess such synergistic association. [ABSTRACT FROM AUTHOR]

The fifth wave of COVID-19 outbreaks in Hong Kong (HK) from January to March 2022 has the highest confirmed cases and deaths compared with previous waves. Severe hospital boarding (to inpatient wards) was noted in various Emergency Departments (EDs). Our objective is to identify factors associated with hospital boarding during Omicron surge in HK. We conducted a retrospective cohort study including all ED visits and inpatient (IP) ward admissions from January 1st to March 31st, 2022. Vector Autoregression model evaluated the effects of a single variable on the targeted hospital boarding variables. Admissions from elderly homes with 6 lag days held the highest positive value of statistical significance (t-stat = 2.827, P <.05) caused prolonged admission waiting time, while medical patients with 4 lag days had the highest statistical significance (t-stat = 2.530, P <.05) caused an increased number of boarding patients. Within one week after impulses, medical occupancy's influence on the waiting time varied from 0.289 on the 1st day to -0.315 on the 7th day. While occupancy of medical wards always positively affected blocked number of patients, and its response was maximized at 0.309 on the 2nd day. Number of confirmed COVID-19 cases was not the sole significant contributor, while occupancy of medical wards was still a critical factor associated with patient boarding. Increasing ward capacity and controlling occupancy were suggested during the outbreak. Moreover, streamlining elderly patients in ED could be an approach to relieve pressure on the healthcare system. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has led to greater attention being given to infectious disease surveillance systems and their notification functionalities. Although numerous studies have explored the benefits of integrating functionalities with electronic medical record (EMR) systems, empirical studies on the topic are rare. The current study assessed which factors influence the effectiveness of EMR-based reporting systems (EMR-RSs) for notifiable disease surveillance. This study interviewed staff from hospitals with a coverage that represented 51.39% of the notifiable disease reporting volume in Taiwan. Exact logistic regression was employed to determine which factors influenced the effectiveness of Taiwan's EMR-RS. The results revealed that the influential factors included hospitals' early participation in the EMR-RS project, frequent consultation with the information technology (IT) provider of the Taiwan Centers for Disease Control (TWCDC), and retrieval of data from at least one internal database. They also revealed that using an EMR-RS resulted in more timely, accurate, and convenient reporting in hospitals. In addition, developing by an internal IT unit instead of outsourcing EMR-RS development led to more accurate and convenient reporting. Automatically loading the required data enhanced the convenience, and designing input fields that may be unavailable in current databases to enable physicians to add data to legacy databases also boosted effectiveness of the reporting system. [ABSTRACT FROM AUTHOR]

We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs. [ABSTRACT FROM AUTHOR]

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen's kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection. [ABSTRACT FROM AUTHOR]

Introduction: European Union intends to enable cross-border health services through a program referred to as "MyHealth@EU". The first main service is the dispensation of medicine by interlinking national electronic prescription systems. The second one is the Patient Summary, which enables providing the basic set of patients' medical data. Methods: The contemporary technical documentation of the project was studied and selected published Key Performance Indicators of the project were analyzed. Where necessary, data were acquired directly from the European Commission. Results: Data from the start of the project (fourth quarter of 2019) until the second quarter of 2022 were analyzed. During this time both the overall number of EU countries with operational cross-border healthcare and their particular abilities in both services have risen. At present, there are eleven countries with capabilities in at least one of the services, of which nine have reported transactions. More countries are in the test phase now and will join the operational phase of the project shortly. Discussion and Conclusion: Nevertheless, the program is still used mostly for testing purposes. It seems that only electronic prescription and dispensation are commonly and widely used so far and only Estonian and Finnish patients usually get their medication dispensed abroad. The rest of the operational countries is still at present missing country pairs with a strong cross-border use case. [ABSTRACT FROM AUTHOR]

Background: In recent years, there has been an increasing interest in electronic health record (EHR) systems and various approaches of encouraging acceptance. Multiple methods of EHR acceptance have been proposed. However, a systematic review of patient's perspectives of their role and challenges in processing EHR remains lacking. Moreover, so far, there has been little discussion about barriers and facilitators of EHR system acceptance and usage from the patients' perspective. Methods: The study was reported according to the PRISMA statement. Six databases were systematically searched using keywords for articles from 2002–2020. We reviewed these data and used an inductive approach to analyse findings. Results: A total of 36 studies met the inclusion criteria. Our systematic literature review results reveal a wide range of barriers and facilitators assigned to four distinct stages of EHR system usage: awareness, adoption, behaviour and perception, and consequences. Results were described in a narrative synthesis of the included empirical studies. Discussion: Results underline the necessity to put a particular emphasis – but not exclusively – on the initial stage of awareness in the future. Further research in the field is therefore strongly recommended in order to develop tailored mediated communication to foster EHR system usage in the long run. [ABSTRACT FROM AUTHOR]

Electronic recording of newborn health information contributes to improving the quality of care. Nonetheless, there is limited evidence on the implementation of perinatal electronic medical records models. We describe the development and implementation of an electronic recording model that includes data on the health care provided to both the mother and the newborn, standardised for six hospitals of a regional health care system. The implementation process was developed in 2 stages. During stage 1, the tool was introduced in hospitals to stablish first contact with the healthcare staff. The second stage consisted in designing a new strategy to stabilise the model. Technical issues were fixed, and a new version was drawn up based on multidisciplinary agreement. Indicators to monitor implementation were measured in both stages and compared using the chi-squared test. During stage 1, nearly every newborn got its electronic medical record with an appropriate connection to the mother's data. However, certain forms that were meant to be filled in by staff were frequently neglected (completion rates: 36.7%-55.3%). In stage 2, there was a statistically significant increase in the completion rates of all these forms. As a result, a standardised discharge report was provided to every newborn at the end of stage 2. The PCR model implemented in the Region of Murcia is an innovative example of how the digitalisation and standardisation of data related to the care of healthy newborns at maternity wards is feasible across an entire network of hospitals. [ABSTRACT FROM AUTHOR]

Patient Electronic Health Records (EHRs) contain valuable clinical data that is useful for medical research and public health inquires. However, patient privacy regulation and improper resource sharing risks limit access to EHR medical data for research and public health purposes. In this paper, we introduce an end-to-end security solution that addresses both concerns and facilitates the sharing of patient EHR data over an unsecured third-party server using a leveled homomorphic encryption (LHE) scheme. Time testing for aggregating queries and linear computations was carried out using an HPE ProLiant DL580 Gen 10 server with an Intel Xeon Platinum 8280 Processor. [ABSTRACT FROM AUTHOR]

This systematic review aimed to identify the factors associated with information breaches in healthcare settings. We searched electronic databases, including PubMed, Embase, Scopus, and Wiley, for all publications until March 6, 2022 (no start date restriction) and outlined the eligibility criteria framework using Population, Concept, and Context (PCC). Our search strategies yielded 2,156 results, with nine studies included in the final review. We grouped the factors attributed to data breaches into four categories: organizational, information technology (IT), professionals/employees, and clients/patients. Hospital type, hospital size (more beds), higher operationalexpenses and revenue, higher admissions and discharges, and higher Electronic Medical Record (EMR) use, being at earlier phases of EMR adoption, were associated with higher information breaches. Professional factors such as the type of personnel involved and the hospital area can potentially impact the breaches' frequency and magnitude. Patients' sociodemographic, clinical, and behavioral characteristics, such as gender and educational attainment, appear to influence the perceived breach of confidentiality in healthcare settings. Identifying different factors contributing to health information breaches is crucial to protecting healthcare organizations and patients from the devastating consequences of data breaches. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the potential of a novel system using outlier detection screening algorithms and to identify medication related risks in an inpatient setting. Methods: In the first phase of the study, we evaluated the transferability of models refined at another medical center using a different electronic medical record system (EMR) on 3 years of historical data (2017–2019), extracted from the local EMR system. Following the retrospective analysis, the system's models were fine-tuned to the specific local practice patterns. In the second, prospective phase of the study, the system was fully integrated in the local EMR and after a short run-in period was activated live. All alerts generated by the system, in both phases, were analyzed by a clinical team of physicians and pharmacists for accuracy and clinical relevance. Results: In the retrospective phase of the study, 226,804 medical orders were analyzed, generating a total of 2731 alerts (1.2% of medical orders). Of the alerts analyzed, 69% were clinically relevant alerts and 31% were false alerts. In the prospective phase of the study, 399 alerts were generated by the system (1.6% of medical orders). The vast majority of the alerts (72%) were considered clinically relevant, and 41% of the alerts caused a change in prescriber behavior (i.e. cancel/modify the medical order). Conclusion: In an inpatient setting of a 600 bed computerized decision support system (CDSS) -naïve medical center, the system generated accurate and clinically valid alerts with low alert burden enabling physicians to improve daily medical practice. [ABSTRACT FROM AUTHOR]

Data derived from the electronic health record (EHR) is frequently extracted using undefined approaches that may affect the accuracy of collected variables. Further, efforts to assess data accuracy often suffer from limited collaboration between clinicians and data analysts who perform the extraction. In this manuscript, we describe the methodology behind creation of a structured, rigorously derived intensive care unit (ICU) data mart based on data automatically and routinely derived from the EHR. This ICU data mart includes high-quality data elements commonly used for quality improvement and research purposes. These data elements were identified by physicians working closely with data analysts to iteratively develop and refine algorithmic definitions for complex outcomes and risk factors. We contend that this methodology can be reproduced and applied across other institution or to other clinical domains to create high quality data marts, inclusive of complex outcomes data. [ABSTRACT FROM AUTHOR]

Asthma in childhood is a common and costly chronic disease. Quality asthma care can lead to better control of asthma thus decreasing use of health services. The gold standard for pediatric asthma diagnosis and management is the National Heart, Lung and Blood Institute (NHLBI) guidelines for Diagnosis and Management of Asthma which center on precisely establishing the severity of asthma, as this precise classification delineates appropriate therapy. However, navigating these guidelines is a challenge for primary care providers that creates a barrier to providing quality care. We aim to improve precision in asthma severity classification in the community healthcare setting through the development of an electronic asthma decision support tool (eADST) incorporating NHLBI guidelines embedded within the electronic health record system. We developed an algorithm for the eADST to guide the health care provider to the appropriate classification and subsequent therapy. We engaged our health system's electronic health record informatics team and together developed and revised the tool. We launched the tool in three academic community clinics and measured precision in asthma classification in the twelve months prior to the availability of the tool and the twelve months following the launch. We found a significant improvement in precision of asthma severity classification following the launch, a necessary first step in improvement of asthma care. The next step will be to evaluate the impact of the tool on asthma outcomes. [ABSTRACT FROM AUTHOR]

Objectives: Structured medical records improve readability and ensure the inclusion of information necessary for correct diagnosis and treatment. This is the first study to assess the quality of computer-generated structured medical records by comparing them to conventional medical records on patients with acute abdominal pain. Materials and methods: A prospective double-blinded study was conducted in a tertiary referral center emergency department between January 2018 and June 2018. Patients were examined by emergency department physicians and by experience and inexperienced researcher. The researchers used a new electronical medical records system, which gathered data during the examination and the system generate structured medical records containing natural language. Conventional medical records dictated by physician and computer-generated medical records were compared by a group of independent clinicians. Results: Ninety-nine patients were included. The overall quality of the computer-generated medical records was better than the quality of conventional human-generated medical records – the structure was similar or better in 99% of cases and the readability was similar or better in 86% of cases, p < 0.001. The quality of medical history, current illness, and findings of physical examinations were likewise better with the computer-generated recording. The results were similar when patients were examined by experienced or inexperienced researcher using the computer-generated recording. Discussion: The quality of computer-generated structured medical records was superior to that of conventional medical records. The quality remained similar regardless of the researcher's level of experience. The system allows automatic risk scoring and easy access for quality control of patient care. We therefore consider that it would be useful in wider practice. [ABSTRACT FROM AUTHOR]

Stroke registries are tools for improving care and advancing research. We aim to describe the methodology and resourcing of existing national stroke registries. We conducted a systematic search of the published, peer-reviewed literature and grey literature examining descriptions of data collection methods and resourcing of national stroke registries published from 2012 to 2023. The systematic review was registered in PROSPERO (CRD42023393841). 101 records relating to 21 registries in 19 countries were identified. They universally employed web-based platforms for data collection. The principal profession of data collectors was nursing. All included the acute phase of care, 28% (6) registered the pre-hospital (ambulance) phase and 14% (3) included rehabilitation. 80% (17) collected outcome data. The registries varied in their approach to outcome data collection; in 9 registries it was collected by hospitals, in 2 it was collected by the registry, and 7 had linkage to national administrative databases allowing follow-up of a limited number of end points. Coverage of the total number of strokes varies from 6 to 95%. Despite widespread use of Electronic Health Records (EHRs) the ability to automatically populate variables remained limited. Governance and management structures are diverse, making it challenging to compare their resourcing. Data collection for clinical registries requires time and necessary skills and imposes a significant administrative burden on the professionals entering data. We highlight the role of clinical registries as powerful instruments for quality improvement. Future work should involve creating a central repository of stroke registries to enable the development of new registries and facilitate international collaboration., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The reuse of healthcare data for various purposes will become increasingly important in the future. To enable the reuse of clinical data, structured and standardized documentation is conditional. However, the primary purpose of clinical documentation is to support high-quality patient care. Therefore, this study investigated the effect of increased structured and standardized documentation on the quality of notes in the Electronic Health Record. A multicenter, retrospective design was used to assess the difference in note quality between 144 unstructured and 144 structured notes. Independent reviewers measured note quality by scoring the notes with the Qnote instrument. This instrument rates all note elements independently using and results in a grand mean score on a 0–100 scale. The mean quality score for unstructured notes was 64.35 (95% CI 61.30–67.35). Structured and standardized documentation improved the Qnote quality score to 77.2 (95% CI 74.18–80.21), a 12.8 point difference (p < 0.001). Furthermore, results showed that structured notes were significantly longer than unstructured notes. Nevertheless, structured notes were more clear and concise. Structured documentation led to a significant increase in note quality. Moreover, considering the benefits of structured data recording in terms of data reuse, implementing structured and standardized documentation into the EHR is recommended. [ABSTRACT FROM AUTHOR]

Excessive amounts of resources in healthcare are wasted due to duplicated or unnecessary health screenings, especially in the diagnosis of low back pain (LBP). Research shows that two-thirds of people will present with LBP at some point throughout their lifetime, but 20–50% of high-tech imaging procedures fail to provide information that improves the patient's condition, representing unnecessary services. The purpose of this study was to evaluate the existence of unnecessary imaging for low back pain throughout healthcare systems in Nebraska based on what was documented in the electronic health record. This study was a retrospective electronic health record analysis of a limited data set focused on procedures related to imaging for LBP extracted from Nebraska Health Information Exchange (HIE) managed by CyncHealth. The sample included 937 patient records with a diagnosis of LBP who received imaging in the state of Nebraska and whose health record was recorded in the Nebraska HIE. To determine necessity, records were categorized in three areas including necessary imaging, likely wasteful imaging, or wasteful imaging based on the criteria from the "First, Do No Harm" study conducted by the Washington Health Alliance. Results revealed a total of 51% of low back pain imaging considered wasteful, 35% likely wasteful, and 14% necessary. Based on these results, further research is warranted to determine specific demographics related to necessary, likely wasteful, and wasteful imaging and the purpose for performing these expensive imaging procedures. [ABSTRACT FROM AUTHOR]

Justifications for the widespread adoption and integration of an electronic health record (EHR) have long leaned on the purported benefits of the technology. However, the performance of the EHR has been underwhelming relative to the promises of immediate access to relevant patient information, clinical decision supports, computerized ordering, and transferable patient data. In this narrative review, we provide an overview of the historical problems and limitations of the EHR, detail the core principles that define agile processes that may overcome the barriers faced by the current EHR, and re-imagine what an integrated, seamless EHR that serves its users and patients might look like. Moving forward, the EHR should be redesigned using a middle-out framework and empowering dual-type champions to maintain the sustainable diffusion of future innovations. [ABSTRACT FROM AUTHOR]

Background: Electronic Health Records (EHRs) can help clinicians to plan, document and deliver care for patients in healthcare services. When used consistently, EHRs can advance patient safety and quality, and reduce clinician's workload. However, usability problems can make it difficult for clinicians to use EHRs effectively, which can negatively impact both healthcare professionals and patients. Objective: To improve usability of EHRs within a mental health service in the UK. Methods: This was a feasibility study conducted with two mental health teams. A mixed-methods approach was employed. Focus group discussions with clinicians identified existing usability problems in EHRs and changes were made to address these problems. Updated EHR assessment forms were evaluated by comparing the following measures pre and post changes: (1) usability testing to monitor time spent completing and duplicating patient information in EHRs, (2) clinician's experience of using EHRs, and (3) proportion of completed EHR assessment forms. Results: Usability testing with clinicians (n = 3) showed that the time taken to complete EHR assessment forms and time spent duplicating patient information decreased. Clinician's experience of completing EHR assessment forms also significantly improved post changes compared to baseline (n = 71; p < 0.005). There was a significant increase in completion of most EHR forms by both teams after EHR usability improvements (all at p < 0.01). Conclusions: Usability improvements to EHRs can reduce the time taken to complete forms, advance clinician's experience and increase usage of EHRs. It is important to engage healthcare professionals in the usability improvement process of EHRs in mental health services. [ABSTRACT FROM AUTHOR]

My Turn is software used to manage several Covid-19 mass vaccination campaigns in California. The objective of this article is to describe the use of My Turn at two points of dispensing in California and comment on process improvements to reduce manual tasks of six identified processes of vaccination–registration, scheduling, administration, documentation, follow-up, and digital vaccine record–and paper. We reviewed publicly available documents of My Turn and patients vaccinated at George R. Moscone Convention Center in San Francisco and Oakland Coliseum Community Vaccination Clinic. For publicly available documents of My Turn, we examined videos of My Turn on YouTube, and documentation from EZIZ, the website for the California Vaccines for Children Program. For patients, we examined publicly available vaccination record cards on Instagram and Google. At the George R. Moscone Convention Center, 329,608 vaccines doses were given. At the Oakland Coliseum Community Vaccination Clinic, more than 500,000 vaccine doses were administered. The use of My Turn can be used to reduce manual tasks and paper for mass vaccinating patients against Covid-19. [ABSTRACT FROM AUTHOR]

The Epic electronic health record (EHR) platform supports structured data entry systems (SDES), which allow developers, with input from users, to create highly customized patient-record templates in order to maximize data completeness and to standardize structure. There are many potential advantages of using discrete data fields in the EHR to capture data for secondary analysis and epidemiological research, but direct data acquisition from clinicians remains one of the largest obstacles to leveraging the EHR for secondary use. Physician resistance to SDES is multifactorial. A 35-item questionnaire based on Unified Theory of Acceptance and Use of Technology, was used to measure attitudes, facilitation, and potential incentives for adopting SDES for clinical documentation among 25 pediatric specialty physicians and surgeons. Statistical analysis included chi-square for categorical data as well as independent sample t-tests and analysis of variance for continuous variables. Mean scores of the nine constructs demonstrated primarily positive physician attitudes toward SDES, while the surgeons were neutral. Those under 40 were more likely to respond that facilitating conditions for structured entry existed as compared to the two older age groups (p = .02). Pediatric surgeons were significantly less positive than specialty physicians about SDES effects on Performance (p = .01) and the effect of Social Influence (p = .02); but in more agreement that use of forms was voluntary (p = .02). Attitudinal differences likely reflect medical training, clinical practice workflows, and division specific practices. Identified resistance indicate efforts to increase SDES adoption should be discipline-targeted rather than a uniform approach.

Current trauma registries suffer from inconsistent collection of data needed to assess health equity. To identify barriers/facilitators to collecting accurate equity-related data elements, we assessed perspectives of national stakeholders, Emergency Department (ED) registration, and Trauma Registry staff. We conducted a Delphi process with experts in trauma care systems and key informant interviews and focus groups with ED patient registration and trauma registry staff at a regional Level I trauma center. Topics included data collection process, barriers/facilitators for equity-related data collection, electronic health record (EHR) entry, trauma registry abstraction, and strategies to overcome technology limitations. Responses were qualitatively analyzed and triangulated with observations of ED and trauma registry staff workflow. Expert-identified barriers to consistent data collection included lack of staff investment in changes and lack of national standardization of data elements; facilitators were simplicity, quality improvement checks, and stakeholder investment in modifying existing technology to collect equity elements. ED staff reported experiences with patients reacting suspiciously to queries regarding race and ethnicity. Cultural resonance training, a script to explain equity data collection, and allowing patients to self-report sensitive items using technology were identified as potential facilitators. Trauma registry staff reported lack of discrete fields, and a preference for auto-populated and designated EHR fields. Identified barriers and facilitators of collection and abstraction of equity-related data elements from multiple stakeholders provides a framework for improving data collection. Successful implementation will require standardized definitions, staff training, use of existing technology for patient self-report, and discrete fields for added elements. [ABSTRACT FROM AUTHOR]

Positive patient identification (PPID) is an integral step to ensure the correct patient identity prior to a healthcare delivery event. Following implementation of a new EHR in November 2017, Vanderbilt University Medical Center (VUMC) experienced frequent and inconsistent failure of barcode scanners which impacted the electronic PPID (ePPID) and blood verification processes. Following multiple iterations of troubleshooting, vendor engagement, and device upgrades, we developed a clinical decision support (CDS) tool as a visual reminder to perform ePPID. If ePPID was initially bypassed, the clinician received a passive alert which remained visible throughout the procedure or until ePPID was completed successfully. We conducted a retrospective observational study using an interrupted time series analysis and analysis of variance pre- and post- CDS intervention. Following CDS intervention, we observed an immediate 20.8% increase in successful ePPID (p < 0.001). The mean success rate of ePPID attempts increased from 62.0% pre-intervention to 94.4% post-intervention (p < 0.001). There were 108 providers who had less than 80.0% success in the six-months prior to CDS intervention, of whom all improved to an average of 95.9% success. Our CDS approach highlights the utility of non-interruptive but continually visible alerts to improve patient safety workflows. By making errors clearly visible to users and their peers, performance improved to only 5.6% of alerts bypassed. [ABSTRACT FROM AUTHOR]

Our aim was to assess the tomographic presence of diabetic macular edema in type 2 diabetes patients screened for diabetic retinopathy with color fundus photographs and an artificial intelligence algorithm. Color fundus photographs obtained with a low-cost smartphone-based handheld retinal camera were analyzed by the algorithm; patients with suspected macular lesions underwent ocular coherence tomography. A total of 366 patients were screened; diabetic macular edema was suspected in 34 and confirmed in 29 individuals, with average age 60.5 ± 10.9 years and glycated hemoglobin 9.8 ± 2.4%; use of insulin, statins, and aspirin were reported in 44.8%, 37.9%, and 34.5% of individuals, respectively; systemic blood hypertension, dyslipidemia, abdominal obesity, chronic kidney disease, and risk for diabetic foot ulcers were present in 100%, 58.6%, 62.1%, 48.3%, and 27.5% of individuals, respectively. Proliferative diabetic retinopathy was present in 31% of patients with macular edema; severity level was associated with albuminuria (p = 0.028). Eyes with macular edema had average central macular thickness 329.89 ± 80.98 m μ ; intraretinal cysts, sub retinal fluid, hyper-reflective foci, epiretinal membrane, and vitreomacular traction were found in 87.2%, 6.4%, 85.1%, 10.6%, and 6.4% of eyes, respectively. Diabetic retinopathy screening overwhelms health systems and is typically based on color fundus photographs, with high false-positive rates for the detection of diabetic macular edema. The present, semi-automated strategy comprising artificial intelligence algorithms integrated with smartphone-based retinal cameras could improve screening in low-resource settings with limited availability of ocular coherence tomography, allowing increased access rates and ultimately contributing to tackle preventable blindness. [ABSTRACT FROM AUTHOR]

To assess competency of residents prior to graduation, the Accreditation Council for Graduate Medical Education (ACGME) maintains a case log system, where residents self-report cases they perform. This mechanism results in underreporting of resident involvement in patient care. To determine if an intraoperative case log reminder would increase the frequency of ACGME case logging amongst anesthesiology residents. An intraoperative ACGME case log reminder was implemented on March 13, 2019. The authors collected data for all 53 PGY2-4 anesthesiology residents at the authors' institution from July 14, 2018 to July 16, 2019 from the electronic medical record and ACGME system to calculate the proportion of cases logged and the "lag time" between case occurrence and logging. Data was analyzed for all residents, classes, and individuals. A total of 16,342 anesthetics were performed, and a total of 11,713 cases were logged. The reminder did not improve overall logging rates. Case-logging rates amongst PGY2 residents remained unchanged and declined for PGY3 and PGY4 residents. The lag time between case occurrence and logging increased. An automatic reminder did not improve logging frequency. This may be because residents are unable to log cases intraoperatively in many instances, or they may not feel as though they have participated enough in a case to log it. Additionally, senior residents may log cases less frequently once they have met required case minimums. An automatic case-logging system that transmits resident information directly to the ACGME may be the best way to increase logging accuracy. [ABSTRACT FROM AUTHOR]

Vancomycin is one of the most prescribed antibiotics in pediatric intensive care units (PICU) in US hospitals. However, a detailed understanding of workflow and information flow among various stakeholders regarding vancomycin treatment processes in clinical settings is lacking. We conducted direct observations and informant interviews to develop the mapping of key processes and information flow for vancomycin treatment, with an emphasis on therapeutic drug monitoring (TDM) dose adjustment decision-making. A health information technology (HIT) sociotechnical framework was used to identify EHR related safety concerns. A total of 27 vancomycin treatment activities were observed over a 60-h duration including infusion administration, infusion completion, trough concentration blood draw and therapeutic decision making processes. Workflow and information flow mappings revealed (1) deviations between the documented timestamp used for TDM decision making and the actual time the tasks executed and (2) the lack of information flow regarding infusion completion and interruption. Missing features, insufficient usability and lack of integration with workflow and communication in the EHR were deemed safety gaps that may affect the accuracy of therapeutic decisions. Our case study identified gaps in information flow among clinical team members via EHR in TDM processes to provide insights for the improvement of the EHR system for antibiotic treatment purposes. In particular, the potential harm of the missing, uncertain, and inaccurate documented TDM task times warrant further investigations. [ABSTRACT FROM AUTHOR]

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient's high-risk markers, and support treatment decisions. [ABSTRACT FROM AUTHOR]

This study aimed to develop a method to enable the financial estimation of each patient's uncertainty without focusing on healthcare technology. We define financial uncertainty (FU) as the difference between an actual amount of claim (AC) and the discounted present value of the AC (DAC). DAC can be calculated based on a discounted present value calculated using a cash flow, a period of investment, and a discount rate. The present study considered these three items as AC, the length of hospital stay, and the predicted mortality rate. The mortality prediction model was built using typical data items in standard level electronic medical records such as sex, age, and disease information. The performance of the prediction model was moderate because an area under curve was approximately 85%. The empirical analysis primarily compares the FU of the top 20 diseases with the actual AC using a retrospective cohort in the University of Miyazaki Hospital. The observational period is 5 years, from April 1, 2013, to March 31, 2018. The analysis demonstrates that the proportion of FU to actual AC is higher than 20% in low-weight children, patients with leukemia, brain tumor, myeloid leukemia, or non-Hodgkin's lymphoma. For these diseases, patients cannot avoid long hospitalization; therefore, the medical fee payment system should be designed based on uncertainty. Our method is both practical and generalizable because it uses a small number of data items that are required in standard electronic medical records. This method contributes to the decision-making processes of health policymakers. [ABSTRACT FROM AUTHOR]

In Japan, since the Next Generation Medical Infrastructure Act regarding anonymized medical data contributing to R&D came into force in 2018, it is expected to exploit medical data for R&D. The Millennial Medical Record Project has been collected a large amount of standardized medical data of a number of hospitals stored in a database under the act. In order for users to widely exploit the medical data when carrying out trial-and-error, there is a difficulty of data access because of a highly secured management of non-anonymous medical data. To solve the data access problem, we develop a general statistical analytical system for executing a variety of statistical significance tests with statistical power analysis in an environment of trial-and-error for users' analyses without programming. In the analytical system, the front-end is a registration form as the input and the analysis results as the output on Microsoft Excel, and the back-end is based on Python, R and SQL. Although the fixed registration form covers limited application for the analysis, since the analysis results using the stored Millennial Medical Record data is provided in a short time without collecting the necessary data for the analysis, the exploitation of medical data could widely and rapidly promote by medical experts/researchers in the manner of trial-and-error. The developed system could apply to make protocols for clinical research and clinical trial, and the potential to discover real-world evidence could be increased. [ABSTRACT FROM AUTHOR]

Rapid Medical Evaluation (RME) is a new Emergency Department (ED) process that initiates testing while patients are in the Waiting Room. Primary goal of this study is to assess the effectiveness of RME pathway on the patient flow through the ED. This was a retrospective, single site, cohort study of patients presenting to the ED 12 months before (PRE group) and 12 months after (POST group) RME implementation. The POST group was divided into those that underwent RME and those managed using standard care pathway (SCP). Data was collected from Electronic Health Record (EHR) database using SQL and consisted of time stamp data for discrete ED patient events. The following metrics were calculated for all ED encounters: Active ED Room Time, Boarding Time, Total ED Room Time, Total ED Time, and Door-to-Provider Time. Patients undergoing RME on average spent 90-min less in ED Treatment Room compared to SCP group and were evaluated by a provider 151 min earlier than if they had waited for an available ED Treatment Room. Implementation of RME helped reduce time patients spend in ED Treatment Room, improved patient throughput, and decreased Door-to-Provider time during the busiest times in the ED. [ABSTRACT FROM AUTHOR]

This study aimed to improve generalizability of our previous study that analyzed clinical pathway (CP) completion. Although our previous study demonstrated that CP completion can reduce the length of hospital stay, it is possible for few medical organizations to extract the implementation of treatment registered on CP from typical electronic medical records. Therefore, we have defined a prospective event for event substitution, called meal completion (MC), in which patients can take their meal daily. Data were collected from April 2013 to March 2018 from the electronic medical records of the University of Miyazaki Hospital. We used propensity score matching to extract records from 8033 patients. Patients were further divided into the MC and non-MC groups; 2577 patients in each group were available for data analysis. The numbers of patients with CP completion were 646 (28.1%) in the MC group and 411 (18.2%) in the non-MC group. The P value of the chi-square test was <0.001. According to this result, there was the causation from MC to increase in CP completion. Additionally, it was possible to consider the inclusion relationship in all treatments (universal set), treatments registered on CP (subset of all treatments), and meals (subset of treatments registered on CP). In conclusion, MC can substitute for CP completion because the demonstration is appropriate for the Prentice criterion, which is often used for the evaluation of a surrogate endpoint. [ABSTRACT FROM AUTHOR]