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BACKGROUND: Californias relatively low smoking rate (10.1% in 2019-2020) (About CHIS, UCLA Center for Health Policy Research, 2024) masks deep disparities among low-income populations, where smoking rates are nearly double that of their middle- to upper-income peers. Low-income smokers report a similar desire to quit and similar rates of recent quit attempts as smokers from other groups; yet, they often face barriers in accessing effective resources to facilitate successful cessation. METHODS: Our team will conduct a pragmatic stepped-wedge cluster, randomized controlled trial of Enhanced Multicomponent Proactive Navigator-Assisted Cessation of Tobacco Use (EMPACT-Us), a suite of tobacco cessation services supported by patient navigators, designed in close partnership with patients, providers, and community stakeholders. The study will take place at Family Health Centers of San Diego (FHCSD), the largest federally qualified health center (FQHC) in San Diego. Eight primary care clinics are included, where 70% (n = 13,496) of smokers at FHCSD receive care. DISCUSSION: We hypothesize that multiple points of engagement and integration of navigation services into the workflow of existing staff will improve utilization and cessation success. This study will examine if the enhanced suite of services offers insights on how to best integrate evidence-based tobacco treatment services into usual care. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05750537, Registered on March 1, 2023. https://clinicaltrials.gov/study/NCT05750537 .

Background: California's relatively low smoking rate (10.1% in 2019–2020) (About CHIS, UCLA Center for Health Policy Research, 2024) masks deep disparities among low-income populations, where smoking rates are nearly double that of their middle- to upper-income peers. Low-income smokers report a similar desire to quit and similar rates of recent quit attempts as smokers from other groups; yet, they often face barriers in accessing effective resources to facilitate successful cessation. Methods: Our team will conduct a pragmatic stepped-wedge cluster, randomized controlled trial of Enhanced Multicomponent Proactive Navigator-Assisted Cessation of Tobacco Use (EMPACT-Us), a suite of tobacco cessation services supported by patient navigators, designed in close partnership with patients, providers, and community stakeholders. The study will take place at Family Health Centers of San Diego (FHCSD), the largest federally qualified health center (FQHC) in San Diego. Eight primary care clinics are included, where 70% (n = 13,496) of smokers at FHCSD receive care. Discussion: We hypothesize that multiple points of engagement and integration of navigation services into the workflow of existing staff will improve utilization and cessation success. This study will examine if the enhanced suite of services offers insights on how to best integrate evidence-based tobacco treatment services into usual care. Trial registration: ClinicalTrials.gov, NCT05750537, Registered on March 1, 2023. https://clinicaltrials.gov/study/NCT05750537. [ABSTRACT FROM AUTHOR]

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed. [ABSTRACT FROM AUTHOR]

Healthcare integrated biobanking describes the annotation and collection of residual samples from hospitalized patients for research purposes. The central idea of the current work is to establish an automated workflow for sample annotation, selection and storage for diabetes mellitus. This is challenging due to incomplete data at the time of sample selection. The study evaluates a machine learning (ML) and natural language processing (NLP) based two-step procedure for timely and precise sample annotation for diabetes mellitus. Electronic health record data of 785 persons were extracted from the hospital information system. In the first step, a conditional inference forest (CIF) model was trained and tested based on laboratory values from the first 72 h of the hospital stay using test- (n = 550) and training data sets (n = 235). Performance was compared with a simple laboratory cut-off classifier (LCC) and a logistic regression (LR) model. Algorithms based on laboratory values, ICD-10 codes or information from discharge summaries extracted by a natural language processing software (NLP-DS) were evaluated as a second (review) step designed to increase the precision of annotations. For the first step, recall/precision/F1-score/accuracy were 71 %/86 %/0.78/0.82 for CIF and 77 %/70 %/0.74/0.75 for LR compared to 73 %/68 %/0.70/0.72 for LCC. NLP-DS was the best-performing second (review) step (93 %/100 %/0.97/0.97). Combining first-step models with NLP-DS increased precision to 100 % for all procedures (66 %/100 %/0.80/0.85 for CIF&NLP-DS, 72 %/100 %/0.84/87.2 for LR&NLP-DS and 66 %/100 %/0.80/0.85 for LCC&NLP-DS). The number of samples removed by NLP-DS was higher for LR&NLP-DS and LCC&NLP-DS (removal rate 35 % and 38 % of initially selected samples) compared to CIF&NLP-DS (removal rate of 20 %). The developed two-step procedure is an efficient implementable method for timely and precise annotation of samples from diabetic hospitalized patients.

Abstract Background Long term oxygen therapy (LTOT) is prescribed for hypoxemia in pulmonary disease. Like other medical therapies, LTOT requires a prescription documenting the dosage (flow rate) and directions (at rest, with activity) which goes to a supplier. Communication with patients regarding oxygen prescription (flow rate, frequency, directions), monitoring (pulse oximetry) and dosage adjustment (oxygen titration) differs in comparison with medication prescriptions. We examined the communication of oxygen management plans in the electronic health record (EHR), and their consistency with patient-reported LTOT use. Study design and methods A cross-sectional study was conducted in 71 adults with chronic lung disease on LTOT. Physician communication regarding oxygen management was obtained from the EHR. Participants were interviewed on their LTOT management plan. The information from each source was compared. Results The study population was, on average, 64 years, two-thirds women, and most used oxygen for over 3 years. Only 45% of both at-rest and with-activity oxygen prescriptions were documented in the Electronic Health Record (EHR). Less than 20% of prescriptions were relayed to the patient in the after-visit summary. Of those with EHR-documented oxygen prescriptions, 44% of patients adhered to prescribed oxygen flow rates. Nearly all patients used a pulse oximeter (96%). Interpretation We identified significant gaps in communication of oxygen management plans from provider to patient. Even when the oxygen prescription was clearly documented, there were differences in patient-reported oxygen management. Critical gaps in oxygen therapy result from the lack of consistent documentation of oxygen prescriptions in the EHR and patient-facing documents. Addressing these issues systematically may improve home oxygen management.

Background: Long term oxygen therapy (LTOT) is prescribed for hypoxemia in pulmonary disease. Like other medical therapies, LTOT requires a prescription documenting the dosage (flow rate) and directions (at rest, with activity) which goes to a supplier. Communication with patients regarding oxygen prescription (flow rate, frequency, directions), monitoring (pulse oximetry) and dosage adjustment (oxygen titration) differs in comparison with medication prescriptions. We examined the communication of oxygen management plans in the electronic health record (EHR), and their consistency with patient-reported LTOT use. Study design and methods: A cross-sectional study was conducted in 71 adults with chronic lung disease on LTOT. Physician communication regarding oxygen management was obtained from the EHR. Participants were interviewed on their LTOT management plan. The information from each source was compared. Results: The study population was, on average, 64 years, two-thirds women, and most used oxygen for over 3 years. Only 45% of both at-rest and with-activity oxygen prescriptions were documented in the Electronic Health Record (EHR). Less than 20% of prescriptions were relayed to the patient in the after-visit summary. Of those with EHR-documented oxygen prescriptions, 44% of patients adhered to prescribed oxygen flow rates. Nearly all patients used a pulse oximeter (96%). Interpretation: We identified significant gaps in communication of oxygen management plans from provider to patient. Even when the oxygen prescription was clearly documented, there were differences in patient-reported oxygen management. Critical gaps in oxygen therapy result from the lack of consistent documentation of oxygen prescriptions in the EHR and patient-facing documents. Addressing these issues systematically may improve home oxygen management. [ABSTRACT FROM AUTHOR]

Patient medical records and their accurate recording, storage, protection, and access are essential elements to high-quality healthcare. While many parts of the world have moved to traditional digital systems and electronic health records (EHRs), these systems require complex evaluation and large infrastructure investments, lack interoperability, and introduce the constantly-increasing challenges of cyber-attacks and digital security. The aim of this study is to address these challenges through a secure and accessible EHR management system, applied to allergy and family records, based on blockchain technology, the InterPlanetary File System (IPFS) protocol, and the health level 7 (HL7) fast healthcare interoperability resources standard. The proposal was carried out in four phases: (1) blockchain architecture design, (2) blockchain network design, (3) interoperability design, and (4) web application design. A performance evaluation of the system was conducted to determine the throughput and latency metrics. The results presented a maximum medical record reading and writing throughput of approximately eight transactions per second, with a write latency averaging 5,926 ms to 51,836 ms and a reading latency of 4,783 ms to 45,500 ms. With the addition of a survey of 21 patients and 10 healthcare professionals indicating that both groups strongly agree that the system meets the criteria of high-quality healthcare, all study results present a framework that could serve as a model for the adoption of standards-based, accessible, and secure EHR systems. [ABSTRACT FROM AUTHOR]

Background: Lipoprotein(a) [Lp(a)] is a low-density lipoprotein variant with atherogenic, thrombogenic, and pro-inflammatory properties that may have numerous pathologic effects, including dyslipidemia. Screening for Lp(a) is clinically significant, due to its causal role in atherosclerotic cardiovascular disease (ASCVD). Among clinicians, however, there remains a general lack of both clinical awareness of Lp(a) and adequate tools to track Lp(a) testing in patients. Objective: To study factors affecting Lp(a) screening by: i) determining the effectiveness of messaging providers at a large community health system about Lp(a) screening and measuring the subsequent percentage of Lp(a) tests requested; and ii) by determining the percentage of patients who obtained Lp(a) testing after being advised by the provider. Methods: From December 2022 through March 2023, messages detailing the need for Lp(a) screening were sent via the Epic EHR™ to providers of patients meeting criteria for Lp(a) testing in advance of scheduled patient appointments. In this prospective study, providers were randomized into 2 groups: those receiving the pre-appointment message (Group 1) and those not receiving the pre-appointment message (Group 2). Results: Sending pre-appointment messages correlated with more Lp(a) orders (16.6 % v. 4.7 %, P < 0.001) and consequently with more tests performed (10.2 % v. 3.7 %, p < 0.001). Among provider types, nurse practitioners and physician assistants had the highest number of Lp(a) results per order (Z = 16.40, P < 0.001), achieving 30.8–39.1 % more test results, even if they did not receive the pre-appointment message. Distribution of Lp(a) values in patients was 59.7 % ≤ 29 mg/dL; 9.7 % > 29 and < 50mg/dL; and 30.6 % ≥ 50 mg/dL. Conclusion: Providers who received pre-appointment messages via an EHR were associated with requesting more tests and consequently receiving more Lp(a) results, compared with providers who did not receive messages.

Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians' workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in‐progress effort to overcome these challenges with cooperative, systematic data extraction and validation.A multi‐disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores.Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (n = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (n = 518 367, 23.7%). The median age was 46.9 years (25th–75th percentiles = 31.1–64.7) at the time of the first encounter. Most patients were female (n = 418 303, 56.5%), a significant proportion were of non‐White race (n = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity.The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de‐identified and converted to a publicly available resource. [ABSTRACT FROM AUTHOR]

This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people's attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1's findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2's results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling. [ABSTRACT FROM AUTHOR]

BackgroundChildhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians.ObjectivesThe objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management.MethodsWe utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process.ResultsA total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process.ConclusionMore research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

Abstract 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.

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed.

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. [ABSTRACT FROM AUTHOR]

In Peru, there is currently no integrated electronic health record (EHR) system that can be automatically shared between healthcare facilities. This leads to increased service costs due to duplicated examinations and records, as well as additional time required to manage patients' clinical information. One alternative for ensuring the secure interoperability of EHRs while preserving data privacy is the use of blockchain technology. However, existing works consider a pre-established format for exchanging EHRs, which is not applicable when systems have different formats, as is the case in Peru. This work proposes an architecture and a web application for exchanging EHRs in heterogeneous systems. The proposed system includes the homologation of an EHR with rapid interoperability resources for medical attention using FHIR HL7, and vice versa, to achieve interoperability. Additionally, it utilizes blockchain technology to ensure data security and privacy. The web application was tested using a case simulation to demonstrate EHR interoperability between clinics in a clear, secure, and efficient manner. In addition, a survey was conducted with 30 patients regarding adoption, and another survey was conducted with 10 doctors from a public hospital in Peru regarding usability. The results demonstrate a very high level of adoption and usability for them all. Unlike other studies, the proposal does not necessitate alterations to existing EHR systems for interoperability. In other words, the proposal presents a feasible and cost-effective alternative to addressing the EHR interoperability issue in clinics and hospitals in Peru. [ABSTRACT FROM AUTHOR]

Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that interrelations of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible. This is because, while they are good at learning piece-wise temporal dependencies, attention-based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode the short-term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. We chose various prediction tasks for the experiments using Electronic Health Records (EHR) data sets since they are great examples with underlying long- and short-term temporal dependencies. Our experiments show exceptional classification results compared to best-performing models on most tasks and data sets.

IntroductionThis study is part of the U.S. Food and Drug Administration (FDA)’s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA’s postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality.Materials and methodsThe study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR.ResultsThe volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met.DiscussionThis study describes the platform’s data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

Background: Most hospitals use electronic health records (EHR) to warn health care professionals of drug hypersensitivity (DH) and other allergies. Indiscriminate recording of patient self-reported allergies may bloat the alert system, leading to unjustified avoidances and increases in health costs. The aim of our study was to analyze hypersensitivities documented in EHR of patients at Lausanne University Hospital (CHUV). Methods: We conducted a retrospective study on patients admitted at least 24 h to CHUV between 2011 and 2021. After ethical clearance, we obtained anonymized data. Because culprit allergen could be either manually recorded or selected through a list, data was harmonized using a reference allergy database before undergoing statistical analysis. Results: Of 192,444 patients, 16% had at least one allergy referenced. DH constituted 60% of all allergy alerts, mainly beta-lactam antibiotics (BLA) (30%), NSAID (11%) and iodinated contrast media (ICM) (7%). Median age at first hospitalization and hospitalization length were higher in the allergy group. Female to male ratio was 2:1 in the allergic group. Reactions were limited to the skin in half of patients, and consistent with anaphylaxis in 6%. In those deemed allergic to BLA, culprit drug was specified in 19%, ‘allergy to penicillin’ otherwise. It was impossible to distinguish DH based on history alone or resulting from specialized work-up. Conclusions: Older age, longer hospital stays, and female sex increase the odds of in-patient allergy documentation. Regarding DH, BLA were referenced in 4% of inpatient records. Specific delabeling programs should be implemented to increase data reliability and patient safety.

Healthcare services and organizations rely on Electronic Health Records (EHRs) to manage, store, and transmit patient data records. Consequently, EHRs play a crucial role in providing high-quality services and maintaining the privacy and security of patients’ sensitive data. However, designing such complex systems with security and privacy concerns is anything but simple. This study aims to propose a Software Reference Architecture (SRA) tailored for Electronic Health Records (EHRs) with security and privacy considerations, intending to enhance the development of these systems. To achieve this goal, we analyze the classification of Reference Architectures (RAs), taking into account the primary security and privacy requirements of EHRs along with well-established architectural design methods. We propose a layered architecture for SRA with privacy and security considerations. Subsequently, we derive the following five architecture views for SRA: the feature diagram, the context diagram, the decomposition view, the layered view, and the deployment view. Each view showcases the SRA software architecture from a different perspective. Moreover, we conducted an evaluation of the proposed SRA through its application in a case study. Specifically, we applied the proposed SRA and derived the application architecture from a study focused on Brazilian EHRs. Our analysis highlights the potential issues arising from the absence of an SRA tailored for EHRs, particularly regarding privacy and security concerns surrounding patient data. Through this case study, we demonstrate the practical applicability of our proposed SRA in enhancing EHR systems.

Background: Teledermoscopy improves teledermatology clinical outcomes, but the practical impact of this and other teleconsultation variables on patient management are unclear. We assessed the impact of these variables, including dermoscopy, on face-to-face (F2F) referrals to optimize effort by imagers and dermatologists. Methods: Using retrospective chart review, we retrieved demographic, consultation, and outcome variables from 377 interfacility teleconsultations sent to San Francisco Veterans Affairs Health Care System (SFVAHCS) between September 2018 to March 2019 from another VA facility and its satellite clinics. Data were analyzed using descriptive statistics and logistic regression models. Results: Of 377 consults, 20 were excluded due to patient F2F self-referral without teledermatologist recommendation. Analysis of consults showed that age, clinical image, and problem number but not dermoscopy were associated with F2F referral. Analysis of problems contained in consults showed that lesion location and diagnostic category were also associated with F2F referral. Skin cancer history and problems on the head/neck were independently associated with skin growths in multivariate regression. Conclusions: Teledermoscopy was associated with variables related to neoplasms but did not affect F2F referral rates. Rather than utilize teledermoscopy for all cases, our data suggests that referring sites prioritize teledermoscopy for consultations with variables associated with a likelihood of malignancy.

IntroductionThe digitalisation of the German healthcare system enables a wide range of opportunities to utilize healthcare data. The implementation of the EHR in January 2021 was a significant step, but compared to other European countries, the implementation of the EHR in the German healthcare system is still at an early stage. The aim of this paper is to characterise the structural factors relating to the adoption of the EHR in more detail from the perspective of representatives of stakeholders working in the German healthcare system and to identify existing barriers to implementation and the need for change.MethodsQualitative expert interviews were conducted with one representative from each of the stakeholder groups health insurance, pharmacies, healthcare research, EHR development and panel doctors.ResultsThe interviews with the various stakeholders revealed that the implementation process of the EHR is being delayed by a lack of a viable basis for decision-making, existing conflicts of interest and insufficient consideration of the needs of patients and service providers, among other things.DiscussionThe current status of EHR implementation is due to deficiency in legal regulations as well as structural problems and the timing of the introduction. For instance, the access rights of various stakeholders to the EHR data and the procedure in the event of a technical failure of the telematics infrastructure are remain unclear. In addition, insufficient information and communication measures have not led to the desired acceptance of EHR use among patients and service providers.

With the continuous growth of cloud-based Electronic Health Record (EHR) systems and medical data, medical organizations are particularly concerned about storing patient data to provide fast services while adhering to privacy and security concerns. Existing EHR systems often face challenges in handling heterogeneous data and maintaining good performance with data growth. These systems mostly use relational databases or partially store data in a knowledge graph, making it challenging to handle big data and allowing flexible schema expansion. Hence, there is a need to address these problems. This paper provides a solution by proposing a novel graph-based EHR system integrating Attribute-Based Encryption and Semantic Web Technologies to ensure fine-grained EHR field-level security of patient records. Our approach leverages semantic context to query through a knowledge graph that stores encrypted medical data in the nodes, making it possible to handle heterogeneous data while ensuring optimal performance and preserving patient privacy.

The factors affecting information systems and technology have become a growing topic in many disciplines. This study focuses on factors affecting the adoption of digital dental technologies and dental informatics in dental practice. There are limited studies in the literature on factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI). Understanding the factors is important for the success of the adoption of technologies. Therefore, this study aims to fill that gap. This paper reviews peer-reviewed literature to analyze factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI) and critically examines an array of technology acceptance models to unveil the underlying determinants of DDT and DI adoption. Usability and practical considerations, work efficiency factors, socioeconomic and organizational aspects, aspects of the learning curve, and system design are the most important factors influencing the adoption of digital dental technologies and dental informatics. The study results identified the conceptual framework for the factors affecting the adoption of digital dentistry. [ABSTRACT FROM AUTHOR]

For several, also vital medications, such as immunosuppressants in solid organ and hematopoietic stem cell transplantation, therapeutic drug monitoring (TDM) remains the only strategy for fine-tuning the dosage to the individual patient. Especially in severe clinical complications, the intraindividual condition of the patient changes abruptly, and in addition, drug-drug interactions (DDIs) can significantly impact exposure, due to concomitant medication alterations. Therefore, a single TDM value can hardly be the sole basis for optimal timely dose adjustment. Moreover, every intraindividually varying situation that affects the drug exposure needs synoptic consideration for the earliest adjustment. To place the TDM value in the context of the patient's most detailed current condition and concomitant medications, the Individual Pharmacotherapy Management (IPM) was implemented in the posttransplant TDM of calcineurin inhibitors assessed by the in-house laboratory. The first strategic pillar are the defined patient scores from the electronic patient record. In this synopsis, the Summaries of Product Characteristics (SmPCs) of each drug from the updated medication list are reconciled for contraindication, dosing, adverse drug reactions (ADRs), and DDIs, accounting for defined medication scores as a second pillar. In parallel, IPM documents the resulting review of each TDM value chronologically in a separate electronic Excel file throughout each patient's transplant course. This longitudinal overview provides a further source of information at a glance. Thus, the applied two-arm concept of TDM and IPM ensures an individually tailored immunosuppression in the severely susceptible early phase of transplantation through digital interdisciplinary networking, with instructive and educative recommendations to the attending physicians in real-time. This concept of contextualizing a TDM value to the precise patient's condition and comedication was established at Halle University Hospital to ensure patient, graft, and drug safety. [ABSTRACT FROM AUTHOR]

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.

To determine the risk of comorbidity following diagnosis of knee or hip osteoarthritis (OA). A cohort study was conducted using the Integrated Primary Care Information database, containing electronic health records of 2.5 million patients from the Netherlands. Adults at risk for OA were included. Diagnosis of knee or hip OA (=exposure) and 58 long-term comorbidities (=outcome) were defined by diagnostic codes following the International Classification of Primary Care coding system. Time between the start of follow-up and incident diagnosis of OA was defined as unexposed, and between diagnosis of OA and the end of follow-up as exposed. Age and sex adjusted hazard ratios (HRs) comparing comorbidity rates in exposed and unexposed patient time were estimated with 99.9% confidence intervals (CI). The study population consisted of 1,890,712 patients. For 30 of the 58 studied comorbidities, exposure to knee OA showed a HR larger than 1. Largest positive associations (HR with (99.9% CIs)) were found for obesity 2.55 (2.29–2.84) and fibromyalgia 2.06 (1.53–2.77). For two conditions a HR < 1 was found, other comorbidities showed no association with exposure to knee OA. For 26 comorbidities, exposure to hip OA showed a HR larger than 1. The largest were found for polymyalgia rheumatica 1.81 (1.41–2.32) and fibromyalgia 1.70 (1.10–2.63). All other comorbidities showed no associations with hip OA. This study showed that many comorbidities were diagnosed more often in patients with knee or hip OA. This suggests that the management of OA should consider the risk of other long-term-conditions. [ABSTRACT FROM AUTHOR]

Abstract Background The electronic health record (EHR), utilized to apply statistical methodology, assists provider decision-making, including during the care of chronic kidney disease (CKD) patients. When estimated glomerular filtration (eGFR) decreases, the rate of that change adds meaning to a patient’s single eGFR and may represent severity of renal injury. Since the cumulative sum chart technique (CUSUM), often used in quality control and surveillance, continuously checks for change in a series of measurements, we selected this statistical tool to detect clinically relevant eGFR decreases and developed CUSUMGFR. Methods In a retrospective analysis we applied an age adjusted CUSUMGFR, to signal identification of eventual ESKD patients prior to diagnosis date. When the patient signaled by reaching a specified threshold value, days from CUSUM signal date to ESKD diagnosis date (earliness days) were measured, along with the corresponding eGFR measurement at the signal. Results Signaling occurred by CUSUMGFR on average 791 days (se = 12 days) prior to ESKD diagnosis date with sensitivity = 0.897, specificity = 0.877, and accuracy = .878. Mean days prior to ESKD diagnosis were significantly greater in Black patients (905 days) and patients with hypertension (852 days), diabetes (940 days), cardiovascular disease (1027 days), and hypercholesterolemia (971 days). Sensitivity and specificity did not vary by sociodemographic and clinical risk factors. Conclusions CUSUMGFR correctly identified 30.6% of CKD patients destined for ESKD when eGFR was > 60 ml/min/1.73 m2 and signaled 12.3% of patients that did not go on to ESKD (though almost all went on to later-stage CKD). If utilized in an EHR, signaling patients could focus providers’ efforts to slow or prevent progression to later stage CKD and ESKD.

Introduction: Electronic Health Record (EHR) is an opportunity to implement healthcare services remotely especially in situations that social distance is necessary, such as in Covid-19 pandemic. The relationship between physician and patient is very important in medicine and it has been described as a basic axis of clinical measures and the foundation stone of proper activities in the health system. However, one of the main factors less considered in the design and deployment of health technologies is the physician-patient relationship, emotions, and feelings. The present study aimed to investigate the role of using electronic health records on physician-patient relationship. Methods: In this study, qualitative data collection was carried out via unstructured and semi-structured interviews and focus group discussion, using a phenomenological approach. A thematic analysis approach was also used to analyze the transcripts. A total of 24 participants, including eight physicians, three specialists (pediatricians, gynecologists, and psychiatrists), four psychologists, five health care providers, and four chief executive officers were selected using purposive sampling. Results: The results of this study revealed using EHR could influence interpersonal communication as well as empathetic and sympathetic relationship between physician and patient. The relevant classes are explained in detail in the main text of the article. Conclusion: While EHR is beneficial, the concerns overshadowing the interactions between physician and patient cause users to change their perception of the benefits and efficiency of EHR.

The Smart4Health (S4H) software application will enable European Union (EU) citizens to manage, analyse, and exchange their aggregated electronic health data. This citizen-centred EU electronic health record (EHR) exchange approach for personalised health services will be the first step for the provision of citizen-centred solutions and services in a digital single market for wellbeing and healthcare. Establishing interoperability between the diverse EU EHR data and citizen-generated health data is mandatory to guarantee adequate usability, reliability, and trust of the service. The Connecting Europe Facility (CEF) building blocks address/fulfil such aspects while complying with EU regulations. Here we demonstrate the current status and applicability of the CEF building blocks in the digital health environment for the envisioned S4H software application. The major findings and success stories resulted from the S4H Project are as follows: (1) a secure and user-friendly eID service for the EU-wide Smart4Health community was successfully integrated into the Smart4Health platform, whereby 7 out of the 13 supported EU Member States are already connected, (2) the eTranslation service was compared to other popular alternatives on the market with the result that eTranslation is a secure and valid tool to address multi-language challenges and (3) we identified several use cases for which Smart4Health can benefit from the usage of CEF building blocks, including the improvement of data quality and increase of trust in data sharing.

Background: Managing hypertension in racial and ethnic minoritized groups (eg, African American/Black patients) in primary care is highly relevant. However, evidence on whether or how electronic health record (EHR)–driven approaches in primary care can help improve hypertension management for patients of racial and ethnic minoritized groups in the United States remains scarce. Objective: This review aims to examine the role of the EHR in supporting interventions in primary care to strengthen the hypertension management of racial and ethnic minoritized groups in the United States. Methods: A search strategy based on the PICO (Population, Intervention, Comparison, and Outcome) guidelines was utilized to query and identify peer-reviewed articles on the Web of Science and PubMed databases. The search strategy was based on terms related to racial and ethnic minoritized groups, hypertension, primary care, and EHR-driven interventions. Articles were excluded if the focus was not hypertension management in racial and ethnic minoritized groups or if there was no mention of health record data utilization. Results: A total of 29 articles were included in this review. Regarding populations, Black/African American patients represented the largest population (26/29, 90%) followed by Hispanic/Latino (18/29, 62%), Asian American (7/29, 24%), and American Indian/Alaskan Native (2/29, 7%) patients. No study included patients who identified as Native Hawaiian/Pacific Islander. The EHR was used to identify patients (25/29, 86%), drive the intervention (21/29, 72%), and monitor results and outcomes (7/29, 59%). Most often, EHR-driven approaches were used for health coaching interventions, disease management programs, clinical decision support (CDS) systems, and best practice alerts (BPAs). Regarding outcomes, out of 8 EHR-driven health coaching interventions, only 3 (38%) reported significant results. In contrast, all the included studies related to CDS and BPA applications reported some significant results with respect to improving hypertension management. Conclusions: This review identified several use cases for the integration of the EHR in supporting primary care interventions to strengthen hypertension management in racial and ethnic minoritized patients in the United States. Some clinical-based interventions implementing CDS and BPA applications showed promising results. However, more research is needed on community-based interventions, particularly those focusing on patients who are Asian American, American Indian/Alaskan Native, and Native Hawaiian/Pacific Islander. The developed taxonomy comprising "identifying patients," "driving intervention," and "monitoring results" to classify EHR-driven approaches can be a helpful tool to facilitate this. [ABSTRACT FROM AUTHOR]

Objective: To assess how patient and caregiver factors influence caregiver readiness for hospital discharge in palliative care patients., Study Setting and Design: This transitional care study uses cross-sectional data from a randomized controlled trial conducted from 2018 to 2023 testing an intervention for caregivers of hospitalized adult patients with a serious or life-limiting illness who received a palliative care consult prior to transitioning out of the hospital., Data Sources and Analytical Sample: Caregiver readiness was measured with the Family Readiness for Hospital Discharge Scale (n = 231). Caregiver demographic, intra- and interpersonal factors were self-reported. Patient demographic, comorbidity score, and risk score for complicated discharge planning were extracted from electronic health records. Stepwise regression models estimated variance explained (r 2 ) in caregiver readiness for patient hospital discharge., Principal Findings: Patient demographics and complexity were not statistically associated with caregiver readiness for discharge. Caregiver depressive symptoms, poor caregiver-patient relationship quality, and fewer hours spent caregiving prior to hospitalization explained 29% of the variance in caregiver readiness., Conclusions: Reliance on patient data may not be sufficient for explaining caregiver readiness for discharge. Assessing caregiver factors may be a better alternative for identifying caregivers at risk for low discharge readiness and those in need of additional support., Trial Registration: ClinicalTrials.gov on November 13, 2017, (No. NCT03339271)., (© 2025 The Author(s). Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Introduction The patient-centered care model emphasizes patient autonomy in recovery, acknowledging each individual's unique journey. Despite challenges in the healthcare system, this model has gained traction nationwide. Advances in healthcare technology have highlighted obstacles to independent decision-making. This study addresses these issues by emphasizing the need for consistent access to health information, which is crucial for empowering patients. We aim to proactively identify information gaps and propose new insights for better data precision and synchronization protocols. Our analysis of nationwide hospital length of stay (LOS) data demonstrates data-driven interventions tailored to patients' needs, aiming to improve the hospital experience and reduce care fragmentation. Methods We examined the complex nature of hospital LOS and various variables across nationwide healthcare settings using CMS data from 2011 to 2021. To enhance our national findings, we incorporated a local perspective by analyzing LOS data from Arrowhead Regional Medical Center (ARMC) and its associated diagnosis-related groups (DRGs). We employed a propensity score to adjust for variables and proactively drive realistic predictions of hospital outcomes. This methodological approach emphasizes the importance of using tools that can be scaled from localized settings to a broader national context. Furthermore, our study highlights the critical need for continuous quality assessment of hospital LOS. This includes measuring LOS and developing innovative tools capable of predicting, analyzing, intervening, and prompting actions based on insights gained from data analysis. The study aims to achieve several core objectives by integrating these components: enhancing patient empowerment through improved communication, refining LOS assessment through innovative techniques, and developing predictive tools to inform clinical practice and policy. Ultimately, this research contributes to a more patient-centered approach to managing inpatient care, improving patient outcomes and satisfaction. Results Our study aspires to transform three pivotal domains that can enhance patient autonomy, optimize hospital recovery, and elevate the overall experience. First, the cost of care reveals that prolonged hospital stays and escalating expenses are often linked to more severe health consequences. Second, our analysis uncovers the intricate relationship between hospital outcomes, such as mortality and readmissions, showing that shorter hospital stays can diminish patients' risk of complications. However, we must tread carefully, as this approach may lead to premature discharges. Lastly, providers can gain more precise insights into these interconnected outcomes by leveraging data tools such as propensity scores. We advocate for the dissolution of care fragmentation through robust health information exchange (HIE), and the adoption of innovative strategies such as blockchain and advanced machine learning (ML) techniques that rise to contemporary medicine and adapt to the growing patient needs., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Arrowhead Regional Medical Center IRB issued approval 22-40. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Allam et al.)

Electronic Health Records (EHRs) aggregate the entire patient’s data from different systems. Achieving interoperability for distributed EHR systems is expected to improve patient safety and care continuity, and therefore it improves the healthcare industry. However, achieving interoperability is challenging because of many standards, medical terminologies and ontologies, and different data formats. These formats make the integration of different systems an impossible process. If the hospital uses one standard to implement all of its medical systems, it will be no problem integrating them. However, hospitals usually depend on multiple standards and data formats to deliver different systems like hospital information systems, radiology information systems, laboratory information systems, etc. Semantic Web presents new technology for achieving EHRs interoperability. In this paper, we propose a novel ontological model to implement interoperability for distributed EHR environments. The proposed semantic ontology-based model can unify different EHRs data formats. In this study, We unify five different and popular healthcare data formats and standards. In addition, the framework could be extended straightforwardly to accept any other EHR data format. By implementing the proposed in real environments, we provide the physician with a single interface with a single terminology to query and interact with distributed healthcare systems that use different standards and data formats. This process is expected to help the physician to collect patient data from different systems quickly, completely, and correctly. The proposed ontological model has two stages. The first stage of the proposed converts each different input source to OWL ontology. In the second stage, it integrates all those ontologies into a merged crisp one. The integrated ontology includes 3753 axioms, 2606 logical axioms, 186 classes, 136 individuals, 126 datatype properties, and 257 object properties. We use SPARQL Protocol and RDF Query Language (SPARQL) and Description Logic (DL) queries to evaluate the output ontology. The obtained results ensure that the proposed framework helps physicians and specialists make a centralized point for all patients’ data. It could aggregate data with any heterogeneous structures with high precision.

Dalam keperawatan jiwa, data riwayat penyakit dan pengobatan sangat diperlukan dalam pemberian asuhan keperawatan. Kesulitan pasien gangguan jiwa untuk menceritakan riwayat kesehatan dan status kesehatannya menjadi kesulitan bagi perawat dalam pengumpulan data, sedangkan data tersebut sangat diperlukan untuk menegakkan diagnosa keperawatan. Untuk itu dibutuhkan pendokumentasian yang terstandar dengan format baku, akurat dan sederhana. Electronic Health Record (EHR) adalah salah satu teknologi pendokumentasian yang sudah terstandar dengan baik. Tujuannya untuk mengidentifikasi artikel penelitian tentang Penggunaaan Electronic Health Record (EHR). Metode tinjauan pustaka adalah untuk menganalisis artikel ilmiah dari 4 database yaitu BMC Health Service Research, BMC Medical Information & Decision Making, Ebscohost, dan Google Scholar. Kriteria inklusi Research Article, Tahun 2011-2021, Bahasa Inggris. Didapatkan 10 jurnal dengan menggunakan perumusan PICO. Teknik analisis artikel penelitian yaitu dengan format tabel yang berisi penulis, judul, tahun, metode (desain, sampel dan analisis), dan hasil. Berdasarkan 10 artikel penelitian yang diperoleh menunjukkan bahwa EHR yang sudah terintegrasi antar pelayanan kesehatan, membuat akses antar layanan kesehatan terbuka sehingga kualitas perawatan klien menjadi lebih baik. Semua artikel menunjukkan hasil selisih yang berbeda-beda.

We proposed architecture for sharing electronic health records. This work is based on an encryption mechanism to encrypt the health data, access control to ensure the privacy and confidentiality of health records. The proposed scheme has used a storage mechanism combining cloud and blockchain. The cloud server stores the encrypted health records, while the blockchain retains traceable log information and encryption keys. The proposal is an adequate solution to share the electronic health record securely. Our solution allows integrity, privacy, and confidentiality to ensure efficient protection of sharing electronic health records.

The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of “5 Vs”—variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.

The health insurance industry requires a secure data management architecture characterized by reliable information processing and fast response. Classical health insurance management system runs on a manually controlled centralized architecture which leverages a vulnerable single point failure and requires frequent human intervention. This makes the model prone to human errors and security attacks. The existing system also lacks the flexibility to choose appropriate health insurance policies for different consumers. To this end, we propose an Ethereum blockchain-based framework, ChainSure, to address this problem. With the power of TOPSIS and smart contracts, ChainSure provides an automated, tamperproof, transparent, scalable system that takes care of all the major functional blocks in a medical insurance environment. We have used the TOPSIS method in this proposed model to help the users to find an insurance policy that best suits their needs. ChainSure works in a decentralized environment and provides an interactive interface for the user. The proposed model is implemented on the Ethereum test network and its performance has been compared empirically with other state-of-art models. ChainSure is found to outperform others in terms of service integrity, latency and cost.

The potential of information technology has influenced the efficiency and quality of healthcare worldwide. Currently, several republics are incorporating electronic health records (EHRs). Due to reluctance of technological adaptation & implementational complexities, electronic health record systems are not in practice. Due to the emphasis on achieving general compatibility, users may perceive systems as being imposed and providing insufficient customizability, which may exacerbate issues in a setting of national implementation. EHS improves patient safety and confidentiality and ensures operative, effective, well-timed, reasonable, and patient-centred care, all of which substantially impact healthcare quality. Blockchain technology has been used by the EHS system, which supports web-based accessibility and availability. The difficulties of exchanging medical data can now be overcome by consumers using an infrastructure based on cloud computing. A variety of cryptographic approaches have been employed to encrypt and safeguard the data. This review paper aims to highlight the role and impact of blockchain in EHR. The proposed research describes cryptography methods, their classifications, and the challenges associated with EHR to identify gaps and countermeasures.

Abstract Background To describe an automated method for assessment of the plausibility of continuous variables collected in the electronic health record (EHR) data for real world evidence research use. Methods The most widely used approach in quality assessment (QA) for continuous variables is to detect the implausible numbers using prespecified thresholds. In augmentation to the thresholding method, we developed a score-based method that leverages the longitudinal characteristics of EHR data for detection of the observations inconsistent with the history of a patient. The method was applied to the height and weight data in the EHR from the Million Veteran Program Data from the Veteran’s Healthcare Administration (VHA). A validation study was also conducted. Results The receiver operating characteristic (ROC) metrics of the developed method outperforms the widely used thresholding method. It is also demonstrated that different quality assessment methods have a non-ignorable impact on the body mass index (BMI) classification calculated from height and weight data in the VHA’s database. Conclusions The score-based method enables automated and scaled detection of the problematic data points in health care big data while allowing the investigators to select the high-quality data based on their need. Leveraging the longitudinal characteristics in EHR will significantly improve the QA performance.

Background: The electronic health record (EHR), utilized to apply statistical methodology, assists provider decision-making, including during the care of chronic kidney disease (CKD) patients. When estimated glomerular filtration (eGFR) decreases, the rate of that change adds meaning to a patient's single eGFR and may represent severity of renal injury. Since the cumulative sum chart technique (CUSUM), often used in quality control and surveillance, continuously checks for change in a series of measurements, we selected this statistical tool to detect clinically relevant eGFR decreases and developed CUSUMGFR.Methods: In a retrospective analysis we applied an age adjusted CUSUMGFR, to signal identification of eventual ESKD patients prior to diagnosis date. When the patient signaled by reaching a specified threshold value, days from CUSUM signal date to ESKD diagnosis date (earliness days) were measured, along with the corresponding eGFR measurement at the signal.Results: Signaling occurred by CUSUMGFR on average 791 days (se = 12 days) prior to ESKD diagnosis date with sensitivity = 0.897, specificity = 0.877, and accuracy = .878. Mean days prior to ESKD diagnosis were significantly greater in Black patients (905 days) and patients with hypertension (852 days), diabetes (940 days), cardiovascular disease (1027 days), and hypercholesterolemia (971 days). Sensitivity and specificity did not vary by sociodemographic and clinical risk factors.Conclusions: CUSUMGFR correctly identified 30.6% of CKD patients destined for ESKD when eGFR was > 60 ml/min/1.73 m2 and signaled 12.3% of patients that did not go on to ESKD (though almost all went on to later-stage CKD). If utilized in an EHR, signaling patients could focus providers' efforts to slow or prevent progression to later stage CKD and ESKD. [ABSTRACT FROM AUTHOR]

Translational research on complex, multifactorial mental health disorders, such as bipolar disorder, major depressive disorder, schizophrenia, and substance use disorders requires databases with large-scale, harmonized, and integrated real-world and research data. The Munich Mental Health Biobank (MMHB) is a mental health-specific biobank that was established in 2019 to collect, store, connect, and supply such high-quality phenotypic data and biosamples from patients and study participants, including healthy controls, recruited at the Department of Psychiatry and Psychotherapy (DPP) and the Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital of the Ludwig-Maximilians-University (LMU), Munich, Germany. Participants are asked to complete a questionnaire that assesses sociodemographic and cross-diagnostic clinical information, provide blood samples, and grant access to their existing medical records. The generated data and biosamples are available to both academic and industry researchers. In this manuscript, we outline the workflow and infrastructure of the MMHB, describe the clinical characteristics and representativeness of the sample collected so far, and reveal future plans for expansion and application. As of 31 October 2021, the MMHB contains a continuously growing set of data from 578 patients and 104 healthy controls (46.37% women; median age, 38.31 years). The five most common mental health diagnoses in the MMHB are recurrent depressive disorder (38.78%; ICD-10: F33), alcohol-related disorders (19.88%; ICD-10: F10), schizophrenia (19.69%; ICD-10: F20), depressive episode (15.94%; ICD-10: F32), and personality disorders (13.78%; ICD-10: F60). Compared with the average patient treated at the recruiting hospitals, MMHB participants have significantly more mental health-related contacts, less severe symptoms, and a higher level of functioning. The distribution of diagnoses is also markedly different in MMHB participants compared with individuals who did not participate in the biobank. After establishing the necessary infrastructure and initiating recruitment, the major tasks for the next phase of the MMHB project are to improve the pace of participant enrollment, diversify the sociodemographic and diagnostic characteristics of the sample, and improve the utilization of real-world data generated in routine clinical practice. [ABSTRACT FROM AUTHOR]