Electronic Health Record Applications Research Articles

Hybrid Architectures Used in the Protection of Large Healthcare Records Based on Cloud and Blockchain Integration: A Review

The management of large medical files poses a critical challenge in the health sector, with conventional systems facing deficiencies in security, scalability, and efficiency. Blockchain ensures the immutability and traceability of medical records, while the cloud allows scalable and efficient storage. Together, they can transform the data management of electronic health record applications. The method used was the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to choose and select the relevant studies that contribute to this research, with special emphasis set on maintaining the integrity and security of the blockchain while tackling the potential and efficiency of cloud infrastructures. The study’s focus is to provide a comprehensive and insightful examination of the modern landscape concerning the integration of blockchain and cloud advances, highlighting the current challenges and building a solid foundation for future development. Furthermore, it is very important to increase the integration of blockchain security with the dynamic potential of cloud computing while guaranteeing information integrity and security remain uncompromised. In conclusion, this paper serves as an important resource for analysts, specialists, and partners looking to delve into and develop the integration of blockchain and cloud innovations.

Design of Interoperable Electronic Health Record (EHR) Application for Early Detection of Lung Diseases Using a Decision Support System by Expanding Deep Learning Techniques.

Electronic health records (EHRs) are live, digital patient records that provide a thorough overview of a person's complete health data. Electronic health records (EHRs) provide better healthcare decisions and evidence-based patient treatment and track patients' clinical development. The EHR offers a new range of opportunities for analyzing and contrasting exam findings and other data, creating a proper information management mechanism to boost effectiveness, quick resolutions, and identifications. The aim of this studywas to implement an interoperable EHR system to improve the quality of care through the decision support system for the identification of lung cancer in its early stages. The main objective of the proposed system was to develop an Android application for maintaining an EHR system and decision support system using deep learning for the early detection of diseases. The second objective was to study the early stages of lung disease to predict/detect it using a decision support system. To extract the EHR data of patients, an android application was developed. The android application helped in accumulating the data of each patient. The accumulated data were used to create a decision support system for the early prediction of lung cancer. To train, test, and validate the prediction of lung cancer, a few samples from the ready dataset and a few data from patients were collected. The valid data collection from patients included an age range of 40 to 70, and both male and female patients. In the process of experimentation, a total of 316 images were considered. The testing was done by considering the data set into 80:20 partitions. For the evaluation purpose, a manual classification was done for 3 different diseases, such as large cell carcinoma, adenocarcinoma, and squamous cell carcinoma diseases in lung cancer detection. The first model was tested for interoperability constraints of EHR with data collection and updations. When it comes to the disease detection system, lung cancer was predicted for large cell carcinoma, adenocarcinoma, and squamous cell carcinoma type by considering 80:20 training and testing ratios. Among the considered 336 images, the prediction of large cell carcinoma was less compared to adenocarcinoma and squamous cell carcinoma. The analysis also showed that large cell carcinoma occurred majorly in males due to smoking and was found as breast cancer in females. As the challenges are increasing daily in healthcare industries, a secure, interoperable EHR could help patients and doctors access patient data efficiently and effectively using an Android application. Therefore, a decision support system using a deep learning model was attempted and successfully used for disease detection. Early disease detection for lung cancer was evaluated, and the model achieved an accuracy of 93%. In future work, the integration of EHR data can be performed to detect various diseases early.

A national survey of digital health company experiences with electronic health record application programming interfaces.

This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs "without special effort" will be crucial to ensure future technology robustness and durability.

DESIGN AND DEVELOPMENT OF EHR SPECIFIC BLOCK CREATION AND POS CUSTOMIZATION USING HIERARCHICAL INTERDEPENDENCY APPROACH

Electronic Health Records (EHR) serve as a pivotal component in leveraging technological capabilities for processing, diagnosis, and informed decision-making in the medical domain. The sharing and endorsement of EHR by medical professionals based on case studies undergo manual procedures, leading to limitations in global accessibility to EHR and resolved case data, impeding expert analysis. In this study, the we propose an exclusive methodology tailored for the establishment and customization of blockchain networks dedicated to EHR applications. The approach centers around a hierarchical interdependency paradigm that governs the fine-tuning of blockchain units and the creation of Proof of Stake (PoS) structures. This technique's calibration is executed within a hospital's private network, ensuring the generalizability of resources within the EHR context. Additionally, the technique introduces deliberate delays in the integration of federated learning models within the blockchain framework, optimizing the alignment of EHR data. Validation of the proposed technique is conducted within the framework of AWS private-public servers, utilizing a dataset sourced from 17 hospitals within the MIMIC-III public EHR dataset. The hierarchical interdependency blockchain is further harmonized with a generalized EHR setup. The study exhaustively deliberates on the outcomes of this setup, demonstrating a discernible enhancement in the performance metrics of EHR blockchains.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review.

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

"Seeing the light in the shade of it": primary caregiver and youth perspectives on using an inpatient portal for pain care during hospitalization.

Studies from multiple countries report that most hospitalized children, especially the youngest and sickest, experience pain that is often severe yet inadequately treated. Evidence suggests this can lead to immediate and lifelong consequences affecting children, families, and communities. Partnership and shared decision-making by children, families, and clinicians is the ideal pediatric healthcare model and can improve care quality and safety, including pain care. A growing evidence base demonstrates that inpatient portals (electronic personal health record applications linked to hospital electronic medical or health records) can improve child and family engagement, outcomes, and satisfaction during hospitalization. This study examined the perspectives of caregivers of hospitalized children and of hospitalized youth about using an inpatient portal to support their engagement in pain care while in hospital. A qualitative descriptive study design was used and 20 participants (15 caregivers and 5 youth) with various painful conditions in one pediatric hospital participated in semistructured interviews. The authors applied a reflexive content analysis to the data and developed 3 broad categories: (1) connecting and sharing knowledge about pain, (2) user-centred designs, and (3) preserving roles. These findings outlined caregiver and youth recommendations for portal configurations that deeply engage and empower children and families in pain care through multidirectional knowledge sharing, supporting caregiver and clinicians' roles without burdening, or replacing human interaction implicit in family-centered pain care. Further research should measure the impact of portals on pain-related outcomes and explore the perspectives of clinicians.

Effectiveness of a cardiovascular health EHR application for cancer survivors in community oncology practice: Results from WF-1804CD.

LBA12007 Background: Practice guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. The Automated Heart-Health Assessment study (AH-HA: WF-1804CD, NCT# 03935282) evaluated a novel electronic health record (EHR) clinical decision support tool based on American Heart Association Simple 7 cardiovascular health (CVH) factors to promote provider-patient CVH discussions in outpatient oncology. Methods: This clinic-randomized trial (R01CA226078), coordinated by the Wake Forest NCORP Research Base (UG1CA189824), randomized NCORP oncology practices using the Epic EHR to the AH-HA tool or usual care and enrolled survivors receiving routine care at least 6 months post-curative cancer treatment. The tool rendered an interactive display of risk factors, automatically populated from the EHR, alongside a tab indicating receipt of cancer treatments with cardiotoxic potential. Survivors at AH-HA sites had tool access during a routine visit; survivors at usual care sites did not. Immediately post-visit, each survivor was asked about counseling on 7 CVH factors [body mass index, physical activity, diet, smoking status, blood pressure, cholesterol, and glucose]. The primary endpoint is survivor-reported discussion of non-ideal or missing CVH factors; with 8 randomized practices (ICC=0.03), the study design had 82% power to detect a difference of 20% reported discussions in usual care versus 40% in AH-HA. Additional clinical endpoints were documentation of CVH discussions, referrals to primary care and cardiology, and change in CVH factors over 12 months (ongoing). A mixed effects logistic regression model assessed the effect of AH-HA on CVH discussions between the arms, with study arm as a fixed effect and practice as a random effect. Results: 5 usual care and 4 AH-HA practices enrolled 645 survivors from 10/1/2020- 2/28/2023. The majority had breast cancer (82%; 8% endometrial, 5% colorectal, 5% prostate, lymphoma or multiple types). Most survivors were female (96%; 84% White/non-Hispanic, 8% Black; 3% Hispanic; mean age= 62 yrs and median time since diagnosis=3.6 yrs). 18 participants (3%) did not complete necessary assessments, leaving 627 evaluable. 87% of providers (n=15) rated the tool utility and ease of use positively. Conclusions: The AH-HA tool was acceptable and effective at promoting CVH discussions during routine follow-up care for survivors and referrals to primary care. Clinical trial information: NCT03935282 . [Table: see text]

Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.

There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care. De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals. Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.

Use of Complementary Pain Management Strategies in Postoperative Cardiac Surgical Patients.

Complementary pain management strategies are effective at reducing pain in postsurgical patients. Cardiac nurses at a large academic hospital reported inconsistent awareness of patient opioid utilization and poor implementation of complementary pain management strategies. A pre/post-quality improvement project was conducted on 2 inpatient cardiac units. Outcomes included nursing staff's perceived knowledge, confidence, and use of complementary pain management strategies and knowledge of patient postsurgical opioid utilization through calculation of morphine milligram equivalence (MME). A comprehensive education program was implemented that included increased patient access to pain management resources, nurse education about complementary pain management strategies, and nurse education and access to MME calculations using a custom electronic health record application. Nursing staff's perceived knowledge, confidence, and use of complementary pain techniques increased. Patient opioid utilization findings were inconclusive. Educational programs about complementary pain management offer promise to improve cardiac postsurgical patient care.

Addressing the Challenges of Penicillin Allergy Delabeling With Electronic Health Records and Mobile Applications

Allergy labels are common, often incorrect, and potentially harmful. There are many opportunities for clinical decision support (CDS) tools integrated in the electronic health record (EHR) and mobile apps to address the challenges with drug allergy management, including penicillin allergy delabeling (PADL). Effective delabeling solutions must consider multidisciplinary clinical workflow and multistep processes, including documentation, assessment, plan (eg, allergy testing and referral), record update, drug allergy alert management, and allergy reconciliation over time. Developing a systematic infrastructure to manage allergies across the EHR is critical to improve the accuracy and completeness of a patient's allergy and avoid inadvertently relabeling. Improving the appropriateness and relevancy of drug allergy alerts is important to reduce alert fatigue. Using alerts to guide clinicians on appropriate antibiotic use may reduce unnecessary β-lactam avoidance. To date, EHR CDS tools have facilitated non-allergists to provide PADL at the point of care. A mobile app was shown to support PADL and provide specialist support and education. Future research is needed to standardize, integrate, and evaluate innovative CDS tools in the EHR to demonstrate patient safety and clinical utility and facilitate wider adoption.

Electronic Health Record Dalam Pelaksanaan Handover Keperawatan Di Rumah Sakit Jakarta

ABSTRACT
 Background: Electronic health record is an electronic record of health-related information that includes patient information such as biodata, medical history, allergy history, test results, and all important care information used in the implementation of Nursing Handover.
 Objective: This study aims to find out more about the experience of nurses in applying electronic health records during handovers in hospitals.
 Design: This study uses a qualitative method with a phenomenological approach using the colaizzi method. Sampling using purposive sampling with the number of participants 8 people. Data collection by means of focus group discussions.
 Results: This study resulted in four main themes: (1) Nurses' understanding of handovers using an electronic format, (2) Advantages of using electronic health records, namely convenience, clarity, security and minimizing errors, (3) Negative and positive experiences of using electronic health records ( 4) Electronic health record constraints in handover.
 Conclusion: The use of electronic health record applications at the time of handover can minimize communication errors and can improve patient safety in hospitals.
 Keywords:
 Electronic Health Records; Handovers; Nurse; Qualitative

Positive Patient Postoperative Outcomes with Pharmacotherapy: A Narrative Review including Perioperative-Specialty Pharmacist Interviews.

The influence of pharmacotherapy regimens on surgical patient outcomes is increasingly appreciated in the era of enhanced recovery protocols and institutional focus on reducing postoperative complications. Specifics related to medication selection, dosing, frequency of administration, and duration of therapy are evolving to optimize pharmacotherapeutic regimens for many enhanced recovery protocolized elements. This review provides a summary of recent pharmacotherapeutic strategies, including those configured within electronic health record (EHR) applications and functionalities, that are associated with the minimization of the frequency and severity of postoperative complications (POCs), shortened hospital length of stay (LOS), reduced readmission rates, and cost or revenue impacts. Further, it will highlight preventive pharmacotherapy regimens that are correlated with improved patient preparation, especially those related to surgical site infection (SSI), venous thromboembolism (VTE), nausea and vomiting (PONV), postoperative ileus (POI), and emergence delirium (PoD) as well as less commonly encountered POCs such as acute kidney injury (AKI) and atrial fibrillation (AF). The importance of interprofessional collaboration in all periprocedural phases, focusing on medication management through shared responsibilities for drug therapy outcomes, will be emphasized. Finally, examples of collaborative care through shared mental models of drug stewardship and non-medical practice agreements to improve operative throughput, reduce operative stress, and increase patient satisfaction are illustrated.

Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.

Implementation of Electronic Psychosocial Screening Among Caregivers in Pediatric Oncology

This study aimed to evaluate the reach and implementation of an electronic psychosocial screening program among caregivers of pediatric oncology patients, as well as characterize caregiver distress. Participants (N = 2,013) included caregivers of patients age 0-17.99 years presenting across 9,280 outpatient oncology visits (median = 2; range = 1-52) from September 2018 to June 2019. At check-ins, caregivers electronically completed the pediatric distress thermometer via a patient-facing electronic health record (EHR) application. Caregiver distress ratings ≥ 8 triggered electronic alerts to medical teams to refer for social work support at point of care. Patient clinical and demographic differences in reach and fidelity were evaluated using univariate chi-square and t-tests. Caregivers reporting high distress were compared with caregivers without reports of high distress using univariate and multivariable logistic regression. The e-screening program was able to reach a caregiver for nearly all children seen during the study period, with 95.5% (1,923/2,013) of patients having a caregiver-completed pediatric distress thermometer. On screeners where caregivers reported high distress, medical teams made appropriate referrals to social work 95.5% (471/493) of the time. Overall, 16.9% (325/1,923) of caregivers ever indicated high distress (score ≥ 8), with caregivers of newly diagnosed (odds ratio = 3.16; 95% CI, 2.12 to 4.71) and on-therapy (odds ratio = 2.81; 95% CI, 2.11 to 3.76) patients being more likely to report high distress, compared with those who were off-treatment for the entire study. Leveraging EHR technology to provide evidence-based psychosocial screening can aid in successfully reaching a significant proportion of caregivers of pediatric oncology patients to identify and respond to ongoing psychosocial distress.

Using a Patient Portal to Increase Enrollment in a Newborn Screening Research Study: Observational Study.

BackgroundMany research studies fail to enroll enough research participants. Patient-facing electronic health record applications, known as patient portals, may be used to send research invitations to eligible patients.ObjectiveThe first aim was to determine if receipt of a patient portal research recruitment invitation was associated with enrollment in a large ongoing study of newborns (Early Check). The second aim was to determine if there were differences in opening the patient portal research recruitment invitation and study enrollment by race and ethnicity, age, or rural/urban home address.MethodsWe used a computable phenotype and queried the health care system’s clinical data warehouse to identify women whose newborns would likely be eligible. Research recruitment invitations were sent through the women’s patient portals. We conducted logistic regressions to test whether women enrolled their newborns after receipt of a patient portal invitation and whether there were differences by race and ethnicity, age, and rural/urban home address.ResultsResearch recruitment invitations were sent to 4510 women not yet enrolled through their patient portals between November 22, 2019, through March 5, 2020. Among women who received a patient portal invitation, 3.6% (161/4510) enrolled their newborns within 27 days. The odds of enrolling among women who opened the invitation was nearly 9 times the odds of enrolling among women who did not open their invitation (SE 3.24, OR 8.86, 95% CI 4.33-18.13; P<.001). On average, it took 3.92 days for women to enroll their newborn in the study, with 64% (97/161) enrolling their newborn within 1 day of opening the invitation. There were disparities by race and urbanicity in enrollment in the study after receipt of a patient portal research invitation but not by age. Black women were less likely to enroll their newborns than White women (SE 0.09, OR 0.29, 95% CI 0.16-0.55; P<.001), and women in urban zip codes were more likely to enroll their newborns than women in rural zip codes (SE 0.97, OR 3.03, 95% CI 1.62-5.67; P=.001). Black women (SE 0.05, OR 0.67, 95% CI 0.57-0.78; P<.001) and Hispanic women (SE 0.07, OR 0.73, 95% CI 0.60-0.89; P=.002) were less likely to open the research invitation compared to White women.ConclusionsPatient portals are an effective way to recruit participants for research studies, but there are substantial racial and ethnic disparities and disparities by urban/rural status in the use of patient portals, the opening of a patient portal invitation, and enrollment in the study.Trial RegistrationClinicalTrials.gov NCT03655223; https://clinicaltrials.gov/ct2/show/NCT03655223

E-health: Evaluation of the implementation of the Personal Electronic Health Record by family doctors in Greece

Introduction&#x0D; Ε-health (eH) is a basic component of current health care provision. The cornerstone of eH is the electronic health record (EHR) system.&#x0D; Material and methods&#x0D; The purpose of this survey is to assess the Greek EHR application by Greek family doctors, taking into consideration the brief period of EHR implementation in Greece. We adapted a preexisting questionnaire, with permission, translating and standardizing it in Greek. The questionnaire focused on computer use capability, EHR usage, and efficiency in daily clinical practice, user satisfaction and global user evaluation of the application. 175 family doctors filled out the questionnaire in February 2019.&#x0D; Results&#x0D; Low usage rates, minimal facilitation in daily practice, competency issues, and moderate user satisfaction were among our findings.&#x0D; Conclusions&#x0D; Greek EHR has several problems and inadequacy areas. Reversing these problems and optimizing the application, then reevaluating the results after a wider period of implementation is important.

Design, Implementation, Utilization, and Sustainability of a Fast Healthcare Interoperability Resources-Based Inpatient Rounding List.

We designed and implemented an application programming interface (API)-based electronic health record (EHR)-integrated rounding list and evaluated acceptability, clinician satisfaction, information accuracy, and efficiency related to the application. We developed and integrated an application, employing iterative design techniques with user feedback. EHR and application user action logs, as well as hospital safety reports, were evaluated. Rounding preparation characteristics were obtained through surveys before and after application integration. To evaluate usability, inpatient providers, including residents, fellows, and attendings were surveyed 2 weeks prior to and 6 months after enterprise-wide EHR application integration. Our primary outcome was provider time savings measured by user action logs; secondary outcomes include provider satisfaction. The application was widely adopted by inpatient providers, with more than 69% of all inpatients queried by the application within 6 months of deployment. Application utilization was sustained throughout the study period with 79% (interquartile range [IQR]: 76, 82) of enterprise-wide unique patients accessed per weekday. EHR action logs showed application users spent -3.24 minutes per day (95% confidence interval [CI]: -6.8, 0.33), p = 0.07 within the EHR compared with nonusers. Median self-reported chart review time for attendings decreased from 30 minutes (IQR: 15, 60) to 20 minutes (IQR: 10, 45) after application integration (p = 0.04). Self-reported sign-out preparation time decreased by a median of 5 minutes (p < 0.01), and providers were better prepared for hand-offs (p = 0.02). There were no increased safety reports during the study period. This study demonstrates successful integration of a rounding application within a commercial EHR using APIs. We demonstrate increasing both provider-reported satisfaction and time savings. Rounding lists provided more accurate and timely information for rounds. Application usage was sustained across multiple specialties at 42 months. Other application designers should consider data density, optimization of provider workflows, and using real-time data transfer using novel tools when designing an application.

Considering non-hospital data in clinical informatics use cases, a review of the National Emergency Medical Services Information System (NEMSIS)

BackgroundThe National Emergency Medical Services (EMS) Information System (NEMSIS) Technical Assistance Center (TAC) collects and curates EMS activation level records for the United States. Originated as an outcomes assessment and service comparison tool, NEMSIS may have other high value clinical and public health uses. MethodsThis study acquired a 100% activation level public dataset for 2019 from NEMSIS TAC and assessed item response quantities. Subsumption of NEMSIS terms within other controlled clinical vocabularies was also considered. ResultsNone of the assessed terminologies (LOINC, ICD10-CM, SNOMED-CT) could describe meaningful volumes of NEMSIS item response codes. The 2019 activation year dataset included 36,525 non-date/time or calculated distinct item responses for 43 activation descriptive items. Said item responses yielded 2,101,844,053 activation distinct non-blank responses. Several NEMSIS item responses had high clinical and public health value. ConclusionsNEMSIS can support multiple public health use cases in addition to EMS outcomes assessment. A comprehensive custom value set is appropriate to integrate NEMSIS item response codes into controlled terminologies, FHIR or hospital Electronic Health Record applications.

Privacy and Utility-Assisted Data Protection Strategy for Secure Data Sharing and Retrieval in Cloud System

ABSTRACT The outsourcing of Electronic Health Records (EHR) on cloud infrastructures has enabled medical data sharing among several healthcare applications. The blockchain offers security by authenticating users with encryption methods. The collaboration with the cloud provides better management but poses threats to the privacy of the patient. This paper devises a novel blockchain-assisted framework for effective data sharing and retrieval using cloud platforms. Here, the data protection model is devised in EHR application for secure transmission. The entities in the cloud platform include data user, data owner, smart agreement, transactional blockchain, and Inter-Planetary File System (IPFS). Here, the data owner includes a data protection model to secure EHR in which secured EHR is transferred to IPFS before sharing with the data user. The data protection is done by preserving data privacy using Tracy-Singh product and proposed Conditional Autoregressive Value at risk (CAViaR)-based Bird swarm algorithm (CAViaR-based BSA) combination of BSA and CAViaR for generating optimal privacy-preserving coefficients. The objective function is newly devised considering privacy and utility. The proposed CAViaR-based BSA outperformed other methods with minimal responsiveness of 251.339 s, maximal genuine user detection of 32.451%, maximal privacy of 96.5%, and minimal information loss of 3.5%.

Using Electronic Health Records to Mitigate Workplace Burnout Among Clinicians During the COVID-19 Pandemic: Field Study in Iran.

BackgroundThe COVID-19 pandemic spread worldwide in 2020. Notably, in the countries dealing with massive casualties, clinicians have worked in new conditions characterized by a heavy workload and a high risk of being infected. The issue of clinician burnout during the pandemic has attracted considerable attention in health care research. Electronic health records (EHRs) provide health care workers with several features to meet a health system’s clinical needs.ObjectiveWe aim to examine how the use of EHR features affects the burnout of clinicians working in hospitals that have special wards for confirmed COVID-19 cases.MethodsUsing an online survey, we collected data from 368 physicians, physician assistants, and nurses working in six hospitals that have implemented EHRs in the city of Tehran in Iran. We used logistic regression to assess the association between burnout and awareness of EHR features, EHR system usability, concerns about COVID-19, technology solutions, hospital technology interventions, hospital preparedness, and professional efficacy adjusted for demographic and practice characteristics.ResultsThe primary outcome of our study was self-reported burnout during the COVID-19 pandemic. Of the 368 respondents, 36% (n=134) reported having at least one symptom of burnout. Participants indicated that the leading cause of EHR-related stress is inadequate training for using technology (n=159, 43%), followed by having less face-to-face time with patients (n=140, 38%). Positive perceptions about the EHR’s ease of use were associated with lower odds of burnout symptoms. More interventions, such as clear communication of regulations; transparency in policies, expectations, and goals regarding the use of technology in the clinical workflow; and hospital preparedness to cope with the challenges of the pandemic, were associated with lower odds of burnout.ConclusionsThe use of EHR applications, hospital pandemic preparation programs, and transparent technology-related policies and procedures throughout the epidemic can be substantial mitigators of technology-based stress and clinician burnout. Hospitals will then be better positioned to devise or modify technology-related policies and procedures to support physicians’ and nurses’ well-being during the COVID-19 pandemic. Training programs, transparency in communications of regulations, and developing a clear channel for informing clinicians of changes in policies may help reduce burnout symptoms among physicians and nurses during a pandemic. Providing easily accessible mentorship through teleconsultation and 24-hour available information technology support may also help to mitigate the odds of burnout.