Making the Case for Electronic Health Records: A Report From ASCO's EHR Symposium.
Ontology highlight
ABSTRACT:
SUBMITTER: Wolfe TE
PROVIDER: S-EPMC2793941 | biostudies-literature | 2008 Jan
REPOSITORIES: biostudies-literature
Publications
Making the Case for Electronic Health Records: A Report From ASCO's EHR Symposium.
Journal of oncology practice 20080101 1
Similar Datasets
Project description:IntroductionEffective electronic health record (EHR)-based training interventions facilitate improved EHR use for healthcare providers. One such training intervention is simulation-based training that emphasises learning actual tasks through experimentation in a risk-free environment without negative patient outcomes. EHR-specific simulation-based training can be employed to improve EHR use, thereby enhancing healthcare providers' skills and behaviours. Despite the potential advantages of this type of training, no study has identified and mapped the available evidence. To fill that gap, this scoping review will synthesise the current state of literature on EHR simulation-based training.Methods and analysisThe Arksey and O'Malley methodological framework will be employed. Three databases (PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature) will be searched for published articles. ProQuest and Google Scholar will be searched to identify unpublished articles. Databases will be searched from inception to 29 January 2020. Only articles written in English, randomised control trials, cohort studies, cross-sectional studies and case-control studies will be considered for inclusion. Two reviewers will independently screen titles and abstracts against inclusion and exclusion criteria. Then, they will review full texts to determine articles for final inclusion. Citation chaining will be conducted to manually screen references of all included studies to identify additional studies not found by the search. A data abstraction form with relevant characteristics will be developed to help address the research question. Descriptive numerical analysis will be used to describe characteristics of included studies. Based on the extracted data, research evidence of EHR simulation-based training will be synthesised.Ethics and disseminationSince no primary data will be collected, there will be no formal ethical review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.
| S-EPMC7422637 | biostudies-literature
Project description:Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (<3% accuracy difference for the models trained on them) while yielding almost-ideal performance in practical privacy metrics.
| S-EPMC10421926 | biostudies-literature
Project description:ObjectiveWe assessed changes in the percentage of providers with positive perceptions of electronic health record (EHR) benefit before and after transition from a local basic to a commercial comprehensive EHR.MethodsChanges in the percentage of providers with positive perceptions of EHR benefit were captured via a survey of academic health care providers before (baseline) and at 6-12 months (short term) and 12-24 months (long term) after the transition. We analyzed 32 items for the overall group and by practice setting, provider age, and specialty using separate multivariable-adjusted random effects logistic regression models.ResultsA total of 223 providers completed all 3 surveys (30% response rate): 85.6% had outpatient practices, 56.5% were >45 years old, and 23.8% were primary care providers. The percentage of providers with positive perceptions significantly increased from baseline to long-term follow-up for patient communication, hospital transitions - access to clinical information, preventive care delivery, preventive care prompt, preventive lab prompt, satisfaction with system reliability, and sharing medical information (P < .05 for each). The percentage of providers with positive perceptions significantly decreased over time for overall satisfaction, productivity, better patient care, clinical decision quality, easy access to patient information, monitoring patients, more time for patients, coordination of care, computer access, adequate resources, and satisfaction with ease of use (P < 0.05 for each). Results varied by subgroup.ConclusionAfter a transition to a commercial comprehensive EHR, items with significant increases and significant decreases in the percentage of providers with positive perceptions of EHR benefit were identified, overall and by subgroup.
| S-EPMC7646958 | biostudies-literature
Project description:This study assessed the factors militating against the effective implementation of electronic health records (EHR) in Nigeria, the computerization of patients' health records with a lot of benefits including improved patients' satisfaction, improved care processes, reduction of patients' waiting time, and medication errors. Despite these benefits, healthcare organizations are slow to adopt the EHR system. Therefore, the study assessed the factors militating against the effective implementation of the EHR system, the level of awareness of EHR, and the utilization of electronic health records; it also investigated the factors militating against the effective implementation of EHR. This is a descriptive cross-sectional study conducted among members of staff of the University of Medical Sciences Teaching Hospital (UNIMEDTH). Purposive sampling was adopted to select the study participants, and a structured questionnaire was used for data collection. Statistical Product and Service Solutions (SPSS) version 27 was used for data analysis, R and Microsoft Excel were used for data visualization. Findings revealed that the respondents had a high level of awareness of EHR with an average mean of 1.29. The study revealed a low utilization rate for EHR (mean 3.01, SD 0.115) and data storage (mean 3.00, SD 0.231), indicating major problems with EHR implementation. Financial limitations (mean 2.63, SD 0.486) and insufficient ICT resources (mean 2.62, SD 0.516) are the main challenges. The regression analysis revealed that EHR utilization is highly impacted by awareness (F = 4.26, p < 0.008), accounting for 15.1% of the variance. Governmental assistance and financial constraints showed a statistically significant negative correlation (r=-0.04, p < 0.05), while inadequate internet connectivity and epileptic power supply showed a statistically significant positive association (r = 0.1, p < 0.05). The results show that, more government support is needed to alleviate financial constraints, and a stable power supply may enhance internet accessibility. The study concluded that epileptic power supply, poor internet connectivity, financial constraints, and other factors mentioned in the study militate against the effective implementation of electronic health records. It was recommended that the management should implement electronic health records effectively for efficient and effective health care delivery.
| S-EPMC11682428 | biostudies-literature
Project description:Adolescent depression, has been identified as one of the important risk factors for adolescent safety. The American Academy of Pediatrics (AAP) recommends screening the adolescent population for depression with a validated screening tool at least once a year. Given the time constraints in primary care, many physicians tend to rely more on clinical questioning to screen depression.This has the potential to miss many adolescents who may have mild to moderate depression which may prove detrimental to their emotional and physical health. Quality measures had consistently indicated that the validated adolescent depression screening rate in our two pediatric clinics was 10-15% in the past two years starting from 2012. There was a need to increase our screening rate for adolescent depression with a validated questionnaire. The stakeholders identified were physicians, nurses and the health information team (HIT). The Patient Health Questionnaire-2 (PHQ-2) is a standardized tool and serves as a good first step rapid screening of the population. A decision was made to implement the use of PHQ-2 to all the adolescents aged 11-21. A clinic flow protocol was developed. As the patient checks in, there will be a computer pop-up reminding nurses to administer the PHQ-2. The PHQ-2 self-scores in the Electronic Health Record (EHR) and if the score is three or more the nurses would automatically administer the PHQ-9 which is also embedded and self-scored in the EHR. After 12 months of implementing this project with four PDSA cycles, the adolescent depression-screening rate improved from 10-15% from the previous two years to 65% (six month period) and 82% at the end of the 12 month period. The rate of referral to mental health services had also increased in the same time period compared to the previous years. In conclusion, screening for adolescent depression with a brief validated tool in a busy primary care office is possible with the help of the EHR.
| S-EPMC4693069 | biostudies-literature
Project description:BackgroundReporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS).AimInvestigate the feasibility of automating the current, manual, HCAI reporting using linked electronic health records (linked-EHR), and assess its level of accuracy.MethodsAll data previously submitted through the HCAI-DCS by the Oxford University Hospitals infection control (IC) team for methicillin-resistant and methicillin-susceptible Staphylococcus aureus (MRSA, MSSA), Clostridium difficile, and Escherichia coli, through March 2017 were downloaded and compared to outputs created from linked-EHR, with detailed comparisons between 2013-2017.FindingsTotal MRSA, MSSA, E. coli and C. difficile cases entered by the IC team vs linked-EHR were 428 vs 432, 795 vs 816, 2454 vs 2450 and 3365 vs 3393 respectively. From 2013-2017, most discrepancies (32/37 (86%)) were likely due to IC recording errors. Patient and specimen identifiers were completed for >98% of cases by both methods, with very high agreement (>97%). Fields relating to the patient at the time the specimen was taken were complete to a similarly high level (>99% IC, >97% linked-EHR), and agreement was fairly good (>80%) except for the main and treatment specialties (57% and 54% respectively) and the patient category (55%). Optional, organism-specific data-fields were less complete, by both methods. Where comparisons were possible, agreement was reasonably high (mostly 70-90%).ConclusionBasic factual information, such as demographic data, is almost-certainly better automated, and many other data fields can potentially be populated successfully from linked-EHR. Manual data collection is time-consuming and inefficient; automated electronic data collection would leave healthcare professionals free to focus on clinical rather than administrative work.
| S-EPMC6221334 | biostudies-literature
Project description:ObjectivesElectronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations. However, this process is time-consuming and expensive. The objective of this paper is to develop an outcome-dependent sampling approach for designing manual chart review, where EHR-derived phenotypes can be used to guide the selection of charts to be reviewed in order to maximize statistical efficiency in the subsequent estimation of risk factor associations.Materials and methodsAfter applying outcome-dependent sampling, an augmented estimator can be constructed by optimally combining the chart-reviewed phenotypes from the selected patients with the error-prone EHR-derived phenotype. We conducted simulation studies to evaluate the proposed method and applied our method to data on colon cancer recurrence in a cohort of patients treated for a primary colon cancer in the Kaiser Permanente Washington (KPW) healthcare system.ResultsSimulations verify the coverage probability of the proposed method and show that, when disease prevalence is less than 30%, the proposed method has smaller variance than an existing method where the validation set for chart review is uniformly sampled. In addition, from design perspective, the proposed method is able to achieve the same statistical power with 50% fewer charts to be validated than the uniform sampling method, thus, leading to a substantial efficiency gain in chart review. These findings were also confirmed by the application of the competing methods to the KPW colon cancer data.DiscussionOur simulation studies and analysis of data from KPW demonstrate that, compared to an existing uniform sampling method, the proposed outcome-dependent method can lead to a more efficient chart review sampling design and unbiased association estimates with higher statistical efficiency.ConclusionThe proposed method not only optimally combines phenotypes from chart review with EHR-derived phenotypes but also suggests an efficient design for conducting chart review, with the goal of improving the efficiency of estimated risk factor associations using EHR data.
| S-EPMC8714286 | biostudies-literature
Project description:Patient-generated health data (PGHD), collected from mobile apps and devices, represents an opportunity for remote patient monitoring and timely interventions to prevent acute exacerbations of chronic illness-if data are seen and shared by care teams. This case report describes the technical aspects of integrating data from a popular smartphone platform to a commonly used EHR vendor and explores the challenges and potential of this approach for disease management. Consented subjects using the Asthma Health app (built on Apple's ResearchKit platform) were able to share data on inhaler usage and peak expiratory flow rate (PEFR) with a local pulmonologist who ordered this data on Epic's EHR. For users who had installed and activated Epic's patient portal (MyChart) on their iPhone and enabled sharing of health data between apps via HealthKit, the pulmonologist could review PGHD and, if necessary, make recommendations. Four patients agreed to share data with their pulmonologist, though only two patients submitted more than one data point across the 4.5-month trial period. One of these patients submitted 101 PEFR readings across 65 days; another submitted 24 PEFR and inhaler usage readings across 66 days. PEFR for both patients fell within predefined physiologic parameters, except once where a low threshold notification was sent to the pulmonologist, who responded with a telephone discussion and new e-prescription to address symptoms. This research describes the technical considerations and implementation challenges of using commonly available frameworks for sharing PGHD, for the purpose of remote monitoring to support timely care interventions.
| S-EPMC6550195 | biostudies-literature
Project description:The privacy of patients and the security of their information is the most imperative barrier to entry when considering the adoption of electronic health records in the healthcare industry. Considering current legal regulations, this review seeks to analyze and discuss prominent security techniques for healthcare organizations seeking to adopt a secure electronic health records system. Additionally, the researchers sought to establish a foundation for further research for security in the healthcare industry. The researchers utilized the Texas State University Library to gain access to three online databases: PubMed (MEDLINE), CINAHL, and ProQuest Nursing and Allied Health Source. These sources were used to conduct searches on literature concerning security of electronic health records containing several inclusion and exclusion criteria. Researchers collected and analyzed 25 journals and reviews discussing security of electronic health records, 20 of which mentioned specific security methods and techniques. The most frequently mentioned security measures and techniques are categorized into three themes: administrative, physical, and technical safeguards. The sensitive nature of the information contained within electronic health records has prompted the need for advanced security techniques that are able to put these worries at ease. It is imperative for security techniques to cover the vast threats that are present across the three pillars of healthcare.
| S-EPMC5522514 | biostudies-other
Project description:Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction of repomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve for the prediction of pathological outcome without and with transfer learning are 0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.
| S-EPMC10439251 | biostudies-literature