Correction: Access to Electronic Personal Health Records Among Patients With Multiple Chronic Conditions: A Secondary Data Analysis.
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ABSTRACT:
SUBMITTER: Greenberg AJ
PROVIDER: S-EPMC9254044 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIn England, almost all general practices (GPs) have implemented GP online services such as electronic personal health records (ePHRs) that allow people to schedule appointments, request repeat prescriptions, and access parts of their medical records. The overall adoption rate of GP online services has been low, reaching just 28% in October 2019. In a previous study, Abd-Alrazaq et al adopted a model to assess the factors that influence patients' use of GP online services in England. According to the previous literature, the predictive power of the Abd-Alrazaq model could be improved by proposing new associations between the existing variables in the model.ObjectiveThis study aims to improve the predictive power of the Abd-Alrazaq model by proposing new relationships between the existing variables in the model.MethodsThe Abd-Alrazaq model was amended by proposing new direct, mediating, moderating, and moderated mediating effects. The amended model was examined using data from a previous study, which were collected by a cross-sectional survey of a convenience sample of 4 GPs in West Yorkshire, England. Structural equation modeling was used to examine the theoretical model and hypotheses.ResultsThe new model accounted for 53% of the variance in performance expectancy (PE), 76% of the variance in behavioral intention (BI), and 49% of the variance in use behavior (UB). In addition to the significant associations found in the previous study, this study found that social influence (SI) and facilitating conditions (FCs) are associated with PE directly and BI indirectly through PE. The association between BI and UB was stronger for younger women with higher levels of education, income, and internet access. The indirect effects of effort expectancy (EE), perceived privacy and security (PPS), and SI on BI were statistically stronger for women without internet access, patients with internet access, and patients without internet access, respectively. The indirect effect of PPS on BI was stronger for patients with college education or diploma than for those with secondary school education and lower, whereas the indirect effect of EE on BI was stronger for patients with secondary school education or lower than for those with college education or a diploma.ConclusionsThe predictive power of the Abd-Alrazaq model improved by virtue of new significant associations that were not examined before in the context of ePHRs. Further studies are required to validate the new model in different contexts and to improve its predictive power by proposing new variables. The influential factors found in this study should be considered to improve patients' use of ePHRs.
| S-EPMC7578819 | biostudies-literature
Project description:BackgroundPersonal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients' medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR.ObjectiveThis scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients' health and health care services.MethodsWe searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query.ResultsOf the 182 eligible papers, 37 (20%) provided sufficient information about patients' data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients' passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction.ConclusionsThe findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.
| S-EPMC9832357 | biostudies-literature
Project description: Not available
| S-EPMC8871105 | biostudies-literature
Project description:ObjectivesThe objective of this study was to assess the impact of electronic health records (EHRs) on health outcomes and care of displaced people with chronic health conditions and determine barriers and facilitators to EHR implementation in displaced populations.DesignA systematic review protocol was developed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Systematic Reviews.Data sourcesMEDLINE, Embase, PsycINFO, CINAHL, Health Technology Assessment, Epub Ahead of Print, In-Process and Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews was searched from inception to 12 April 2021.Eligibility criteria for selected studiesInclusion criteria were original research articles, case reports and descriptions of EHR implementation in populations of displaced people, refugees or asylum seekers with related chronic diseases. Grey literature, reviews and research articles unrelated to chronic diseases or the care of refugees or asylum populations were excluded. Studies were assessed for risk of bias using a modified Cochrane, Newcastle-Ottawa and Joanna Briggs Institute tools.Data extraction and synthesisTwo reviewers independently extracted data from each study using Covidence. Due to heterogeneity across study design and specific outcomes, a meta-analysis was not possible. An inductive thematic analysis was conducted using NVivo V.12 (QSR International, Melbourne, Australia). An inductive analysis was used in order to uncover patterns and themes in the experiences, general outcomes and perceptions of EHR implementation.ResultsA total of 32 studies across nine countries were included: 14 in refugee camps/settlements and 18 in asylum countries. Our analysis suggested that EHRs improve health outcomes for chronic diseases by increasing provider adherence to guidelines or treatment algorithms, monitoring of disease indicators, patient counselling and patient adherence. In asylum countries, EHRs resource allocation to direct clinical care and public health services, as well as screening efforts. EHR implementation was facilitated by their adaptability and ability to integrate into management systems. However, barriers to EHR development, deployment and data analysis were identified in refugee settings.ConclusionOur results suggest that well-designed and integrated EHRs can be a powerful tool to improve healthcare systems and chronic disease outcomes in refugee settings. However, attention should be paid to the common barriers and facilitating actions that we have identified such as utilising a user-centred design. By implementing adaptable EHR solutions, health systems can be strengthened, providers better supported and the health of refugees improved.
| S-EPMC9453995 | biostudies-literature
Project description:ObjectiveNo relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk.MethodsUsing data from a clinic-based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set (n = 1435) and tested the model performance in an independent validation set of MS patients (n = 186). This iterative process identified prior 1-year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor-intensive chart review is impractical. We pursued two-stage algorithm development: (1) L1 -regularized logistic regression (LASSO) to phenotype past 1-year relapse status from contemporaneous EHR data, (2) LASSO to predict future 1-year relapse risk using imputed prior 1-year relapse status and other algorithm-selected features.ResultsThe final model, comprising age, disease duration, and imputed prior 1-year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1-year relapse history. The predicted risk probability declined with disease duration and age.ConclusionOur novel machine-learning algorithm predicts 1-year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR-based two-stage approach of outcome prediction may have application to neurological disease beyond MS.
| S-EPMC8045951 | biostudies-literature
Project description:RATIONALE: Use of a web-based risk assessment tool may help improve screening rates for colorectal cancer.
PURPOSE: This randomized clinical trial is studying personal electronic health records to see how well they work in improving screening rates for colorectal cancer.
| 2082240 | ecrin-mdr-crc
Project description:BackgroundPassive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection.MethodsZip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.ResultsPotential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection.ConclusionsGI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection.
| S-EPMC3377951 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pone.0232346.].
| S-EPMC7514035 | biostudies-literature
Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:Many healthcare facilities enforce security on their electronic health records (EHRs) through a corrective mechanism: some staff nominally have almost unrestricted access to the records, but there is a strict ex post facto audit process for inappropriate accesses, i.e., accesses that violate the facility's security and privacy policies. This process is inefficient, as each suspicious access has to be reviewed by a security expert, and is purely retrospective, as it occurs after damage may have been incurred. This motivates automated approaches based on machine learning using historical data. Previous attempts at such a system have successfully applied supervised learning models to this end, such as SVMs and logistic regression. While providing benefits over manual auditing, these approaches ignore the identity of the users and patients involved in a record access. Therefore, they cannot exploit the fact that a patient whose record was previously involved in a violation has an increased risk of being involved in a future violation. Motivated by this, in this paper, we propose a collaborative filtering inspired approach to predicting inappropriate accesses. Our solution integrates both explicit and latent features for staff and patients, the latter acting as a personalized "finger-print" based on historical access patterns. The proposed method, when applied to real EHR access data from two tertiary hospitals and a file-access dataset from Amazon, shows not only significantly improved performance compared to existing methods, but also provides insights as to what indicates an inappropriate access.
| S-EPMC3967851 | biostudies-literature