scholarly journals Online electronic healthcare records: Comparing the views of cancer patients and others

2020 ◽  
Vol 26 (4) ◽  
pp. 2915-2929
Author(s):  
Hanife Rexhepi ◽  
Jonas Moll ◽  
Isto Huvila
Keyword(s):  
Electronic Health Records ◽  
Cancer Patients ◽  
Patient Survey ◽  
General Interest ◽  
Health Records ◽  
Positive Effects ◽  
Health History ◽  
Survey Response Rate ◽  
National Patient ◽  
Electronic Health

This study investigates differences in attitudes towards, and experiences with, online electronic health records between cancer patients and patients with other conditions, highlighting what is characteristic to cancer patients. A national patient survey on online access to electronic health records was conducted, where cancer patients were compared with all other respondents. Overall, 2587 patients completed the survey (response rate 0.61%). A total of 347 respondents (13.4%) indicated that they suffered from cancer. Results showed that cancer patients are less likely than other patients to use online electronic health records due to general interest (p < 0.001), but more likely for getting an overview of their health history (p = 0.001) and to prepare for visits (p < 0.001). Moreover, cancer patients rate benefits of accessing their electronic health records online higher than other patients and see larger positive effects regarding improved communication with and involvement in healthcare.

scholarly journals Patients’ Experiences of Accessing Their Electronic Health Records: National Patient Survey in Sweden

2018 ◽  
Vol 20 (11) ◽  
pp. e278 ◽  
Author(s):  
Jonas Moll ◽  
Hanife Rexhepi ◽  
Åsa Cajander ◽  
Christiane Grünloh ◽  
Isto Huvila ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Patient Survey ◽  
Health Records ◽  
National Patient ◽  
Electronic Health

scholarly journals Patients’ Experiences of Accessing Their Electronic Health Records: National Patient Survey in Sweden (Preprint)

2017 ◽  
Author(s):  
Jonas Moll ◽  
Hanife Rexhepi ◽  
Åsa Cajander ◽  
Christiane Grünloh ◽  
Isto Huvila ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Care Professionals ◽  
Patient Survey ◽  
Patient Portal ◽  
Health Records ◽  
Web Based ◽  
Number Of Patients ◽  
National Patient ◽  
Electronic Health

BACKGROUND Internationally, there is a movement toward providing patients a Web-based access to their electronic health records (EHRs). In Sweden, Region Uppsala was the first to introduce patient-accessible EHRs (PAEHRs) in 2012. By the summer of 2016, 17 of 21 county councils had given citizens Web-based access to their medical information. Studies on the effect of PAEHRs on the work environment of health care professionals have been conducted, but up until now, few extensive studies have been conducted regarding patients’ experiences of using PAEHRs in Sweden or Europe, more generally. OBJECTIVE The objective of our study was to investigate patients’ experiences of accessing their EHRs through the Swedish national patient portal. In this study, we have focused on describing user characteristics, usage, and attitudes toward the system. METHODS A national patient survey was designed, based on previous interview and survey studies with patients and health care professionals. Data were collected during a 5-month period in 2016. The survey was made available through the PAEHR system, called Journalen, in Sweden. The total number of patients that logged in and could access the survey during the study period was 423,141. In addition to descriptive statistics reporting response frequencies on Likert scale questions, Mann-Whitney tests, Kruskal-Wallis tests, and chi-square tests were used to compare answers between different county councils as well as between respondents working in health care and all other respondents. RESULTS Overall, 2587 users completed the survey with a response rate of 0.61% (2587/423,141). Two participants were excluded from the analysis because they had only received care in a county council that did not yet show any information in Journalen. The results showed that 62.97% (1629/2587) of respondents were women and 39.81% (1030/2587) were working or had been working in health care. In addition, 72.08% (1794/2489) of respondents used Journalen about once a month, and the main reason for use was to gain an overview of one’s health status. Furthermore, respondents reported that lab results were the most important information for them to access; 68.41% (1737/2539) of respondents wanted access to new information within a day, and 96.58% (2454/2541) of users reported that they are positive toward Journalen. CONCLUSIONS In this study, respondents provided several important reasons for why they use Journalen and why it is important for them to be able to access information in this way—several related to patient empowerment, involvement, and security. Considering the overall positive attitude, PAEHRs seem to fill important needs for patients.

scholarly journals Leveraging Genetic Reports and Electronic Health Records for Predicting Primary Cancers Based on FHIR and RDF (Preprint)

2020 ◽  
Author(s):  
Nansu Zong ◽  
Victoria Ngo ◽  
Daniel J. Stone ◽  
Andrew Wen ◽  
Yiqing Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Cancer Patients ◽  
Genetic Data ◽  
Precision Oncology ◽  
Primary Cancer ◽  
Health Records ◽  
Web Resource ◽  
Cancer Types ◽  
Electronic Health

BACKGROUND Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnose, and treatments. A key research area focuses on early detection of primary cancers and the potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. OBJECTIVE This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict unknown primaries. METHODS We extracted the genetic data elements from a collection of oncology genetic reports of 1,011 cancer patients, and corresponding phenotypical data from the Mayo Clinic electronic health records (EHRs). We modeled both genetic and EHR data with HL7 Fast Healthcare Interoperability Resources (FHIR). The semantic web Resource Description Framework (RDF) was employed to generate the network-based data representation (i.e., patient-phenotypic-genetic network). Based on RDF data graph, graph embedding algorithm Node2vec was applied to generate features, and then multiple machine learning and deep learning backbone models were adopted for cancer prediction. RESULTS With six machine-learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types and predicting unknown primaries. To demonstrate the interpretability, phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review. CONCLUSIONS Accurate prediction of cancer types can be achieved with existing EHR data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnose stage for cancer patients.

Abstract P148: Heart Disease Among Breast Cancer Patients

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Tekeda F Ferguson ◽  
Sunayana Kumar ◽  
Denise Danos
Keyword(s):  
Breast Cancer ◽  
Heart Disease ◽  
Electronic Health Records ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Health Concern ◽  
Breast Cancer Patients ◽  
Health Records ◽  
Electronic Health

Purpose: In conjunction with women being diagnosed earlier with breast cancer and a rapidly aging population, advances in cancer therapies have swiftly propelled cardiotoxicity as a major health concern for breast cancer patients. Frequent cardiotoxicity outcomes include: reduced left ventricular ejection fraction (LVEF), myocardial infarction, asymptomatic or hospitalized heart failure, arrhythmias, hypertension, and thromboembolism. The purpose of this study was to use an electronic health records system determine if an increased odds of heart disease was present among women with breast cancer. Methods: Data from the Research Action for Health Network (REACHnet) was used for the analysis. REACHnet is a clinical data research network that uses the common data model to extract electronic health records (EHR) from health networks in Louisiana (n=100,000).Women over the age of 30 with data (n=35,455) were included in the analysis. ICD-9 diagnosis codes were used to classify heart disease (HD) (Hypertensive HD, Ischemic HD, Pulmonary HD, and Other HD) and identify breast cancer patients. Additional EHR variables considered were smoking status, and patient vitals. Chi-square tests, crude, and adjusted logistic regression models were computed utilizing SAS 9.4. Results: Utilizing diagnoses codes our study team has estimated 28.6% of women over the age of 30 with a breast cancer diagnosis (n=816) also had a heart disease diagnosis, contrasted with 15.6% of women without a breast cancer diagnosis. Among patients with heart disease, there was no significant difference in the distribution of the type of heart disease diagnoses by breast cancer status (p=0.87). There was a 2.21 (1.89, 2.58) crude odds ratio of having a CVD diagnoses among breast cancer cases when referenced to cancer free women. After adjusting for age (30-49, 50-64, 65+), race (black/white), and comorbidities (obesity/overweight, diabetes, current smoker) there was an increased risk of heart disease (OR: 1.24 (1.05, 1.47)). Conclusion: The short-term and long-term consequences of cardiotoxicity on cancer treatment risk-to-benefit ratio, survivorship issues, and competing causes of mortality are increasingly being acknowledged. Our next efforts will include making advances in predictive risk modeling. Maximizing benefits while reducing cardiac risks needs to become a priority in oncologic management and monitoring for late-term toxic effects.

scholarly journals Enrichment sampling for a multi-site patient survey using electronic health records and census data

2018 ◽  
Vol 26 (3) ◽  
pp. 219-227 ◽  
Author(s):  
Nathaniel D Mercaldo ◽  
Kyle B Brothers ◽  
David S Carrell ◽  
Ellen W Clayton ◽  
John J Connolly ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Census Data ◽  
Sampling Strategy ◽  
Fold Increase ◽  
Patient Survey ◽  
United States Census ◽  
Sampling Frame ◽  
Health Records ◽  
Adult Age ◽  
Electronic Health

Abstract Objective We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.

Electronic health record use among cancer patients: Insights from the Health Information National Trends Survey

2017 ◽  
Vol 25 (1) ◽  
pp. 83-90 ◽  
Author(s):  
Yulia A Strekalova
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Cancer Patients ◽  
Health Information ◽  
Information Seeking ◽  
Health Record ◽  
Online Information ◽  
Health Records ◽  
Electronic Health ◽  
National Trends

Over 90% of US hospitals provide patients with access to e-copy of their health records, but the utilization of electronic health records by the US consumers remains low. Guided by the comprehensive information-seeking model, this study used data from the National Cancer Institute’s Health Information National Trends Survey 4 (Cycle 4) and examined the factors that explain the level of electronic health record use by cancer patients. Consistent with the model, individual information-seeking factors and perceptions of security and utility were associated with the frequency of electronic health record access. Specifically, higher income, prior online information seeking, interest in accessing health information online, and normative beliefs were predictive of electronic health record access. Conversely, poorer general health status and lack of health care provider encouragement to use electronic health records were associated with lower utilization rates. The current findings provide theory-based evidence that contributes to the understanding of the explanatory factors of electronic health record use and suggest future directions for research and practice.

USE OF AN ELECTRONIC HEALTH RECORDS REGISTRY TO IDENTIFY CARE GAPS IN CARDIOVASCULAR CARE OF CANCER PATIENTS

2018 ◽  
Vol 71 (11) ◽  
pp. A698
Author(s):  
Steven Philips ◽  
Duwayne Willett ◽  
Sandeep Das ◽  
Evan Sara ◽  
Vaishnavi Kannan ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Cancer Patients ◽  
Health Records ◽  
Care Gaps ◽  
Electronic Health ◽  
Cardiovascular Care
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