Access to Personal Health Records and Screening for Breast and Cervical Cancer Among Women with a Family History of Cancer

Effects of electronic personal health information technology on American women's cancer screening behaviors mediated through cancer worry: Differences and similarities between 2017 and 2020

Backgrounds

Thanks to their accessibility and low cost, electronic personal health information (ePHI) technologies have been widely used to facilitate patient-physician communication and promote health prevention behaviors (e.g. cancer screening). Despite that empirical evidence has supported the association between ePHI technology use and cancer screening behaviors, the underlying mechanism through which ePHI technology use influences cancer screening behaviors remains a topic of discussion.

Objective

This study investigates the relationship between ePHI technology uses and cancer screening behaviors of American women and examines the mediating role of cancer worry.

Methods

Data for this study were from the Health Information National Trends Survey (HINTS) collected in 2017 (HINTS 5 Cycle 1) and 2020 (HINTS 5 Cycle 4). The final sample included 1914 female respondents in HINTS 5 Cycle 1 and 2204 in HINTS 5 Cycle 4. Mann-Whitney U test, two-sample t-test, and mediation analysis were performed. We also referred to the regression coefficients generated by min-max normalization as percentage coefficients (bp) for the comparison.

Results

This study reports increased usage of ePHI technologies (from 1.41 in 2017 to 2.19 to 2020), increased cancer worry (from 2.60 in 2017 to 2.84 in 2020), and a stable level of cancer screening behaviors (from 1.44 in 2017 to 1.34 in 2020) among American women. Cancer worry was found to mediate the ePHI effect on cancer screening behaviors (bp = 0.005, 95% confidence interval [0.001, 0.010]) in a positive complementary mediation in 2020.

Conclusions

The research findings support a positive association between ePHI technology use and cancer screening behaviors, and cancer worry has been identified as a salient mediator. An understanding of the mechanism that prompts US women's cancer screening practices provides practical implications for health campaign practitioners.

Electronic Medical Record-Based Electronic Messaging Among Patients with Breast Cancer: A Systematic Review

BACKGROUND

Electronic medical record (EMR) systems and electronic messages are an increasingly common conduit between physicians and patients. Clear benefits of this type of communication have been established, especially among cancer patients. Studies suggest that patient portals and electronic messaging platforms can help with care coordination between oncology providers and facilitate asynchronous patient-provider communication. Despite the many benefits, there is little research regarding EMR and secure messaging use, particularly among patients with breast cancer.

OBJECTIVES

The objective of this systematic review was to examine the evidence supporting the use of EMR-based messaging systems in patients with breast cancer.

METHODS

A systematic literature search of Ovid MEDLINE, PubMed, Scopus, Web of Science CINAHL, and Cochrane Library was conducted. Studies were required to be published between 2005 and 2022 and report data on demographic information and electronic messaging between patients and providers. Studies were excluded if they reported insufficient data, did not include breast cancer patients, or were not published in English.

RESULTS

This study identified 10 articles that met inclusion criteria. The resulting studies investigated topics such as: patterns of messaging and medication adherence, cancer screening, messaging as a predictor of behavior or outcomes, and symptom management. The literature indicates that electronic messaging with providers was associated with clinical benefits for breast cancer patients and improved screening behaviors.

CONCLUSION

This review uncovered multiple areas to focus future research on, including ideal volume of electronic messages sent and their relation to prescription adherence, studies focusing solely on the breast cancer population, racial disparities in electronic messaging, and provider perceptions of electronic messaging. It is vital that more work be done to understand barriers and gaps in EMR usage to ensure that all individuals can access this increasingly essential medical service while minimizing physician workload and burnout.

Novel Lagrange interpolation polynomials for dynamic access control in a healthcare cloud system

The authority of user personal health records (PHRs) is usually determined by the owner of a cloud computing system. When a PHR file is accessed, a dynamic access control algorithm must be used to authenticate the users. The proposed dynamic access control algorithm is based on a novel Lagrange interpolation polynomial with timestamps, mainly functioning to authenticate the users with key information. Moreover, the inclusion of timestamps allows user access within an approved time slot to enhance the security of the healthcare cloud system. According to the security analysis results, this healthcare cloud system can effectively resist common attacks, including external attacks, internal attacks, collaborative attacks and equation-based attacks. Furthermore, the overall computational complexity of establishing and updating the polynomials is O(n*m* (log m)²), which is a promising result, where m denotes the degree of $ polynomial~G\left(x, y\right) $ and n denotes the number of secure users in the hierarchy.

Effectiveness of Using Personal Health Records to Improve Recommended Breast Cancer Screening and Reduce Racial and Geographic Disparities Among Women

This paper examines the effectiveness of using personal health records (PHRs), which contains detailed health information to improve the use of recommended screening mammography among women at potential risk for breast cancer. It also explores the potential of PHR in reducing disparities in recommended mammography use experienced by minority and underserved women. The primary data used for this study were obtained from the 2015 Health Information National Trends Survey, and they were supplemented by the 2016 Area Health Resource Files. The study sample included women aged 40-75 years with no prior diagnosis of cancer. Because the use of PHRs as a key predictor of breast cancer screening may be endogenously determined, we used the instrumental variable (IV) approach to correct for estimation bias. We found a significant and positive association between the use of PHRs and recommended mammography use among women, with the likelihood of mammography screening increasing with more frequent use of PHRs. Furthermore, the effects of PHR use on mammography screening were found to be greater among Hispanic and Black women and those living in non-metropolitan areas compared with White women and those living in metropolitan areas, respectively. The use of PHRs for health information seeking can empower women at potential risk for breast cancer to participate in recommended screening mammography, particularly among those underserved and racial/ethnic minorities. Policymakers should consider developing policies and programs that can promote PHR use by women, especially medically underserved and minority women.

The Use of Multiple Imputation to Handle Missing Data in Secondary Datasets: Suggested Approaches when Missing Data Results from the Survey Structure

Secondary datasets are used in healthcare research because of its cost advantages, its convenience, and the size of the datasets. However, missing data can cause problems that are difficult to resolve. This manuscript reviews possible causes for missing data, and how to address them. Many researchers use multiple imputation as a solution, which consists of three phases: (a) the imputation phase, (b) the analysis phase, and (c) the pooling phase. When missing data is caused by a refusal to answer or by insufficient knowledge, multiple imputation works well. However, difficulties arise when there are problems with screening questions. If respondents do not answer a screening question, possible answers could be either “yes” or “no.” This paper suggests identifying “yes” responses on the screening question, and setting them aside for use in the analysis. The reasons for this approach are the impossibility of conducting multiple imputation twice, the problem of imputation based on the population after sample weight, and the difficulty of producing logical errors on the estimation in imputation phase. This manuscript uses as an example the techniques used to address missing data from screening questions in a national US dataset. These techniques of multiple imputation using examples from the dataset could be used by researchers in future healthcare research that relies on secondary datasets.