Dataset Information

Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health: A Cross-sectional Survey.

ABSTRACT:

Background

To our knowledge, no evidence is available on health care professionals' use of electronic personal health records (ePHRs) for their health management. We therefore focused on nurses' personal use of ePHRs using a modified technology acceptance model.

Objectives

To examine (1) the psychometric properties of the ePHR acceptance model, (2) the associations of perceived usefulness, ease of use, data privacy and security protection, and perception of self as health-promoting role models to nurses' own ePHR use, and (3) the moderating influences of age, chronic illness and medication use, and providers' use of electronic health record (EHRs) on the associations between the ePHR acceptance constructs and ePHR use.

Methods

A convenience sample of registered nurses, those working in one of 12 hospitals in the Maryland and Washington, DC areas and members of the nursing informatics community (AMIA and HIMSS), were invited to respond to an anonymous online survey; 847 responded. Multiple logistic regression identified associations between the model constructs and ePHR use, and the moderating effect.

Results

Overall, ePHRs were used by 47%. Sufficient reliability for all scales was found. Three constructs were significantly related to nurses' own ePHR use after adjusting for covariates: usefulness, data privacy and security protection, and health-promoting role model. Nurses with providers that used EHRs who perceived a higher level of data privacy and security protection had greater odds of ePHR use than those whose providers did not use EHRs. Older nurses with a higher self-perception as health-promoting role models had greater odds of ePHR use than younger nurses.

Conclusions

Nurses who use ePHRs for their personal health might promote adoption by the general public by serving as health-promoting role models. They can contribute to improvements in patient education and ePHR design, and serve as crucial resources when working with their individual patients.

SUBMITTER: Gartrell K

PROVIDER: S-EPMC4493327 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health: A Cross-sectional Survey.

Gartrell K K Trinkoff A M AM Storr C L CL Wilson M L ML Gurses A P AP

Applied clinical informatics 20150408 2

<h4>Background</h4>To our knowledge, no evidence is available on health care professionals' use of electronic personal health records (ePHRs) for their health management. We therefore focused on nurses' personal use of ePHRs using a modified technology acceptance model.<h4>Objectives</h4>To examine (1) the psychometric properties of the ePHR acceptance model, (2) the associations of perceived usefulness, ease of use, data privacy and security protection, and perception of self as health-promotin ...[more]

PMID: 26171072

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Project description:BackgroundPhysician burnout has a direct impact on the delivery of high-quality health care, with health information technology tools such as electronic health records (EHRs) adding to the burden of practice inefficiencies.ObjectiveThe aim of this study was to determine the extent of burnout among physicians and learners (residents and fellows); identify significant EHR-related contributors of physician burnout; and explore the differences between physicians and learners with regard to EHR-related factors such as time spent in EHR, documentation styles, proficiency, training, and perceived usefulness. In addition, the study aimed to address gaps in the EHR-related burnout research methodologies by determining physicians' patterns of EHR use through usage logs.MethodsThis study used a cross-sectional survey methodology and a review of administrative data for back-end log measures of survey respondents' EHR use, which was conducted at a large Canadian academic mental health hospital. Chi-square and Fisher exact tests were used to examine the association of EHR-related factors with general physician burnout. The survey was sent out to 474 individuals between May and June 2019, including physicians (n=407), residents (n=53), and fellows (n=14), and we measured physician burnout and perceptions of EHR stressors (along with demographic and practice characteristics).ResultsOur survey included 208 respondents, including physicians (n=176) and learners (n=32). The response rate was 43.2% for physicians (full-time: 156/208, 75.0%; part-time: 20/199, 10.1%), and 48% (32/67) for learners. A total of 25.6% (45/176) of practicing physicians and 19% (6/32) of learners reported having one or more symptoms of burnout, and 74.5% (155/208) of all respondents who reported burnout symptoms identified the EHR as a contributor. Lower satisfaction and higher frustration with the EHRs were significantly associated with perceptions of EHR contributing toward burnout. Physicians' and learners' experiences with the EHR, gathered through open-ended survey responses, identified challenges around the intuitiveness and usability of the technology as well as workflow issues. Metrics gathered from back-end usage logs demonstrated a 13.6-min overestimation in time spent on EHRs per patient and a 5.63-hour overestimation of after-hours EHR time, when compared with self-reported survey data.ConclusionsThis study suggests that the use of EHRs is a perceived contributor to physician burnout. There should be a focus on combating physician burnout by reducing the unnecessary administrative burdens of EHRs through efficient implementation of systems and effective postimplementation strategies.

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Dataset Information

Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health: A Cross-sectional Survey.

Background

Objectives

Methods

Results

Conclusions

Publications

Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health: A Cross-sectional Survey.

Similar Datasets

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