Electronic Health Record Portal Adoption: a cross country analysis.
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ABSTRACT: Background
This study's goal is to understand the factors that drive individuals to adopt Electronic Health Record (EHR) portals and to estimate if there are differences between countries with different healthcare models.Methods
We applied a new adoption model using as a starting point the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by incorporating the Concern for Information Privacy (CFIP) framework. To evaluate the research model we used the partial least squares (PLS) - structural equation modelling (SEM) approach. An online questionnaire was administrated in the United States (US) and Europe (Portugal). We collected 597 valid responses.Results
The statistically significant factors of behavioural intention are performance expectancy ([Formula: see text] total?=?0.285; P?total?=?0.160; P?total?=?0.198; P?total?=?-0.141; P?total?=?0.152; P?total?=?0.255; P?total?=?0.145; P?total?=?0.480; P?ConclusionsOur study identified critical factors for the adoption of EHR portals and significant differences between the countries. Confidentiality issues do not seem to influence acceptance. The EHR portals usage patterns are significantly higher in US compared to Portugal.
SUBMITTER: Tavares J
PROVIDER: S-EPMC5499062 | biostudies-literature | 2017 Jul
REPOSITORIES: biostudies-literature
Publications
Electronic Health Record Portal Adoption: a cross country analysis.
Tavares Jorge J Oliveira Tiago T
BMC medical informatics and decision making 20170705 1
<h4>Background</h4>This study's goal is to understand the factors that drive individuals to adopt Electronic Health Record (EHR) portals and to estimate if there are differences between countries with different healthcare models.<h4>Methods</h4>We applied a new adoption model using as a starting point the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by incorporating the Concern for Information Privacy (CFIP) framework. To evaluate the research model we used the partial le ...[more]
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Project description:BackgroundThe future of health care delivery is becoming more patient-focused, and electronic health record (EHR) portals are gaining more attention from worldwide governments that consider this technology as a valuable asset for the future sustainability of the national health care systems. Overall, this makes the adoption of EHR portals an important field to study.ObjectiveThe aim of this study is to understand the factors that drive individuals to adopt EHR portals.MethodsWe applied a new adoption model that combines 3 different theories, namely, extended unified theory of acceptance and use of technology, health belief model, and the diffusion of innovation; all the 3 theories provided relevant contributions for the understanding of EHR portals. To test the research model, we used the partial least squares causal modeling approach. We executed a national survey based on randomly generated mobile phone numbers. We collected 139 questionnaires.ResultsPerformance expectancy (beta=.203; t=2.699), compatibility (beta=.530; t=6.189), and habit (beta=.251; t=2.660) have a statistically significant impact on behavior intention (R2=76.0%). Habit (beta=.378; t=3.821), self-perception (beta=.233; t=2.971), and behavior intention (beta=.263; t=2.379) have a statistically significant impact on use behavior (R2=61.8%). In addition, behavior intention (beta=.747; t=10.737) has a statistically significant impact on intention to recommend (R2=69.0%), results demonstrability (beta=.403; t=2.888) and compatibility (beta=.337; t=2.243) have a statistically significant impact on effort expectancy (R2=48.3%), and compatibility (beta=.594; t=6.141) has a statistically significant impact on performance expectancy (R2=42.7%).ConclusionsOur research model yields very good results, with relevant R2 in the most important dependent variables that help explain the adoption of EHR portals, behavior intention, and use behavior.
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Project description:BackgroundElectronic health record (EHR) systems have been shown to be associated with improvements in care processes, quality of care, and patient outcomes. EHR also has a crucial role in the delivery of substance use disorder (SUD) treatment and is considered important for addressing SUD crises, including the opioid epidemic. However, little is known about the adoption of EHR in SUD treatment programs or the organizational-level factors associated with the adoption of EHR in SUD treatment.ObjectiveWe examined the adoption of EHR in SUD programs, with a focus on changes in adoption from 2014 to 2017, and identified organizational-level factors associated with EHR adoption.MethodsWe used data from the 2014 and 2017 National Drug Abuse Treatment System Surveys. Our analysis included 1027 SUD programs (531 in 2014 and 496 in 2017). We used chi-square and Mann-Whitney U tests for categorical and continuous variables, respectively, to assess changes in EHR adoption, technology use, program, and client characteristics. We also investigated differences in characteristics and barriers to adoption by EHR adoption status (adopted EHR vs had not adopted or were planning to adopt EHR). We then conducted multivariate logistic regressions to examine internal and external factors associated with EHR adoption.ResultsThe adoption of EHR increased significantly from 57.6% (306/531) in 2014 to 69.2% (343/496) in 2017 (P<.001), showing that nearly one-third (153/496, 30.8%) of SUD programs had not yet adopted an EHR system by 2017. We identified a significant increase in technology use and ownership by a parent company (P=.01 and P<.001) and a decrease in the percentage of uninsured patients in 2017 (P<.001), compared to 2014. Our analysis further showed significant differences by adoption status for three major barriers to adoption: (1) start-up costs, (2) ongoing financial costs, and (3) privacy or security concerns (P<.001). Programs that used computerized scheduling (adjusted odds ratio [AOR] 3.02, 95% CI 2.23-4.09) and billing systems (AOR 2.29, 95% CI 1.62-3.25) were more likely to adopt EHR. Similarly, ownership type, such as private nonprofit (AOR 1.86, 95% CI 1.31-2.65) and public (AOR 2.14, 95% CI 1.27-3.67), or interest in participating in a patient-centered medical home (AOR 1.93, 95% CI 1.29-2.92), were associated with an increased likelihood to adopt EHR. Overall, SUD programs were more likely to adopt an EHR system in 2017 compared to 2014 (AOR 1.44, 95% CI 1.07-1.94).ConclusionsOur findings highlighted that SUD programs may be on track to achieve widespread EHR adoption. However, there is a need for focused strategies, resources, and policies explicitly designed to systematically address barriers and tackle obstacles to expanding the adoption of EHR systems. These efforts must be holistic and address factors at multiple organizational levels.
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Project description:BackgroundThe adoption of an electronic health record (EHR) in the healthcare system has the potential to make healthcare service delivery effective and efficient by providing accurate, up-to-date, and complete information. Despite its great importance, the adoptions of EHR in low-income country settings, like Ethiopia, were lagging and increasingly failed. Assessing the readiness of stakeholders before the actual adoption of EHR is considered the prominent solution to tackle the problem. However, little is known about healthcare providers' EHR readiness in this study setting. Accordingly, this research was conducted aiming at examining healthcare providers' readiness for EHR adoption and associated factors in southwestern Ethiopia.MethodsAn institutional-based cross-sectional study was conducted from September 1 to October 30, 2021. A total of 423 healthcare providers working in public hospitals were selected using a simple random sampling technique. Multivariable logistic regression was fitted to identify determinant factors of overall healthcare providers' readiness after the other covariates were controlled.ResultIn this study, the overall good readiness level of EHR adoption was 52.8% (n = 204) [95% CI of 47.9% to 56.6%]. Age, computer literacy, computer access at health facilities, attitude towards EHRs, awareness about EHRs, perceived benefit, and perceived technology self-efficacy were significantly associated with the overall health care providers' readiness for the adoption of EHR using a cut point of P-value less than 0.05.ConclusionAround half of the respondents had a good level of overall healthcare providers' readiness for the adoption of EHR which was considered inadequate. This finding implied that a huge effort is required to improve readiness before the actual implementation of EHRs. The finding implied that younger-aged groups were more ready for such technology which in turn implied; the older one needs more concern. Enhancing computer literacy, confidence building to raise self-efficacy of such technology, addressing the issue of computer availability at health facilities, building a positive attitude, awareness campaign of EHR, and recognizing the usefulness of such systems were the necessary measures to improve EHR readiness in this setting. Additionally, further studies are recommended to encompass all types of EHR readiness such as organizational readiness, technology readiness, societal readiness, and so on. Additionally, exploring the healthcare provider opinion with qualitative study and extending the proposed study to other implementation settings are recommended to be addressed by future works.
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Project description:OBJECTIVE: To describe small area variation in ambulatory electronic health record (EHR) adoption and assess evidence of a "digital divide" in whether adoption is lagging in traditionally underserved communities. DATA SOURCES: Survey data on U.S. ambulatory health care sites (261,973 sites representing 716,160 providers) collected by SK&A Information Services in 2011. STUDY DESIGN: We examined cross-sectional variation in two measures of local area EHR adoption: share of providers at sites using an EHR with e-prescribing functionality; and predicted probability of EHR adoption for the average site. Local areas were defined as Public Use Microdata Areas (n = 2,068). Using multivariate regression, we examined the association between adoption and three area characteristics: high concentration of minority population; high concentration of low-income population; and metropolitan status. PRINCIPAL FINDINGS: EHR adoption varied significantly across local areas, ranging from 8 to 88 percent with a median of 41 percent. Adoption was lower in large metropolitan areas; areas with high concentration of minority population in the Northeast and West; and areas with high concentration of low-income population in the Midwest. CONCLUSIONS: Our 2011 estimates suggest there was substantial room for increased EHR adoption across the United States, including some underserved areas with relatively low EHR adoption rates. Further research should monitor policy initiatives in these areas and examine sources of heterogeneity in low- and high-adoption communities.
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Project description:BackgroundThe rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system.ObjectiveThis study addresses two important questions: (1) "which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?", and (2) "which variables are associated with high- and extreme-risk severity ratings?"MethodsThis study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach's alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined.ResultsA total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type "extended EHR unavailability". The lowest overall risk level was associated with "selecting incorrectly from a list of items". In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest ( P<.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels ( P<.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors.ConclusionsBased on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately.
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Project description:ObjectiveTo assess how medical staffing mix changed over time in association with the adoption of electronic health records (EHRs) in community health centers (CHCs).Study settingCommunity health centers within the 50 states and Washington, DC.Study designEstimated how the change in the share of total medical staff full-time equivalents (FTE) by provider category between 2007 and 2013 was associated with EHR adoption using fractional multinomial logit.Data collection2007-2013 Uniform Data System, an administrative data set of Section 330 federal grant recipients; and Readiness for Meaningful Use and HIT and Patient Centered Medical Home Recognition Survey responses collected from Section 330 recipients between December 2010 and February 2011.Principal findingsHaving an EHR system did significantly shift the share of workers over time between physicians and each of the other categories of health care workers. While an EHR system significantly shifted the share of physician and other medical staff, this effect did not significantly vary over time. CHCs with EHRs by the end of the study period had a relatively greater proportion of other medical staff compared to the proportion of physicians.ConclusionsElectronic health records appeared to influence staffing allocation in CHCs such that other medical staff may be used to support adoption of EHRs as well as be leveraged as an important care provider.
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Project description:Rationale & objectiveElectronic health record portals are increasingly emphasized in chronic kidney disease (CKD). However, associations of portal use with clinical and patient-centered outcomes remain unknown.Study designCross-sectional survey (April 2015 to March 2018).Setting & participantsNondialysis patients with CKD from nephrology clinics within 1 academic medical center.ExposuresPatient demographics (age, sex, race, ethnicity, education, and income), kidney function.OutcomesAssociation between portal use as an outcome and exposures. Additionally, associations of portal use and patient demographics with 4 patient-centered outcomes (CKD-specific knowledge, stress, and 2 self-ratings of health).Analytic approachLogistic regression to examine associations between patient portal use, demographics, and kidney function. Linear regression to examine associations between portal use and patient-centered outcomes.ResultsOf 245 participants, mean age was 60 ± 17 (SD) years, 182 (77%) were White, 121 (49%) were women, 230 (96%) had a high school education or higher, and 96 (45%) had <$50,000 annual income. Examining portal use, 159 (65%) used the portal as follows: checking laboratory test results, 157 (99%); managing appointments, 133 (84%); messaging providers, 131 (82%); viewing medical history, 127 (80%); reviewing educational resources, 113 (71%); and renewing prescriptions, 98 (62%). African Americans (OR, 0.34; 95% CI, 0.16-0.72 vs White patients), patients with less formal education (OR, 0.06; 95% CI, 0.01-0.36), and those with lower income (OR, 0.28; 95% CI, 0.13-0.60; and OR, 0.26; 95% CI, 0.12-0.54 comparing income < $25,000 and $25,000-$50,000, respectively, with ≥$50,000) had lower odds of using the portal. In adjusted analysis, only lower income predicted lower portal use. Examining patient-centered outcomes in univariable analysis, portal users had higher knowledge (β = 4.89; P = 0.02), higher ratings of current health (β = 0.28; P = 0.03), and lower CKD-related stress (β = -0.18; P = 0.05). In adjusted analysis, only patient demographics and/or kidney function remained independent predictors of patient-centered outcomes.LimitationsCross-sectional study design, cannot determine causality.ConclusionsInterventions are needed to ensure that all patients have access to portals to mitigate disparities in care.
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