Relationship between Hospital Profitability and Mobile-Based Personal Health Record Adoption

PNS109 Reducing the Racial/Ethnic Disparities in Personal Health Record (PHR) Adoption in the US

Personal health records (PHRs) are intended to increase patients’ access to and ownership over their health care information for self-management purposes. The purpose of this study was to determine the association of graph literacy with adoption of an online PHR and, among adopters with self-reported skills, the frequency of use and intent to return to use the PHR . We conducted a cross-sectional survey of veterans receiving outpatient care. We measured health literacy, numeracy, graph literacy, and Internet and PHR adoption and use. We compared subgroups of veterans using analyses of covariance. We used hierarchical logistic regression models to estimate the effects of the literacy variables on PHR use. A total of 600 veterans (age = 22–94) participated in the survey. After we adjusted for known covariates, we found that adopters of a PHR were more likely to demonstrate higher health and graph literacy than nonadopters. Among PHR adopters, self-reported frequent and skillful users were more likely to have higher graph literacy than lower frequency and less skillful users. Adopters with higher intentions to return to use the PHR were more likely to show lower graph literacy than those less likely to return to use the PHR. Inadequate graph literacy was associated with lower adoption of a PHR and, among users, with lower self-reported frequent use and skills . As PHR use becomes more widespread, stakeholders will need to consider patients’ levels of graph literacy when implementing PHRs.

BackgroundHealthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is to investigate factors associated with healthcare organizations’ adoption of PHR systems in South Korea.MethodsThe units of analysis were hospitals with more than 100 beds. Study data of 313 hospitals were collected from May 1 to June 30, 2020. The PHR adoption status for each hospital was collected from PHR vendors and online searches. Adoption was then confirmed by downloading the hospital’s PHR app and the PHR app was examined to ascertain its available functions. One major outcome variable was PHR adoption status at hospital level. Data were analysed by logistic regressions using SAS 9.4 version.ResultsOut of 313 hospitals, 103 (32.9%) hospitals adopted PHR systems. The nurse-patient ratio was significantly associated with PHR adoption (OR 0.758; 0.624 to 0.920, p = 0.005). The number of health information management staff was associated with PHR adoption (OR 1.622; 1.228 to 2.141, p = 0.001). The number of CTs was positively associated with PHR adoption (OR 5.346; 1.962 to 14.568, p = 0.001). Among the hospital characteristics, the number of beds was significantly related with PHR adoption in the model of standard of nursing care (OR 1.003; 1.001 to 1.005, p < 0.001), HIM staff (OR 1.004; 1.002 to 1.006, p < 0.001), and technological infrastructure (OR 1.050; 1.003 to 1.006, p < 0.001).ConclusionsOne-third of study hospitals had adopted PHR systems. Standard of nursing care as well as information technology infrastructure in terms of human resources for health information management and advanced technologies were significantly associated with adoption of PHR systems. A favourable environment for adopting new technologies in general may be associated with the adoption and use of PHR systems.

The adoption of a personal health record (PHR) is a crucial element in quality healthcare, allowing patients to permit the storage of their health information to create a more inclusive, reliable health record. However, the embracing of PHRs has been slow compared to other healthcare-related systems due to the poor design and behavioral aspects. The objective of this research is to study user acceptance factors to identify a better design for PHR systems and to promote healthy behaviors that support individuals' performance. The study proposes an integrative adoption model for PHRs that integrates theoretical factors from the health belief model with the user acceptance determinants from the technology acceptance model and innovation diffusion theory. Using structural equation modeling with the R “Lavaan” package, the study tested the hypothesis relationships of the constructs. The data were captured from individuals through Amazon’s MTurk. Among the nine relationships studied, the research revealed six significant relationships that inform the final PHR integrative adoption model. The research provides great insights into the factors that influence individuals’ PHR adoption. The results introduce a novel integration model to the current body of knowledge. This model will contribute to a better theoretical understanding of the actual use of healthcare-related technologies and bring greater estimates of patient engagement in healthy activities.

In this paper, we present a discussion of personal health records (PHRs). The development of PHRs in the USA has occurred as a result of the Institute of Medicine's direction to improve healthcare quality and make it more patient-centred, as well as demands from patients for more control of their health data. The PHR addresses timeliness, patient safety and equity. The PHR is also integral to the US National Health Information Network (NHIN) being designed to give all Americans access to electronic health records by 2014. Despite increasing access to PHRs via employers, insurance companies, healthcare providers and independent entities, adoption and successful implementation of the PHR as well as other types of electronic records (Electronic Medical Record (EMR) and Electronic Health Record (EHR)) is unclear. The specific interaction and distinction between these records is also unclear. This article differentiates the PHR from the other records, introduces the notion of ownership of medical information and presents a basic research model regarding PHR adoption and diffusion.

Communication between patient and healthcare provider is an essential part of care planning. A patient's personal health record (PHR) contains medical information that can be easily accessed, updated, and used via the Internet to facilitate and improve treatment plans. However, PHRs are not widely adopted. The goal of this study was to explore the challenges and design a roadmap to adoption. Data were collected via semistructured face-to-face interviews and analyzed with a qualitative approach. This study included 35 subjects selected from a sample of CEOs, chief information officers (CIOs), and health informatics project managers (HIPMs); the sample included key executives and project managers in the health information technology business. This study revealed that executives see the PHR as a tool to improve communication and patient care and reduce cost. However, a lack of electronic health record (EHR) systems, poor PHR integration with EHR systems, lack of user experience, and computer and health illiteracy were identified as barriers to PHR adoption. Therefore, adoption was not found to be a top priority for executives. Nevertheless, the PHR ought to be part of the information technology strategy in every hospital. Effective staff training, better patient orientation, and dedicated health informaticians can ensure a smooth and safe journey toward a useful PHR.

With the adoption of Personal Health Record (PHR), patients take an active role in monitoring their health and can store and access a wide array of credible health information. However, patients are often concerned about the confidentiality and privacy of their health information in PHR. This study was carried out to determine the effect of confidentiality and privacy concerns on PHR adoption. This was a cross – sectional study in which 175 patients referred to teaching hospitals of Tabriz University of Medical Sciences were randomly selected. Patients’ perception regarding the effect of confidentiality and privacy concern on PHR adoption was examined by an extended model of Technology Acceptance Model (TAM). Collected data were analyzed by correlation and regression tests. The final model was tested by Structural Equation Modeling (SEM) and presented by AMOS. The results show that the extended model of TAM, proposed in this study, can explain about 63% of the variance of the PHR adoption. Also, the results confirmed the significant effect of confidentiality and privacy concerns about PHR adoption. PHR adoption has several significant advantages both to patients and health care providers. This study clearly acknowledged that confidentiality and privacy concerns were two main obstacles that should be considered when comprehensive implementation of PHR is in progress.

BackgroundClinical trials constitute an important pillar in medical research. It is beneficial to support recruitment for clinical trials using software tools, so-called patient recruitment support systems; however, such information technology systems have not been frequently used to date. Because medical information systems' underlying data collection methods strongly influence the benefits of implementing patient recruitment support systems, we investigated patient recruitment support system requirements and corresponding electronic record types such as electronic medical record, electronic health record, electronic medical case record, personal health record, and personal cross-enterprise health record.ObjectiveThe aim of this study was to (1) define requirements for successful patient recruitment support system deployment and (2) differentiate and compare patient recruitment support system–relevant properties of different electronic record types.MethodsIn a previous study, we gathered requirements for patient recruitment support systems from literature and unstructured interviews with stakeholders (15 patients, 3 physicians, 5 data privacy experts, 4 researchers, and 5 staff members of hospital administration). For this investigation, the requirements were amended and categorized based on input from scientific sessions. Based on literature with a focus on patient recruitment support system–relevant properties, different electronic record types (electronic medical record, electronic health record, electronic medical case record, personal health record and personal cross-enterprise health record) were described in detail. We also evaluated which patient recruitment support system requirements can be achieved for each electronic record type.ResultsPatient recruitment support system requirements (n=16) were grouped into 4 categories (consent management, patient recruitment management, trial management, and general requirements). All 16 requirements could be partially met by at least 1 type of electronic record. Only 1 requirement was fully met by all 5 types. According to our analysis, personal cross-enterprise health records fulfill most requirements for patient recruitment support systems. They demonstrate advantages especially in 2 domains (1) supporting patient empowerment and (2) granting access to the complete medical history of patients.ConclusionsIn combination with patient recruitment support systems, personal cross-enterprise health records prove superior to other electronic record types, and therefore, this integration approach should be further investigated.

BackgroundPersonal Health Records (PHRs) are electronic health records controlled, shared or maintained by patients to support patient centered care. The potential for PHRs to transform health care is significant; however, PHRs do not always achieve their potential. One reason for this may be that not all health conditions are sensitive to the PHR as an intervention. The goal of this review was to discover which conditions were potentially sensitive to the PHR as an intervention, that is, what conditions have empirical evidence of benefit from PHR-enabled management.MethodsA systematic review of Medline and CINAHL was completed to find articles assessing PHR use and benefit from 2008 to 2014 in specific health conditions. Two researchers independently screened and coded articles. Health conditions with evidence of benefit from PHR use were identified from the included studies.Results23 papers were included. Seven papers were RCTs. Ten health conditions were identified, seven of which had documented benefit associated with PHR use: asthma, diabetes, fertility, glaucoma, HIV, hyperlipidemia, and hypertension. Reported benefits were seen in terms of care quality, access, and productivity, although many benefits were measured by self-report through quasi-experimental studies. No study examined morbidity/mortality. No study reported harm from the PHR.ConclusionThere is a small body of condition specific evidence that has been published. Conditions with evidence of benefit when using PHRs tended to be chronic conditions with a feedback loop between monitoring in the PHR and direct behaviours that could be self-managed. These findings can point to other potentially PHR sensitive health conditions and guide PHR designers, implementers, and researchers. More research is needed to link PHR design, features, adoption and health outcomes to better understand how and if PHRs are making a difference to health outcomes.

To report the results of a preliminary test of validity of a proposed theoretical framework of electronic personal health record adoption by older adults. Personal health records can assist older adults in managing or co-managing their chronic health conditions. Although there is interest by this population, their adoption of this technology has been slow. Without a comprehensive understanding of the reasons for this phenomenon, facilitating adoption will be difficult. Therefore, we developed a theoretical framework of adoption barriers and facilitators. Survey. A modified e-Delphi methodology was used to validate the framework by obtaining consensus from a panel of experts in related healthcare fields. These data were collected over 3 months in 2010. Of 16 expert panellists, 13 completed Round 1, 12 completed Rounds 2 and 3, and 9 completed Round 4. Central tendencies and percent agreement were computed to establish consensus. The e-Delphi resulted in expert consensus on the concepts, variables, and empirical indicators that were retained in (or added to) the framework to describe barriers and/or facilitators to older adults' adoption of personal health records for self-management of chronic disease. The validated model will be used as the basis for developing an instrument to identify barriers and facilitators to personal health records adoption and use by older adults with chronic disease. With better understanding of the factors that influence personal health record adoption in this population, more effective strategies may be developed to increase adoption and thus improve chronic disease management.

Usability and cognitive load in the design of a personal health record

The Personal Health Record (PHR) is an emerging web-based healthcare IT system, which is a hot topic of discussion among healthcare professionals and many other people. It is a very new concept and many companies are now offering PHR services to their customers. The PHR is created and kept up to date by the user, healthcare providers or insurance companies. Users can enter their information and manage their PHR. The PHR can include everything from medical histories to test results, physician's notes, prescriptions and information from their pharmacies and insurance companies. Hence PHR empowers the patients and gives them full access to their healthcare data. Many employers, insurance companies, hospitals and physicians are offering PHR services to their consumers. There are also many websites from where consumers can sign up for free PHR services. Due to the many benefits associated with PHR services, consumers find it very attractive to use. But there are also many concerns hindering the adoption of PHR. In this paper we shall review the factors affecting adoption of PHR systems using the Technology Acceptance Model (TAM) and formulate hypotheses for PHR adoption.

Personal health record (PHR) is an emerging Web-based healthcare it system, which is a hot topic of discussion among healthcare professionals and many common people. Its a very new concept and many companies are offering PHR services to their customers. PHR is created and kept up-to-date by the user, healthcare providers or insurance companies. User can enter their information and manage their PHR. PHR can include everything from medical histories to test results, physicians notes, prescriptions and information from their pharmacies and insurance companies. Hence PHR empowers the patients and gives them full access to their healthcare data. Many employers, insurance companies, hospitals, and physicians are offering PHR services to their consumers. There are also many Web-sites from where consumers can sign up for free PHRs services. Due to many benefits associated with PHR services, consumers find it very attractive to use PHR. But there are also many concerns hindering adoption of PHR. In this paper we shall review the factors affecting adoption of PHR systems using technology acceptance model (TAM) and formulate hypothesis for PHR adoption.

The inadvertent disclosure of personal health information through peer-to-peer file sharing programs

Health information technology, applied to electronic health record (EHR), has evolved with the adoption of standards for defining patient health records. However, there are many standards for defining such data, hindering communication between different healthcare providers. Even with adopted standards, patients often need to repeatedly provide their health information when they are taken care of at different locations. This problem hinders the adoption of personal health record (PHR), with the patients' health records under their own control. Therefore, the purpose of this paper is to propose an interoperability model for PHR use. The methodology consisted prototyping an application model named OmniPHR, to evaluate the structuring of semantic interoperability and integration of different health standards, using a real database from anonymized patients. We evaluated health data from a hospital database with 38645 adult patients' medical records processed using different standards, represented by openEHR, HL7 FHIR, and MIMIC-III reference models. OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP). Although the first executions reached a 76.39% F1-score and required retraining of the machine-learning process, the final score was 87.9%, presenting a way to obtain the original data from different standards on a single format. Unlike other models, OmniPHR presents a unified, structural semantic and up-to-date vision of PHR for patients and healthcare providers. The results were promising and demonstrated the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.