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Journal articles on the topic 'Electronic personal health record'

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Published: 10 December 2022

Last updated: 29 January 2023

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1

Abd, Lobna, Ahmed Sharaf, and Mona Mohamed. "Personal Integrated Electronic Health Record." International Journal of Computer Applications 150, no. 12 (September 24, 2016): 44–47. http://dx.doi.org/10.5120/ijca2016911656.

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McCartney, Patricia R. "The Electronic Personal Health Record." MCN, The American Journal of Maternal/Child Nursing 33, no. 6 (November 2008): 390. http://dx.doi.org/10.1097/01.nmc.0000341262.00620.dc.

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Karagiannis, George E., Vasileios G. Stamatopoulos, Michael Rigby, Takis Kotis, Elisa Negroni, Adolfo Munoz, and Loannis Mathes. "Web-based personal health records: the personal electronic health record (pEHR) multicentred trial." Journal of Telemedicine and Telecare 13, no. 1_suppl (July 2007): 32–34. http://dx.doi.org/10.1258/135763307781645086.

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A multicentre trial of a Web-based personal electronic health record (pEHR) service was conducted in three different European hospitals. A total of 150 patients and 22 health-care professionals were involved. The service was customised according to the needs of three groups of patients who had congenital heart disease, Parkinson's disease and type 2 diabetes. Two structured questionnaires, one for patients and one for health-care professionals, were used to collect their views on the pEHR service. The questions were about usability and user friendliness, safety and trustworthiness, reliability, functionality, satisfaction and the potential revenue model of the service in the case of future deployment. Patients perceived the service as very motivating and felt that it could help them in managing their clinical information. Health-care professionals showed a very positive attitude towards the use of the service and its potential for future large-scale deployment. They were also keen to recommend the service to their patients. Both study groups were unwilling to pay for the service and preferred it to be sponsored by a third party (e.g. the National Health Service).

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Hawthorne, Kisha Hortman, and Lorraine Richards. "Personal health records: a new type of electronic medical record." Records Management Journal 27, no. 3 (November 20, 2017): 286–301. http://dx.doi.org/10.1108/rmj-08-2016-0020.

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Purpose This paper examines existing research on the topic of personal health records (PHRs). Areas covered include PHR/patient portal, recordkeeping, preservation planning, access and provider needs for future reuse of health information. Patient and physician PHR use and functionality, as well as adoption facilitators and barriers, are also reviewed. Design/methodology/approach The paper engages in a review of relevant literature from a variety of subject domains, including personal information management, medical informatics, medical literature and archives and records management literature. Findings The review finds that PHRs are extensions of electronic records. In addition, it finds a lack of literature within archives and records management that may lead to a less preservation-centric examination of the new PHR technologies that are desirable for controlling the lifecycle of these important new records-type. Originality/value Although the issues presented by PHRs are issues that can best be solved with the use of techniques from records management, there is no current literature related to PHRs in the records management literature, and that offered in the medical informatics literature treats the stewardship aspects of PHRs as insurmountable. This paper offers an introduction to the aspects of PHRs that could fruitfully be examined in archives and records management.

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Cahill, Jennifer E., Mark R. Gilbert, and Terri S. Armstrong. "Personal health records as portal to the electronic medical record." Journal of Neuro-Oncology 117, no. 1 (January 30, 2014): 1–6. http://dx.doi.org/10.1007/s11060-013-1333-x.

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Lee, PS, WS Jian, CH Kuo, and YC Li. "Electronic Health Record Goes Personal World-wide." Yearbook of Medical Informatics 18, no. 01 (August 2009): 40–43. http://dx.doi.org/10.1055/s-0038-1638636.

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Summary Objective Increasing patient demand for convenient access to their own healthcare data has led to more personal use of the Electronic Health Record (EHR). With “consumer empowerment” being an important issue of EHR, we are seeing a more “patient-centric” approach of EHR from countries around the world. Researchers have reported on issues in EHR sharing including concerns on privacy and security, consumer empowerment, competition among providers, and content standards. This study attempts to analyze prior research and to synthesize comprehensive, empirically-based conceptual models of EHR for personal use. Methods We use “B2C(2B)” to represent this new behavior of EHR sharing and exchange, with “consumer” in the center stage. ResultsBased on different information sharing mechanisms, we summarized the “B2C(2B)” behavior into three models, namely, the Inexpensive data media model, the Internet patient portal model and the Personal portable device model. Models each have their own strengths and weaknesses in their ways to share patient data and to address privacy and security concerns. Conclusion Personal use of EHR under the B2C(2B) model does look promising based on our study. We started to observe a trend that governments around the world are embarking on related projects. With multiple stake-holders involved, we are only beginning to understand the complexity of such undertakings.

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Alami, MasoomehRahimi, Mahdieh Nemayande, Omid Yousefianzadeh, Mahnaz Samadbeik, Amir Abbas Azizi, Robabeh Motaghedi, Atefeh Zare, et al. "Personal Electronic Health Record for Patients with Diabetes; Health Technology Assessment Protocol." Internal Medicine and Medical Investigation Journal 2, no. 4 (October 5, 2017): 132. http://dx.doi.org/10.24200/imminv.v2i4.100.

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Introduction: In recent decades, diabetes has contributed significantly to the burden of disease in developed and developing countries, due to the considerable prevalence and involvement of various age groups in the communities.Today, a variety of ways to manage and control the disease are used, one of which is the use of personal electronic health records. Recently there has been a remarkable upsurge in activity surrounding the adoption of personal electronic health records systems for patients and consumers. personal electronic health records systems are more than just static repositories for patient data; they combine data, knowledge, and software tools, which help patients to become active participants in their own care.The present study was conducted with the goal of Health Technology Assessment the impact of personal electronic health records in Patients with Diabetes.Methods: Writing is based on PRISMA standards. This was a Health Technology Assessment study. It aimed to evaluate the technology of personal electronic health record . The scoping review was conducted to evaluate 8 dimensions (Health Problem and Current Use of the Technology, Description and technical characteristics of technology, Safety, Costs and economic evaluation, Ethical analysis, Organisational aspects, Patients and Social aspects, Legal aspects) of Personal electronic health record . This study was based on answering questions which were developed based on Health Diagnostics Technology Assessment Documents Framework and HTA Core Model 3.0 . A systematic review was conducted to evaluate the Clinical Effectiveness dimension of personal electronic health record in controlling diabetes. In order to gather evidences, Ovid databases, Cochrane Library, PubMed, CRD, Trip database and EMBASE, and Randomized Controlled Trial Registries, such as the Clinical Trial and Trial Registry, were searched using specific keywords and strategies. .Articles are evaluated on the basis of the quality criteria of JADAD.The data is analyzed by the STATA software.Dissemination:The results of the study will be published in a peer-reviewed journal and presented at relevant conferences.Policy makers and healthcare decision-makers can use these results.

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Gartrell, Kyungsook, Carla L. Storr, Alison M. Trinkoff, Marisa L. Wilson, and Ayse P. Gurses. "Electronic personal health record use among registered nurses." Nursing Outlook 63, no. 3 (May 2015): 278–87. http://dx.doi.org/10.1016/j.outlook.2014.11.013.

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Mandal, Ajaya, Prakriti Dumaru, Sagar Bhandari, Shreeti Shrestha, and Subarna Shakya. "Decentralized Electronic Health Record System." Journal of the Institute of Engineering 15, no. 1 (February 16, 2020): 77–80. http://dx.doi.org/10.3126/jie.v15i1.27716.

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With a view to overcome the shortcomings of traditional Electronic Health Record (EHR) system so as to assure the interoperability by providing open access to sensitive health data, while still preserving personal data privacy, anonymity and avoiding data misuse, Decentralized Electronic Health Record System was developed. The aforementioned issue concerning traditional EHR system can be addressed by implication of emerging technology of the era namely Block chain, together with Inter Planetary File System (IPFS) which enables data sharing in decentralized and transactional fashion, thereby maintaining delicate balance between privacy and accessibility of electronic health records. A block chain based EHR system has been built for secure, efficient and interoperable access to medical records by both patients and doctors while preserving privacy of the sensitive patient’s information. Patients can easily and comprehensively access to their medical records across providers and treatment sites using unique properties of block chain and decentralized storage. A separate portal for both the patients and doctors has been built enabling the smart contracts to handle further interaction between doctors and patients. So, in this system, it is demonstrated how principles of decentralization and block chain architectures could contribute to EHR system using Ethereum smart contracts and IPFS to orchestrate a suitable system governing the medical record access while providing patients with comprehensive record review along with consideration for audit ability and data sharing.

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Li, Yu-Chuan, Don E. Detmer, Syed-Abdul Shabbir, Phung Anh Nguyen, Wen-Shan Jian, George I. Mihalas, Edward H. Shortliffe, Paul Tang, Reinhold Haux, and Michio Kimura. "A global travelers' electronic health record template standard for personal health records: Figure 1." Journal of the American Medical Informatics Association 19, no. 1 (January 2012): 134–36. http://dx.doi.org/10.1136/amiajnl-2011-000323.

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Bologna, Silvio, Alessandro Bellavista, Pietro Paolo Corso, and Gianluca Zangara. "Electronic Health Record in Italy and Personal Data Protection." European Journal of Health Law 23, no. 3 (June 14, 2016): 265–77. http://dx.doi.org/10.1163/15718093-12341403.

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The present article deals with the Italian Electronic Health Record (hereinafter ehr), recently introduced by Act 221/2012, with a specific focus on personal data protection. Privacy issues — e.g., informed consent, data processing, patients’ rights and minors’ will — are discussed within the framework of recent e-Health legislation, national Data Protection Code, the related Data Protection Authority pronouncements and eu law. The paper is aimed at discussing the problems arising from a complex, fragmentary and sometimes uncertain legal framework on e-Health.

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Dontje, Katherine, William D. Corser, and Greg Holzman. "Understanding Patient Perceptions of the Electronic Personal Health Record." Journal for Nurse Practitioners 10, no. 10 (November 2014): 824–28. http://dx.doi.org/10.1016/j.nurpra.2014.09.009.

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Caligtan, Christine A., and Patricia C. Dykes. "Electronic Health Records and Personal Health Records." Seminars in Oncology Nursing 27, no. 3 (August 2011): 218–28. http://dx.doi.org/10.1016/j.soncn.2011.04.007.

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Sadre, Atharva, Akshat Bhargava, Pranav Doshi, and Deepak M. Shinde. "Personal Health Record Manager and Appointment Scheduler (PHARMAS)." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2206–14. http://dx.doi.org/10.22214/ijraset.2022.42797.

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Abstract: The adaptation of digitalization in the banking and finance industry around the world is rapidly growing. IT infrastructure is used in various fields such as smart telephony, text services, and transportation i.e., coordinating trains and aviation timetables and signals based on GPRS location. Despite such progress in other areas where digitalization continues to evolve and grow in popularity, the use in the healthcare sector is significantly limited. It must quickly adapt to meet the demands of modern-day patients and healthcare professionals. But the current systems lack many features. Our aim is to create a one-stop system that satisfies the requirement of managing users’ data and providing its access on-demand to respective healthcare professionals along with various facilities such as a system to schedule an appointment with a specific doctor or any hospital, a timeline of patient’s medical history along with medical reports and medication provided to the patient, a messing facility (chat) for the patients to doctors (accessible only after consulting), recommendations and reviews for the same. Keywords: Digitization, EHR Electronic Health Record, EMR Electronic Medical Record, PHR Personal Health Record FHIR Fast Healthcare Interoperability Resources, JSON, API

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Conway, Nicholas Thomas, Brian Allardice, Deborah Jane Wake, and Scott Gordon Cunningham. "User Experiences of an Electronic Personal Health Record for Diabetes." Journal of Diabetes Science and Technology 13, no. 4 (December 26, 2018): 744–50. http://dx.doi.org/10.1177/1932296818818837.

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Background: My Diabetes My Way (MDMW) is an electronic personal health record (ePHR) that provides access to educational resources and clinical data to people with diabetes in Scotland. This questionnaire study aims to assess user experience, barriers to access, and inform future development. Methods: All active MDMW users (n = 3797) were invited to complete an online questionnaire in May 2015, surveying usage patterns and system utility. A “utility score” was calculated, based on responses to Likert-scale questions and used as the dependent variable within regression analysis, with demographic features as independent predictors. Free-text responses were analyzed thematically and presented using descriptive statistics. Results: A total of 1095/3797 (27.5%) active users completed the survey. Of them, 690/1095 (63%) were male. There was representation of all age and socioeconomic groups. Respondents were positive regarding the system utility, which met expectations. The majority of respondents believed that online access to diabetes information has the potential to improve diabetes self-care within the population. The most valued features were personal clinical data associated visualizations. Th main problems cited were data accuracy and system access (ie, log-in procedure). Perceived usefulness of the system was inversely associated with duration of diabetes, which was the only significant predictor of utility score. Conclusions: This study has demonstrated that MDMW users find the system useful in supporting diabetes self-management. The system was found to have greatest utility among those most recently diagnosed with diabetes. This study has informed further development of the service, including enhancing data visualization and the need to improve access to the system.

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Liu, Zhenhua, Yaohui Liu, and Yaqing Fan. "Searchable Attribute-Based Signcryption Scheme for Electronic Personal Health Record." IEEE Access 6 (2018): 76381–94. http://dx.doi.org/10.1109/access.2018.2878527.

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Evans, Beth A., Claudia J. Beverly, Pao-Feng Tsai, Mallikarjuna Rettiganti, Leanne L. Lefler, and Rachida F. Parks. "Older Adults’ Live Demonstration of Electronic Personal Health Record Use." CIN: Computers, Informatics, Nursing 36, no. 12 (December 2018): 603–9. http://dx.doi.org/10.1097/cin.0000000000000448.

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Lee, Sol-Bee, Jung-Hyok Kwon, Eui-Jik Kim, and Jaehoon Park. "Implementation of Integrated Electronic Health Record and Mobile Personal Health Record Datasets for Improving Healthcare Services." Sensors and Materials 30, no. 8 (August 31, 2018): 1885. http://dx.doi.org/10.18494/sam.2018.1896.

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Giordanengo, Alain, Meghan Bradway, Rune Pedersen, Astrid Grøttland, Gunnar Hartvigsen, and Eirik Årsand. "Integrating data from apps, wearables and personal Electronic Health Record (pEHR) systems with clinicians’ Electronic Health Records (EHR) systems." International Journal of Integrated Care 16, no. 5 (November 9, 2016): 16. http://dx.doi.org/10.5334/ijic.2565.

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Moghbeli, Fateme, Tina Adel, Mostafa Langarizadeh, and Mitra Hakim Shoushtari. "A Conceptual Model of Personal Health Record for Autistic Patients." Frontiers in Health Informatics 8, no. 1 (August 27, 2019): 18. http://dx.doi.org/10.30699/fhi.v8i1.197.

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Introduction: The personal health record of patient with autism has not been adequately addressed. While, these records play an important role to provide a good perspective on how to diagnose autism, negotiating, coordinating and designing services and interventions by families and physicians during delivery of multiple services. The present study is aimed to present a conceptual model of electronic personal health record for patients with autism.Material and Methods: This study was conducted on six psychiatric practitioners and six pediatricians from Raoul-e-Akram Hospital and Ali Asghar Hospital with purposeful sampling. A questionnaire has been used for data gathering and identifying information needs. The content validity was measured using expert panel method and test-retest method (r=0.79) was used to perform the reliability test.Results: According to the findings, the most important data elements of the conceptual model of the personal health record for patients with autism from the viewpoint of physicians are divided into two demographic data segments including first name, father's name, national code, Age, gender, marital status, address and telephone, and clinical data including maternal gestational age, family history, parental relationship, history of developmental delay, name of patient's drug use, test results, age of the first diagnosis of autism , seizure, mental retardation, early diagnosis, treatment history and individual function level (based on valid clinical scales) such as a general clinical measure .Conclusion: Since autism patients’ long life clinical information that recorded in their electronic health records system plays an important role in diagnosis and management of patients' clinical problems, the conceptual model of personal health record for patients with autism could have a significant effect in managing and integrating patient clinical information, increasing the quality of care, and reducing the cost of treatment and diagnosis.

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Park, Hyun-A. "The Study on Health Information Characteristics and Privacy." Journal of Medical Imaging and Health Informatics 10, no. 11 (November 1, 2020): 2543–50. http://dx.doi.org/10.1166/jmihi.2020.3267.

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Because health information has some different properties from other general data, it is important to understand 'information subject,' 'subject of information generation,' 'subject of information management' according to the characteristics of each medical information. It makes it possible to develop the appropriate security technology under the current legal regulations. In this paper, we identify some incorrect uses in existing papers, we show that "Patient-Participated on Electronic Health Record Systems" is more appropriate expression rather than "Patient-Controlled on Electronic Health Record Systems." We discuss three key factors (information subject, subject of information generation, subject of information management) of medical information and 'personal information self-determination.' As a solution for privacy, we suggest the 'Secure and dynamic consent system' and 'Personally-controlled health record on PHR (Personal Health Records)' should be developed under the current law and the current (or future) integrated medical information system.

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Park, Hyun-A. "The Study on Health Information Characteristics and Privacy." Journal of Medical Imaging and Health Informatics 10, no. 11 (November 1, 2020): 2543–50. http://dx.doi.org/10.1166/jmihi.2020.32672543.

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Because health information has some different properties from other general data, it is important to understand 'information subject,' 'subject of information generation,' 'subject of information management' according to the characteristics of each medical information. It makes it possible to develop the appropriate security technology under the current legal regulations. In this paper, we identify some incorrect uses in existing papers, we show that "Patient-Participated on Electronic Health Record Systems" is more appropriate expression rather than "Patient-Controlled on Electronic Health Record Systems." We discuss three key factors (information subject, subject of information generation, subject of information management) of medical information and 'personal information self-determination.' As a solution for privacy, we suggest the 'Secure and dynamic consent system' and 'Personally-controlled health record on PHR (Personal Health Records)' should be developed under the current law and the current (or future) integrated medical information system.

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Nurhayati, Yunita Wisda Tumarta Arif, and Ahmad Yusron Yunizar. "Rancang Bangun Website Rekam Medis Elektronik di Fasilitas Pelayanan Kesehatan Praktik Dokter." Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan 10, no. 2 (September 28, 2020): 49–54. http://dx.doi.org/10.47701/infokes.v10i2.1033.

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Electronic medical records record electronic medical records which contain personal data, demographic data, social data, clinical / medical data. Processing of medical record documents at doctor's practice health facilities is still done manually, starting from patient registration, writing examination history, and storing medical record documents. One of the efforts to overcome these obstacles is by building an Electronic Medical Record website. The website development method uses the development life cycle system. Medical records are processed from input patient data, diagnostic data, action data, drug data, officer data, registration data, examination data. Then the data is processed to produce reports, including patient data, and examination data. The electronic medical record website used can simplify the processing of medical record data.

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Ong, Rebecca Yoke Chan, and Sandy Sabapathy. "Enhancing patient privacy protection under Hong Kong’s Electronic Health Record Sharing System." Common Law World Review 49, no. 1 (March 2020): 4–30. http://dx.doi.org/10.1177/1473779520914290.

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While it is true that the expanded use of health information and electronic health records (eHRs) can help deliver better healthcare, there remains the need to reconcile citizens’ legitimate concerns for privacy protection and confidentiality in the use of their personal health data, and the potential for violation of their privacy. Under the Hong Kong’s Electronic Health Record Sharing System (eHRSS), the eHR of the individual patient can be accessed and shared between healthcare providers for healthcare-related purposes. Although the Electronic Health Record Sharing System Ordinance (Cap 625) (the ‘eHRSSO’) and the Personal Data (Privacy) Ordinance (Cap 486) (the ‘PD(P)O’) provide protection for personal data and patients’ privacy, the eHRSS has come under greater scrutiny given the rise in data breaches experienced globally and in Hong Kong. The article’s objective is twofold. It first examines the eHRSS specifically with regard to some of the more pertinent provisions of the eHRSSO and the PD(P)O, to critically evaluate the extent to which these provisions ensure and protect patient privacy. Thence it offers suggestions and recommendations as to how protection for patient privacy can be enhanced and, indeed, altogether better ensured.

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Kim, Dong Myung, and Baeg Weon Hong. "Personal Privacy Protection of Electronic Medical Record in Health Information System." Journal of Korean Society of Medical Informatics 13, no. 3 (2007): 249. http://dx.doi.org/10.4258/jksmi.2007.13.3.249.

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Cunningham, Scott G., Deborah J. Wake, Annalu Waller, Andrew D. Morris, and James Walker. "My Diabetes My Way: an electronic personal health record for diabetes." British Journal of Diabetes & Vascular Disease 13, no. 3 (May 2013): 143–49. http://dx.doi.org/10.1177/1474651413493336.

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Pagliari, Claudia, Don Detmer, and Peter Singleton. "Potential of electronic personal health records." BMJ 335, no. 7615 (August 16, 2007): 330–33. http://dx.doi.org/10.1136/bmj.39279.482963.ad.

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FAUNTLEROY, GLENDA. "Public Resists Electronic Personal Health Records." Clinical Psychiatry News 35, no. 5 (May 2007): 54. http://dx.doi.org/10.1016/s0270-6644(07)70332-6.

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McDermott, Donna S., Jessica L. Kamerer, and Andrew T. Birk. "Electronic Health Records." International Journal of Cyber Research and Education 1, no. 2 (July 2019): 42–49. http://dx.doi.org/10.4018/ijcre.2019070104.

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Electronic health records (EHRs) pose unique concerns for administrators and information technology professionals with regard to cybersecurity. Due to the sensitive nature and increasing value of personal health information, cyber risks and information protection should be a high priority. A literature review was conducted to identify potential threat categories and best practices in protecting EHR information. Potential threats were identified and categorized into five areas; physical, portable devices, insider use, technical, and administrative. Government policies have created administrative, physical, and technical safeguards to keep EHR information safe. Despite these efforts, EHRs are being targeted by cyber-criminals due to flaws in personal and organizational management of protected healthcare information. This paper aims to educate, inform, and advocate for the proper handling of EHRs to alleviate the burden caused by compromised electronic documents.

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Horne, William Connor, and Zina Ben Miled. "Making the Case for a P2P Personal Health Record." Information 11, no. 11 (October 31, 2020): 512. http://dx.doi.org/10.3390/info11110512.

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Improved health care services can benefit from a more seamless exchange of medical information between patients and health care providers. This exchange is especially important considering the increasing trends in mobility, comorbidity and outbreaks. However, current Electronic Health Records (EHR) tend to be institution-centric, often leaving the medical information of the patient fragmented and more importantly inaccessible to the patient for sharing with other health providers in a timely manner. Nearly a decade ago, several client–server models for personal health records (PHR) were proposed. The aim of these previous PHRs was to address data fragmentation issues. However, these models were not widely adopted by patients. This paper discusses the need for a new PHR model that can enhance the patient experience by making medical services more accessible. The aims of the proposed model are to (1) help patients maintain a complete lifelong health record, (2) facilitate timely communication and data sharing with health care providers from multiple institutions and (3) promote integration with advanced third-party services (e.g., risk prediction for chronic diseases) that require access to the patient’s health data. The proposed model is based on a Peer-to-Peer (P2P) network as opposed to the client–server architecture of the previous PHR models. This architecture consists of a central index server that manages the network and acts as a mediator, a peer client for patients and providers that allows them to manage health records and connect to the network, and a service client that enables third-party providers to offer services to the patients. This distributed architecture is essential since it promotes ownership of the health record by the patient instead of the health care institution. Moreover, it allows the patient to subscribe to an extended range of personalized e-health services.

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Ross, M. K., Wei Wei, and L. Ohno-Machado. "“Big Data” and the Electronic Health Record." Yearbook of Medical Informatics 23, no. 01 (August 2014): 97–104. http://dx.doi.org/10.15265/iy-2014-0003.

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Summary Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on “big data” in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. Methods: We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to “big data” and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Results: Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. Conclusion: The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of “big data”, and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

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Jones, Dixie A., Jean P. Shipman, Daphne A. Plaut, and Catherine R. Selden. "Characteristics of personal health records: findings of the Medical Library Association/National Library of Medicine Joint Electronic Personal Health Record Task Force." Journal of the Medical Library Association : JMLA 98, no. 3 (July 2010): 243–49. http://dx.doi.org/10.3163/1536-5050.98.3.013.

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Kroth, Philip J., Nancy Morioka-Douglas, Sharry Veres, Katherine Pollock, Stewart Babbott, Sara Poplau, Katherine Corrigan, and Mark Linzer. "The electronic elephant in the room: Physicians and the electronic health record." JAMIA Open 1, no. 1 (June 11, 2018): 49–56. http://dx.doi.org/10.1093/jamiaopen/ooy016.

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AbstractObjectivesDetermine the specific aspects of health information and communications technologies (HICT), including electronic health records (EHRs), most associated with physician burnout, and identify effective coping strategies.Materials and methodsWe performed a qualitative analysis of transcripts from 2 focus groups and a burnout assessment of ambulatory physicians—each at 3 different health care institutions with 3 different EHRs.ResultsOf the 41 clinicians, 71% were women, 98% were physicians, and 73% worked in primary care for an average of 11 years. Only 22% indicated sufficient time for documentation. Fifty-six percent noted “a great deal of stress” because of their job. Forty-two percent reported “poor” or “marginal” control over workload. Even though 90% reported EHR proficiency, 56% indicated EHR time at home was “excessive” or “moderately high.” Focus group themes included HICT “successes” where all patients’ information is accessible from multiple locations. HICT “stressors” included inefficient user interfaces, unpredictable system response times, poor interoperability between systems and excessive data entry. “Adverse outcomes” included ergonomic problems (eg, eye strain and hand, wrist, and back pain) and decreased attractiveness of primary care. Suggested “organizational changes” included EHR training, improved HICT usability, and scribes. “Personal/resilience” strategies focused on self-care (eg, exercise, maintaining work-life boundaries, and positive thinking).Discussion and conclusionHICT use, while beneficial in many ways for patients and providers, has also increased the burden of ambulatory practice with personal and professional consequences. HICT and clinic architectural and process redesign are likely necessary to make significant overall improvements.

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Trinkoff, A. M., C. L. Storr, M. L. Wilson, A. P. Gurses, and K. Gartrell. "Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health." Applied Clinical Informatics 06, no. 02 (2015): 224–47. http://dx.doi.org/10.4338/aci-2014-11-ra-0107.

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SummaryBackground: 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.Citation: Gartrell K, Trinkoff AM, Storr CL, Wilson ML, Gurses AP. Testing the electronic personal health record acceptance model by nurses for managing their own health: A cross-sectional survey. Appl Clin Inf 2015; 6: 224–247http://dx.doi.org/10.4338/ACI-2014-11-RA-0107

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GARTRELL, KYUNGSOOK, ALISON M. TRINKOFF, CARLA L. STORR, and MARISA L. WILSON. "Electronic Personal Health Record Use Among Nurses in the Nursing Informatics Community." CIN: Computers, Informatics, Nursing 33, no. 7 (July 2015): 306–14. http://dx.doi.org/10.1097/cin.0000000000000163.

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Shashi, Dr Manish. "Leveraging Blockchain-Based Electronic Health Record Systems in Healthcare 4.0." International Journal of Innovative Technology and Exploring Engineering 12, no. 1 (December 30, 2022): 1–5. http://dx.doi.org/10.35940/ijitee.a9359.1212122.

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Digitalization has become a crucial part of healthcare 4.0 by transforming systems such as electronic health records (EHR), electronic medical records (EMR), and electronic personal medical records (ePHR). Healthcare 4.0 is derived from industry 4.0 and aims to enhance collaboration, virtualization, coherence, and convergence, which helps transform modern healthcare into more personalized and predictive. Healthcare 4.0 also aims to develop digital enablers which will support coordination among various stakeholders and seamless information flow in the patient journey towards wellbeing. These systems enhance patient care through the timely sharing of patient data across different providers globally. Timely sharing helps, but it also makes the electronic system vulnerable to alteration and breaches. In healthcare, blockchain application is widely used in various areas, such as health information exchange, pharmaceutical counterfeit, clinical trials, health supply chain management, patient data management, insurance claims, and product recall in case of adverse events. This research paper aims to identify how blockchain technology can help enhance the privacy and security of electronic health record systems. This paper discusses various blockchain-based systems, which provide a more efficient and secure option than client-server architecture-based traditional EHR systems.

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Roehrs, Alex, Cristiano Andre da Costa, Rodrigo da Rosa Righi, Sandro Jose Rigo, and Matheus Henrique Wichman. "Toward a Model for Personal Health Record Interoperability." IEEE Journal of Biomedical and Health Informatics 23, no. 2 (March 2019): 867–73. http://dx.doi.org/10.1109/jbhi.2018.2836138.

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Clarke, Janice L., and Deborah C. Meiris. "Electronic Personal Health Records Come of Age." American Journal of Medical Quality 21, no. 3_suppl (May 2006): 5S—15S. http://dx.doi.org/10.1177/1062860606287642.

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Kidd, Michael R. "Personal electronic health records: MySpace or HealthSpace?" BMJ 336, no. 7652 (May 6, 2008): 1029–30. http://dx.doi.org/10.1136/bmj.39567.550301.80.

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Alsahafi, A. Yaser A., and B. Valerie Gay. "An overview of electronic personal health records." Health Policy and Technology 7, no. 4 (December 2018): 427–32. http://dx.doi.org/10.1016/j.hlpt.2018.10.004.

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Lazarova, Elena, Sara Mora, Norbert Maggi, Carmelina Ruggiero, Alessandro Cosolito Vitale, Paolo Rubartelli, and Mauro Giacomini. "An Interoperable Electronic Health Record System for Clinical Cardiology." Informatics 9, no. 2 (June 13, 2022): 47. http://dx.doi.org/10.3390/informatics9020047.

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Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.

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Day, Jeams. "Cyberinfo: Privacy and Personal Health Data in Cyberspace." Journal of Contemporary Dental Practice 1, no. 4 (1999): 19–24. http://dx.doi.org/10.5005/jcdp-1-4-19.

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Abstract In this series of articles, the author discusses the potential risks, benefits, and liabilities in using electronic communications and computer-based record keeping for patients’ medical data. Article #1 in this series reviews the foundations of privacy for personal information and the current practices of collecting disaggregated private personal and medical data made possible on the Internet by software and hardware configurations.

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Kim, G. R. "Linking Personal, Public Health Data via Electronic Health Records." AAP Grand Rounds 30, no. 3 (September 1, 2013): 33. http://dx.doi.org/10.1542/gr.30-3-33.

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Chen, Ming-Te, and Tsung-Hung Lin. "A Provable and Secure Patient Electronic Health Record Fair Exchange Scheme for Health Information Systems." Applied Sciences 11, no. 5 (March 8, 2021): 2401. http://dx.doi.org/10.3390/app11052401.

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In recent years, several hospitals have begun using health information systems to maintain electronic health records (EHRs) for each patient. Traditionally, when a patient visits a new hospital for the first time, the hospital’s help desk asks them to fill in relevant personal information on a piece of paper and verifies their identity on the spot. This patient will find that many of her personal electronic records are in many hospital’s health information systems that she visited in the past, and each EHR in these hospital’s information systems cannot be accessed or shared between these hospitals. This is inconvenient because this patient will again have to provide their personal information. This is time-consuming and not practical. Therefore, in this paper, we propose a practical and provable patient EHR fair exchange scheme for each patient. In this scheme, each patient can securely delegate the information system of a current hospital to a hospital certification authority (HCA) to apply migration evidence that can be used to transfer their EHR to another hospital. The delegated system can also establish a session key with other hospital systems for later data transmission, and each patient can protect their anonymity with the help of the HCA. Additionally, we also provide formal security proofs for forward secrecy and functional comparisons with other schemes.

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Luo, Yan. "ELECTRONIC PERSONAL HEALTH RECORD USE AMONG ELDERLY CANCER SURVIVORS AND NON-CANCER SURVIVORS." Innovation in Aging 3, Supplement_1 (November 2019): S325. http://dx.doi.org/10.1093/geroni/igz038.1185.

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Abstract Background Electronic personal health records (ePHRs) are potential tools to improve clinical outcomes through increasing patients’ self-management. Although elderly people, especially elderly cancer survivors, is a growing population who can benefit from ePHRs, little is known about its utilization among the elderly, particularly among those diagnosed with cancer. Objective By applying Anderson’s Behavioral Model of Health Services Use, this study aims to examine and compare the associated factors with ePHRs use among elderly cancer survivors and non-cancer survivors. Methods The data collected from the 2018 Health Information National Trends Survey (HINTS) was analyzed. The level of access to ePHRs among the elderly were assessed. Predictors of ePHRs use among elderly cancer survivors and non-cancer survivors were compared by conducting multiple linear regression. According to Anderson’s Model, predisposing factors, enabling factors, and need factors were included in the statistical model. Results The overall use of ePHRs remained low among 577 participants (mean = .87, SD = 1.72, range from 0 to 4). Non-Cancer survivors reported lower ePHRs use (mean = .83, SD = 1.77, range from 0 to 4). Race/ethnicity, education, regular health care providers, health insurance, social support, and medical conditions were associated with ePHRs use among non-cancer survivors, while age, gender, social support, and self-reported health status were related to ePHRs use among cancer survivors. Conclusion This study suggests additional efforts to increase ePHRs utilization among the elderly, especially the elderly cancer survivors. The predictive findings reported in this study will contribute valuable implications to enhance the ePHRs use.

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Califano, Licia. "The Electronic Health Record (EHR): Legal framework and issues about personal data protection." Pharmaceuticals Policy and Law 19, no. 3-4 (October 17, 2018): 141–59. http://dx.doi.org/10.3233/ppl-180454.

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Rybynok, V. O., P. A. Kyriacou, J. Binnersley, and A. Woodcock. "MyCare Card Development: Portable GUI Framework for the Personal Electronic Health Record Device." IEEE Transactions on Information Technology in Biomedicine 15, no. 1 (January 2011): 66–73. http://dx.doi.org/10.1109/titb.2010.2091143.

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Czaja, Sara J., Christina Zarcadoolas, Wendy L. Vaughon, Chin Chin Lee, Maxine L. Rockoff, and Joslyn Levy. "The Usability of Electronic Personal Health Record Systems for an Underserved Adult Population." Human Factors: The Journal of the Human Factors and Ergonomics Society 57, no. 3 (September 5, 2014): 491–506. http://dx.doi.org/10.1177/0018720814549238.

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Williamson, Rebecca S., Brooke O. Cherven, Jordan Gilleland Marchak, Paula Edwards, Michael Palgon, Cam Escoffery, Lillian R. Meacham, and Ann C. Mertens. "Meaningful Use of an Electronic Personal Health Record (ePHR) among Pediatric Cancer Survivors." Applied Clinical Informatics 26, no. 01 (2017): 250–64. http://dx.doi.org/10.4338/aci-2016-11-ra-0189.

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Summary Background and Objectivs: Survivors of pediatric and adolescent cancer are at an increased risk of chronic and debilitating health conditions and require life-long specialized care. Stand-alone electronic personal health records (ePHRs) may aid their self-management. This analysis characterizes young adult survivors and parents who meaningfully use an ePHR, Cancer SurvivorLinkTM, designed for survivors of pediatric and adolescent cancer. Methods: This was a retrospective observational study of patients seen at a pediatric survivor clinic for annual survivor care. Young adult survivors and/or parent proxies for survivors <18 years old who completed ePHR registration prior to their appointment or within 90 days were classified as registrants. Registrants who uploaded or downloaded a document and/or shared their record were classified as meaningful users. Results: Overall, 23.7% (148/624) of survivors/parents registered and 38% of registrants used SurvivorLink meaningfully. Young adult registrants who transferred to adult care during the study period were more likely to be meaningful users (aOR: 2.6 (95% CI: 1.1, 6.1)) and used the ePHR twice as frequently as those who continued to receive care in our institution’s pediatric survivor clinic. Among survivors who continued to receive care at our institution, being a registrant was associated with having an annual follow-up visit (aOR: 2.6 (95% CI: 1.2, 5.8)). Conclusions: While ePHRs may not be utilized by all survivors, SurvivorLink is a resource for a subset and may serve as an important bridge for patients who transfer their care. Using SurvivorLink was also associated with receiving recommended annual survivor care.

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Liu, Yang, Zhaoxiang Yu, and Yunlong Yang. "Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis." Journal of Healthcare Engineering 2021 (March 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/6678526.

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In today’s society, the development of information technology is very rapid, and the transmission and sharing of information has become a development trend. The results of data analysis and research are gradually applied to various fields of social development, structured analysis, and research. Data mining of electronic medical records in the medical field is gradually valued by researchers and has become a major work in the medical field. In the course of clinical treatment, electronic medical records are edited, including all personal health and treatment information. This paper mainly introduces the research of diabetes risk data mining method based on electronic medical record analysis and intends to provide some ideas and directions for the research of diabetes risk data mining method. This paper proposes a research strategy of diabetes risk data mining method based on electronic medical record analysis, including data mining and classification rule mining based on electronic medical record analysis, which are used in the research experiment of diabetes risk data mining method based on electronic medical record analysis. The experimental results in this paper show that the average prediction accuracy of the decision tree is 91.21%, and the results of the training set and the test set are similar, indicating that there is no overfitting of the training set.

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