From a simple EHR to the market lead: what technologies to add

Electronic health records (EHRs) can store, capture, and present patient data in an organized way that improves physicians’ workflow and patient care. This makes EHRs key to addressing many of today’s health care challenges. An interdisciplinary review and qualitative study of artificial intelligence, machine learning, natural language processing, and real-time location services in health care was conducted. The results show that in an industry where digitization is key, several recommendations can be made to leverage these technologies in ways that can improve current systems and help EHR vendors become the market lead

A Review of Electronic Health Records Systems Around the World

Electronic Medical Records (EMR) are digitised versions of the paper charts in clinician offices, clinics and hospitals. The information in an EMR is usually stored locally at a practice or a hospital, and it contains the medical and treatment history of a patient. [1] [2] [3] Electronic Health Records (EHR) focus on the total health of a patient, and are designed to reach out beyond the health organisation. The EHR systematically collate and store digitised data on patients from the different healthcare and medical organisations and providers. They also enable the secure electronic sharing of these data between the different healthcare settings, and in some instances, the patient. The information, which includes the EMR, moves with the patient between different healthcare settings, providing a more holistic view of the state of a patient across time. The EHR can also provide information on population health by aggregating relevant data (permissions providing). Sometimes EHR is also referred to as an Electronic Patient Record (EPR). [1] [2] [3

Perceptions of Electronic Health Records Effects on Staffing, Workflow, & Productivity in Community Health Centers

Significant Federal investments under the Health Information Technology for Economic and Clinical Health Act of 2009 and the Affordable Care Act have motivated many community health centers (CHCs) to implement electronic health records (EHRs) in the past few years. Evidence suggests that EHR implementation causes significant changes in how primary care clinicians spend their time and may be associated with changes in staff and facility level productivity. However, the mechanisms to explain these changes were mostly speculative. The goals of this project were to understand how, from the perspective of clinicians, support staff, and administrators, CHCs’ implementation of EHRs has changed staffing models, staff roles, and workflow, as well as the mechanisms by which EHRs influence staff productivity, coordination between providers, and quality of care. Key Questions How has EHR implementation changed staffing models in CHCs? How has EHR implementation changed staff roles and workflow in CHCs? How have these changes influenced CHC productivity and quality of care

Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governance surrounding its use and integration is often poorly understood. What the Health AI Partnership (HAIP) aims to do in this research is to better define the requirements for adequate organizational governance of AI systems in healthcare settings and support health system leaders to make more informed decisions around AI adoption. To work towards this understanding, we first identify how the standards for the AI adoption in healthcare may be designed to be used easily and efficiently. Then, we map out the precise decision points involved in the practical institutional adoption of AI technology within specific health systems. Practically, we achieve this through a multi-organizational collaboration with leaders from major health systems across the United States and key informants from related fields. Working with the consultancy IDEO.org, we were able to conduct usability-testing sessions with healthcare and AI ethics professionals. Usability analysis revealed a prototype structured around mock key decision points that align with how organizational leaders approach technology adoption. Concurrently, we conducted semi-structured interviews with 89 professionals in healthcare and other relevant fields. Using a modified grounded theory approach, we were able to identify 8 key decision points and comprehensive procedures throughout the AI adoption lifecycle. This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States. We hope these findings can inform future efforts to build capabilities to promote the safe, effective, and responsible adoption of emerging technologies in healthcare

Understanding the processes of Information Systems deployment and evaluation: The challenges facing e-health

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Information Systems (IS) innovations in healthcare sector are seen as panacea to control burgeoning demand on healthcare resources and lack of streamlining in care delivery. Two particular manifestations of such innovations are telehealth and electronic records in its two forms: the electronic medical records and the electronic health records. Deployment efforts concerning both of these IS-innovations have encountered a rough terrain and have been slow. Problems are also faced while evaluating the effectiveness of innovations on health and care delivery outcomes through strategies such as randomised controlled trials- particularly in case of telehealth. By taking these issues into account, this research investigates the issues that affect IS innovation deployment and its evaluation. The strategy adopted in this research was informed by recursive philosophy and theoretical perspectives within IS that strived to expound this recursive relationship. It involved conducting two longitudinal case studies that are qualitative in nature. The first study involved telehealth deployment and its evaluation in the UK, while the second case study involved the deployment of electronic medical/health records in the US. Data was collected through focus group discussions, interviews and online discussion threads; and was analysed thematically. The results of this research indicate that there are nine issues that arise and affect the deployment and evaluation of IS innovation in healthcare; and these are design, efficiency and effectiveness, optimality and equity, legitimacy, acceptance, demand and efficacy, expertise, new interaction patterns, and trust. These issues are attributes of relationships between the IS innovation, context of healthcare and the user. The significance of these attributes varies during the deployment and evaluation process, and due to iterative nature of IS innovation. This research further indicates that all the attributes have either direct or indirect impact on work practices of the user.Multidisciplinary Assessment of Technology Centre for Healthcare (MATCH

Strategies Rural Hospital Leaders Use to Implement Electronic Health Record

The Centers for Medicare and Medicaid Services issued over 144,000 payments totaling $7.1 billion to medical facilities that have adopted and successfully demonstrated meaningful use of certified electronic health record (EHR). Hospital organizations can increase cost savings by using the electronic components of EHRs to improve medical coding and reduce medical errors and transcription costs. Despite the incentives, some rural health care facilities are failing to progress. The purpose of this multiple case study was to explore the strategies rural hospital leaders used to implement an EHR. The target population consisted of rural hospital leaders who were involved in the successful implementation of an EHR in South Texas. The conceptual framework chosen for this study was the sociotechnical systems theory. Data were collected through telephone interviews using open-ended semistructured interviews with 5 participants from 4 rural hospitals who were involved in the EHR implementation. Data analysis occurred using Yin\u27s 5-step process which includes compiling, disassembling, reassembling, interpreting, and concluding. Data analysis included collecting information from government websites, company documents, and open-ended information to develop recurring themes. Several themes emerged including ongoing training, provider buy-in, constant communication, use of super users, and workflow maintenance. The findings could influence social change by making the delivery of health care more efficient and improving quality, safety, and access to health care services for patients

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Health information technology and digital innovation for national learning health and care systems

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public–private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public–private partnerships, and ethically and safely apply artificial intelligence in the National Health Service

Managerial Strategies for Maximizing Benefits From Electronic Health Record Systems

In 2009, the U.S. government allocated $27 billion to health care agencies for electronic health records (EHRs) implementation. The increased use of EHR systems is expected to drive down health care costs and increase profits. To meet this anticipated return on investment (ROI), hospital managers need to be able to successfully design, deploy, and manage EHR systems. The purpose of this single case study was to explore organizational management strategies that hospital managers can use to ensure their investments in EHRs meet targeted ROIs and work efficiency goals. The conceptual framework for this study was based on the technology acceptance model. Primary data were collected from a criterion sample of 6 hospital managers with direct experience designing and implementing successful EHRs in a small hospital in the Northeastern United States. Secondary data were collected using public financial records available on the Internet. After cataloging and grouping the raw data, 4 emergent themes were identified: (a) training, (b) the role of organizational management strategies, (c) technological barriers, and (d) ongoing support and maintenance. Findings may contribute to social change through an increase in the quality of patient care and making health care records more accessible to doctors in isolated areas