Healthcare Information Technology Research Articles

The Interplay between Healthcare Information Technologies and Denied Claims

The Interplay between Healthcare Information Technologies and Denied Claims

Enhancing Healthcare IT Cybersecurity Resilience: Integrating CMMC Controls with HIPAA Compliance

Enhancing Healthcare IT Cybersecurity Resilience: Integrating CMMC Controls with HIPAA Compliance

Sustainability Practices in IT Infrastructure for Healthcare

In recent years, sustainability has become a critical focus across industries, and healthcare is no exception. The growing digitalization in healthcare, while improving efficiency and patient care, has also raised concerns about the environmental impact of IT infrastructure. Healthcare organizations increasingly rely on advanced technologies such as electronic health records (EHR), telemedicine, and AI-driven diagnostics, all of which demand robust IT systems. However, the energy consumption and e-waste generated by these systems can be substantial. To address this, healthcare IT leaders are adopting sustainability practices that include energy-efficient data centers, cloud computing, and hardware recycling programs. Transitioning to green technologies, such as renewable energy-powered data centers and virtualization, helps reduce the carbon footprint of IT infrastructure. Additionally, implementing software solutions that optimize resource use, reducing energy consumption in hospitals through smart systems, and adopting sustainable procurement policies are becoming essential strategies. The healthcare sector is also focusing on reducing e-waste by extending the life of IT equipment through proper maintenance and disposal practices. By integrating sustainability into IT strategies, healthcare organizations not only contribute to environmental protection but also improve operational efficiency and reduce costs. As patient data continues to grow, scalable, environmentally conscious infrastructure will become key. This shift towards sustainable IT solutions is not just an ethical responsibility but also a smart business strategy, aligning healthcare operations with global sustainability goals. By 2024 and beyond, the healthcare industry is set to witness a significant transformation in its IT infrastructure, driven by both technological advancements and an increasing commitment to sustainability.

Enhancing Healthcare Data Entry Efficiency and Accuracy through Voice Assistant Systems: A Case Study of Obijackson Hospital Okija

Traditional data entry methods are often time-consuming and prone to errors, negatively impact patient care and administrative workflows. To address these challenges, this research proposed a voice-assisted data entry system that leverages advanced speech recognition and natural language processing technologies. Object Oriented Analysis and Design Methods were used to implement a system that allows healthcare providers to input patient data and medical information through voice commands, streamlining the data entry process and reducing the cognitive load on healthcare professionals. The system’s design incorporates user-centred principles, ensuring it is intuitive and seamlessly integrated into existing healthcare IT infrastructures. Key features include real-time speech-to-text conversion, contextual understanding of medical terminology, and robust data security measures to protect patient information. The implementation phase involved rigorous testing in a simulated healthcare environment, assessing the system’s accuracy, speed, and user satisfaction. Results indicated significant improvements in data entry efficiency and reduced error rates compared to manual entry methods. Feedback from healthcare professionals highlighted the system’s potential to enhance productivity and patient care quality. This dissertation contributes to the field of healthcare informatics by providing a practical solution to a critical problem, demonstrating the feasibility and benefits of voice-assisted technologies in medical data management. Future work will explore further refinements and the potential for broader adoption across diverse healthcare settings.

The use of Information and Communication Technologies in Primary Health Care in Brazil - the period of 2014 to 2018.

Information and Communication Technologies in Health allow the storage and processing of digital data, access to information and remote communications. The objective of this article was to describe the use of these Technologies to support clinical practice and continuing education by primary health care teams in Brazil the period of 2014 to 2018, within the scope of the Access and Quality Improvement Program Basic, according to characteristics of the geopolitical context. It's a cross-sectional study that analyzed the data collected from the teams of the Basic Health Units. A growing use by the teams of Telehealth resources, the Telemedicine University Network and the Open University of the Unified Health System was observed to aid clinical practice and permanent health education. In the North and Northeast regions, the use of these Technologies doubled, from cycle II to cycle III. The need for investments in infrastructure, human resources in Primary Health Care, qualification and professional training is a way to strengthen the Unified Health System and its Health Care Network, contributing to a continuous flow of care, with quality and access universal.

Evolution of the availability of Information and Communication Technologies in primary health care in Brazil, 2012 to 2018.

To verify the evolution of the availability of information and communication technology equipment and inputs in primary health care services that participated in the external evaluation of the Access and Quality Improvement Program in Primary Care and its distribution according to context characteristics social and geographic. Cross-sectional study, analyzed the distribution of information and communication technology equipment in basic health units in Brazil, during the three cycles (2012 to 2018) of the Program for Improving Access and Quality in Primary Care. The variables were examined at the municipal level and stratified by geopolitical region. Univariate analysis was performed, using the chi-square test and testing the distributions of exposures among themselves and between the outcome and exposures. The availability of information and communication technology equipment increased from 9.4% (2012) to 17.5% (2018), with emphasis on the Southeast and South regions, in municipalities with a population size of up to 10,000 inhabitants², with greater family health coverage and high/very high HDI-M. Over the period from 2012 to 2018, basic units joined the program and increased availability of information and communication technologies, such as Internet access, which ranged from 45.2% (n=6,249) to 74.0% (n=21,423), with emphasis on the Northeast region, which increased from 19.1% (n=970) to 58.8% (n=7,087). Investment in technologies and constant evaluation of primary care in the country is necessary, contributing to its strengthening.

Embracing digital health: German otolaryngology patients' usage and prospects of digital information and communication technologies for cross-sectoral care.

The usage of digital information and communication technologies in European healthcare is growing. Unlike numerous technological possibilities, the present use of these technologies and perspectives towards them in relation to otolaryngology care have so far been of less interest. This study evaluates the utilisation of and attitudes towards digital information and communication technologies in cross-sectoral otolaryngology care among German patients. A structured interview-based study was conducted at the outpatient facility of a tertiary hospital in Germany. It focused on chief complaints, current use of digital technologies, estimated benefits of increased digital technology use in otolaryngology care, and sociodemographic data. The detailed statistical analysis employed Chi-squared tests and multivariate logistic regression. A total of 208 otolaryngology patients completed the interview. Digital communication technologies exhibited a high penetration rate (91.8%) and were regularly used in daily life (78.7%) and for health reasons (73.3%). Younger age (p ≤ 0.003) and higher education levels (p ≤ 0.008) were significantly correlated with the increased digital communication technology use. The overall potential of eHealth technologies was rated significantly higher by younger patients (p ≤ 0.001). The patients' chief complaints showed no significant influence on the current and potential use of these technologies for cross-sectoral otolaryngology care. Regardless of their chief complaints, German otolaryngology patients regularly use digital information and communication technologies for health reasons and express interest in their further use for cross-sectoral care. To enhance digital patient communication in otolaryngology, attention should be given to treatment quality, usability, data security and availability and financial remuneration for service providers.

Innovation and development of traditional Chinese medicine telemedicine service model

Abstract The innovation and development of Traditional Chinese Medicine (TCM) telemedicine service models are increasingly gaining attention as an important component of the modern healthcare system. This paper discusses the innovative service models that integrate TCM with telemedicine technology. Firstly, it introduces the background of TCM telemedicine services, pointing out that while providing convenient and efficient medical services, it also promotes the inheritance and development of TCM culture. Subsequently, the paper focuses on the innovation of TCM remote diagnosis and treatment technologies, including the innovative application of the four diagnostic methods (observation, listening and smelling, inquiry, and pulse-taking and palpation) in telemedicine services and the implementation strategies of TCM syndrome differentiation and treatment in telemedicine services. In addition, this paper explores the development strategies of TCM telemedicine service models, including improving relevant policies, emphasizing the importance of technological innovation, and combining them with modern medical technologies. The successful application of the "cloud TCM" platform in telemedicine services and its diagnostic accuracy and treatment effectiveness are demonstrated through case analysis. This paper aims to provide useful references for the innovation and development of TCM telemedicine service models, promote the deep integration of the healthcare industry and information technology, improve service quality, and facilitate the broader dissemination of TCM culture.

Psychometric Properties of the Arabic Version of the Self-assessment Nursing Informatics Competency Scale

Background: Nursing informatics competency is a critical component of nursing practice, since it plays a major role in promoting the quality of patient care and safety. The Selfassessment Nursing Informatics Competency Scale (SANICS) has been developed in English to assess the nursing informatics competency level. However, a valid and reliable Arabic version of the SANICS is lacking. Therefore, it is essential to translate the SANICS instrument into Arabic language and assess its psychometric properties. Purpose: This study aims to evaluate the psychometric proprieties of the Arabic version of the selfassessment nursing informatics competency scale among Arabic-speaking nurses. Methods: A methodological, cross-sectional study was carried out on 176 nurses using a translated SANICS questionnaire. The nurses’ selection was based on the non-probability convenience sampling technique, from three governmental hospitals. The psychometric proprieties were assessed using content validity, exploratory factor analysis, confirmatory factor analysis, convergent and divergent validity, and reliability tests. Results: The scale content validity index (S-CVI) was excellent, amounting to 0.93. Exploratory factor analysis revealed five factors with eigenvalues greater than 1.00, accounting for 71.62% of the total variance. Confirmatory factor analysis showed that the fit indices for the tested scale model were acceptable as follows: χ2/df = 1.994, p <0.001, RMSEA = 0.075, CFI = 0.916, SRMR= 0.070, TLI= 0.901, IFI = 0.917. The internal consistency of the overall scale was supported by Cronbach's α, McDonald's omega, composite reliability, and split-half reliability ranging between 0.959 and 0.984. Conclusion: The psychometric proprieties of the Arabic version of SANICS were examined using content validity, EFA, CFA, convergent validity, divergent validity, and internal consistency methodologies. It was concluded that the Arabic version of SANICS is valid and reliable. This scale can be used to assess the nursing informatics competency level among nurses and nursing students in the Arab region. Implications for Nursing: Nursing informatics plays a significant role in the areas of practice, education, research, and administration. Healthcare facilities can accurately assess a nurse's informatics competency level by using this tool. In addition, the presented tool can serve as a valuable resource for designing educational strategies aimed at enhancing nursing students’ informatics competencies within the dynamic healthcare information technology environment.

Quality discrimination in healthcare markets

AbstractRecent advances in healthcare information technologies allow healthcare providers to more accurately track patient characteristics and predict the future treatment costs of previously treated patients, which increases the scope for providers to quality discriminate across different patient types. We theoretically analyze the potential implications of such quality discrimination in a duopoly setting with profit‐maximizing hospitals, fixed prices, and heterogeneous patients. Our analysis shows that the ability to quality discriminate tends to intensify competition and lead to higher quality provision, which benefits patients but makes the hospitals less profitable. Nevertheless, the effect on social welfare is a priori ambiguous, since quality discrimination also leads to an inefficient allocation of patients across hospitals.

Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence.

Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breastcancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.

Advancing Through Data: A Critical Review of the Evolution of Medical Information Management Systems

The evolution of medical information management (MIM) is a critical aspect of modern healthcare, reflecting significant technological and regulatory advancements. This article offers a comprehensive review of the development and transformation of MIM systems, from rudimentary paper-based records to sophisticated digital platforms. We explore the early adoption of electronic health records (EHR), the integration of big data analytics, and the emerging role of artificial intelligence (AI) and machine learning in enhancing healthcare delivery. The review also addresses the pivotal challenges of data privacy and security, highlighting regulatory frameworks like HIPAA and GDPR. Through case studies, we analyze both successful implementations and notable challenges in the MIM field, providing a balanced perspective. The article discusses the crucial role of interoperability in facilitating efficient data exchange among diverse healthcare systems and underscores the importance of patient-generated data in future MIM systems. We conclude by identifying potential future trends, including the application of blockchain technology and the Internet of Things (IoT) in healthcare. This critical review serves as a guide for healthcare professionals, policymakers, and IT specialists, offering insights into the past, present, and future of medical information management.

Healthcare information management and operational cost performance: empirical evidence.

Healthcare knowledge management systems can mitigate hospitals' operational inefficiency. As a healthcare information technology, the electronic health record (EHR) receives much attention from medical institutions due to its considerable impact on operational cost performance. This paper focuses on EHR systems to address operational inefficiency by which patients pay more for health care services, and many U.S. hospitals are filing for bankruptcy. From the theoretical perspective of the practice-based view, this paper introduces a path to implement EHR systems for improving cost performance. The empirical investigation is archival data of 200 hospitals collected from the U.S. healthcare agencies. Findings contribute to prior work by hypothesizing moderating and mediating roles in EHR systems implementation. This paper introduces absorptive capacity and monitoring mechanisms as enablers of implementing EHR systems. The results showed that hospital monitoring strengthens the relationship between absorptive capacity and electronic health record systems implementation, which results in better operational cost performance. Theoretically, this study supports the long-term potential benefits of EHR adoption, and its findings are consistent with optimizing efficiency through data standardization and interoperability. From a practical perspective, this study supports hospitals' investments in evolving healthcare information technology systems through the development of a knowledge-based system employing EHR, particularly when hospitals are merging or need a financial strategic plan to control expenses.

Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.

Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study.

Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology's performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.

Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.

The Large-Scale Implementation of a Health Information System in Brazilian University Hospitals: Process and Outcomes.

Governments around the globe are paving the way for healthcare services that can have a profound impact on the overall well-being and development of their nations. However, government programs to implement health information technologies on a large-scale are challenging, especially in developing countries. In this article, the process and outcomes of the large-scale implementation of a hospital information system for the management of Brazilian university hospitals are analyzed. Based on a qualitative approach, this research involved 21 hospitals and comprised a documentary search, interviews with 24 hospital managers and two system user focus groups, and a questionnaire of 736 respondents. Generally, we observed that aspects relating to the wider context of system implementation (macro level), the managerial structure, cultural nuances, and political dynamics within each hospital (meso level), as well as the technology, work activities, and individuals themselves (micro level) acted as facilitators and/or obstacles to the implementation process. The dynamics and complex interactions established between these aspects had repercussions on the process, including the extended time necessary to implement the national program and the somewhat mixed outcomes obtained by hospitals in the national network. Mostly positive, these outcomes were linked to the eight emerging dimensions of practices and work processes; planning, control, and decision making; transparency and accountability; optimization in the use of resources; productivity of professionals; patient information security; safety and quality of care; and improvement in teaching and research. We argued here that to maximize the potential of information technology in healthcare on a large-scale, an integrative and cooperative vision is required, along with a high capacity for change management, considering the different regional, local, and institutional contexts.

Electronic connectivity between hospital pairs: impact on emergency department-related utilization.

To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. We combined 2018 Medicare fee-for-service claims to identify beneficiaries with 2 ED encounters within 30days, and Definitive Healthcare and AHA IT Supplement data to identify hospital participation in HIE networks (HIOs and EHR vendor networks). We determined whether the 2 encounters for the same beneficiary occurred at: the same organization, different organizations connected by HIE, or different organizations not connected by HIE. Outcomes were: (1) whether any repeat imaging occurred during the second ED visit; (2) for beneficiaries with a treat-and-release ED visit followed by a second ED visit, whether they were admitted to the hospital after the second visit; (3) for beneficiaries discharged from the hospital followed by an ED visit, whether they were admitted to the hospital. In adjusted mixed effects models, for all outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging. HIE was associated with lower likelihood of hospital admission following a treat-and-release ED visit (1.83 percentage points [-3.44 to -0.21]) but higher likelihood of admission following hospital discharge (2.78 percentage points [0.48-5.08]). Lower utilization for beneficiaries returning to the same organization could reflect better access to information or other factors such as aligned incentives. HIE is not consistently associated with utilization outcomes reflecting more coordinated care in the ED setting.

The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

ABSTRACT In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

Electronic Health Record Adolescent Confidentiality in a Safety Net Setting.

The 21st Century Cures Act provides improved access to one's medical record but presents new challenges to adolescent confidentiality in the domain of health care information technology. Adolescent patients, who have the right to confidential care in certain areas as dictated by state law, face the prospect of parents and guardians erroneously accessing their confidential health information. Our institution, the largest safety net hospital in our region, sought to quantify the proportion of adolescent patient portal accounts likely being accessed by guardians and to implement corrective measures for the patient portal sign-up process in our electronic health record (EHR) system. We manually reviewed our institution's adolescent patient portal accounts based on email addresses associated with adolescents' accounts. We implemented EHR changes to address the potential for breach of confidentiality as a result of adolescent patient portal account creation by guardians. One intervention was creating a "guardrail rule" to prevent guardians from creating adolescent patient portal accounts with their own email addresses. After email reminders to adolescent patients to update their accounts, we deactivated accounts with concern for erroneous guardian access. Sixty percent of our institution's adolescent patient portal accounts had possible direct access by guardians. Following requests to update account access, 425 (18.8%) of accounts had email addresses updated by adolescent patients and 1,830 (81.2%) accounts were deactivated. More nuanced EHR functionality for adolescent patients and their guardians can help health care systems provide confidential, patient-centered care for adolescents, while allowing guardians to access appropriate health information to facilitate care. There is an opportunity for a national consensus on how adolescents and their guardians can access health information by patient portal.