Electronic health records (EHRs) provide comprehensive patient data which could be better used to enhance informed decision-making, resource allocation, and coordinated care, thereby optimising healthcare delivery. However, in mental healthcare, critical information, such as on risk factors, precipitants, and treatment responses, is often embedded in unstructured text, limiting the ability to automate at scale measures to identify and prioritise local populations and patients, which potentially hinders timely prevention and intervention. We describe the development and proof-of-concept implementation of VIEWER, a clinical informatics platform designed to enhance direct patient care and population health management by improving the accessibility and usability of EHR data. We further outline strategies that were employed in this work to foster informatics innovation through interdisciplinary and cross-organisational collaboration to support integrated, personalised care, and detail how these advancements were piloted and implemented within a large UK mental health National Health Service Foundation Trust to improve patient outcomes at an individual patient, clinician, clinical team,
Background: As value-based care expands across the U.S. healthcare system, reducing health disparities has become a priority. Social determinants of health (SDoH) indices, like the widely used Area Deprivation Index (ADI), guide efforts to manage patient health and costs. However, the ADI's reliance on housing-related variables (e.g., median home value) may reduce its effectiveness, especially in high-cost regions, by masking inequalities and poor health outcomes. Methods: To overcome these limitations, we developed the balanced ADI (bADI), a new SDoH index that reduces dependence on housing metrics through standardized construction. We evaluated the bADI using data from millions of Medicare Fee-for-Service and Medicare Advantage beneficiaries. Correlation analyses measured its association with clinical outcomes, life expectancy, healthcare use, and cost, and compared results to the ADI. Results: The bADI showed stronger correlations with clinical outcomes and life expectancy than the ADI. It was less influenced by housing costs in expensive regions and more accurately predicted healthcare use and costs. While ADI-based research suggested both the most and least disadvantaged group
Digital Twin (DT) technology has emerged as a transformative approach in healthcare, but its application in personalized patient care remains limited. This paper aims to present a practical implementation of DT in the management of chronic diseases. We introduce a general DT framework for personalized care planning (DT4PCP), with the core components being a real-time virtual representation of a patient's health and emerging predictive models to enable adaptive, personalized care. We implemented the DT4PCP framework for managing Type 2 Diabetes (DT4PCP-T2D), enabling real-time collection of behavioral data from patients with T2D, predicting emergency department (ED) risks, simulating the effects of different interventions, and personalizing care strategies to reduce ED visits. The DT4PCP-T2D also integrates social determinants of health (SDoH) and other contextual data, offering a comprehensive view of the patient's health to ensure that care recommendations are tailored to individual needs. Through retrospective simulations, we demonstrate that integrating DTs in T2D management can lead to significant advancements in personalized medicine. This study underscores the potential of DT
Post-discharge care management coordinates patients' referrals to improve their health after being discharged from hospitals, especially elderly and chronically ill patients. In a care management setting, health referrals are processed by a specialized unit in the managed care organization (MCO), which interacts with many other entities including inpatient hospitals, insurance companies, and post-discharge care providers. In this paper, a machine-learning-guided discrete event simulation framework to improve health referrals processing is proposed. Random-forest-based prediction models are developed to predict the LOS and referral type. Two simulation models are constructed to represent the as-is configuration of the referral processing system and the intelligent system after incorporating the prediction functionality, respectively. By incorporating a prediction module for the referral processing system to plan and prioritize referrals, the overall performance was enhanced in terms of reducing the average referral creation delay time. This research will emphasize the role of post-discharge care management in improving health quality and reducing associated costs. Also, the paper de
This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DMAIC approach in the context of epidemic outbreaks. First, we define specific challenges posed by epidemic outbreaks to populational health, health systems, as well as economic challenges to different industries such as retailing, education and manufacturing. Second, we present a review of medical testing and statistical sampling methods for data collection, as well as existing efforts in data management and data visualization. Third, we discuss the importance to realizing the full potential of data for epidemic insights, and emphasize the need to leverage analytical methods and tools for decision support. Fourth, an epidemic brings imperative changes to health systems. We discuss the new trend of healthcare solutions to improve system resil
Demand for health care is constantly increasing due to the ongoing demographic change, while at the same time health service providers face difficulties in finding skilled personnel. This creates pressure on health care systems around the world, such that the efficient, nationwide provision of primary health care has become one of society's greatest challenges. Due to the complexity of health care systems, unforeseen future events, and a frequent lack of data, analyzing and optimizing the performance of health care systems means tackling a wicked problem. To support this task for primary care, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models the interactions of patients and primary care physicians on an individual level. By tracking agent interactions, it enables modelers to assess multiple key indicators such as patient waiting times and physician utilization. Based on these indicators, primary care systems can be assessed and compared. Moreover, changes in the infrastructure, patient behavior, and service design can be directly evaluated. To showcase the opportunities offered by SiM-Care and aid model validation, we present a case study for
The growing demand for home healthcare calls for tools that can support care delivery. In this study, we explore automatic health assessment from voice using real-world home care visit data, leveraging the diverse patient information it contains. First, we utilize Large Language Models (LLMs) to integrate Subjective, Objective, Assessment, and Plan (SOAP) notes derived from unstructured audio transcripts and structured vital signs into a holistic illness score that reflects a patient's overall health. This compact representation facilitates cross-visit health status comparisons and downstream analysis. Next, we design a multi-stage preprocessing pipeline to extract short speech segments from target speakers in home care recordings for acoustic analysis. We then employ an Audio Language Model (ALM) to produce plain-language descriptions of vocal biomarkers and examine their association with individuals' health status. Our experimental results benchmark both commercial and open-source LLMs in estimating illness scores, demonstrating their alignment with actual clinical outcomes, and revealing that SOAP notes are substantially more informative than vital signs. Building on the illness
With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and data privacy in the prediction of health behaviors of the elderly. The model achieves accurate prediction and dynamic management of health behaviors of the elderly through modules such as multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. In the experimental design, based on multi-source data sets and market research results, the model demonstrates excellent performance in health behavior prediction, emergency detection, and personalized services. The experimental results show that the model can effectively improve the accuracy and robustness of health behavior prediction and meet the actual application needs in the field of smart elderly care. In the future, with the integration of more data and further optimization of technology, the model will provide more powerful technical support for smart elderly car
Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and poli
This review underscores the vital role of interoperability in digital health, advocating for a standardized framework. It focuses on implementing a Fast Healthcare Interoperability Resources (FHIR) server, addressing technical, semantic, and process challenges. FHIR's adaptability ensures uniformity within Primary Care Health Information Systems, fostering interoperability. Patient data management complexities highlight the pivotal role of semantic interoperability in seamless patient care. FHIR standards enhance these efforts, offering multiple pathways for data search. The ADR-guided FHIR server implementation systematically addresses challenges related to patient identity, biometrics, and data security. The detailed development phases emphasize architecture, API integration, and security. The concluding stages incorporate forward-looking approaches, including HHIMS Synthetic Dataset testing. Envisioning FHIR integration as transformative, it anticipates a responsive healthcare environment aligned with the evolving digital health landscape, ensuring comprehensive, dynamic, and interconnected systems for efficient data exchange and access.
Long-term care service for old people is in great demand in most of the aging societies. The number of nursing homes residents is increasing while the number of care providers is limited. Due to the care worker shortage, care to vulnerable older residents cannot be fully tailored to the unique needs and preference of each individual. This may bring negative impacts on health outcomes and quality of life among institutionalized older people. To improve care quality through personalized care planning and delivery with limited care workforce, we propose a new care planning model assisted by artificial intelligence. We apply bandit algorithms which optimize the clinical decision for care planning by adapting to the sequential feedback from the past decisions. We evaluate the proposed model on empirical data acquired from the Systems for Person-centered Elder Care (SPEC) study, a ICT-enhanced care management program.
Healthcare organization is implementing Customer Relationship Management (CRM) as a strategy for managing interactions with patients involving technology to organize, automate, and coordinate business processes. Web-based CRM provides healthcare organization with the ability to broaden service beyond its usual practices in achieving a complex patient care goal, and this paper discusses and demonstrates how a new approach in CRM based on Web 2.0 or Social CRM helps healthcare organizations to improve their customer support, and at the same time avoiding possible conflicts, and promoting better healthcare to patients. A conceptual framework of the new approach will be proposed and highlighted. The framework includes some important features of Social CRM such as customer's empowerment, social interactivity between healthcare organization-patients, and patients-patients. The framework offers new perspective in building relationships between healthcare organizations and customers and among customers in e-health scenario. It is developed based on the latest development of CRM literatures and case studies analysis. In addition, customer service paradigm in social network's era, the import
Objectieve:This review aims to deliver a comprehensive analysis of Large Language Models (LLMs) utilization in mental health care, evaluating their effectiveness, identifying challenges, and exploring their potential for future application. Materials and Methods: A systematic search was performed across multiple databases including PubMed, Web of Science, Google Scholar, arXiv, medRxiv, and PsyArXiv in November 2023. The review includes all types of original research, regardless of peer-review status, published or disseminated between October 1, 2019, and December 2, 2023. Studies were included without language restrictions if they employed LLMs developed after T5 and directly investigated research questions within mental health care settings. Results: Out of an initial 313 articles, 34 were selected based on their relevance to LLMs applications in mental health care and the rigor of their reported outcomes. The review identified various LLMs applications in mental health care, including diagnostics, therapy, and enhancing patient engagement. Key challenges highlighted were related to data availability and reliability, the nuanced handling of mental states, and effective evaluation
Abundant evidence has tracked the labour market and health assimilation of immigrants, including static analyses of differences in how foreign-born and native-born residents consume health care services. However, we know much less about how migrants' patterns of health care usage evolve with time of residence, especially in countries providing universal or quasi-universal coverage. We investigate this process in Spain by combining all the available waves of the local health survey, which allows us to separately identify period, cohort, and assimilation effects. We find that the evidence of health assimilation is limited and solely applies to migrant females' visits to general practitioners. Nevertheless, the differential effects of ageing on health care use between foreign-born and native-born populations contributes to the convergence of utilisation patterns in most health services after 20 years in Spain. Substantial heterogeneity over time and by region of origin both suggest that studies modelling future welfare state finances would benefit from a more thorough assessment of migration.
Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue.
Industry 4.0 in health care has evolved drastically over the past century. In fact, it is evolving every day, with new tools and strategies being developed by physicians and researchers alike. Health care and technology have been intertwined together with the advancement of cloud computing and big data. This study aims to analyze the impact of industry 4.0 in health care systems. To do so, a systematic literature review was carried out considering peer-reviewed articles extracted from the two popular databases: Scopus and Web of Science (WoS). PRISMA statement 2015 was used to include and exclude that data. At first, a bibliometric analysis was carried out using 346 articles considering the following factors: publication by year, journal, authors, countries, institutions, authors' keywords, and citations. Finally, qualitative analysis was carried out based on selected 32 articles considering the following factors: a conceptual framework, schedule problems, security, COVID-19, digital supply chain, and blockchain technology. Study finding suggests that during the onset of COVID-19, health care and industry 4.0 has been merged and evolved jointly, considering various crisis such as d
Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. This invited article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science" edited by Subhashis Ghosha
A sound performance of health care organizations depends on vigorous financing system, skilled and well paying personnel, trustworthy information, well sustained health services and reliable technologies they used. The management and organization of health care systems and other health care settings can deeply have an effect on health result, excellence of care, and patient pleasure. Environmental assessment of an organization must considered political, technical and environmental aspects. This research is paying attention on dangerous and unresolved issues resulting from waste. The insufficient managing of hospital waste be capable of creating a danger to people, in the spreading of infectious diseases, and also for the environment. Issues are classified in different groups like dangerous chemical waste, cytotoxic with carcinogenic, mutagenic and teratogenic risk and radioactive. This study focuses on the analysis of above mentioned risks and decision will be made through Multi-Criteria Decision Analysis (MCDA). MCDA techniques give considerable enhancement in decision making. Survey is used for collecting information and results are evaluated by applying some statistical tool.
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrate
Big Data are revolutionizing nearly every aspect of the modern society. One area where this can have a profound positive societal impact is the field of Health Care Informatics (HCI), which faces many challenges. The key idea behind this study is: can we use some of the experience and technical and methodological solutions from the fields that have successfully adapted to the Big Data era, namely astronomy and space science, to help accelerate the progress of HCI? We illustrate this with examples from the Virtual Observatory framework, and the NCI EDRN project. An effective sharing and reuse of tools, methods, and experiences from different fields can save a lot of effort, time, and expense. HCI can thus benefit from the proven solutions to big data challenges from other domains.