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Technologies and decision-support tools for health systems management: a scoping review of features and use cases

INTRODUCTION

In the face of growing patient volumes, health requirements, and economic constraints, modern healthcare systems are in dire need of innovative management solutions. Despite a range of tools available to tackle these challenges, there's a gap in the understanding of how these tools are tailored for health systems management.

AREAS COVERED

This review, conducted in October 2023 across key health administration and technology databases Medline, Embase, IEEE Xplore, and ACM Digital Library, aims to examine the applications, characteristics, and real-world impacts of health systems management tools. From a total of 2,048 records, 12 studies were selected that span a variety of technologies, including decision analysis tools, machine learning algorithms, and data dashboards, all of which have proven critical in optimizing resource allocation, financial management, and patient care.

EXPERT OPINION

These technologies have shown substantial promise in improving decision-making processes, operational efficiency, and overall healthcare outcomes. This review highlights the role these technologies and tools play in enhancing the manageability and sustainability of complex healthcare systems. It also underscores the need for ongoing research to further align technological developments with the evolving needs of the healthcare sector, ultimately aiming to boost system efficiency and improve patient care.

A Systematic Review of Electronic Community Resource Referral Systems

INTRODUCTION

Community Resource Referral Systems delivered electronically through healthcare information technology systems (e.g., electronic medical records) have become more common in efforts to address patients' unmet health-related social needs. Community Resource Referral System connects patients with social supports such as food assistance, utility support, transportation, and housing. This systematic review identifies barriers and facilitators that influence the Community Resource Referral System's implementation in the U.S. by identifying and synthesizing peer-reviewed literature over a 15-year period.

METHODS

This systematic review was conducted following PRISMA guidelines. A search was conducted on five scientific databases to capture the literature published between January 2005 and December 2020. Data analysis was conducted from August 2021 to July 2022.

RESULTS

This review includes 41 articles of the 2,473 initial search results. Included literature revealed that Community Resource Referral Systems functioned to address a variety of health-related social needs and were delivered in different ways. Integrating the Community Resource Referral Systems into clinic workflows, maintenance of community-based organization inventories, and strong partnerships between clinics and community-based organizations facilitated implementation. The sensitivity of health-related social needs, technical challenges, and associated costs presented as barriers. Overall, electronic medical records-integration and automation of the referral process was reported as advantageous for the stakeholders.

DISCUSSION

This review provides information and guidance for healthcare administrators, clinicians, and researchers designing or implementing electronic Community Resource Referral Systems in the U.S. Future studies would benefit from stronger implementation science methodological approaches. Sustainable funding mechanisms for community-based organizations, clear stipulations regarding how healthcare funds can be spent on health-related social needs, and innovative governance structures that facilitate collaboration between clinics and community-based organizations are needed to promote the growth and sustainability of Community Resource Referral Systems in the U.S.

How do nurses work in chronic management in the age of artificial intelligence? development and future prospects

AI is undeniably revolutionizing medical research and patient care across diverse fields. Chronic disease nursing care, a pivotal aspect of clinical management, has significantly reaped the benefits of AI across numerous dimensions. Understanding the operational principles of artificial intelligence before implementation is crucial, avoiding indiscriminate replacement of all tasks with AI. Nurses serve as the primary force in symptom group research, expanding beyond diabetes to encompass various chronic diseases; their primary responsibility involves recording patients' daily symptoms and vital signs. However, a substantial portion of current AI research excludes nurses from the developmental phase, encompassing them solely in user and feedback populations. The comprehensive design of the symptom analysis and long-term management approach necessitates the guidance and oversight of nurses; however, their current insufficient involvement might stem from nursing staff's comparatively limited comprehension of AI and their ambiguous perception of their role's value in AI. Therefore, an imperative exploration of nurses' roles in symptom analysis and long-term management, leveraging the latest research in these areas, is vital to pinpoint breakthroughs in nurses' AI involvement in the future.

Artificial Intelligence Applications in Health Care Practice: Scoping Review

BACKGROUND

Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.

OBJECTIVE

The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?

METHODS

A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.

RESULTS

Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.

CONCLUSIONS

Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

The Perceived Impact and Usability of a Care Management and Coordination System in Delivering Services to Vulnerable Populations: Mixed Methods Study

Background

People with complex needs, such as those experiencing homelessness, require concurrent, seamless support from multiple social service agencies. Sonoma County, California has one of the nation’s largest homeless populations among largely suburban communities. To support client-centered care, the county deployed a Care Management and Coordination System (CMCS). This system comprised the Watson Care Manager (WCM), a front-end system, and Connect 360, which is an integrated data hub that aggregates information from various systems into a single client record.

Objective

The aim of this study is to evaluate the perceived impact and usability of WCM in delivering services to the homeless population in Sonoma County.

Methods

A mixed methods study was conducted to identify ways in which WCM helps to coordinate care. Interviews, observations, and surveys were conducted, and transcripts and field notes were thematically analyzed and directed by a grounded theory approach. Responses to the Technology Acceptance Model survey were analyzed.

Results

A total of 16 participants were interviewed, including WCM users (n=8) and department leadership members (n=8). In total, 3 interdisciplinary team meetings were observed, and 8 WCM users were surveyed. WCM provided a central shared platform where client-related, up-to-date, comprehensive, and reliable information from participating agencies was consolidated. Factors that facilitated WCM use were users’ enthusiasm regarding the tool functionalities, scalability, and agency collaboration. Constraining factors included the suboptimal awareness of care delivery goals and functionality of the system among the community, sensitivities about data sharing and legal requirements, and constrained funding from government and nongovernment organizations. Overall, users found WCM to be a useful tool that was easy to use and helped to enhance performance.

Conclusions

WCM supports the delivery of care to individuals with complex needs. Integration of data and information in a CMCS can facilitate coordinated care. Future research should examine WCM and similar CMCSs in diverse populations and settings.

Precision Medicine, AI, and the Future of Personalized Health Care

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.