Clinical Decision Support Systems (CDSS) are essential tools in contemporary healthcare, enhancing clinicians\' decisions and patient outcomes. The integration of artificial intelligence (AI) is now revolutionizing CDSS even further. This review delves into AI technologies transforming CDSS, their applications in healthcare decision-making, associated challenges, and the potential trajectory toward fully realizing AI-CDSS\'s potential. The review begins by laying the groundwork with a definition of CDSS and its function within the healthcare field. It then highlights the increasingly significant role that AI is playing in enhancing CDSS effectiveness and efficiency, underlining its evolving prominence in shaping healthcare practices. It examines the integration of AI technologies into CDSS, including machine learning algorithms like neural networks and decision trees, natural language processing, and deep learning. It also addresses the challenges associated with AI integration, such as interpretability and bias. We then shift to AI applications within CDSS, with real-life examples of AI-driven diagnostics, personalized treatment recommendations, risk prediction, early intervention, and AI-assisted clinical documentation. The review emphasizes user-centered design in AI-CDSS integration, addressing usability, trust, workflow, and ethical and legal considerations. It acknowledges prevailing obstacles and suggests strategies for successful AI-CDSS adoption, highlighting the need for workflow alignment and interdisciplinary collaboration. The review concludes by summarizing key findings, underscoring AI\'s transformative potential in CDSS, and advocating for continued research and innovation. It emphasizes the need for collaborative efforts to realize a future where AI-powered CDSS optimizes healthcare delivery and improves patient outcomes.

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Digital communication tools have demonstrated significant potential to improve health literacy which ultimately leads to better health outcomes. In this article, we examine the power of digital communication tools such as mobile health apps, telemedicine and online health information resources to promote health and digital literacy. We outline evidence that digital tools facilitate patient education, self-management and empowerment possibilities. In addition, digital technology is optimising the potential for improved clinical decision-making, treatment options and communication among providers. We also explore the challenges and limitations associated with digital health literacy, including issues related to access, reliability and privacy. We propose leveraging digital communication tools is key to optimising engagement to enhance health literacy across demographics leading to transformation of healthcare delivery and driving better outcomes for all.

This editorial investigates the development and efficacy of Japanese learn-to-rank approach systems in family medicine, emphasizing their establishment by Dr. Keijiro Torigoe and their significance in rural community hospitals. Initiated in 1977, Dr. Torigoe\'s innovative system integrated international medical knowledge with technology, yielding a comprehensive database of 7,000 registered diseases. These learn-to-rank approaches, notably the listwise method, address technological gaps in extracting data on differential diseases and enhance the predictive performance of clinical decision support systems, offering a holistic, culturally resonant healthcare approach. They are especially vital in rural medicine, aiding in managing the volatility, uncertainty, complexity, and ambiguity prevalent among older patients, streamlining diagnoses, and improving healthcare delivery in resource-constrained settings. In conclusion, integrating Japanese learn-to-rank approach systems is pivotal in revolutionizing disease diagnosis, catering to diverse rural health needs, and fostering sustainability in rural healthcare systems. By harmonizing medical insights with innovation, they demonstrate the potential for a comprehensive and contextually relevant approach to healthcare in Japan.

Introduction Drug-drug interactions (DDIs) have the potential to harm patients. Hence, DDI alerts are meant to prevent harm; as a result, their usefulness is reduced when most alerts displayed to providers are ignored. This study aims to explore the rates and reasons for overriding alerts of DDI. Methods This is a retrospective study of DDI alert overrides that occurred between January 2020 and December 2020 within the inpatient medical records at a tertiary hospital, Medina City, Kingdom of Saudi Arabia. Results A total of 7,098 DDI alerts were generated from inpatient settings, of which 6,551(92.2%) were overridden by the physicians at the point of prescribing. \"Will Monitor as Recommended\" (33%) was the most common reason for the override, followed by \'Will Adjust the Dose as Recommended (27.1%),\" \"The Patient Has Already Tolerated the Combination\" (25.7%), and \"No Overridden Reason Selected\" (13.0%). Discussion The DDI alert overriding is still high and is comparable to other studies. However, this study reveals that physicians are ready to deal with the consequences of around 58% of DDI alerts. Additionally, 13% of physicians were not willing to report the reason for overriding. This indicates an urgent need to review and restructure the DDI alert system. Conclusion The DDI alert override rates are high, and this is undesirable. It is recommended to revise the DDI alert system. Future studies should dig deep for real reasons for overriding and seek inputs from all stakeholders, including developing actionable metrics to track and monitor DDI alerting system.

Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events (\"flags\") through suggesting evidence-based courses of action. However, extant literature shows multiple barriers-perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.-to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic.
The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI2). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach-starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories\' relationships with client and medication-subtype characteristics were tested.
The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors-the clients\' environment, their clinical relationships, and medical needs-mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ2 = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up0 and Not-followed up1 flags (M0 = 31.78; M1 = 45.55; U = 12,119; p < 0.001; η2 = .05).
These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.

Continued efforts are required to reduce preventable child deaths. User-friendly Integrated Management of Childhood Illness (IMCI) implementation tools and supervision systems are needed to strengthen the quality of child health services in South Africa. A 2018 pilot implementation of electronic IMCI case management algorithms in KwaZulu-Natal demonstrated good uptake and acceptance at primary care clinics. We aimed to investigate whether ongoing electronic IMCI implementation is feasible within the existing Department of Health infrastructure and resources.
In a mixed methods descriptive study, the electronic IMCI (eIMCI) implementation was extended to 22 health facilities in uMgungundlovu district from November 2019 to February 2021. Training, mentoring, supervision and IT support were provided by a dedicated project team. Programme use was tracked, quarterly assessments of the service delivery platform were undertaken and in-depth interviews were conducted with facility managers.
From December 2019 - January 2021, 9 684 eIMCI records were completed across 20 facilities, with a median uptake of 29 records per clinic per month and a mean (range) proportion of child consultations using eIMCI of 15% (1-46%). The local COVID-19-related movement restrictions and epidemic peaks coincided with declines in the monthly eIMCI uptake. Substantial inter- and intra-facility variations in use were observed, with the use being positively associated with the allocation of an eIMCI trained nurse (p < 0.001) and the clinician workload (p = 0.032).
The ongoing eIMCI uptake was sporadic and the implementation undermined by barriers such as low post-training deployment of nurses; poor capacity in the DoH for IT support; and COVID-19-related disruptions in service delivery. Scaling eIMCI in South Africa would rely on resolving these challenges.

Medications Dexamethasone, Remdesivir or Colchicine, used to treat COVID-19 patients, have significant interactions with other medications and the human genome. The study presented in this paper investigates how to use the Personalized Medicine Therapy Optimization Method (PM-TOM) to minimize these interactions in polypharmacy therapies of COVID-19 patients. We applied PM-TOM on the EMR database of Harvard Personal Genome Project (PGP), drug database DrugBank and Comprehensive Toxicogenomics Database (CTD) to analyze polypharmacy therapies augmented with these medications. The main finding is that these COVID-19 medications significantly increase the drug and gene interactions in partially optimized (or unoptimized) therapies, which is not the case in the fully optimized ones. For example, the test results show that in polypharmacy treatments for patients having between 3 and 8 conditions, the average number of drug and gene interactions in partially optimized therapies ranges from 3 to 18 after adding Remdesivir, 4.3 to 20 Colchicine, and 4.7 to 23 Dexamethasone. On the other hand, these interactions in fully optimized therapies range only 0.6 to 5.2, 1.2 to 7, and 2.7 to 11, respectively. These results suggest that polypharmacy therapies should be carefully examined before adding these medications. This recommendation applies to all other situations when polypharmacy patients may conduct new serious conditions, such as COVID-19, requiring additional medications with a high number of drug and gene interactions.

Multiple studies show that therapies for multi-diseases can lead to dangerous reactions and high healthcare costs due to their adverse drug-drug, drug-gene, and drug-condition interactions. In this paper, we present the results of using PM-TOM (Personalized Medicine Therapy Optimization Method) for finding therapies that minimize these interactions. The testing of the method was performed on the repository of electronic medical records of the Harvard Personal Genome Project (PGP), and the public databases of the drug and genetic information: DrugBank and Comprehensive Toxicogenomics Database (CTD). The results presented in this paper showed a significant potential of PM-TOM for reducing the cumulative adverse drug interactions in therapies for common multi-diseases.

New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, highlighted by multiple international organizations as a core issue in future healthcare. Despite the myriad of exciting new developments, each application and system is designed and implemented for specific purposes and lacks the flexibility to support different healthcare concerns. Some of the known problems of such developments are the integration issues between applications and existing healthcare systems, the reusability of technical knowledge in the creation of new and more sophisticated systems and the usage of data gathered from multiple sources in the generation of new knowledge. This paper proposes a framework for the development of chronic disease support systems and applications as an answer to these shortcomings. Through this framework our pursuit is to create a common ground methodology upon which new developments can be created and easily integrated to provide better support to chronic patients, medical staff and other relevant participants. General requirements are inferred for any support system from the primary attention process of chronic patients by the Business Process Management Notation. Numerous technical approaches are proposed to design a general architecture that considers the medical organizational requirements in the treatment of a patient. A framework is presented for any application in support of chronic patients and evaluated by a case study to test the applicability and pertinence of the solution.