Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Background: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms' performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models' performance, 11.1% assessed ML algorithms' performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion: Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored

Utilizing artificial intelligence in perioperative patient flow:systematic literature review

Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Investigating opportunities to improve surgical site infection prevention through social and technological innovation

Background Surgical site infections (SSIs) are a common cause of morbidity and mortality and pose a significant problem for patients, health systems, and society. There is a wealth of literature on SSI prevention interventions, yet SSIs remain a problem. Surveillance of SSI rates on a local and national scale has been shown to be fundamental to reducing rates and improving patient safety. The national surveillance program for SSI rates in England is focused mostly on orthopaedic SSIs and in-hospital surveillance, and data collection is done manually despite advances in automation. Aims This thesis aims to address four key research questions emerging around SSI prevention through surveillance at the interface of two concepts: social innovation and technological innovation. Four questions arising from gaps in the literature are 1) which surgery types should be targeted for SSI surveillance, 2) what are healthcare workers’ perceptions and beliefs about SSI prevention and surveillance, 3) how can technology enhance SSI surveillance, and 4) how can post-discharge SSI surveillance be improved? Study design Quantitative methods were used to synthesise data on SSI risk, burden, cost, and national reporting requirements in different surgery types in England to inform decisions on how to prioritise surveillance. To better understand perceptions and drivers of SSI prevention and surveillance practices, qualitative interviews with staff stakeholders at a large London NHS Trust were analysed thematically. A mixed-methods case study used quantitative validation of a semi-automated in-hospital surveillance algorithm and qualitative workshops with staff to explore barriers and facilitators to implementation. The final studies used a realist review and patient focus groups to assess post-discharge surveillance methods. Results Current practices for SSI surveillance do not match the medical or economic burden posed by SSIs in different surgical categories. The highest contributors of SSIs in England are large bowel surgery and caesarean section, which are under voluntary surveillance or no national surveillance respectively. Differences in the perceived responsibility for SSI prevention (whole team and patients) versus accountability for rates (consultant surgeons) create tensions in the team, but surveillance can help stimulate engagement. Electronic systems to improve SSI surveillance are a promising and obvious solution to chronic resource problems, but poor technological infrastructure and difficulties proving their cost-effectiveness prevent a universal solution. Patients are often required to contribute to post-discharge surveillance of SSIs but need to see this task as useful and easy. Conclusion Improvements in technological infrastructure in the NHS would facilitate enhanced SSI surveillance, while top-down encouragement from national bodies and hospital managers to broaden surveillance could provide the social support needed to re-prioritise surveillance. On a local level, team accountability of SSI rates could precipitate social change by facilitating stakeholder engagement.Open Acces

Structured and unstructured data integration with electronic medical records

In recent years there has been a great population and technological evolution all over the world. At the same time, more areas beyond technology and information technology have also developed, namely medicine, which has led to an increase in average life expectancy which in turn, leads to a greater need for healthcare. In order to provide the best possible treatments and healthcare services, nowadays the hospitals store large amounts of data regarding patients and diseases (in the form of electronic medical records) or the logistics of some departments in their storage systems. Therefore, computer science techniques such as data mining and natural language processing have been used to extract knowledge and value from these information-rich sources in order not only to develop, for example, new models for disease prediction, as well as improving existing processes in healthcare centres and hospitals. This data storage can be done in one of three ways: structured, unstructured or semi-structured. In this paper, the author tested the integration of structured and unstructured data from two different departments of the same Portuguese hospital, in order to extract knowledge and improve hospital processes. Aiming to reduce the value loss of loading data that is not used in the healthcare providers systems.Nos últimos anos tem-se assistido a uma grande evolução populacional e tecnológica por todo o mundo. Paralelamente, mais áreas para além da tecnologia e informática têm-se também desenvolvido, nomeadamente a área da medicina, o que tem permitido um aumento na esperança média de vida que por sua vez leva a uma maior necessidade de cuidados de saúde. Com o intuito de fornecer os melhores serviços de saúde possíveis, nos dias que hoje os hospitais guardam nos seus sistemas informáticos grandes quantidades de dados relativamente aos pacientes e doenças (sobre a forma de registos médicos eletrónicos) ou relativos à logística de alguns departamentos dos hospitais, etc. Por conseguinte, a estes dados têm vindo a ser utilizadas técnicas da área das ciências da computação como o data mining e o processamento da língua natural para extrair conhecimento e valor dessas fontes ricas em informação com o intuito não só de desenvolver, por exemplo, novos modelos de predição de doenças, como também de melhorar processos já existentes em centros de saúde e hospitais. Este armazenamento de dados pode ser feito em uma de três formas: de forma estruturada, não estruturada ou semi-estruturada. Neste trabalho o autor testou a integração de dados estruturados e não estruturados de dois departamentos diferentes do mesmo hospital português, com o intuito de extrair conhecimento e melhorar os processos do hospital. Com o intuito de reduzir a perda do armazenamento de dados que não são utilizados

Wright State University\u27s Celebration of Research, Scholarship and Creative Activities Book of Abstracts from Friday, April 21, 2017

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Annual Celebration of Research, Scholarship and Creative Activities on April 21, 2017.https://corescholar.libraries.wright.edu/urop_celebration/1024/thumbnail.jp