5,406 research outputs found
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
Information technologies for pain management
Millions of people around the world suffer from pain, acute or chronic and this raises the
importance of its screening, assessment and treatment. The importance of pain is attested by
the fact that it is considered the fifth vital sign for indicating basic bodily functions, health
and quality of life, together with the four other vital signs: blood pressure, body
temperature, pulse rate and respiratory rate. However, while these four signals represent an
objective physical parameter, the occurrence of pain expresses an emotional status that
happens inside the mind of each individual and therefore, is highly subjective that makes
difficult its management and evaluation. For this reason, the self-report of pain is considered
the most accurate pain assessment method wherein patients should be asked to periodically
rate their pain severity and related symptoms. Thus, in the last years computerised systems
based on mobile and web technologies are becoming increasingly used to enable patients to
report their pain which lead to the development of electronic pain diaries (ED). This approach
may provide to health care professionals (HCP) and patients the ability to interact with the
system anywhere and at anytime thoroughly changes the coordinates of time and place and
offers invaluable opportunities to the healthcare delivery. However, most of these systems
were designed to interact directly to patients without presence of a healthcare professional
or without evidence of reliability and accuracy. In fact, the observation of the existing
systems revealed lack of integration with mobile devices, limited use of web-based interfaces
and reduced interaction with patients in terms of obtaining and viewing information. In
addition, the reliability and accuracy of computerised systems for pain management are
rarely proved or their effects on HCP and patients outcomes remain understudied.
This thesis is focused on technology for pain management and aims to propose a monitoring
system which includes ubiquitous interfaces specifically oriented to either patients or HCP
using mobile devices and Internet so as to allow decisions based on the knowledge obtained
from the analysis of the collected data. With the interoperability and cloud computing
technologies in mind this system uses web services (WS) to manage data which are stored in a
Personal Health Record (PHR).
A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness
of the proposed computerised monitoring system. The six weeks RCT evidenced the
advantages provided by the ubiquitous access to HCP and patients so as to they were able to
interact with the system anywhere and at anytime using WS to send and receive data. In
addition, the collected data were stored in a PHR which offers integrity and security as well
as permanent on line accessibility to both patients and HCP. The study evidenced not only
that the majority of participants recommend the system, but also that they recognize it
suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented
treatments with reduced therapist time. The study also revealed that the guidance of HCP at
the beginning of the monitoring is crucial to patients' satisfaction and experience stemming
from the usage of the system as evidenced by the high correlation between the
recommendation of the application, and it suitability to improve pain management and to
provide medical information. There were no significant differences regarding to
improvements in the quality of pain treatment between intervention group and control group.
Based on the data collected during the RCT a clinical decision support system (CDSS) was
developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This
CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the
combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer)
with an imputation model based on linear regression. This system resulted in fully accuracy
related to decisions suggested by the system compared with the medical diagnosis, and
therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems
capability to deal with different complex data sources with varied complexities and
accuracies, an innovative model was proposed. This model is characterized by a qualitative
analysis stemming from the data fusion method combined with a quantitative model based on
the comparison of the standard deviation together with the values of mathematical
expectations. This model aimed to compare the effects of technological and pen-and-paper
systems when applied to different dimension of pain, such as: pain intensity, anxiety,
catastrophizing, depression, disability and interference. It was observed that pen-and-paper
and technology produced equivalent effects in anxiety, depression, interference and pain
intensity. On the contrary, technology evidenced favourable effects in terms of
catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy
to implement and low time and resources consuming. Further work is needed to evaluate the
proposed system to follow up participants for longer periods of time which includes a
complementary RCT encompassing patients with chronic pain symptoms. Finally, additional
studies should be addressed to determine the economic effects not only to patients but also
to the healthcare system
Undergraduate nursing student situation awareness during simulation
Graduate nurses encounter complex and rapidly changing patient care situations that require attentiveness, careful surveillance, and the recognition of subtle changes and patterns that will lead to appropriate decisions. Many researchers concur that new graduates are ill-equipped to meet these challenges, resulting in significant risk to patient safety. Situation Awareness (SA) is a skill that has been taught in the field of aviation to facilitate decision-making in complex, dynamic situations; however, there is little known about how nursing students develop SA. This mixed methods explorative study contrasted sophomore and senior nursing studentsâ (n=33) measured levels of SA during simulations of deteriorating patients, and gathered information from the students regarding how they came to be aware of changes. The results indicate students do not have complete SA (avg. score 69%). There is also evidence of significant differences between sophomore and senior nursing studentsâ scores on the comprehensive scale (F(1,31) = 10.394, p = .002) with senior scores significantly higher than sophomore scores. Students described how they became aware of the situation through developing expectations, determining salience and processing the information to create a meaningful whole. These themes support the proposed definition of situation awareness specific to nursing. This study found that nursing students develop Situation Awareness during the course of their nursing program indicating the necessity for deliberate development of this important skill. These study results can be also used to improve nursing education by teaching students specific skills including recognition of changes in respiratory rate and habits of frequent reassessment for patients whose condition is changing. Together these skills will help address the lack of SA which impairs clinical judgment and contributes to unsafe nursing care. Recommendations include further study and measurement of nursing student SA as well as teaching strategies aimed at developing SA
Clinical decision-making in acute paediatrics : evaluation of the impact of an internet-delivered paediatric decision support system
Imperial Users onl
Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact
This report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public.
This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of âextended personalized medicineâ, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: âfake-basedâ, âpatient-generatedâ, and âscientifically tailoredâ views.
This Report presents:
- An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new âTechnology Availability Scaleâ is defined to evaluate and compare their current status.
- Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources.
- A âVisual Overview of AI and AI-mediated technologies in Medicine and Healthcareâ, in which two figures show, respectively, a (newly proposed) classification according to their ethical and social impact, and the most relevant ethical and social aspects considered for such classification. Some key questions, controversies, significant, and conflicting issues are outlined for each aspect.
- A âStructured Overviewâ, with a sorted list of technologies and their implementations, including perspectives, conflicting views and potential pitfalls, and a corresponding, extensive list of references.
- A conclusive set of policy challenges, namely the need of informed citizens, key aspects (of AI and AI-mediated technologies in Medicine and Healthcare) to evaluate, and some recommendations towards a European leadership in this sector.
- We finally relate our study with an update on the use of AI technologies to fight the SARS-CoV-2 virus and COVID-19 pandemic disease.JRC.A.5-Scientific Developmen
Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact
ComisiĂłn Europea. Joint Research Centre.
Serie: JRC Science for Police ReportThis report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public.This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of âextended personalized medicineâ, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: âfake-basedâ, âpatient-generatedâ, and âscientifically tailoredâ views.This Report presents:- An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new âTechnology Availability Scaleâ is defined to evaluate and compare their current status.- Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources.- A âVisual Overview of AI and AI-mediated technologies in Medicine and Healthcareâ, in which two figures show, respeComisiĂłn Europea. Joint Research Centr
A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care
Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care.
An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases.
The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS.
The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include:
(a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology.
(b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patientsâ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems.
(c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation.
A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution.
The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems.
The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies
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