On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review

Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review

Although artificial intelligence (AI) is considered to be a promising tool, evidence for the effectiveness of AI-supported clinical practice for lowering blood pressure (BP) in the real world is scarce. We conducted a systematic review to elucidate whether AI-supported clinical care improves BP control. We identified two randomized control trials (RCTs) in a literature search. The results revealed no significant difference between AI-supported care and usual care in a random-effects model meta-analysis of RCTs (AI vs. usual care: systolic/diastolic BP difference: -2.13 [95% confidence interval: -4.72 to 0.46] / -1.03 [-2.52 to 0.46]). In this review, we were unable to clarify whether AI-supported clinical practice improved BP control compared with usual care. Further studies will be needed to provide robust evidence for the effectiveness of AI-supported care in clinical settings.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Humanizing AI in medical training: ethical framework for responsible design

The increasing use of artificial intelligence (AI) in healthcare has brought about numerous ethical considerations that push for reflection. Humanizing AI in medical training is crucial to ensure that the design and deployment of its algorithms align with ethical principles and promote equitable healthcare outcomes for both medical practitioners trainees and patients. This perspective article provides an ethical framework for responsibly designing AI systems in medical training, drawing on our own past research in the fields of electrocardiogram interpretation training and e-health wearable devices. The article proposes five pillars of responsible design: transparency, fairness and justice, safety and wellbeing, accountability, and collaboration. The transparency pillar highlights the crucial role of maintaining the explainabilty of AI algorithms, while the fairness and justice pillar emphasizes on addressing biases in healthcare data and designing models that prioritize equitable medical training outcomes. The safety and wellbeing pillar however, emphasizes on the need to prioritize patient safety and wellbeing in AI model design whether it is for training or simulation purposes, and the accountability pillar calls for establishing clear lines of responsibility and liability for AI-derived decisions. Finally, the collaboration pillar emphasizes interdisciplinary collaboration among stakeholders, including physicians, data scientists, patients, and educators. The proposed framework thus provides a practical guide for designing and deploying AI in medicine generally, and in medical training specifically in a responsible and ethical manner.

Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead

The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices.

Multiple sclerosis and computational biology (Review)

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

AIM

The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications.

BACKGROUND

In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications.

EVALUATION

International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison.

KEY ISSUES

Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network.

CONCLUSION

The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care.

IMPLICATIONS FOR NURSING MANAGEMENT

Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.

Understanding Cardiology Practitioners' Interpretations of Electrocardiograms: An Eye-Tracking Study

Background

Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners’ visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately.

Objective

This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs.

Methods

A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated.

Results

Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation.

Conclusions

The results show that visual expertise for ECG interpretation is linked to the practitioner’s role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels.

Mutuality in nursing: A conceptual framework on the relationship between patient and nurse

AIMS

To describe and develop a conceptual framework for the process of mutuality between nurse and patient.

DESIGN

This was a qualitative study with a grounded theory (GT) design following the constructivist approach of Charmaz (SAGE handbook of research, 2014).

METHODS

A sample of 33 patients with one or more chronic diseases and 35 nurses were interviewed between July and October 2020. Comparative and simultaneous data analyses were conducted. Theoretical sampling and saturation of categories were used to define the sample size.

RESULTS

A conceptual framework for mutuality between nurse (mean age 42 SD ±7 years, 89% female) and patient (mean age 63 SD ±8 years, 42% female) was developed, including the mutuality process, potential influencing factors for both nurses and patients, and outcomes. The mutuality process was characterised by three dimensions: developing and going beyond, being a reference, and deciding and sharing care. Influencing factors for nurses were personal characteristics and professional organisation, while for patients these were age and past experiences. Nurse outcomes were satisfaction and quality of life; patient outcomes were improved self-care and reduction of hospitalisation and emergency admissions.

CONCLUSION

This study described a new conceptual framework for mutuality between nurse and patient, which could improve our understanding of the relationship between nurses and patients, thus enhancing both nurse and patient outcomes.

Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.

Impact of Physicians' Competence and Warmth on Chronic Patients' Intention to Use Online Health Communities

In China, medical resources are unevenly distributed, and hospitals are very congested. Online health communities (OHCs) provide a new way for patients to communicate and obtain health-related information, thereby alleviating the pressure of treatment in hospitals. However, little is known about how to increase individuals’ use intention for OHCs from the perspective of physicians. This study aims to investigate the impact of physicians’ competence and warmth on chronic patients’ intention to use physician-centered OHCs based on the technology acceptance model. A formal investigation was anonymously conducted through a web-based questionnaire survey addressed to participants, and 710 valid responses were received. A research model was constructed and the hypotheses were tested by structural equation modeling. The findings suggest that competence and warmth positively affect chronic patients’ behavioral intention to use (BIU) OHCs through the mediation of perceived usefulness (PU) and perceived ease of use (PEOU). All hypotheses were supported at the 0.05 significant level. Compared with competence, warmth has a slightly stronger impact on PU and PEOU. PEOU has a stronger impact on chronic patients’ BIU OHCs than PU. This study provides a comprehensive understanding of the impacts of physicians’ characteristics in physician-driven OHCs. Compared with competence, physicians’ warmth should be paid more attention to motivate more chronic patients to use OHCs. Enhancing physicians’ warmth and the ease of use are the preferred ways to improve chronic patients’ intention to use OHCs.