scholarly journals What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

2022 ◽  
Author(s):  
Bas de Boer ◽  
Olya Kudina
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Providers ◽  
Medical Diagnosis ◽  
Clinical Decision Support Systems ◽  
Active Role ◽  
Machine Learning Techniques ◽  
Care Providers ◽  
Technological Mediation ◽  
Medical Diagnoses

AbstractIn this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

Adherence to treatment in psychiatry

2020 ◽  
pp. 193-204
Author(s):  
Amy Chan ◽  
Rob Horne
Keyword(s):  
Health Care ◽  
Treatment Adherence ◽  
Health Care Providers ◽  
Patient Perspective ◽  
Good Health ◽  
Active Role ◽  
Care Providers ◽  
Adherence To Treatment ◽  
Factors Influencing ◽  
Adherence Support

Adherence to treatment in psychiatry is pivotal for achieving and maintaining good health outcomes. Yet, despite the vast amount of research into adherence, treatment adherence remains suboptimal. There is a need for everyone to take an active role in addressing non-adherence if we are to realize the full benefits of available treatments. This chapter introduces the concept of adherence and discusses the factors influencing adherence in psychiatry. The adherence literature is then reviewed, and results from past adherence interventions summarized to explain why non-adherence occurs from an individual patient perspective. A perceptions and practicalities approach to adherence is then presented to help guide the design and delivery of patient-centred adherence support. This chapter serves as a practical guide to adherence for health care providers and others interested in supporting adherence to treatment in psychiatry.

scholarly journals Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis

2018 ◽  
Vol 38 (4) ◽  
pp. 46-54 ◽  
Author(s):  
Devida Long ◽  
Muge Capan ◽  
Susan Mascioli ◽  
Danielle Weldon ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
User Interface ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers

BACKGROUND Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE To gain insight into clinical decision support systems–based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient’s clinical condition rather than regulatory guidelines. CONCLUSIONS The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

scholarly journals Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 245-250
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Differential Diagnosis ◽  
Medical Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Care Practice ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Classification Prediction

Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.

scholarly journals Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models

2021 ◽  
Author(s):  
Daniela A. Gomez-Cravioto ◽  
Ramon E. Diaz-Ramos ◽  
Francisco J. Cantu-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Health Care Providers ◽  
Time Series Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Feature Importance

AbstractTo understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

scholarly journals Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

2021 ◽  
Author(s):  
Mostafa Shanbehzadeh ◽  
Azam Orooji ◽  
Hadi Kazemi-Arpanahi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Hospital Mortality ◽  
Health Care Providers ◽  
Prediction Models ◽  
Confusion Matrix ◽  
World Health ◽  
Medical Decision ◽  
Support Vector ◽  
Care Providers

Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)

scholarly journals What Do Patients Say About Doctors Online? A Systematic Review of Studies on Patient Online Reviews (Preprint)

2018 ◽  
Author(s):  
Y Alicia Hong ◽  
Chen Liang ◽  
Tiffany A Radcliff ◽  
Lisa T Wigfall ◽  
Richard L Street
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Providers ◽  
Health Care Quality ◽  
Online Reviews ◽  
The United States ◽  
Care Quality ◽  
Small Sample ◽  
Care Providers ◽  
Study Characteristics

BACKGROUND The number of patient online reviews (PORs) has grown significantly, and PORs have played an increasingly important role in patients’ choice of health care providers. OBJECTIVE The objective of our study was to systematically review studies on PORs, summarize the major findings and study characteristics, identify literature gaps, and make recommendations for future research. METHODS A major database search was completed in January 2019. Studies were included if they (1) focused on PORs of physicians and hospitals, (2) reported qualitative or quantitative results from analysis of PORs, and (3) peer-reviewed empirical studies. Study characteristics and major findings were synthesized using predesigned tables. RESULTS A total of 63 studies (69 articles) that met the above criteria were included in the review. Most studies (n=48) were conducted in the United States, including Puerto Rico, and the remaining were from Europe, Australia, and China. Earlier studies (published before 2010) used content analysis with small sample sizes; more recent studies retrieved and analyzed larger datasets using machine learning technologies. The number of PORs ranged from fewer than 200 to over 700,000. About 90% of the studies were focused on clinicians, typically specialists such as surgeons; 27% covered health care organizations, typically hospitals; and some studied both. A majority of PORs were positive and patients’ comments on their providers were favorable. Although most studies were descriptive, some compared PORs with traditional surveys of patient experience and found a high degree of correlation and some compared PORs with clinical outcomes but found a low level of correlation. CONCLUSIONS PORs contain valuable information that can generate insights into quality of care and patient-provider relationship, but it has not been systematically used for studies of health care quality. With the advancement of machine learning and data analysis tools, we anticipate more research on PORs based on testable hypotheses and rigorous analytic methods. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO) CRD42018085057; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=85057 (Archived by WebCite at http://www.webcitation.org/76ddvTZ1C)

scholarly journals An Exploration and Forecast of COVID-19 in Mexico with Machine Learning

2020 ◽  
Author(s):  
Daniela A. Gomez-Cravioto ◽  
Ramon E. Diaz-Ramos ◽  
Francisco J. Cantu-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Health Care Providers ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Logistic Regression Models ◽  
Feature Importance

Abstract Background: To understand and approach the COVID-19 spread, Machine Learning offers fundamental tools. This study presents the use of machine learning techniques for the projection of COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. Methods: The methods used are linear, polynomial, and generalized logistic regression models to evaluate the growth of the COVID-19 incidents in the country. Additionally, machine learning and time-series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with mobility rates obtained from Google’s Mobility Reports and climate variables acquired from Weather Online. Results: The results suggest that the logistic growth model fits best the behavior of the pandemic in Mexico, that there is a significant correlation of climate and mobility variables with the disease numbers, and that LSTM is a more suitable approach for the prediction of daily cases. Conclusion: We hope that this study can make some contributions to the world’s response to this epidemic as well as give some references for future research.

Chronic Health Care

2009 ◽  
Vol 22 (1) ◽  
pp. 43-48
Author(s):  
Brenda Scruggs
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Active Role ◽  
Illness Management ◽  
Positive Outcomes ◽  
Care Providers ◽  
Chronic Health ◽  
Acute Health Care ◽  
Acute Injuries

The traditional acute health care model works well for acute injuries and illnesses but is unlikely to achieve good outcomes in managing chronic health care conditions. Positive outcomes in chronic illness management requires multiple referrals from the primary care provider for a multidisciplinary team, relinquished control by the health care providers as they coach the patient into the active role of self-care life skills and daily management of their health, and active learning, participation, and accountability of the patient.

scholarly journals Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jiancheng Ye ◽  
Liang Yao ◽  
Jiahong Shen ◽  
Rethavathi Janarthanam ◽  
Yuan Luo
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Critically Ill ◽  
Health Care Providers ◽  
Critically Ill Patients ◽  
Care Providers ◽  
Learning Models ◽  
Clinical Notes ◽  
Risk Of Mortality ◽  
Machine Learning Models

Abstract Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

Patients’ Perceptions of Health Care Providers’ Dismissive Communication

2021 ◽  
pp. 152483992110275
Author(s):  
Grace M. Hildenbrand ◽  
Evan K. Perrault ◽  
Rachel HeeJoon Rnoh
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Active Role ◽  
Care Providers ◽  
Supportive Communication ◽  
Patient Centered ◽  
Communication Behaviors ◽  
Speak Up ◽  
Patients Experience ◽  
Health Care Provider Communication

Some patients experience negative interactions with health care providers, such as when they perceive that their concerns are ignored by providers. The present study, guided by patient-centered communication, examined health care provider communication behaviors that resulted in patients feeling dismissed, and whether there were differences in providers who dismissed being perceived as (dis)similar to the patients in gender, race/ethnicity, or age. U.S. adults claiming they felt dismissed by a provider were asked to recall demographic information they perceived about the provider and what the provider said that was dismissive. Responses were coded for emergent themes. Results revealed that younger, female, and non-White participants most frequently reported being dismissed by a dissimilar provider. Patients felt dismissed when they perceived that providers were rude or did not take action, provided poor information, did not believe patients, rushed the visit, or were uninformed. Providers may want to avoid these behaviors and could consider obtaining training in supportive communication behaviors such as providing validation to patients and listening to patients in order to enhance patient satisfaction. Patients can also receive communication training to take a more active role in their medical encounters by learning to effectively ask questions, express preferences, and speak up for themselves.

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