Background: In paternalistic models, healthcare providers’ responsibility is to decide what is best for patients. The main concern is that such models fail to respect patient autonomy and do not promote patient respo...Background: In paternalistic models, healthcare providers’ responsibility is to decide what is best for patients. The main concern is that such models fail to respect patient autonomy and do not promote patient responsibility. Aim: To evaluate mental healthcare team members’ perceptions of their own role in encouraging elderly persons to participate in shared decision-making after implementation of the CCM. The CCM is not an explanatory theory, but an evidence-based guideline and synthesis of best available evidence. Methods: Data were collected from two teams that took part in a focus group interview, and the transcript was analysed by means of qualitative thematic analysis. Results: One overall theme emerged—Preventing the violation of human dignity based on three themes, namely, Changing understanding and attitudes, Increasing depressed elderly persons’ autonomy and Clarifying the mental healthcare team coordinator’s role and responsibility. The results of this study reveal that until recently, paternalism has been the dominant decision-making model within healthcare, without any apparent consideration of the patient perspective. Community mental healthcare can be improved by shared decision-making in which team members initiate a dialogue focusing on patient participation to prevent the violation of human dignity. However, in order to determine how best to empower the patient, team members need expert knowledge and intuition.展开更多
The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to co...The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to construct the error function. In addition, we introduce the concept of deviation variable in the definition of the error function. The algorithm of the new approach is summarized in three main steps: first, we transform the original fuzzy problem into a deterministic one by choosing a specific level . second, we solve separately each uni-criteria problem and we compute the error function for each criteria. Finally, we minimize the sum of error functions in order to obtain the desired compromise solution. A numerical example is done for a comparative study with some existing approaches to show the effectiveness of the new approach.展开更多
Background:Given the strikingly high diagnostic error rate in hospitals,and the recent development of Large Language Models(LLMs),we set out to measure the diagnostic sensitivity of two popular LLMs:GPT-4 and PaLM2.Sm...Background:Given the strikingly high diagnostic error rate in hospitals,and the recent development of Large Language Models(LLMs),we set out to measure the diagnostic sensitivity of two popular LLMs:GPT-4 and PaLM2.Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results,with GPT-4 demonstrating high accuracy in diagnosing test cases.However,larger evaluations on real electronic patient data are needed to provide more reliable estimates.Methods:To fill this gap in the literature,we used a deidentified Electronic Health Record(EHR)data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston.This data set contained blood,imaging,microbiology and vital sign information as well as the patients'medical diagnostic codes.Based on the available EHR data,doctors curated a set of diagnoses for each patient,which we will refer to as ground truth diagnoses.We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients.Results:Based on the proportion of correctly predicted ground truth diagnoses,we estimated the diagnostic hit rate of GPT-4 to be 93.9%.PaLM2 achieved 84.7%on the same data set.On these 1000 randomly selected EHRs,GPT-4 correctly identified 1116 unique diagnoses.Conclusion:The results suggest that artificial intelligence(AI)has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year.However,human oversight of AI remains essential:LLMs cannot replace clinicians,especially when it comes to human understanding and empathy.Furthermore,a significant number of challenges in incorporating AI into health care exist,including ethical,liability and regulatory barriers.展开更多
文摘Background: In paternalistic models, healthcare providers’ responsibility is to decide what is best for patients. The main concern is that such models fail to respect patient autonomy and do not promote patient responsibility. Aim: To evaluate mental healthcare team members’ perceptions of their own role in encouraging elderly persons to participate in shared decision-making after implementation of the CCM. The CCM is not an explanatory theory, but an evidence-based guideline and synthesis of best available evidence. Methods: Data were collected from two teams that took part in a focus group interview, and the transcript was analysed by means of qualitative thematic analysis. Results: One overall theme emerged—Preventing the violation of human dignity based on three themes, namely, Changing understanding and attitudes, Increasing depressed elderly persons’ autonomy and Clarifying the mental healthcare team coordinator’s role and responsibility. The results of this study reveal that until recently, paternalism has been the dominant decision-making model within healthcare, without any apparent consideration of the patient perspective. Community mental healthcare can be improved by shared decision-making in which team members initiate a dialogue focusing on patient participation to prevent the violation of human dignity. However, in order to determine how best to empower the patient, team members need expert knowledge and intuition.
文摘The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to construct the error function. In addition, we introduce the concept of deviation variable in the definition of the error function. The algorithm of the new approach is summarized in three main steps: first, we transform the original fuzzy problem into a deterministic one by choosing a specific level . second, we solve separately each uni-criteria problem and we compute the error function for each criteria. Finally, we minimize the sum of error functions in order to obtain the desired compromise solution. A numerical example is done for a comparative study with some existing approaches to show the effectiveness of the new approach.
文摘Background:Given the strikingly high diagnostic error rate in hospitals,and the recent development of Large Language Models(LLMs),we set out to measure the diagnostic sensitivity of two popular LLMs:GPT-4 and PaLM2.Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results,with GPT-4 demonstrating high accuracy in diagnosing test cases.However,larger evaluations on real electronic patient data are needed to provide more reliable estimates.Methods:To fill this gap in the literature,we used a deidentified Electronic Health Record(EHR)data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston.This data set contained blood,imaging,microbiology and vital sign information as well as the patients'medical diagnostic codes.Based on the available EHR data,doctors curated a set of diagnoses for each patient,which we will refer to as ground truth diagnoses.We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients.Results:Based on the proportion of correctly predicted ground truth diagnoses,we estimated the diagnostic hit rate of GPT-4 to be 93.9%.PaLM2 achieved 84.7%on the same data set.On these 1000 randomly selected EHRs,GPT-4 correctly identified 1116 unique diagnoses.Conclusion:The results suggest that artificial intelligence(AI)has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year.However,human oversight of AI remains essential:LLMs cannot replace clinicians,especially when it comes to human understanding and empathy.Furthermore,a significant number of challenges in incorporating AI into health care exist,including ethical,liability and regulatory barriers.
