Examining Cognitive Empathy Elements within AI Chatbots for Healthcare Systems

Empathy is an essential part of communication in healthcare. It is a multidimensional concept and the two key dimensions: emotional and cognitive empathy allow clinicians to understand a patient’s situation, reasoning, and feelings clearly (Mercer and Reynolds, 2002). As artificial intelligence (AI) is increasingly being used in healthcare for many routine tasks, accurate diagnoses, and complex treatment plans, it is becoming more crucial to incorporate clinical empathy into patient-faced AI systems. Unless patients perceive that the AI is understanding their situation, the communication between patient and AI may not sustain efficiently. AI may not really exhibit any emotional empathy at present, but it has the capability to exhibit cognitive empathy by communicating how it can understand patients’ reasoning, perspectives, and point of view. In my dissertation, I examine this issue across three separate lab experiments and one interview study. At first, I developed AI Cognitive Empathy Scale (AICES) and tested all empathy (emotional and cognitive) components together in a simulated scenario against control for patient-AI interaction for diagnosis purposes. In the second experiment, I tested the empathy components separately against control in different simulated scenarios. I identified six cognitive empathy elements from the interview study with first-time mothers, two of these elements were unique from the past literature. In the final lab experiment, I tested different cognitive empathy components separately based on the results from the interview study in simulated scenarios to examine which element emerges as the most effective. Finally, I developed a conceptual model of cognitive empathy for patient-AI interaction connecting the past literature and the observations from my studies. Overall, cognitive empathy elements show promise to create a shared understanding in patients-AI communication that may lead to increased patient satisfaction and willingness to use AI systems for initial diagnosis purposes

INVESTIGATING THE IMPACT OF EXPLANATION ON REPAIRING TRUST IN AI DIAGNOSTIC SYSTEMS FOR RE-DIAGNOSIS

AI systems are increasingly being fielded to support diagnoses and healthcare advice for patients. One promise of AI application is that they might serve as the first point of contact for patients, replacing routine tasks, and allowing health care professionals to focus on more challenging and critical aspects of healthcare. For AI systems to succeed, they must be designed based on a good understanding of how physicians explain diagnoses to patients, and how prospective patients understand and trust the systems providing the diagnosis, as well as the explanations they expect. In this thesis, I examine this problem across three studies. In the first study, I interviewed physicians to explore their explanation strategies in re-diagnosis scenarios. I identified five broad categories of explanation strategies and I developed a generic diagnostic timeline of explanations from the interviews. For the second study, I tested an AI diagnosis scenario and found that explanation helps improve patient satisfaction measures for re-diagnosis. Finally, in a third study I implemented different forms of explanation in a similar diagnosis scenario and found that visual and example-based explanation integrated with rationales had a significantly better impact on patient satisfaction and trust than no explanations, or with text-based rationales alone. Based on these studies and the review of the literature, I provide some design recommendations for the explanations offered for AI systems in the healthcare domain

Examining the effect of explanation on satisfaction and trust in AI diagnostic systems

Background: Artificial Intelligence has the potential to revolutionize healthcare, and it is increasingly being deployed to support and assist medical diagnosis. One potential application of AI is as the first point of contact for patients, replacing initial diagnoses prior to sending a patient to a specialist, allowing health care professionals to focus on more challenging and critical aspects of treatment. But for AI systems to succeed in this role, it will not be enough for them to merely provide accurate diagnoses and predictions. In addition, it will need to provide explanations (both to physicians and patients) about why the diagnoses are made. Without this, accurate and correct diagnoses and treatments might otherwise be ignored or rejected. Method: It is important to evaluate the effectiveness of these explanations and understand the relative effectiveness of different kinds of explanations. In this paper, we examine this problem across two simulation experiments. For the first experiment, we tested a re-diagnosis scenario to understand the effect of local and global explanations. In a second simulation experiment, we implemented different forms of explanation in a similar diagnosis scenario. Results: Results show that explanation helps improve satisfaction measures during the critical re-diagnosis period but had little effect before re-diagnosis (when initial treatment was taking place) or after (when an alternate diagnosis resolved the case successfully). Furthermore, initial “global” explanations about the process had no impact on immediate satisfaction but improved later judgments of understanding about the AI. Results of the second experiment show that visual and example-based explanations integrated with rationales had a significantly better impact on patient satisfaction and trust than no explanations, or with text-based rationales alone. As in Experiment 1, these explanations had their effect primarily on immediate measures of satisfaction during the re-diagnosis crisis, with little advantage prior to re-diagnosis or once the diagnosis was successfully resolved. Conclusion: These two studies help us to draw several conclusions about how patient-facing explanatory diagnostic systems may succeed or fail. Based on these studies and the review of the literature, we will provide some design recommendations for the explanations offered for AI systems in the healthcare domain

Persistence of anti-HBs and immunologic memory in children immunized with hepatitis B vaccine

Background:  We aimed to examine the persistence of anti-HBs in Bangladeshi children aged 5 and 10 years after primary vaccination, and this response to a booster dose. Methods: A total of 100 children were enrolled who were divided into two groups (A and B). Group A comprised of 50 children vaccinated 5 years ago, and group B had 50 children vaccinated 10 years ago. Hepatitis B surface antibody titer was measured, and a booster dose of the vaccine was administered to those who had anti-HBs less than 10 mlU/ml. Seventeen such children from group A and 27 from group B were vaccinated with a booster dose. After one month, 12 children from group A and 18 children from group B were retested for hepatitis B surface antibody levels. Results: After 5 and 10 years of primary vaccination, 66.0% and 46.0% children had protective antibody levels. After one month of booster dose, 91.6% children responded to the increased level of anti-HBs in group A. Among them, 66.6% showed an adequate response. In group B, 88.8% had an increased level of anti-HBs antibody where 83.3% had an adequate response. Geometric mean titre of anti-HBs antibody boosted by 35 and 75 times from pre-booster time to post-booster vaccination in group A and B, respectively. Conclusion: Children had protective levels of anti-HBs antibodies at 5 and 10 years after completion of the primary vaccinations. Anamnestic response to booster vaccination confirmed the persistence of an effective immunological memory in vaccines. Bangabandhu Sheikh Mujib Medical University Journal 2023;16(2): 101-10

Association between severity of chronic liver disease with grading of oesophageal varices in children

Chronic liver disease is a uncommon manifestation in the children and many of them presents with oesophageal varices. The aim of this study was to observe the association between severity of chronic liver diseases determined by Child- Pugh score with grading of oesophageal varices. 62 cases (male, 34) were included in the present study. Mean age of the study population was 9.5±3.3 years. Male to female ratio was 1.2:1. Wilson’s disease was the most common etiology of chronic liver disease (64.5%). Of the 62 children, 30.7% had Child class A, 16.1% had Child class B and the remaining 53.2% had Child class C cirrhosis. Oesophageal varices were found in 43 (69.3%) children. On univariate ananlysis low platelet count and splenomegaly were found to be associated with the presence of esophageal varices. Splenomegaly was found as independent predictor for presence of varices on multivariate analysis (OR; 15.51, 95% CI, 3.7-63.5). Furthermore, splenomegaly was also independent risk factor for large esophageal varices. No association was found between Child-Pugh classification (child A, B, C) with grading of oesophageal varices (Grade - I, II, III, IV). Our study showed no positive association between Child-Pugh classifications with grading of esophageal varices. Splenomegaly predicts the presence of oesophageal varices as well as the presence of large esophageal varices. BSMMU J 2022; 15(1): 29-3

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Assessing Clustering Methods to Establish Reliability and Consensus in Card Sorting Tasks

Human factors researchers often collect qualitative data that involve statements about a system or tool. Establishing consistent patterns in such data is important for making conclusions about the data. When a theoretically motivated coding scheme has not been established, one might use card sorting techniques to have independent raters generate a similarity space in order to create a bottom-up taxonomy. In this paper, we will explore how clustering and scaling techniques can be used to derive a common taxonomy from multiple car sorting results and judge how consistent the groupings are. We examine this process on two datasets, one with the qualitative data from an interview study with physicians and another with the data regarding a website design for usability purposes. Results showed that the different clustering methods had very similar high-level results, and these had high within-group similarity across groups, suggesting inter-rater reliability. Finally, we will discuss the benefits of different algorithms for clustering and scaling and propose measures to assess strong consensus and inter-group sorting reliability