Expert assessment of ChatGPT's ability to generate illness scripts: an evaluative study.

BACKGROUND: An illness script is a specific script format geared to represent patient-oriented clinical knowledge organized around enabling conditions, faults (i.e., pathophysiological process), and consequences. Generative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could help the comprehension of key features of diseases and increase diagnostic accuracy. No systematic summary of specific examples of illness scripts has been reported since illness scripts are unique to each physician. OBJECTIVE: This study investigated whether generative AI can generate illness scripts. METHODS: We utilized ChatGPT-4, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum in Japan for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: "A" denotes that the content of each disease's illness script proves sufficient for training medical students, "B" denotes that it is partially lacking but acceptable, and "C" denotes that it is deficient in multiple respects. RESULTS: By leveraging ChatGPT-4, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received "A," "B," and "C" ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. CONCLUSION: Useful illness scripts were seamlessly and instantaneously created using ChatGPT-4 by employing prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to key features of diseases.

Association Between Physician Empathy and Difficult Patient Encounters: a Cross-Sectional Study.

BACKGROUND: Physicians frequently experience patients as difficult. Our study explores whether more empathetic physicians experience fewer patient encounters as difficult. OBJECTIVE: To investigate the association between physician empathy and difficult patient encounters (DPEs). DESIGN: Cross-sectional study. PARTICIPANTS: Participants were 18 generalist physicians with 3-8 years of experience. The investigation was conducted from August-September 2018 and April-May 2019 at six healthcare facilities. MAIN MEASURES: Based on the Jefferson Scale of Empathy (JSE) scores, we classified physicians into low and high empathy groups. The physicians completed the Difficult Doctor-Patient Relationship Questionnaire-10 (DDPRQ-10) after each patient visit. Scores ≥ 31 on the DDPRQ-10 indicated DPEs. We implemented multilevel mixed-effects logistic regression models to examine the association between physicians' empathy and DPE, adjusting for patient-level covariates (age, sex, history of mental disorders) and with physician-level clustering. KEY RESULTS: The median JSE score was 114 (range: 96-126), and physicians with JSE scores 96-113 and 114-126 were assigned to low and high empathy groups, respectively (n = 8 and 10 each); 240 and 344 patients were examined by physicians in the low and high empathy groups, respectively. Among low empathy physicians, 23% of encounters were considered difficulty, compared to 11% among high empathy groups (OR: 0.37; 95% CI = 0.19-0.72, p = 0.004). JSE scores and DDPRQ-10 scores were negatively correlated (r = -0.22, p < 0.01). CONCLUSION: Empathetic physicians were less likely to experience encounters as difficult. Empathy appears to be an important component of physician perception of encounter difficulty.

Improving decision accuracy using a clinical decision support system for medical students during history-taking: a randomized clinical trial.

BACKGROUND: A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google. METHODS: This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared. RESULTS: The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1). CONCLUSIONS: Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases. TRIAL REGISTRATION: This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).

Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study.

BACKGROUND: To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews. METHODS: The effects of the two feedback methods on student performance in medical interviews were compared using a prospective observational trial. Seventy-nine medical students in a clinical clerkship were assigned to receive either speech-recognition feedback (n = 39; SRS feedback group) or voice-recording feedback (n = 40; IC recorder feedback group). All students' medical interviewing skills during mock patient encounters were assessed twice, first using a mini-clinical evaluation exercise (mini-CEX) and then a checklist. Medical students then made the most appropriate diagnoses based on medical interviews. The diagnostic accuracy, mini-CEX, and checklist scores of the two groups were compared. RESULTS: According to the study results, the mean diagnostic accuracy rate (SRS feedback group:1st mock 51.3%, 2nd mock 89.7%; IC recorder feedback group, 57.5%-67.5%; F(1, 77) = 4.0; p = 0.049), mini-CEX scores for overall clinical competence (SRS feedback group: 1st mock 5.2 ± 1.1, 2nd mock 7.4 ± 0.9; IC recorder feedback group: 1st mock 5.6 ± 1.4, 2nd mock 6.1 ± 1.2; F(1, 77) = 35.7; p < 0.001), and checklist scores for clinical performance (SRS feedback group: 1st mock 12.2 ± 2.4, 2nd mock 16.1 ± 1.7; IC recorder feedback group: 1st mock 13.1 ± 2.5, 2nd mock 13.8 ± 2.6; F(1, 77) = 26.1; p < 0.001) were higher with speech recognition-based feedback. CONCLUSIONS: Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores. TRIAL REGISTRATION: This study was registered in the Japan Registry of Clinical Trials on June 14, 2022. Due to our misunderstanding of the trial registration requirements, we registered the trial retrospectively. This study was registered in the Japan Registry of Clinical Trials on 7/7/2022 (Clinical trial registration number: jRCT1030220188).

The influence of Gamification on medical students' diagnostic decision making and awareness of medical cost: a mixed-method study.

BACKGROUND: The gamification of learning increases student enjoyment, and motivation and engagement in learning tasks. This study investigated the effects of gamification using decision-making cards (DMCs) on diagnostic decision-making and cost using case scenarios. METHOD: Thirty medical students in clinical clerkship participated and were randomly assigned to 14 small groups of 2-3 medical students each. Decision-making was gamified using DMCs with a clinical information heading and medical cost on the front, and clinical information details on the back. First, each team was provided with brief clinical information on case scenarios. Subsequently, DMCs depending on the case were distributed to each team, and team members chose cards one at a time until they reached a diagnosis of the case. The total medical cost was then scored based on the number and contents of cards drawn. Four case scenarios were conducted. The quantitative outcomes including confidence in effective clinical decision-making, motivation to learn diagnostic decision-making, and awareness of medical costs were measured before and after our gamification by self-evaluation using a 7-point Likert scale. The qualitative component consisted of a content analysis on the benefits of learning clinical reasoning using DMCs. RESULT: Confidence in effective clinical decision-making, motivation to learn diagnostic decision-making, and awareness of medical cost were significantly higher after the gamification. Furthermore, comparing the clinical case scenario tackled last with the one tackled first, the average medical cost of all cards drawn by students decreased significantly from 11,921 to 8,895 Japanese yen. In the content analysis, seven advantage categories of DMCs corresponding to clinical reasoning components were extracted (information gathering, hypothesis generation, problem representation, differential diagnosis, leading or working diagnosis, diagnostic justification, and management and treatment). CONCLUSION: Teaching medical students clinical reasoning using DMCs can improve clinical decision-making confidence and learning motivation, and reduces medical cost in clinical case scenarios. In addition, it can help students to acquire practical knowledge, deepens their understanding of clinical reasoning, and identifies several important clinical reasoning skills including diagnostic decision-making and awareness of medical costs. Gamification using DMCs can be an effective teaching method for improving medical students' diagnostic decision-making and reducing costs.

Validating the representation of distance between infarct diseases using word embedding.

BACKGROUND: The pivot and cluster strategy (PCS) is a diagnostic reasoning strategy that automatically elicits disease clusters similar to a differential diagnosis in a batch. Although physicians know empirically which disease clusters are similar, there has been no quantitative evaluation. This study aimed to determine whether inter-disease distances between word embedding vectors using the PCS are a valid quantitative representation of similar disease groups in a limited domain. METHODS: Abstracts were extracted from the Ichushi Web database and subjected to morphological analysis and training using Word2Vec, FastText, and GloVe. Consequently, word embedding vectors were obtained. For words including "infarction," we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method. RESULTS: Seventy-one words included "infarction," of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. When using Word2Vec, the CCC was most significant at 0.8690 (metric and method: euclidean and centroid), whereas the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete). FastText and GloVe generally resulted in the same trend as Word2Vec, and the metric and method that maximized CCC differed from the ones that maximized the external validity measures. CONCLUSIONS: The metric and method that maximized the internal validity measure differed from those that maximized the external validity measures; both produced different results. The cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence. The distributed representation, when trained by Word2Vec on the "infarction" domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS.

Effective situation-based delirium simulation training using flipped classroom approach to improve interprofessional collaborative practice competency: a mixed-methods study.

BACKGROUND: Interprofessional collaborative practice competency (ICPC) is key to providing safe, high-quality, accessible, patient-centred care. Effective delirium management, particularly, requires a multi-component intervention, including the use of interprofessional teams at care point. This research aims to investigate the effectiveness of the flipped classroom approach for improving ICPC in simulation-based delirium case management. METHOD: An embedded mixed-methods study was designed to investigate the effects of the flipped classroom approach on health professionals' performance in delirium management. The study population comprised nine health professionals (three physicians, nurses, and pharmacists each). They used pre-class study materials about delirium management via a digital learning platform before a simulation case training session. A readiness assurance process test was conducted on key concepts, covered in the pre-class study material. Participants were randomly assigned to three teams, each of which included health professionals. Each team participated in a simulation case scenario. For the quantitative outcome measures, the Chiba Interprofessional Competency Scale (CICS29), a validated scale for measuring competencies of interprofessional practice, was used before, after, and three months after the educational intervention. The qualitative component consisted of a post-training questionnaire and semi-structured focused group interviews about the impact of the flipped classroom approach. RESULT: The CICS29 measured after the intervention and three months after was noted to be significantly higher than before the intervention. Three semi-structured focused group interviews were conducted (n=9), which, upon analysis revealed that the flipped classroom approach effected on four stages of Bloom's taxonomy level. A total of nine categories and 17 subcategories were identified corresponding to four levels of the revised Bloom's taxonomy: remember (1), understand (12), apply (23), and analyse (3). CONCLUSION: The simulation-based skill training using flipped classroom approach can be an effective method for improving ICPC for health professionals. In this approach, an elevated level of cognitive activity is practiced in the Bloom's taxonomy, and the participants worked on an application-based case simulation that promoted higher level learning and engagement in interprofessional collaborative practice. This approach also established a basic common language of delirium assessment and management, thus facilitating communication among health professionals and improving ICPC.

Feedback of patient survey on medication improves the management of polypharmacy: a pilot trial.

BACKGROUND: Patient awareness surveys on polypharmacy have been reported previously, but no previous study has examined the effects of sending feedback to health professionals on reducing medication use. Our study aimed to conduct a patient survey to examine factors contributing to polypharmacy, feedback the results to health professionals, and analyze the resulting changes in the number of polypharmacy patients and prescribed medications. METHODS: After conducting a questionnaire survey of patients in Study 1, we provided its results to the healthcare professionals, and then surveyed the number of polypharmacy patients and oral medications using a before-after comparative study design in Study 2. In Study 1, we examined polypharmacy and its contributing factors by performing logistic regression analysis. In Study 2, we performed a t-test and a chi-square test. RESULTS: In the questionnaire survey, significant differences were found in the following 3 items: age (odds ratio (OR) = 3.14; 95% confidence interval (CI) = 2.01-4.91), number of medical institutions (OR = 2.34; 95%CI = 1.50-3.64), and patients' difficulty with asking their doctors to deprescribe their medications (OR = 2.21; 95%CI = 1.25-3.90). After the feedback, the number of polypharmacy patients decreased from 175 to 159 individuals and the mean number of prescribed medications per patient decreased from 8.2 to 7.7 (p < 0.001, respectively). CONCLUSIONS: Providing feedback to health professionals on polypharmacy survey results may lead to a decrease in the number of polypharmacy patients. Factors contributing to polypharmacy included age (75 years or older), the number of medical institutions (2 or more institutions), and patients' difficulty with asking their physicians to deprescribe their medications. Feedback to health professionals reduced the percentage of polypharmacy patients and the number of prescribed medications. TRIAL REGISTRATION: UMIN. Registered 21 June 2020 - Retrospectively registered, https://www.umin.ac.jp/ctr/index-j.htm.

Lists of potential diagnoses that final-year medical students need to consider: a modified Delphi study.

BACKGROUND: Contrastive learning is known to be effective in teaching medical students how to generate diagnostic hypotheses in clinical reasoning. However, there is no international consensus on lists of diagnostic considerations across different medical disciplines regarding the common signs and symptoms that should be learned as part of the undergraduate medical curriculum. In Japan, the national model core curriculum for undergraduate medical education was revised in 2016, and lists of potential diagnoses for 37 common signs, symptoms, and pathophysiology were introduced into the curriculum. This study aimed to validate the list of items based on expert consensus. METHODS: The authors used a modified Delphi method to develop consensus among a panel of 23 expert physician-teachers in clinical reasoning from across Japan. The panel evaluated the items on a 5-point Likert scale, based on whether a disease should be hypothesized by final-year medical students considering given signs, symptoms, or pathophysiology. They also added other diseases that should be hypothesized. A positive consensus was defined as both a 75% rate of panel agreement and a mean of 4 or higher with a standard deviation of less than 1 on the 5-point scale. The study was conducted between September 2017 and March 2018. RESULTS: This modified Delphi study identified 275 basic and 67 essential other than basic items corresponding to the potential diagnoses for 37 common signs, symptoms, and pathophysiology that Japanese medical students should master before graduation. CONCLUSIONS: The lists developed in the study can be useful for teaching and learning how to generate initial hypotheses by encouraging students' contrastive learning. Although they were focused on the Japanese educational context, the lists and process of validation are generalizable to other countries for building national consensus on the content of medical education curricula.

Flexible e-learning video approach to improve fundus examination skills for medical students: a mixed-methods study.

BACKGROUND: Training for the fundus examination using traditional teaching is challenging, resulting in low generalist physicians' confidence in performing the funduscopic examination. There is growing evidence suggesting a flexible e-learning video approach's value in teaching physical examination procedures. However, whether the flexible e-learning video approach is superior to the traditional, face-to-face (F2F) lecture-based teaching for the funduscopic exam and the cognitive processes supporting its effectiveness has not yet been determined. METHODS: We conducted a sequential explanatory mixed-method study to compare the flexible e-learning video approach's effectiveness versus the F2F lecture-based approach for teaching the funduscopic exam to medical students at Chiba University in Japan. Medical students were randomly assigned to either a flexible e-learning video approach group or a F2F lecture approach group. We then quantitatively measured the diagnostic accuracy of funduscopic findings before and after attending the specific classrooms. Next, we conducted student focus groups to explore the students' thinking processes in the flexible e-learning video approach vs. the F2F lecture-based teaching of fundus examination. The qualitative data were analyzed using the qualitative content analysis method. RESULTS: The mean diagnostic accuracy scores in the post-test significantly increased from pre-test in the intervention group (36.6 to 63.4%, p < 0.001). Post-post comparisons across the two groups revealed a significant difference (intervention group 63.4% vs. control group 34.6%, p < 0.001). Six semi-structured focused group interviews were conducted (n = 36). In the flexible e-learning video approach group, we identified ten categories corresponding to four levels of the revised Bloom's taxonomy: remember, understand, apply, analyze. Five categories were identified in the traditional F2F lecture approach group corresponding to three revised Bloom's taxonomy levels: understand, apply, analyze. Interrater reliability was substantial (Cohen's kappa = 0.81). CONCLUSIONS: Teaching medical students funduscopic examination using the flexible e-learning video approach leads to improved diagnostic accuracy of funduscopic examinations. The flexible e-learning video teaching method enabled higher cognitive activity levels than the traditional, lecture-based classroom, as assessed using the revised Bloom's taxonomy. TRIAL REGISTRATION: This study was registered with the University Hospital Medical Information Network Clinical Trials Registry on 08/02/2020 (Unique trial number: UMIN 000039434 ).

Alternative approaches for clinical clerkship during the COVID-19 pandemic: online simulated clinical practice for inpatients and outpatients-A mixed method.

BACKGROUND: The COVID-19 pandemic has created a need for educational materials and methods that can replace clinical clerkships (CCs) for online simulated clinical practice (online-sCP). This study evaluates the impact of using simulated electronic health records (sEHR) for inpatients, and electronic problem-based learning (e-PBL) and online virtual medical interviews (online-VMI) for outpatients, for an online-sCP using a learning management system (LMS) and online meeting system facilitated by a supervising physician. METHODS: The sEHR was reviewed by medical students and subsequently discussed with a supervising physician using an online meeting system. In the e-PBL, medical students reviewed the simulated patients and discussed on the LMS. For the online-VMI, a faculty member acted as an outpatient and a student acted as the doctor. Small groups of students discussed the clinical reasoning process using the online meeting system. A mixed-method design was implemented. Medical students self-assessed their clinical competence before and after the online-sCP. They answered questionnaires and participated in semi-structured focus group interviews (FGIs) regarding the advantages and disadvantages of the practice. RESULTS: Forty-three students completed the online-sCP during May and June 2020. All students indicated significant improvement in all aspects of self-evaluation of clinical performance after the online-sCP. Students using sEHR reported significant improvement in writing daily medical records and medical summaries. Students using e-PBL and online-VMI reported significant improvement in medical interviews and counseling. Students also indicated CCs as more useful for learning associated with medical interviews, physical examinations, and humanistic qualities like professionalism than the online-sCP. Eight FGIs were conducted (n = 42). The advantages of online-sCP were segregated into five categories (learning environment, efficiency, accessibility, self-paced learning, and interactivity); meanwhile, the disadvantages of online-sCP were classified into seven categories (clinical practice experience, learning environment, interactivity, motivation, memory retention, accessibility, and extraneous cognitive load). CONCLUSIONS: Online-sCP with sEHR, e-PBL, and online-VMI could be useful in learning some of the clinical skills acquired through CC. These methods can be implemented with limited preparation and resources.

Influence of psychiatric or social backgrounds on clinical decision making: a randomized, controlled multi-centre study.

BACKGROUND: Frequent and repeated visits from patients with mental illness or free medical care recipients may elicit physicians' negative emotions and influence their clinical decision making. This study investigated the impact of the psychiatric or social background of such patients on physicians' decision making about whether to offer recommendations for further examinations and whether they expressed an appropriate disposition toward the patient. METHODS: A randomized, controlled multi-centre study of residents in transitional, internal medicine, or emergency medicine was conducted in five hospitals. Upon randomization, participants were stratified by gender and postgraduate year, and they were allocated to scenario set 1 or 2. They answered questions pertaining to decision-making based on eight clinical vignettes. Half of the eight vignettes presented to scenario set 1 included additional patient information, such as that the patient had a past medical history of schizophrenia or that the patient was a recipient of free care who made frequent visits to the doctor (biased vignettes). The other half included no additional information (neutral vignettes). For scenario set 2, the four biased vignettes presented to scenario set 1 were neutralized, and the four neutral vignettes were rendered biased by providing additional information. After reading, participants answered decision-making questions regarding diagnostic examination, interventions, or patient disposition. The primary analysis was a repeated-measures ANOVA on the mean management accuracy score, with patient background information as a within-subject factor (no bias, free care recipients, or history of schizophrenia). RESULTS: A total of 207 questionnaires were collected. Repeated-measures ANOVA showed that additional background information had influence on mean accuracy score (F(7, 206) = 13.84, p <  0.001 partial η2 = 0.063). Post hoc pairwise multiple comparison test, Sidak test, showed a significant difference between schizophrenia and no bias condition (p <  0.05). The ratings for patient likability were lower in the biased vignettes compared to the neutral vignettes, which was associated with the lower utilization of medical resources by the physicians. CONCLUSIONS: Additional background information on past medical history of schizophrenia increased physicians' mistakes in decision making. Patients' psychiatric backgrounds should not bias physicians' decision-making. Based on these findings, physicians are recommended to avoid being influenced by medically unrelated information.

One novel and two uncommon MEFV mutations in Japanese patients with familial Mediterranean fever: a clinicogenetic study.

Familial Mediterranean fever (FMF) is an autoinflammatory disease caused by mutations in the MEFV gene and characterized by recurrent episodes of fever and polyserositis. To date, over 317 MEFV mutations have been reported, only nine of which account for almost all Japanese patients with FMF. Therefore, the prevalence of rare MEFV variants and their clinical characteristics remains unclear. This study identified MEFV mutations previously unreported in the Japanese population and described their clinical features. We performed MEFV genetic testing in 488 Japanese patients with clinically suspected FMF. Of these patients, we retrospectively analyzed three patients with novel or very uncommon MEFV mutations. In all patients, the clinical diagnosis of FMF was made according to Tel-Hashomer's criteria. One novel missense mutation (N679H) and two rare mutations (T681I and R410H) were identified in the MEFV gene. These mutations were found in compound heterozygous or complex genotypes with other known mutations in exons 1 or 2. According to clinical images, all three patients exhibited typical FMF symptoms. A number of patients with FMF caused by novel or uncommon MEFV variants might exist in the Japanese population; therefore, careful genetic testing is required for accurate diagnosis of this curable genetic disorder.

How Accurate Are First Visit Diagnoses Using Synchronous Video Visits with Physicians?

BACKGROUND: Telediagnosis (TD), which uses information and communications technology, has recently undergone rapid development. Since no studies have compared the diagnostic precision of TD to that of face-to-face diagnosis (FD), we examined and compared the diagnostic accuracy of these diagnostic approaches among general medicine outpatients. METHODS: Data of 97 patients (45 men and 52 women with a mean age of 52 years) who underwent initial examinations at a regional hospital were analyzed. Two fully trained general medicine physicians were selected from a group of three physicians to perform FD and TD. Levels of agreement (as κ coefficients) were determined between TD and FD diagnoses as well as between final diagnoses and TD and FD diagnoses. RESULTS: The κ coefficients were 0.75 for TD and FD and 0.81 for both, the final diagnoses and the TD and FD diagnoses, revealing a sufficiently high level of diagnostic agreement. CONCLUSIONS: TD can provide the same level of diagnostic accuracy as FD among general medicine outpatients for adults. The help of medical assistants and the utilization of physical examination devices might enable medical staff to provide TD care similar in quality to FD. TD could be a useful diagnostic tool when medical work force is limited (e.g., in remote areas, during natural disasters, and in at-home care).