Journal of Medical Systems最新文献

Clinicians and patients seeking electronic health applications face challenges in selecting effective solutions due to a high market failure rate. Conversational agent applications (“chatbots”) show promise in increasing healthcare user engagement by creating bonds between the applications and users. It is unclear if chatbots improve patient adherence or if past trends to include chatbots in electronic health applications were due to technology hype dynamics and competitive pressure to innovate. We conducted a systematic literature review using Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology on health chatbot randomized control trials. The goal of this review was to identify if user engagement indicators are published in eHealth chatbot studies. A meta-analysis examined patient clinical trial retention of chatbot apps. The results showed no chatbot arm patient retention effect. The small number of studies suggests a need for ongoing eHealth chatbot research, especially given the claims regarding their effectiveness made outside the scientific literatures.

Transvaginal oocyte retrieval is an outpatient procedure performed under local anaesthesia. Hypno-analgesia could be effective in managing comfort during this procedure. This study aimed to assess the effectiveness of a virtual reality headset as an adjunct to local anaesthesia in managing nociception during oocyte retrieval. This was a prospective, randomized single-centre study including patients undergoing oocyte retrieval under local anaesthesia. Patients were randomly assigned to the intervention group (virtual reality headset + local anaesthesia) or the control group (local anaesthesia). The primary outcome was the efficacy on the ANI^®, which reflects the relative parasympathetic tone. Secondary outcomes included pain, anxiety, conversion to general anaesthesia rate, procedural duration, patient’s and gynaecologist’s satisfaction and virtual reality headset tolerance. ANI was significantly lower in the virtual reality group during the whole procedure (mean ANI: 79 95 CI [77; 81] vs 74 95 CI [72; 76]; p < 0.001; effect size Cohen’s d -0.53 [-0.83, -0.23]), and during the two most painful moments: infiltration (mean ANI: 81 +/- 11 vs 74 +/- 13; p < 0.001; effect size Cohen’s d -0.54[-0.85, -0.24]) and oocytes retrieval (mean ANI: 78 +/- 11 vs 74.40 +/- 11; p = 0.020; effect size Cohen’s d -0.37 [-0.67, -0.07]).There was no significant difference in pain measured by VAS. No serious adverse events related were reported. The integration of virtual reality as an hypnotic tool during oocyte retrieval under local anaesthesia in assisted reproductive techniques could improve patient’s comfort and experience.

The aim of the study is to evaluate and compare the quality and readability of responses generated by five different artificial intelligence (AI) chatbots—ChatGPT, Bard, Bing, Ernie, and Copilot—to the top searched queries of erectile dysfunction (ED). Google Trends was used to identify ED-related relevant phrases. Each AI chatbot received a specific sequence of 25 frequently searched terms as input. Responses were evaluated using DISCERN, Ensuring Quality Information for Patients (EQIP), and Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) metrics. The top three most frequently searched phrases were “erectile dysfunction cause”, “how to erectile dysfunction,” and “erectile dysfunction treatment.” Zimbabwe, Zambia, and Ghana exhibited the highest level of interest in ED. None of the AI chatbots achieved the necessary degree of readability. However, Bard exhibited significantly higher FKRE and FKGL ratings (p = 0.001), and Copilot achieved better EQIP and DISCERN ratings than the other chatbots (p = 0.001). Bard exhibited the simplest linguistic framework and posed the least challenge in terms of readability and comprehension, and Copilot’s text quality on ED was superior to the other chatbots. As new chatbots are introduced, their understandability and text quality increase, providing better guidance to patients.

Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of $84.37\%$ .

Anesthesiologists have a significant responsibility to provide care at all hours of the day, including nights, weekends, and holidays. This call burden carries a significant lifestyle constraint that can impact relationships, affect provider wellbeing, and has been associated with provider burnout. This quality improvement study analyzes the effects of a dynamic call marketplace, which allows anesthesiologists to specify how much call they would like to take across a spectrum of hypothetical compensation levels, from very low to very high. The system then determines the market equilibrium price such that every anesthesiologist gets exactly the amount of desired call. A retrospective analysis compared percentage participation in adjusting call burden both pre- and post-implementation of a dynamic marketplace during the years of 2017 to 2023. Additionally, a 2023 post-implementation survey was sent out assessing various aspects of anesthesiologist perception of the new system including work-life balance and job satisfaction. The dynamic call marketplace in this study enabled a more effective platform for adjusting call levels, as there was a statistically significant increase in the percentage of anesthesiologists participating in call exchanged during post- compared to pre-implementation (p < 0.0001). The satisfaction survey suggested agreement among anesthesiologists that the dynamic call marketplace positively affected professional satisfaction and work-life balance. Further, the level of agreement with these statements was most prevalent among middle career stage anesthesiologists (11-20 years as attending physician). The present system may target elements with the capacity to increase satisfaction, particularly among physicians most at risk of burnout within the anesthesia workforce.

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GW_non-MEWS and GW_MEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GW_non-MEWS and 71,023 GW_MEWS. The numbers of inpatients who underwent an unplanned ICU admission in GW_non-MEWS and GW_MEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GW_MEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

In healthcare professions, soft skills contribute to critical thinking, decision-making, and patient-centered care. While important to the delivery of high-quality medical care, soft skills are often underemphasized during healthcare training in low-and-middle-income countries. Despite South Asia's large population, the efficacy and viability of a digital soft skills curriculum for South Asian healthcare practitioners has not been studied to date. We hypothesized that a web-based, multilingual, soft skills course could aid the understanding and application of soft skills to improve healthcare practitioner knowledge, confidence, attitudes, and intent-to-change clinical practice.In September 2019 a needs assessment observing soft skills practices was conducted in several Indian states. We developed a communication-focused soft skills curriculum that comprised seven 10-minute video lectures, recorded in spoken English and Hindi. Participants consisted of any practicing healthcare professionals and trainees in select South Asian countries age 18 and over. Participant knowledge, confidence, attitudes, and intent-to-change clinical practice were evaluated using pre- and post-course tests and surveys. Statistical analyses were performed using STATA and SPSS.From July 26, 2021 to September 26, 2021, 5750 registered and attempted the course, 2628 unique participants completed the pre-test, and 1566 unique participants completed the post-test. Participants demonstrated small but statistically significant gains in confidence (𝑝<0.001), attitudes toward course topics relevance (𝑝<0.001), and intent-to-change clinical practice (𝑝<0.001). There was no statistically significant gain in knowledge. A digital soft-skills massive open online course for healthcare practitioners in South Asia could serve as a viable approach to improve the quality of soft skills training in low-to-middle income countries.

Intraoperative cardiopulmonary variables are well-known predictors of postoperative pulmonary complications (PPC), traditionally quantified by median values over the duration of surgery. However, it is unknown whether cardiopulmonary instability, or wider intra-operative variability of the same metrics, is distinctly associated with PPC risk and severity. We leveraged a retrospective cohort of adults (n = 1202) undergoing major non-cardiothoracic surgery. We used multivariable logistic regression to evaluate the association of two outcomes (1)moderate-or-severe PPC and (2)any PPC with two sets of exposure variables- (a)variability of cardiopulmonary metrics (inter-quartile range, IQR) and (b)median intraoperative cardiopulmonary metrics. We compared predictive ability (receiver operating curve analysis, ROC) and parsimony (information criteria) of three models evaluating different aspects of the intra-operative cardiopulmonary metrics: Median-based: Median cardiopulmonary metrics alone, Variability-based: IQR of cardiopulmonary metrics alone, and Combined: Medians and IQR. Models controlled for peri-operative/surgical factors, demographics, and comorbidities. PPC occurred in 400(33%) of patients, and 91(8%) experienced moderate-or-severe PPC. Variability in multiple intra-operative cardiopulmonary metrics was independently associated with risk of moderate-or-severe, but not any, PPC. For moderate-or-severe PPC, the best-fit predictive model was the Variability-based model by both information criteria and ROC analysis (area under the curve, AUC_{Variability-based} = 0.74 vs AUC_Median-based = 0.65, p = 0.0015; AUC_{Variability-based} = 0.74 vs AUC_Combined = 0.68, p = 0.012). For any PPC, the Median-based model yielded the best fit by information criteria. Predictive accuracy was marginally but not significantly higher for the Combined model (AUC_Combined = 0.661) than for the Median-based (AUC_Median-based = 0.657, p = 0.60) or Variability-based (AUC_{Variability-based} = 0.649, p = 0.29) models. Variability of cardiopulmonary metrics, distinct from median intra-operative values, is an important predictor of moderate-or-severe PPC.

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers. We defined the occurrence of progressive disease after MRI evaluation within 12 months as a poor prognosis and constructed a 3D CNN-based deep learning model to predict prognosis. Images from 111 cases were used as the training and internal validation data; images from 31 cases were used as the external validation data. Internal validation of the AI model with stratified 5-fold cross-validation resulted in a significant difference in progression-free survival (PFS) between good and poor prognostic cases (2-year PFS, 91.2% versus [vs.] 61.1%, P = 0.0002). The AI model clearly stratified good and poor prognostic cases in the external validation cohort (2-year PFS, 92.9% vs. 55.6%, P = 0.004), with an area under the receiver operating characteristic curve of 0.804. According to Grad-CAM, the MRI signals of the spleen and bones of the vertebrae and pelvis contributed to prognosis prediction. This study is the first to show that image analysis of whole-body MRI using a 3D CNN without any other clinical data is effective in predicting the prognosis of patients with MM.