Telehealth-Based Family Conferences with Implementation of Shared Decision Making Concepts and Humanistic Communication Approach: A Mixed-Methods Prospective Cohort Study

Palliative Care for Adult Patients Undergoing Hemodialysis in Asia: Challenges and Opportunities

This article underscores the importance of integrating comprehensive palliative care for noncancer patients who are undergoing hemodialysis, with an emphasis on the aging populations in Asian nations such as Taiwan, Japan, the Republic of Korea, and China. As the global demographic landscape shifts towards an aging society and healthcare continues to advance, a marked increase has been observed in patients undergoing hemodialysis who require palliative care. This necessitates an immediate paradigm shift to incorporate this care, addressing the intricate physical, psychosocial, and spiritual challenges faced by these individuals and their families. Numerous challenges impede the provision of effective palliative care, including difficulties in prognosis, delayed referrals, cultural misconceptions, lack of clinician confidence, and insufficient collaboration among healthcare professionals. The article proposes potential solutions, such as targeted training for clinicians, the use of telemedicine to facilitate shared decision-making, and the introduction of time-limited trials for dialysis to overcome these obstacles. The integration of palliative care into routine renal treatment and the promotion of transparent communication among healthcare professionals represent key strategies to enhance the quality of life and end-of-life care for people on hemodialysis. By embracing innovative strategies and fostering collaboration, healthcare providers can deliver more patient-centered, holistic care that meets the complex needs of seriously ill patients within an aging population undergoing hemodialysis.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study

BACKGROUND

An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life.

OBJECTIVE

This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings.

METHODS

This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance.

RESULTS

From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase.

CONCLUSIONS

We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.

Natural language processing and network analysis in patients withdrawing from life-sustaining treatments: a retrospective cohort study

BACKGROUND

Providing palliative care to patients who withdraw from life-sustaining treatments is crucial; however, delays or the absence of such services are prevalent. This study used natural language processing and network analysis to identify the role of medications as early palliative care referral triggers.

METHODS

We conducted a retrospective observational study of 119 adult patients receiving specialized palliative care after endotracheal tube withdrawal in intensive care units of a Taiwan-based medical center between July 2016 and June 2018. Patients were categorized into early integration and late referral groups based on the median survival time. Using natural language processing, we analyzed free texts from electronic health records. The Palliative trigger index was also calculated for comparison, and network analysis was performed to determine the co-occurrence of terms between the two groups.

RESULTS

Broad-spectrum antibiotics, antifungal agents, diuretics, and opioids had high Palliative trigger index. The most common co-occurrences in the early integration group were micafungin and voriconazole (co-correlation = 0.75). However, in the late referral group, piperacillin and penicillin were the most common co-occurrences (co-correlation = 0.843).

CONCLUSION

Treatments for severe infections, chronic illnesses, and analgesics are possible triggers for specialized palliative care consultations. The Palliative trigger index and network analysis indicated the need for palliative care in patients withdrawing from life-sustaining treatments. This study recommends establishing a therapeutic control system based on computerized order entry and integrating it into a shared-decision model.

Determining the Development Strategy and Suited Adoption Paths for the Core Competence of Shared Decision-Making Tasks through the SAA-NRM Approach

Shared decision making (SDM) is an interactive process that involves patients and their healthcare professionals reaching joint decisions about medical care through negotiation. As the initiators of medical decision-making in daily routine, physicians should be aware of and concerned about the SDM process. Thus, professional competency development for SDM has become increasingly critical for physicians' training. Therefore, this study investigates the professional competency and the important competency development aspects/criteria of SDM tasks through expert interviews and literature research. The study adopts the SAA (satisfaction-attention analysis) method to assess the status of competency development aspects/criteria and determine the NRM (network relation map) based on the DEMATEL (decision-making trial and evaluation laboratory) technique. The results demonstrate that the CE (concept and evaluation) aspect is the dominant aspect, and the CR (communication and relationship) aspect is the aspect being dominated. The CE aspect influences the aspects of SP (skill and practice), JM (joint information and decision making) and CR, and the SP aspect affects the aspects of JM and CR. Then, the JM aspect affects the CR aspect. The study also suggests suitable adoption paths of competency development for SDM tasks using the NRM approach. It provides recommendations and strategic directions for SDM competency development and sustainable training programs.

Improving the Process of Shared Decision-Making by Integrating Online Structured Information and Self-Assessment Tools

The integration of face-to-face communication and online processes to provide access to information and self-assessment tools may improve shared decision-making (SDM) processes. We aimed to assess the effectiveness of implementing an online SDM process with topics and content developed through a participatory design approach. We analyzed the triggered and completed SDM cases with responses from participants at a medical center in Taiwan. Data were retrieved from the Research Electronic Data Capture (REDCap) database of the hospital for analysis. Each team developed web-based patient decision aids (PDA) with empirical evidence in a multi-digitized manner, allowing patients to scan QR codes on a leaflet using their mobile phones and then read the PDA content online. From July 2019 to December 2020, 48 web-based SDM topics were implemented in the 24 clinical departments of this hospital. The results showed that using the REDCap system improved SDM efficiency and quality. Implementing an online SDM process integrated with face-to-face communication enhanced the practice and effectiveness of SDM, possibly through the flexibility of accessing information, self-assessment, and feedback evaluation.