BMC Medical Informatics and Decision Making最新文献

Background: Determining the appropriate end-of-life (EOL) care model within a short time frame is challenging and requires extensive experience. To the best of our knowledge, no studies have developed automatic systems for identifying the hospice care models: hospice home (HHC), inpatient (HIC), and shared care (HSC). This study aimed to determine the optimal hospice care model for EOL patients with machine learning (ML) methods based on health assessment data.

Methods: We employed high-performance ML methods to build prediction models that could predict the most appropriate hospice care service for each patient using their health assessment data. Furthermore, we employed the knowledge distillation technique to transfer knowledge from the best-performing ML model to a decision tree model for classification interpretation.

Results: Experiments were conducted on a dataset of 3,468 hospice patients from National Cheng Kung University Hospital (2005-2020). ML models were built and validated, achieving high performance, with a macro-F1 score of 0.88 and an area under the precision-recall curve (AUPRC) of 0.95. In addition, an interpretable decision tree model was generated, which maintained high performance while providing clear, visualizable decision paths for the best hospice care model.

Conclusion: ML models were developed using health assessment data to explore their potential in guiding the selection of hospice care services for end-of-life patients. The findings demonstrate a data-driven approach that may support more informed and personalized clinical decisions, while representing an initial proof of concept for integrating ML into hospice care planning.

Background: The Short Physical Performance Battery (SPPB) is a widely used assessment tool to evaluate lower extremity function in older adults. However, it requires clinical settings which may not be feasible in all circumstances. This study aimed to develop alternative methods for indirectly estimating SPPB scores using questionnaire responses related to functional abilities.

Methods: We analyzed data from Round 12 of the National Health and Aging Trends Study, using 4,988 participants for statistical analyses, and 2,035 participants (1,628 for training and 407 for testing) for model development and validation. A total of 27 questionnaire items, covering basic and instrumental activities of daily living and physical activities, were used as predictors. Three artificial intelligence models were developed: a tree-based classifier, a multilayer perceptron (MLP) classifier, and a tree-based regressor. For comparison, summed abilities of each ability category and simplified summed ability derived from Shapley Additive Explanations analysis were used. Multiclass and binary classifications were performed using predefined SPPB cutoff values (scores ≤ 3 and ≥ 10).

Results: In analysis comparing SPPB score groups (0-3, 4-9, 10-12), all 27 questionnaire variables were statistically significant. The summed abilities showed a Pearson correlation of 0.716 with total SPPB scores. In multiclass classification, the MLP classifier outperformed other models with a mean AUC of 0.803 (95% CI: 0.767-0.839). For binary classification, distinguishing between individuals with severe impairment (SPPB ≤ 3) and unimpaired function (SPPB ≥ 10), the MLP classifier again demonstrated the highest AUCs (0.907 for SPPB ≤ 3; 0.920 for SPPB ≥ 10). Summed abilities outperformed AI models in detecting severe impairment, with the total ability score reaching an AUC of 0.915. However, for detecting unimpaired function, AI models consistently outperformed summed abilities (maximum AUC of 0.898).

Conclusions: The proposed AI methods enable prediction of SPPB component scores, supporting indirect functional assessment when SPPB testing is not feasible. These tools can help reduce unnecessary clinical burden and cost by guiding SPPB administration decisions.

Background: Shared decision making (SDM) is highly relevant in oncology and cancer care, yet its application within multiple myeloma (MM) remains underexplored. This study aims to (1) investigate SDM implementation in MM clinical practice, (2) assess the role of various stakeholders next to haematologists in the SDM process, and (3) identify barriers and potential solutions to SDM implementation in MM care.

Methods: This qualitative study consisted of semi-structured interviews with patients (n = 39), haematologists (n = 15), and haematology nurses (n = 5) from nine countries in Europe and Israel. Interviews were analysed thematically.

Results: MM patients expressed diverse preferences for involvement in treatment decisions, emphasising the importance of receiving information, engaging in discussions, and having their opinions considered. However, participants reported varied experiences regarding the application of SDM. While most haematologists believed SDM was consistently attempted, patients frequently indicated that their preferences, concerns, and desired level of involvement were not explicitly solicited. Discussions about the option of no treatment were notably under-discussed, as observed by patients and acknowledged by haematologists. Patients uniformly reported that the assessment of their preferred information-seeking approach was consistently overlooked, a critical step in SDM. Haematology nurses, the multidisciplinary team, family members, and patient organisations were found to play an invaluable role in the SDM process, each having their own complementary role alongside haematologists. Barriers to SDM implementation included haematologists' reluctance to inform or involve patients, patients' emotional status, lack of reliable patient-focused information, absence of haematology nurses, and time constraints. Patient decision aids (PtDAs) were perceived as tools to facilitate SDM, with a majority of participants expressing positive attitudes towards them, recognising their value in specific contexts.

Conclusion: While SDM is partially applied in MM care, there remains room for improvement. This can be done by amplifying the role of haematology nurses and other multidisciplinary team members in the SDM process. Additionally, efforts should focus on increasing the role of patient organisations in raising awareness about SDM and empowering patients to actively participate in SDM. The recommendations derived from this study along with the insights for PtDA development can serve as an initial stride towards increasing SDM implementation in MM care.

Artificial intelligence (AI) has emerged as a promising tool to enhance medical practice and improve patient outcomes. However, introducing AI in interactions between patients, support persons (SPs) and physicians may create real or perceived information asymmetries and may not always be well accepted by end-users. To ensure that AI contributes to patient empowerment rather than undermining it, there is a need to better understand how AI-based tools affect communication, trust and decision-making in clinical encounters. Research should focus on identifying how AI can support patients' autonomy, trust and acceptance, how it may strengthen the role of SPs and promote transparent and ethically sound care. With these findings, applying a human-centered design with established technology acceptance frameworks (e.g. TAM, UTAUT) will be crucial to guide evidence-based implementation. Only by involving patients, SPs and physicians in AI development can these technologies unfold their full potential to deliver equitable, interpretable and patient-centered healthcare.