Integrated Electronic Health Record of Multidisciplinary Professionals Throughout the Cancer Care Pathway: A Pilot Study Exploring Patient-Centered Information in Breast Cancer Patients.

Purpose: The aim of this pilot study was to first aggregate and then integrate the medical records of various healthcare professionals involved with breast cancer patients to reveal if and how patient-centered information is documented in multidisciplinary cancer care. Patients and Methods: We aggregated 20 types of medical records from various healthcare professionals such as physicians, nurses and allied healthcare professionals (AHPs) throughout three breast cancer patients' care pathways in a department of breast surgery at a university hospital. Purposeful sampling was used, and three cases were examined. The number of integrated type of records was 14, 14, 17 in case 1, 2 and 3, respectively. We manually annotated and analyzed them exploratively using a thematic analysis. The tags were produced using both a deductive template approach and a data-driven inductive approach. All records were then given tags. We defined patient-centered information related tags and biomedical information related tags and then analyzed for if and how patient-centered information was documented. Results: The number of patient-centered information related tags accounted for 30%, 30% and 20% of the total in case 1, 2 and 3, respectively. In all cases, patient-centered information was distributed across various medical records. The Progress Note written by doctors provided much of the patient-centered information, while other records contained information not described elsewhere in the Progress Notes. The records of nurses and AHPs included more patient-centered information than the doctors' notes. Each piece of patient-centered information was documented in fragments providing from each of the healthcare professionals' viewpoints. Conclusion: The documented information throughout the breast cancer care pathway in the cases examined was dominated by biomedical information. However, our findings suggest that integrating fragmented patient-centered information from various healthcare professionals' medical records produces holistic patient-centered information from multiple perspectives and thus may facilitate an enhanced multidisciplinary patient-centered care.

An important paradigm shift within healthcare is the shift toward patient-centered care and away from disease-centered treatment. Patient-centered care is based on shared decision-making, respecting an individual patient's preferences, needs and values, and considering social context and best available research evidence to improve the quality of care. A multidisciplinary team (MDT) approach plays an important role in patient-centered care and MDTs are already adopted into daily oncology practices in many countries, especially in breast cancer care. Previous studies have shown that an effective MDT needs more patient-centered information but often that patient-centered information is notably absent from medical records. We investigated if and how patient-centered information such as psychosocial entries exists in patient records. For this purpose, we performed an exploratory pilot study in which the patient records of three patients with breast cancer, including two patients with advanced stage disease, were studied throughout their care pathway. We observed that the documentation of patient-centered information was fragmented and scattered across various medical records written by multidisciplinary professionals. Moreover, these pieces of scattered information were recorded from different perspectives and viewpoints. Our findings point to a significant role that healthcare informatics could play, as integrating the various healthcare professionals' electronic health record could likely produce multifaceted and more holistic patient-centered information which could be shared and used in shared decision-making and MDTs with a view to considering both patient and clinical perspectives, potentially improving the quality of care.

Utility of a Clinical Decision Support System in Weight Loss Prediction After Head and Neck Cancer Radiotherapy.

PURPOSE: To evaluate the utility of a clinical decision support system (CDSS) using a weight loss prediction model. METHODS: A prediction model for significant weight loss (loss of greater than or equal to 7.5% of body mass at 3-month post radiotherapy) was created with clinical, dosimetric, and radiomics predictors from 63 patients in an independent training data set (accuracy, 0.78; area under the curve [AUC], 0.81) using least absolute shrinkage and selection operator logistic regression. Four physicians with varying experience levels were then recruited to evaluate 100 patients in an independent validation data set of head and neck cancer twice (ie, a pre-post design): first without and then with the aid of a CDSS derived from the prediction model. At both evaluations, physicians were asked to predict the development (yes/no) and probability of significant weight loss for each patient on the basis of patient characteristics, including pretreatment dysphagia and weight loss and information from the treatment plan. At the second evaluation, physicians were also provided with the prediction model's results for weight loss probability. Physicians' predictions were compared with actual weight loss, and accuracy and AUC were investigated between the two evaluations. RESULTS: The mean accuracy of the physicians' ability to identify patients who will experience significant weight loss (yes/no) increased from 0.58 (range, 0.47 to 0.63) to 0.63 (range, 0.58 to 0.72) with the CDSS ( P = .06). The AUC of weight loss probability predicted by physicians significantly increased from 0.56 (range, 0.46 to 0.64) to 0.69 (range, 0.63 to 0.73) with the aid of the CDSS ( P < .05). Specifically, more improvement was observed among less-experienced physicians ( P < .01). CONCLUSION: Our preliminary results demonstrate that physicians' decisions may be improved by a weight loss CDSS model, especially among less-experienced physicians. Additional study with a larger cohort of patients and more participating physicians is thus warranted for understanding the usefulness of CDSSs.

The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System.

PURPOSE: Clinical data collection and development of outcome prediction models by machine learning can form the foundation for a learning health system offering precision radiation therapy. However, changes in clinical practice over time can affect the measures and patient outcomes and, hence, the collected data. We hypothesize that regular prediction model updates and continuous prospective data collection are important to prevent the degradation of a model's predication accuracy. METHODS AND MATERIALS: Clinical and dosimetric data from head and neck patients receiving intensity modulated radiation therapy from 2008 to 2015 were prospectively collected as a routine clinical workflow and anonymized for this analysis. Prediction models for grade ≥2 xerostomia at 3 to 6 months of follow-up were developed by bivariate logistic regression using the dose-volume histogram of parotid and submandibular glands. A baseline prediction model was developed with a training data set from 2008 to 2009. The selected predictor variables and coefficients were updated by 4 different model updating methods. (A) The prediction model was updated by using only recent 2-year data and applied to patients in the following test year. (B) The model was updated by increasing the training data set yearly. (C) The model was updated by increasing the training data set on the condition that the area under the curve (AUC) of the recent test year was less than 0.6. (D) The model was not updated. The AUC of the test data set was compared among the 4 model updating methods. RESULTS: Dose to parotid and submandibular glands and grade of xerostomia showed decreasing trends over the years (2008-2015, 297 patients; P < .001). The AUC of predicting grade ≥2 xerostomia for the initial training data set (2008-2009, 41 patients) was 0.6196. The AUC for the test data set (2010-2015, 256 patients) decreased to 0.5284 when the initial model was not updated (D). However, the AUC was significantly improved by model updates (A: 0.6164; B: 0.6084; P < .05). When the model was conditionally updated, the AUC was 0.6072 (C). CONCLUSIONS: Our preliminary results demonstrate that updating prediction models with prospective data collection is effective for maintaining the performance of xerostomia prediction. This suggests that a machine learning framework can handle the dynamic changes in a radiation oncology clinical practice and may be an important component for the construction of a learning health system.

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

OBJECTIVE: We explore whether a knowledge-discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible. METHODS AND MATERIALS: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume-organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data. RESULTS: Among 391 patients identified, WL predictors during RT planning were International Classification of Diseases diagnosis; dose to masticatory and superior constrictor muscles, larynx, and parotid; and age. At EOT, patient-reported oral intake, diagnosis, N stage, nausea, pain, dose to larynx, parotid, and low-dose planning target volume-larynx distance were significant predictive factors. The area under the curve during RT and EOT was 0.773 and 0.821, respectively. CONCLUSIONS: We demonstrate the feasibility and potential value of an informatics infrastructure that has facilitated insight into the prediction of WL using the CART algorithm. The prediction accuracy significantly improved with the inclusion of additional treatment-related data and has the potential to be leveraged as a strategy to develop a learning health system.