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Background Health care providers are now required to provide their patients access to their consultation and progress notes. Early research of this concept, known as “OpenNotes,” showed promising results in terms of provider acceptability and patient adoption, yet objective evaluations relating to patients' interactions with the notes are limited. Objectives To assess the effect of the complexity level of notes and number of accesses (initial read vs. continuous access) on the user's performance, perceived usability, cognitive workload, and satisfaction with the notes. Methods We used a 2*2 mixed subjects experimental design with two independent variables: (1) note's complexity at two levels (simple vs. complex) and (2) number of accesses to notes at two levels (initial vs. continuous). Fifty-three participants were randomly assigned to receive a simple versus complex radiation oncology clinical note and were tested on their performance for understanding the note content after an initial read, and then with continuous access to the note. Performance was quantified by comparing each participant's answers to the ones developed by the research team and assigning a score of 0 to 100 based on participants' understanding of the notes. Usability, cognitive workload, and satisfaction scores of the notes were quantified using validated tools. Results Performance for understanding was significantly better in simple versus complex notes with continuous access (p = 0.001). Continuous access to the notes was also positively associated with satisfaction scores (p = 0.03). The overall perceived usability, cognitive workload, and satisfaction scores were considered low for both simple and complex notes. Conclusion Simplifying notes can improve understanding of notes for patients/families. However, perceived usability, cognitive workload, and satisfaction with even the simplified notes were still low. To make notes more useful for patients and their families, there is a need for dramatic improvements to the overall usability and content of the notes.

For the majority of patients with non-metastatic breast cancer, radiation therapy (RT) plays an integral role as part of breast-conserving therapy and in the post-mastectomy setting. RT reduces the risk of local and regional recurrence and, albeit to a lesser degree, distant recurrence and mortality. Improvements in RT-based planning/delivery technologies have improved the therapeutic ratio (i.e. increased effectiveness and/or reduced morbidity). These techniques are better able to restrict the therapeutic dose to the target tissues and evolving technologies may yield further improvements. Given the integral role of RT as part of the overall treatment approach, radiation oncologists should be part of the multidisciplinary team from the initial phase of the diagnostic–therapeutic pathway. The interaction between local–regional and systemic treatments is complex. Most of the benefit of RT on survival is expected for low-risk patients, who do not receive systemic treatment, and for high-risk patients, receiving effective systemic therapy.

A method to predict radiation-induced pneumonitis (RP) using an artificial neural network (ANN) was investigated. A retrospective study was applied to the clinical data from 142 patients who have been treated with three-dimensional conformal radiotherapy for tumors in the thoracic region. These data were classified, based on their treatment outcome, into two patient clusters: with RP (Np=26) and without RP (Np= 116). An ANN was designed as a classifier. To perform the classification, a patient-treatment outcome with RP was assigned a value of 1, and a patient treatment outcome without RP was assigned a value of -1. The input of the ANN was limited to the patient lung dose-volume data only. A volume vector (VD) that describes patient lung subvolumes receiving more than a set of threshold doses was used as the network input variable. A zero value was used as the threshold to set the output value into -1 or 1. Three ANNs (ANN_1, ANN_2, and ANN_3), each with three layers, were trained to perform this classification function and to show the effect of training data on the ANN performance. Radial basis function was applied as the hidden layer neuron activation function and a sigmoid function was selected as the output layer neuron function. Backpropagation with a conjugate gradient algorithm was used to train the network. ANN_1 was trained and tested by using the leave-one-out method. ANN_2 was trained by randomly selecting 2/3 of the patient data, and tested by the remaining 1/3 of the data. ANN_3 was trained by a user selecting 2/3 of the patient data, and tested by the remaining 1/3 of the data. The predictive accuracy was verified as the area under a receiver operator characteristic (ROC) curve. The correct classification rates of 73% for RP cases, and 99% for non-RP cases were obtained from ANN_1. The corresponding correct classification rates of 44% for RP cases, and 89% for non-RP cases were obtained from ANN_2. From the ANN_3 test phase, the corresponding correct classification rates of 55% for RP cases, and 95% non-RP cases were achieved. The area under ROC curve was 0.85+/-0.05, 0.68+/-0.10, and 0.81+/-0.09 for ANN_1, ANN _2, and ANN_3, respectively, within its asymmetric 95% confidence interval. The sensitivity was 95%, 57%, and 71%, and the specificity was 94%, 88%, and 90% for ANN_1, ANN_2, and ANN_3, respectively. Preliminary results suggest that the ANN approach provides a useful tool for the prediction of radiation-induced lung pneumonitis, using the patient lung dose-volume information.