Keyword-augmented and semi-automatic generation of FESS reports: a proof-of-concept study.

INTRODUCTION: Surgical reports are usually written after a procedure and must often be reproduced from memory. Thus, this is an error-prone, and time-consuming task which increases the workload of physicians. In this proof-of-concept study, we developed and evaluated a software tool using Artificial Intelligence (AI) for semi-automatic intraoperative generation of surgical reports for functional endoscopic sinus surgery (FESS). MATERIALS AND METHODS: A vocabulary of keywords for developing a neural language model was created. With an encoder-decoder-architecture, artificially coherent sentence structures, as they would be expected in general operation reports, were generated. A first set of 48 conventional operation reports were used for model training. After training, the reports were generated again and compared to those before training. Established metrics were used to measure optimization of the model objectively. A cohort of 16 physicians corrected and evaluated three randomly selected, generated reports in four categories: "quality of the generated operation reports," "time-saving," "clinical benefits" and "comparison with the conventional reports." The corrections of the generated reports were counted and categorized. RESULTS: Objective parameters showed improvement in performance after training the language model (p < 0.001). 27.78% estimated a timesaving of 1-15 and 61.11% of 16-30 min per day. 66.66% claimed to see a clinical benefit and 61.11% a relevant workload reduction. Similarity in content between generated and conventional reports was seen by 33.33%, similarity in form by 27.78%. 66.67% would use this tool in the future. An average of 23.25 ± 12.5 corrections was needed for a subjectively appropriate surgery report. CONCLUSION: The results indicate existing limitations of applying deep learning to text generation of operation reports and show a high acceptance by the physicians. By taking over this time-consuming task, the tool could reduce workload, optimize clinical workflows and improve the quality of patient care. Further training of the language model is needed.

Depression, anxiety, fatigue, and quality of life in a large sample of patients suffering from head and neck cancer in comparison with the general population.

BACKGROUND: Treatment of head and neck cancer (HNC) often leads to visible and severe functional impairments. In addition, patients often suffer from a variety of psychosocial problems, significantly associated with a decreased quality of life. We aimed to compare depression, anxiety, fatigue and quality of life (QoL) between HNC patients and a large sample of the general population in Germany and to examine the impact of sociodemographic, behavioral and clinical factors on these symptoms. METHODS: We assessed data of HNC patients during the aftercare consultation at the Leipzig University Medical Center with a patient reported outcome (PRO) tool named "OncoFunction". Depression, anxiety, fatigue and QoL were assessed using validated outcome measures including the PHQ-9, the GAD-2, and the EORTC QLQ-C30 questionnaire. RESULTS: A total of 817 HNC patients were included in our study and compared to a sample of 5018 individuals of the general German population. HNC patients showed significantly higher levels of impairment in all dimensions assessed. Examination of association between depression, anxiety, fatigue and QoL and clinical as well as sociodemographic variables showed significant relationships between occupational status, ECOG-state, body mass index and time since diagnosis. CONCLUSIONS: HNC patients suffer significantly from psychological distress. The used questionnaires are suitable for the use in daily routine practice and can be helpful to increase the detection of depression, anxiety and fatigue and therefore can improve HNC aftercare.

[Introduction of the Screening Tool OncoFunction for Functional Follow-up of Head and Neck Patients]. / Vorstellung des Screeningsystems (OncoFunction) für Funktionsstörungen im Kopf-Hals-Tumor-Follow-up.

INTRODUCTION: The follow-up for head and neck cancer (HNC) focussed on therapy control. Accessory long term functionality is important. Impairment of function is observed, but a comparable documentation is not established. Additional we frequently see psychooncological comorbidities, what complicates the assessment. This was the reason why Tschiesner et al. developed on the base of the "ICF Core set for head and neck cancer" a guideline for the Assessment of Function in HNC. In consequence of good results in other tumour entities we developed an electronic version (OncoFunction). METHODS: In a proof of concept study all patients of our follow up consultation from 07/13 to 03/14 were included. OncoFunction was given to patients in a digital form using tablet computers. The results were visible to the physician in a concentrated form before consultation and were supplemented by a physician questionnaire. Furthermore we evaluated the usability in 202 patients. RESULTS: We had 682 patient contacts. 530 patient contacts (77, 7%) used the questionnaire. The physician questionnaire was answered in 470 times. Finally there are from 69.8% of the patient contacts full datasets available. Between users and non-users of the questionnaire we see no difference. CONCLUSION: The use of a computer-based screening and feedback system (OncoFunction) in clinical use is feasible and excellent assessed by patients. The patient data are visible in a compact form for the physician and problems can clear addressed to the patient. One more benefit is the standardized follow up documentation and the use of comparable data in research.