Towards identifying cancer patients at risk to miss out on psycho-oncological treatment via machine learning

Experience in psychological counseling supported by artificial intelligence technology

BACKGROUND

In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare.

OBJECTIVE

This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes.

METHODS

Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling.

RESULTS

The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling.

CONCLUSION

In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.

Evaluation of the e-Mental Health Intervention Make It Training From Patients' Perspectives: Qualitative Analysis Within the Reduct Trial

BACKGROUND

Make It Training is an e-mental health intervention designed for individuals with cancer that aims to reduce psychological distress and improve disease-related coping and quality of life.

OBJECTIVE

This study evaluated the experienced usefulness and usability of the web-based Make It Training intervention using a qualitative approach.

METHODS

In this study, semistructured interviews were conducted with participants at different cancer stages and with different cancer entities. All participants had previously taken part in the Reduct trial, a randomized controlled trial that assessed the efficacy of the Make It Training intervention. The data were coded deductively by 2 independent researchers and analyzed iteratively using thematic codebook analysis.

RESULTS

Analysis of experienced usefulness resulted in 4 themes (developing coping strategies to reduce psychological distress, improvement in quality of life, Make It Training vs traditional psychotherapy, and integration into daily life) with 11 subthemes. Analysis of experienced usability resulted in 3 themes (efficiency and accessibility, user-friendliness, and recommendations to design the Make It Training intervention to be more appealing) with 6 subthemes. Make It Training was evaluated as a user-friendly intervention helpful for developing functional coping strategies to reduce psychological distress and improve quality of life. The consensus regarding Make It Training was that it was described as a daily companion that integrates well into daily life and that it has the potential to be routinely implemented within oncological health care either as a stand-alone intervention or in addition to psychotherapy.

CONCLUSIONS

e-Mental health interventions such as Make It Training can target both the prevention of mental health issues and health promotion. Moreover, they offer a cost-efficient and low-threshold option to receive psycho-oncological support.

The application and use of artificial intelligence in cancer nursing: A systematic review

PURPOSE

Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing.

METHODS

CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.

RESULTS

Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models.

CONCLUSION

Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.

A short screening tool identifying systemic barriers to distress screening in cancer care

INTRODUCTION

International guidelines on cancer treatment recommend screening for early detection and treatment of distress. However, screening rates are insufficient. In the present study, a survey was developed to assess perceived systemic barriers to distress screening.

METHODS

A three-step approach was used for the study. Based on qualitative content analysis of interviews and an expert panel, an initial survey with 53 questions on barriers to screening was designed. It was completed by 98 nurses in a large comprehensive cancer center in Switzerland. From this, a short version of the survey with 24 questions was derived using exploratory principal component analysis. This survey was completed by 150 nurses in four cancer centers in Switzerland. A confirmatory factor analysis was then performed on the shortened version, yielding a final set of 14 questions.

RESULTS

The initial set of 53 questions was reduced to a set of 14 validated questions retaining 53% of the original variance. These 14 questions allow for an assessment within 2-3 min that identifies relevant barriers to distress screening from the perspective of those responsible for implementation of distress screening. Across several hospitals in Switzerland, the timing of the first distress screening, lack of capacity, patient and staff overload, and refusal of distressed patients to be referred to support services emerged as major problems.

CONCLUSION

The validated 14 questions on barriers to screening cancer patients for distress enable clinicians and hospital administrators to quickly identify relevant issues and take action to improve screening programs.

Mindfulness and skills-based eHealth intervention to reduce distress in cancer-affected patients in the Reduct trial: Intervention protocol of the make it training optimized

INTRODUCTION

Cancer-affected patients experience high distress due to various burdens. One way to expand psycho-oncological support is through digital interventions. This protocol describes the development and structure of a web-based psycho-oncological intervention, the Make It Training optimized. This intervention is currently evaluated in the Reduct trial, a multicenter randomized controlled trial.

METHODS

The Make It Training optimized was developed in six steps: A patient need and demand assessment, development and acceptability analysis of a prototype, the formation of a patient advisory council, the revision of the training, implementation into a web app, and the development of a motivation and evaluation plan.

RESULTS

Through a process of establishing cancer-affected patients' needs, prototype testing, and patient involvement, the Make It Training optimized was developed by a multidisciplinary team and implemented in a web app. It consists of 16 interactive self-guided modules which can be completed within 16 weeks.

DISCUSSION

Intervention protocols can increase transparency and increase the likelihood of developing effective web-based interventions. This protocol describes the process and results of developing a patient-oriented intervention. Future research should focus on the further personalization of web-based psycho-oncological interventions and the potential benefits of combining multiple psychotherapeutic approaches.