IntroductionAlthough molecular testing is crucial for many patients with lung cancer, the decision to carry out molecular testing is not easy to make in actual clinical scenarios. Using a specific decision aid (DA) to conduct shared decision-making (SDM) may help ameliorate this problem. However, no DA currently exists for lung cancer molecular testing (DA_LCMT). We aim to develop an evidence-based, iteratively refined DA, which may facilitate SDM and improve the quality of SDM.Methods and analysisAfter considering the Ottawa Decision Support Framework, International Patient Decision Aid Standards and Food and Drug Administration guidance about methods to identify what is important to patients, semistructured interviews with qualitative research methods will be used to generate the decision-making needs of patients with lung cancer diagnosed with lung adenocarcinoma by intraoperative frozen pathological sections. Input will be provided by patients and other stakeholders, including thoracic surgeons, nurses, hospital administrators, molecular testing company staff and insurance company staff. Then, a modified Delphi method will be used to develop the DA_LCMT V.1.0 (DA_LCMT 1.0). Structured interviews with qualitative research methods will be used in the cognitive debriefing (alpha tests) and field testing (beta tests) to revise and improve the DA_LCMT from version 1.0 to the final version, version 3.0. Descriptive statistics will be used to summarise the baseline characteristics of the patients and other stakeholders. Qualitative data will be analysed using the three steps of grounded theory: generate a codebook, update the codebook and create a comprehensive list of related items.Ethics and disseminationEthics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital approved this study. This protocol is based on the latest version 1.0, dated 31 October 2021. The study was also approved by the Ethics Committees of The Third People’s Hospital of Chengdu, Zigong First People’s Hospital and Jiangyou People’s Hospital. The results of this study will be presented at medical conferences and published in peer-reviewed journals.Trial registration numberNCT05191485.
BACKGROUND Longitudinal patient-reported outcomes studies require questionnaire assessments to be administered remotely multiple times, burdening research staff. OBJECTIVE To define and quantify the burden that researcher may experience during patient follow-up. METHODS Data were collected via interviews and a questionnaire. This study is an exploratory sequential mixed-methods study. Traditional content analysis was used for the qualitative data. Quantitative data were analyzed using Spearman’s correlation, and significance was tested using the chi-square test. Learning curves of healthcare staff regarding follow-up calls were generated using cumulative summation analysis. RESULTS We constructed a three-dimension conceptual framework for staff burden: (a) time-related burden, (b) technical-related burden, and (c) emotional-related burden. The quantitative analysis found that follow-up time was significantly correlated with staff experience, workload, and learning curve periods. There was a significant difference between the lost-to-follow-up rate of staff with and without follow-up experience with this program. Staff working on a daily assessment schedule had a higher lost-to-follow-up rate than those on a twice-a-week schedule. Additionally, inexperienced follow-up staff needed 113 calls to achieve stable follow-up time and quality, while experienced staff needed only 55 calls. CONCLUSIONS Researchers in longitudinal PROs projects suffer from a multidimensional burden during remote follow-up. Our results may help establish a proper PROs follow-up protocol to reduce the burden on research staff without sacrificing data quality.
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