Speaking up for patient safety by hospital-based health care professionals: a literature review

BACKGROUND: Speaking up is important for patient safety, but often, health care professionals hesitate to voice concerns. Understanding the influencing factors can help to improve speaking-up behaviour and team communication. This review focused on health care professionals’ speaking-up behaviour for patient safety and aimed at (1) assessing the effectiveness of speaking up, (2) evaluating the effectiveness of speaking-up training, (3) identifying the factors influencing speaking-up behaviour, and (4) developing a model for speaking-up behaviour. METHODS: Five databases (PubMed, MEDLINE, CINAHL, Web of Science, and the Cochrane Library) were searched for English articles describing health care professionals’ speaking-up behaviour as well as those evaluating the relationship between speaking up and patient safety. Influencing factors were identified and then integrated into a model of voicing behaviour. RESULTS: In total, 26 studies were identified in 27 articles. Some indicated that hesitancy to speak up can be an important contributing factor in communication errors and that training can improve speaking-up behaviour. Many influencing factors were found: (1) the motivation to speak up, such as the perceived risk for patients, and the ambiguity or clarity of the clinical situation; (2) contextual factors, such as hospital administrative support, interdisciplinary policy-making, team work and relationship between other team members, and attitude of leaders/superiors; (3) individual factors, such as job satisfaction, responsibility toward patients, responsibility as professionals, confidence based on experience, communication skills, and educational background; (4) the perceived efficacy of speaking up, such as lack of impact and personal control; (5) the perceived safety of speaking up, such as fear for the responses of others and conflict and concerns over appearing incompetent; and (6) tactics and targets, such as collecting facts, showing positive intent, and selecting the person who has spoken up. CONCLUSIONS: Hesitancy to speak up can be an important contributing factor to communication errors. Our model helps us to understand how health care professionals think about voicing their concerns. Further research is required to investigate the relative importance of different factors

Impact of loss-to-follow-up on cancer survival estimates for small populations: a simulation study using Hospital-Based Cancer Registries in Japan

Objectives: The accuracy of the ascertainment of vital status impacts the validity of cancer survival. This study assesses the potential impact of loss-to-follow-up on survival in Japan, both nationally and in the samples seen at individual hospitals. Design: Simulation study Setting and participants: Data of patients diagnosed in 2007, provided by the Hospital-Based Cancer Registries of 177 hospitals throughout Japan. Primary and secondary outcome measures: We performed simulations for each cancer site, for sample sizes of 100, 1000 and 8000 patients, and for loss-to-follow-up ranging from 1% to 5%. We estimated the average bias and the variation in bias in survival due to loss-to-follow-up. Results: The expected bias was not associated with the sample size (with 5% loss-to-follow-up, about 2.1% for the cohort including all cancers), but a smaller sample size led to more variable bias. Sample sizes of around 100 patients, as may be seen at individual hospitals, had very variable bias: with 5% loss-to-follow-up for all cancers, 25% of samples had a bias of 3.06%. Conclusion: Survival should be interpreted with caution when loss-to-follow-up is a concern, especially for poor-prognosis cancers and for small-area estimates

Impact of loss-to-follow-up on cancer survival estimates for small populations: a simulation study using Hospital-Based Cancer Registries in Japan

Objectives: The accuracy of the ascertainment of vital status impacts the validity of cancer survival. This study assesses the potential impact of loss-to-follow-up on survival in Japan, both nationally and in the samples seen at individual hospitals. Design: Simulation study Setting and participants: Data of patients diagnosed in 2007, provided by the Hospital-Based Cancer Registries of 177 hospitals throughout Japan. Primary and secondary outcome measures: We performed simulations for each cancer site, for sample sizes of 100, 1000 and 8000 patients, and for loss-to-follow-up ranging from 1% to 5%. We estimated the average bias and the variation in bias in survival due to loss-to-follow-up. Results: The expected bias was not associated with the sample size (with 5% loss-to-follow-up, about 2.1% for the cohort including all cancers), but a smaller sample size led to more variable bias. Sample sizes of around 100 patients, as may be seen at individual hospitals, had very variable bias: with 5% loss-to-follow-up for all cancers, 25% of samples had a bias of 3.06%. Conclusion: Survival should be interpreted with caution when loss-to-follow-up is a concern, especially for poor-prognosis cancers and for small-area estimates

Usability of Clinical Information in Discharge Summary Data in the Diagnosis Procedure Combination Survey for Cancer Patients

Valid data are required to monitor and measure the quality of cancer treatment. This study aims to assess the usability of diagnosis procedure combination (DPC) survey discharge summary data. DPC survey data were analyzed by linking them to the hospital-based cancer registries (HBCR) from 231 hospitals. We focused on patients who were aged 20 years or older and diagnosed in 2013 with stomach, colorectal, liver, lung, or breast cancer. We assessed the percentage of unknown/missing values in supplementary data for patients with five common cancers and compared DPC cancer stage information to that of HBCR. In total, 279,451 discharge data sets for 180,399 patients were analyzed. The percentages of unknown data for smoking index and height/weight were 10.5% and 2.3%, respectively, and varied from 0.0% to 93.0% between hospitals. In the activity of daily living component, the rates of missing data for climbing stairs (3.6%) and bathing (2.9%) at admission were slightly higher than for other elements. Unexpectedly low concordance rate of tumor, node, and metastasis classification between DPC survey and HBCR data was observed as 80.6%, which means 20.4% of the data showed discrepancies. The usability of DPC survey discharge summary data is generally acceptable, but some variables had substantial amounts of missing values

Critical Points for Interpreting Patients’ Survival Rate Using Cancer Registries: A Literature Review

Background: Survival rate is used to develop cancer control plans. However, there are limitations and biases when interpreting patient survival rate data. This study aimed to identify and account for potential biases and/or limitations on estimating survival rate to enable more effective control of cancer. Methods: The authors searched PubMed from December 2010 to December 2015 for articles that investigated or described biases in estimating patient survival using cancer registries. Articles that only described the tendency of survival rate and investigated relationships between patient characteristics, treatment, and survival rate were excluded. Results: In total, 50 articles met the inclusion criteria. The identified potential biases were categorized into three areas, as follows: 1) the quality of registry data (eg, the completeness of cancer patients, accuracy of data, and follow-up rates); 2) limitations related to estimated methods of survival rates (eg, misclassification of cause of death for cause-specific survival rate or a lack of comparability of background mortality for relative survival rate); and 3) the comparability of survival rates among different groups (eg, age-adjustment or patients with multiple cancers). Conclusion: We concluded that survival rate can be suitable for answering questions related to health policy and research. Several factors should be considered when interpreting survival rates estimated using cancer registries