The complexity of lung cancer care mandates interaction between clinicians with different skill sets and practice cultures in the routine delivery of care. Using team science principles and a case-based approach, we exemplify the need for the development of real care teams for patients with lung cancer to foster coordination among the multiple specialists and staff engaged in routine care delivery. Achieving coordinated lung cancer care is a high-priority public health challenge because of the volume of patients, lethality of disease, and well-described disparities in quality and outcomes of care. Coordinating mechanisms need to be cultivated among different types of specialist physicians and care teams, with differing technical expertise and practice cultures, who have traditionally functioned more as coactively working groups than as real teams. Coordinating mechanisms, including shared mental models, high-quality communication, mutual trust, and mutual performance monitoring, highlight the challenge of achieving well-coordinated care and illustrate how team science principles can be used to improve quality and outcomes of lung cancer care. To develop the evidence base to support coordinated lung cancer care, research comparing the effectiveness of a diverse range of multidisciplinary care team approaches and interorganizational coordinating mechanisms should be promoted.

Background: When talking about a probabilistic neural network a new concept on survival is introduced. Using advanced statistics, different characteristics amongst patients such as oncogenic driver mutation, smoking status or age can be correlated to calculate the probability of survival in each individual and even for each attending institution. Method: We built a neural network model that allowed us to estimate the probability of mortality according to the variables: Oncogenic addiction (EGFR mutation and ALK rearrangements), expression of PDL-1, age, gender and exposure to cigarette. These variables were obtained from a cohort of patients with advanced non-small cell lung carcinoma who received treatment in our institution during the last 3 years. Result: Thirty-one patients were included in the model. The accuracy of the neural network was 78.57% for the patients that died and 82.35% for the patients still alive. It was possible to calculate the probability of overall survival and the probability of dying in relation to age, exposure to cigarette, gender, oncogenic addiction and PDL-1 expression for each person and for the whole cohort. Individual results and global results (Kaplan Meier) are shown in table and graphic. Conclusion: The minimum data sample required to produce the model was used (n¼31), thus it would not be correct to talk about global conclusions. However, this is a representative model of a proof of concept related to predictive analytics related to probability of survival in a real world setting using a neural network that worked on an individual basis according to clinical and biological characteristics. Results for the entire cohort are also available. Using a greater amount of data will increase the accuracy of the neural network to predict survival and other outcomes in an individual and even institutional basis that will improve data analytics in real oncologic settings.

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Background: Most of lung cancer patients diagnosed in advanced stage. The reasons for the delay of the diagnosis might be from patient and/or health care system. Currently, Indonesia, as a developing country in Asia, has National Health Insurance System (Jaminan Kesehatan Nasional) of which the patients is feasibly referred to the referral hospital without worrying the cost. In Persahabatan Hospital-the National Referral for Respiratory Diseases many cases have diagnostic delayed that might contribute to the prognosis. We had been conducting a study to evaluate diagnostic time and diagnostic cost to diagnose lung cancer. Method:We conducted an observational study in Persahabatan Hospital Jakarta of which newly diagnosed lung cancer patients were reviewed. We evaluated the time and cost needed from the first visit until definitive diagnosis by histopathology obtained. We also evaluated the factors that have correlated with time and cost of lung cancer diagnosis. Result: One hundred and ten subjects were enrolled in this study. Eighty four (76,36%) were male and 26 (23,64%) were female. The median age was 57 years old with range 26 to 86 years old. Data have shown that 53 (48,2%) subjects were diagnosed under target time ( less than 2 weeks) but 57 subjects (51,8%) had diagnostic time more than 2 weeks. The median time of diagnostic was 15 days with the range of 1 to 68 days. Diagnostic delay was correlated with: early stage of the diseases, good performance status, no financial resource. The median cost of diagnosis was 13.025.