Toward the Measure of Credibility of Hospital Administrative Datasets in the Context of DRG Classification

Poor quality of coded clinical data in hospital administrative databases may negatively affect decision making, clinical and health care services research and billing. In this paper, we assessed the level of credibility of a nationwide Portuguese inpatient database concerning the codification of pneumonia, with a special emphasis on identifying suspicious cases of upcoding affecting proper APR-DRG (All-Patient Refined Diagnosis-Related Groups) classification and hospital funding. Using data on pneumonia-related hospitalizations from 2015, we compared six hospitals with similar complexity regarding the frequency of all pneumonia-related diagnosis codes in order to identify codes that were significantly overreported in a given facility relatively to its peers. To verify whether the discrepant codes could be related to upcoding, we built Support Vector Machine (SVM) models to simulate the APR-DRG system and assess its response to each discrepant code. Findings demonstrate that hospitals significantly differed in coding six pneumonia conditions, with five of them playing a major role in increasing APR-DRG complexity, being thus suspicious cases of upcoding. However, those comprised a minority of cases and the overall credibility concerning upcoding of pneumonia was above 99% for all evaluated hospitals. Our findings can not only be relevant for planning future audit processes by signalizing errors impacting APR-DRG classification, but also for discussing credibility of administrative data, keeping in mind their impact on hospital financing. Hence, the main contribution of this paper is a reproducible method that can be employed to monitor the credibility and to promote data quality management in administrative databases

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models. Methods: 352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (+/- SE). Results: The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (+/- 0.04) vs. 0.802 (+/- 0.04) in the first model (p = 0.19) and 0.781 (+/- 0.05) vs. 0.808 (+/- 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively. Conclusion: The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies

The willingness of final year medical and dental students to perform bystander cardiopulmonary resuscitation in an Asian community

Background Despite the importance of early effective chest compressions to improve the chance of survival of an out-of-hospital cardiac arrest victim, it is still largely unknown how willing our Malaysian population is to perform bystander cardiopulmonary resuscitation (CPR). Aims We conducted a voluntary, anonymous self-administered questionnaire survey of a group of 164 final year medical students and 60 final year dental students to unravel their attitudes towards performing bystander CPR. Methods Using a 4-point Likert scale of “definitely yes,” “probably yes,” “probably no,” and “definitely no,” the students were asked to rate their willingness to perform bystander CPR under three categories: chest compressions with mouth-to-mouth ventilation (CC + MMV), chest compressions with mask-to-mouth ventilation (CC + PMV), and chest compressions only (CC). Under each category, the students were given ten hypothetical victim scenarios. Categorical data analysis was done using the McNemar test, chi-square test, and Fisher exact test where appropriate. For selected analysis, “definitely yes” and “probably yes” were recoded as a “positive response.” Results Generally, we found that only 51.4% of the medical and 45.5% of the dental students are willing to perform bystander CPR. When analyzed under different hypothetical scenarios, we found that, except for the scenario where the victim is their own family member, all other scenarios showed a dismally low rate of positive responses in the category of CC + MMV, but their willingness was significantly improved under the CC + PMV and CC categories. Conclusion This study shows that there are unique sociocultural factors that contribute to the reluctance of our students to perform CC + MMV. Keywords Cardiopulmonary resuscitation Mouth-to-mouth resuscitation Basic cardiac life support Asian communit