Quality improvement has been widely used in healthcare in the last few decades. The US healthcare sector has made significant strides in quality improvement, technological advancement, and innovations in the last century, yet it is often referenced as a complex, expensive and an industry with many inefficient processes and systems. One of the widely used key performance metrics in US hospitals is the 30-day emergency department visit after a surgical procedure. ED visits within 30 days of a surgical procedure are considered one of the key quality outcome measures, adding millions of dollars each year as a cost burden to US healthcare. This study aimed to identify key predictors that are known prior to the patient’s surgery date contributing to undesirable ED visits within 30 days of a bariatric surgical procedure. The study was conducted in three phases. The first phase of the study engaged a panel of experts to narrow down important preoperative factors for patients undergoing bariatric surgery in the form of a Delphi study. The second phase of the study included quantitative data analysis, which utilized the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program Participant Use Data File from 2019 to identify statistically significant preoperative factors that can contribute to the likelihood of patients returning to the emergency department within 30 days of bariatric surgery. There were N = 193,774 cases with complete information from 868 MBSAQIP-accredited bariatric surgery centers across the United States in the Data File among which 15,533 (8% of the total cases) visited an ED without needing admission as inpatients. Examining the feasibility of developing a predictive model with only statistically significant factors and checking if the model has an acceptable fit was also part of the analysis. The third phase of the study reengaged the same panel of experts from the first phase to validate the findings from the second phase and to document the subject matter experts’ perception regarding the model developed and the overall findings. Out of 33 preoperative variables, only 9 variables were selected in the first phase of the study with the help of a panel of experts. Out of the 9 selected variables, 8 variables i.e., Pre-Op GERD requiring medication, Number of Hypertensive Medications, Pre-Op BMI closest to bariatric surgery, Highest Recorded Pre-Op BMI, Pre-Op vein thrombosis requiring therapy, Pre-Op diabetes mellitus, Pre-Op history of COPD, and Pre-Op Steroid/Immunosuppressant Use for Chronic Condition significantly contributed to the likelihood of patients coming back to ED within 30 days of a bariatric procedure. The second phase of the study also yielded a predictive model using only the statistically significant and weighted variables, and each predictor exhibited statistical significance. Although Omnibus Tests of Model Coefficients showed that the Chi-square value was highly significant (χ^2=505.052, df=8,p<0.001) suggesting the revised model after addition of exploratory variables being statistically significant, Hosmer & Lemeshow test suggested that the revised model was not a good fit to the data (χ^2=22.152,df=8,p< 0.05). In the third phase of the study, a panel of experts weighted in mostly with positive feedback deeming the study to be clinically and operationally valuable for the bariatric patient population. Practical implication of this study is that the model can be used by the MBSAQIP Centers to determine the probability of patient’s likelihood of coming back to ED after a bariatric surgical procedure. Based on the set criteria, if the patient has higher chance of coming back to ED, care team can take interventions during and in the first few days or weeks of the discharge to prevent potential ED visit. Limitations of this study include but is not limited to the timeframe of the data was limited to only one year i.e., 2019 and size of the panel of experts was limited to 5. For future research studies, subject matter experts recommended taking the study to the next level by applying the findings to prevent future ED visits that are avoidable. Researchers can also take the findings from this study and expand the research by increasing the timeframe of the dataset and explore other variables that could also be important from day-to-day operations perspective which were not part of this study.