E. A. Armstrong1, R. K. Pickard4, K. L. Johnson4, S. Abdel-Misih5,6 4Ohio State University,Cancer Program Analytics, James Cancer Hospital And Solove Research Institute, Comprehensive Cancer Center,Columbus, OH, USA 5Ohio State University,Department Of Surgery,Columbus, OH, USA 6Ohio State University,Division Of Surgical Oncology,Columbus, OH, USA 1Ohio State University,College Of Medicine,Columbus, OH, USA
Introduction:
Hepato-pancreatico-biliary (HPB) and gastrointestinal (GI) cancer patients receiving surgical care are at a significant risk for post-operative hospital readmission. At a tertiary academic center, a Readmission Risk Model was developed to identify cancer patients at increased risk for readmission with a list of suggested post-discharge interventions intended to lower readmission rates for HPB and GI Surgical Oncology (SONC) services. This study investigates the utility of these interventions in lowering patient readmission rates.
Methods:
153 patients with 163 surgical admissions related to their cancer diagnosis between September 1, 2016 and September 30, 2017 were analyzed. Patients were stratified into one of four risk categories based on variables in the established Readmission Risk Model. A chi-square analysis of readmission rates before and after implementation of the Readmission Risk Model Suggested Interventions (RRMSI) for HPB and SONC was performed as well as for each risk category. Chi-square analysis was further performed to determine difference in patient readmission rates based on type of surgery performed and difference in number of days to readmission before and after RRMSI. Additionally, compliance for each suggested intervention was analyzed using univariate analysis.
Results:
There were no significant differences in readmission rates among HPB or SONC patients before and after implementation of the RRMSI. There were also no significant differences for readmission rates based on patient Readmission Risk Category. There was also no difference in readmission rates for patients based on type of surgery performed. Median number of days to readmission was not significantly changed after the RRMSI. While "moderate risk" patients in both the pre-RRMSI and post-RRMSI groups were readmitted at rates betwen 0% and 14%, patients in the "high risk" pre- and post-RRMSI groups were readmitted at rates ranging between 33% and 45%. The HPB service showed overall a greater rate of compliance for the suggested interventions, ranging from 39.3% and 79.1% for individual interventions, while SONC showed a compliance ranged from 6.7% to 70.0%.
Conclusion:
The RRMSI did not affect patient readmission rates for any analyzed group. Implementation of more robust interventions for patients to avoid readmission and compliance improvement strategies should be goals for future clinical practice. Because of the discrepancy between predicted and actual readmission rates among "high risk" patients, additional studies should look into the ability of this Readmission Risk Model to accurately predict surgical cancer patient readmission rates.?
