AB214. 48. Patient satisfaction with preoperative assessment clinic

Objective To determine the predictors of length of stay (LOS), readmission within 30 days, and unplanned return to the operating room (OR) within 30 days in head and neck free flap patients. Study Design Case series with chart review. Setting Tertiary academic cancer hospital. Subjects and Methods All head and neck free flap patients at The Ohio State University (OSU, 2006-2012) were assessed. Multivariable logistic regression to assess the impact of patient factors, flap and wound factors, and intraoperative factors on the aforementioned quality metric outcomes. Results In total, 515 patients were identified, of whom 66% had oral cavity cancers, 33% had recurrent tumors, and 28% underwent primary radiotherapy. Of the patients, 31.5% had a LOS greater than 9 days, predicted by longer operative time, oral cavity and pharyngeal tumor sites, blood transfusion, diabetes mellitus, and any complication. A total of 12.6% of patients were readmitted within 30 days predicted by absent OSU preoperative assessment clinic attendance and any complication, and 14.8% of patients had an unplanned OR return predicted by advanced age. Conclusions When assessing quality metrics, adjustment for the complexity involved in managing patients with head and neck cancer with a high comorbidity index, clean contaminated wounds, and a high degree of primary radiotherapy is important. Patients seen in a preoperative assessment clinic had a lower risk of readmission postoperatively, and this should be recommended for all head and neck free flap patients. Quality improvement projects should focus on predictors and prevention of complications as this was the number one predictor of both increased length of stay and readmission.

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Evaluation of an online preoperative assessment tool

BMJ Innovations ◽

10.1136/bmjinnov-2019-000344 ◽

2020 ◽

Vol 6 (4) ◽

pp. 127-131

Author(s):

John O'Shea ◽

Margaret Coleman ◽

Saad Mahdy ◽

Mel Corbett ◽

Ger Curley

Keyword(s):

Web Application ◽

Assessment Tool ◽

Preoperative Assessment ◽

Misclassification Rate ◽

Online Assessment ◽

Assessment Clinic ◽

Patient Acceptability ◽

Patient Risk ◽

Consultant Anaesthetist ◽

At Home

Triaging patients into and away from preoperative assessment clinics remains a challenge. Anaesthesia Preoperative Assessment Tool (APAT) is a web application that delivers an online 22 question survey to patients at home, and uses an artificially intelligent algorithm to stratify patient risk and identify the need for non routine preoperative investigation and intervention. We assess APATs accuracy and patient acceptability in this prospective observational study. Patients were recruited at preoperative assessment clinic, where they were assessed by a consultant anaesthetist. Anaesthetist (ASA) grade, need for nonstandard investigation and intervention were recorded (gold standard). Patients were invited to complete an APAT assessment on their PC or smartphone at home, and the results of both assessments compared. 22 patients completed conventional clinical assessment by consultant anaesthetist and online assessment by APAT. APAT score correlates with clinicians ASA grade (rτ=0.6075, p=0.0008). APAT predicts patient risk group (misclassification rate of 0%, Area Under the Curve (AUC)=0.9825). APAT predicts the need for additional investigation (AUC=0.8077) and preoperative intervention (AUC=0.7193). Online assessment was acceptable to 92% of patients. Our findings support the hypothesis that APAT accurately predicts patients perioperative risk and predicts the need for investigation and intervention. Further studies are needed to confirm that APAT may be used to identify ASA 1 and 2 patients who could safely bypass preoperative assessment clinic.

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