Quantifying patient satisfaction with process metrics using a weighted bundle approach.

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ID: 33382
2019
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Abstract
Current patient satisfaction assessment results are delayed and obtained from select patient surveys. As a result, these assessments may not represent the experience of the entire patient population. This study developed a method to measure and evaluate all patients' experiences while they are within the care episode and link it to processes within the organisation.Using the Six Sigma methodology, sites assembled diverse teams to categorise and analyse negative experience comments from patients to understand the drivers of dissatisfaction. These customer expectations lead to the development of the four components in the Patient Experience Bundle (PEB): communication, environment, basic needs/comfort and logistics. Individual process elements were ranked to create a numerical relationship between service and the needs expressed by the voice of the customer. Sites created surveys incorporating questions that focused on the bundle elements and measured daily bundle compliance. Graphical analysis and hypothesis testing enabled sites to determine key drivers of patient dissatisfaction within the bundle elements. Improvement strategies were developed and implemented to address the key drivers of patient dissatisfaction.After implementing process improvements focused on issues identified by the PEB, bundle compliance improved from an average of 51% to an average of 82.5% and Press Ganey Likelihood to Recommend (PG LTR) scores improved from an average of 64.73% to an average 74.64%. The data demonstrated that the trends in improving PEB are followed by meaningful changes in PG LTR scores.This work is built on the identification of common elements of care that impact patient satisfaction and detailed mathematical analysis of the relationship between factors. Using the bundle concept, these improvement efforts maintain highly reliable processes to drive outcomes and provide real-time feedback on patient experience.
Reference Key
riebling2019quantifyingbmj Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Riebling, Nancy B;Norouzzadeh, Shaghayegh;Reeder, George;Mouradian, Christina;Hillier, Alison;Cowan, Ryan;Doerfler, Martin;
Journal BMJ open quality
Year 2019
DOI
10.1136/bmjoq-2018-000458
URL
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