Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Abstract

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes.

Publication DOI: https://doi.org/10.1136/bmjopen-2017-019947
Divisions: College of Engineering & Physical Sciences
Additional Information: © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Publication ISSN: 2044-6055
Last Modified: 25 Mar 2024 08:29
Date Deposited: 10 Dec 2018 10:43
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Related URLs: http://bmjopen. ... pen-2017-019947 (Publisher URL)
PURE Output Type: Article
Published Date: 2018-12-04
Accepted Date: 2018-11-01
Authors: Litchfield, Ian
Hoye, Ciaron
Shukla, David
Backman, Ruth
Turner, Alice
Lee, Mark
Weber, Phil

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