SP4.8 Can we risk stratify surgical waiting lists using patient level and demographic data? A novel waiting list analyser tool
Introduction 7.2 million patients are currently awaiting NHS treatment. It is imperative that trusts are able to interrogate waiting lists in order to understand the impact of ‘long waits’, on both patients and hospital services. An ability to prioritise, optimise and risk stratify care would be inv...
Saved in:
| Published in: | British journal of surgery Vol. 110 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Oxford University Press
01.09.2023
|
| Subjects: | |
| ISSN: | 0007-1323, 1365-2168 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Introduction 7.2 million patients are currently awaiting NHS treatment. It is imperative that trusts are able to interrogate waiting lists in order to understand the impact of ‘long waits’, on both patients and hospital services. An ability to prioritise, optimise and risk stratify care would be invaluable. In collaboration with clinicians, managers and patient level information and costing (PLICS) team we present a big data waiting list analyser tool that allows the delivery of patient focused decision making and analysis. Methods Using Structured Query Language (SQL), the analyser pulls data including waiting times (RTT), patient ethnicity and deprivation deciles directly from trust systems. The analyser cross references associated morbidity and hospital attendances to delineate the impact and resource utilisation of acute attendance whilst awaiting treatment. Coding is used to calculate additional bed or theatre requirements for every patient awaiting treatment. Financial costs, lost bed days and associated morbidity are analysed and used to identify at risk patient groups. Results The analyser itself takes the form of a fully interactive database. It provides dynamic analysis for any surgical procedure across three zones; waiting list times, acute presentation, and resources required to deliver elective activity. There is also a population health management section, which includes ethnicity and deprivation deciles. Conclusions This first of a kind analyser is an exciting new tool that will aid data driven decision making. It will allow us to target patients based not only on their waiting time, re-attendance risk but also based on their socioeconomic risk factors. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0007-1323 1365-2168 |
| DOI: | 10.1093/bjs/znad241.052 |