Two‐year outcomes from the Australian and New Zealand Emergency Laparotomy Audit‐Quality Improvement pilot study

2021 ◽  
Author(s):  
◽  
R James Aitken ◽  
Ben Griffiths ◽  
Jill Van Acker ◽  
Edmond O'Loughlin ◽  
...  
Keyword(s):  
Quality Improvement ◽  
New Zealand ◽  
Pilot Study ◽  
Audit Quality ◽  
Emergency Laparotomy

scholarly journals 244 Does Direct Admission to ICU In Emergency Laparotomy Patients with A NELA Score ≥ 10% Affect Outcome at Fiona Stanley Hospital?

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
T Shepherd ◽  
A Foster
Keyword(s):  
Critical Care ◽  
Quality Improvement ◽  
New Zealand ◽  
Surgical Patients ◽  
Emergency Laparotomy ◽  
Improvement Project ◽  
Code Blue ◽  
Retrospective Audit ◽  
Direct Admission ◽  
Surgical Unit

Abstract Introduction The Australian and New Zealand Emergency Laparotomy Audit (ANZELA) is a quality improvement project based on UK NELA. Direct admission to ICU post-operatively for patients with a NELA ≥ 10% is recommended. In the current pandemic, the use of critical care beds must be rationalised. We investigated if patients with NELA ≥ 10% experienced worse outcomes if admitted to the ward post-operatively (instead of ICU). Method We performed a retrospective audit of emergency laparotomies at Fiona Stanley Hospital over 6 months December 2019 – May 2020. NELA scores were obtained from the ANZELA database and patient notes reviewed to identify post-operative unplanned ICU admissions and mortalities. Results Twenty-four (30%) emergency laparotomy patients had a NELA ≥ 10%. Ten (42%) patients were admitted to the ward post-operatively. There were no unplanned ICU admissions in this group. Two (20%) patients had a documented ‘code blue’ but were managed conservatively on the ward. No patients in this group died within 30 days. Conclusions Post-operative ward admission in selected patients with NELA ≥ 10% does not result in unplanned ICU admissions or increased mortality at a tertiary Acute Surgical Unit. This data is reassuring as we expect future ICU bed shortages for non-COVID surgical patients during the pandemic.

Quality Improvement Pilot Study of the Living Your Best Weight Program: A Health at Every Size Approach

2020 ◽  
Vol 81 (2) ◽  
pp. 91-93
Author(s):  
Anna Angelinas ◽  
Roseann Nasser ◽  
Amanda Geradts ◽  
Justine Herle ◽  
Kristen Schott ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Pilot Study ◽  
Response Rate ◽  
Participant Satisfaction ◽  
Health At Every Size ◽  
Outpatient Program ◽  
To Receive

Purpose: Living Your Best Weight (LYBW) is an outpatient program based on Health at Every Size (HAES) principles for adults interested in managing their weight. The purpose of this pilot study was to determine perceptions of participants and their satisfaction with the LYBW program. Methods: A survey was developed to determine participant satisfaction of the LYBW program. Fifty-six participants who completed the LYBW program from June 2017 to February 2018 were contacted via telephone and invited to participate in the study. Forty-five participants agreed to receive the survey by mail or email. Results: Thirty-four participants completed the survey for a response rate of 61%. The average age of respondents was 52 years. Seventy-nine percent of respondents agreed that the program helped them to focus on health instead of weight. Eighty-two percent agreed that the program helped them respond to internal cues of hunger and fullness, and 94% were satisfied with the program. Conclusion: Participants reported that they were satisfied with the LYBW program and perceived improvements in their health. Future programming may benefit from using a HAES-based approach with adults.

National quality improvement indicators project: an initial descriptive study

2021 ◽  
pp. 0310057X2110278
Author(s):  
Daniel P Ramsay ◽  
Phillip Quinn ◽  
Veronica Gin ◽  
Timothy D Starkie ◽  
Robert A Fry ◽  
...  
Keyword(s):  
Quality Improvement ◽  
New Zealand ◽  
Respiratory Distress ◽  
Postoperative Nausea ◽  
Postoperative Nausea And Vomiting ◽  
Public Hospitals ◽  
Nausea And Vomiting ◽  
Post Anaesthesia Care Unit ◽  
National Quality ◽  
Anaesthesia Care

Background Anaesthesia Quality Improvement New Zealand developed a set of five quality improvement indicators pertaining to postoperative nausea and vomiting, pain, respiratory distress, hypothermia and a prolonged post-anaesthesia care unit stay. This study sought to assess the proportion of eligible institutions that were able to measure and provide data on these indicators, produce an initial national estimate of these, and a measure of variability in the quality improvement indicators across hospitals in New Zealand. Methods All public hospitals that provide a representative to Anaesthesia Quality Improvement New Zealand were eligible for inclusion. Participating institutions were required to provide the number and proportion of patients with each of the five quality improvement indicators over a continuous 2-week period between 1 June 2019 and 25 October 2019. The overall percentage of patients and the median percentage with each outcome were calculated. Results A total of 79.2% of eligible hospitals participated. The median incidence of the indicators ranged from 1.67% for respiratory distress to 6.31% for prolonged post-anaesthesia care unit stay. The indicator with the largest interquartile range was hypothermia and the smallest was respiratory distress (13.48 and 2.29, respectively). A large variation was seen for prolonged post-anaesthesia care unit stay, hypothermia, pain and postoperative nausea and vomiting. Conclusion The majority of eligible institutions were able to measure and provide data on the quality improvement indicators. There was a low rate of respiratory distress with low variability. A large amount of variability was observed in the other indicators. Future studies are needed to explore the nature of this variability.

EP.FRI.857 Audit and quality improvement project; data entry into the NELA database from Royal Stoke university hospital

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Nandu Nair ◽  
Vasileios Kalatzis ◽  
Madhavi Gudipati ◽  
Anne Gaunt ◽  
Vishnu Machineni
Keyword(s):  
Quality Improvement ◽  
Data Entry ◽  
Quality Improvement Project ◽  
University Hospital ◽  
Emergency Laparotomy ◽  
Data Set ◽  
Improvement Project ◽  
Hospital Database ◽  
Case Ascertainment ◽  
Project Data

Abstract Aims During the period December-2018 to November-2019 a total of 84 cases were entered on the NELA website, corresponding to HES data suggesting 392 laparotomies. This suggests a possible case acquisition of 21% prompting us to look at our data acquisition in detail. Methods Interrogation of the NELA data from January–March 2020 was done from NELA website and hospital records. Results Analysis revealed that during this period 45 patients had laparotomy recorded whereas hospital database recorded 68 laparotomies. Of the 45 cases entered on the NELA database, only 1 patient had a complete data set entered.  22 cases had 87% data entry and 22 cases had <50% of the data fields completed. Firstly, we were not capturing all patients who underwent an emergency laparotomy and secondly our data entry for the patients we did report was incomplete.  This led us to engage in a quality improvement project with following measures - Conclusions We re-assessed the case ascertainment and completeness of data collection in the period April 2020 – June 2020 and case ascertainment rate increased to 54% and all the entries were complete and locked.

Towards the development of a system-wide quality improvement maturity model: a synthesis using systematic review and expert opinion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adeel Akmal ◽  
Nataliya Podgorodnichenko ◽  
Richard Greatbanks ◽  
Jeff Foote ◽  
Tim Stokes ◽  
...  
Keyword(s):  
Systematic Review ◽  
Quality Improvement ◽  
New Zealand ◽  
Phase 1 ◽  
Successful Implementation ◽  
Maturity Model ◽  
True Nature ◽  
Content Type ◽  
Current State ◽  
Full Control

Purpose The various quality improvement (QI) frameworks and maturity models described in the health services literature consider some aspects of QI while excluding others. This paper aims to present a concerted attempt to create a quality improvement maturity model (QIMM) derived from holistic principles underlying the successful implementation of system-wide QI programmes. Design/methodology/approach A hybrid methodology involving a systematic review (Phase 1) of over 270 empirical research articles and books developed the basis for the proposed QIMM. It was followed by expert interviews to refine the core constructs and ground the proposed QIMM in contemporary QI practice (Phase 2). The experts included academics in two academic conferences and 59 QI managers from the New Zealand health-care system. In-depth interviews were conducted with QI managers to ascertain their views on the QIMM and its applicability in their respective health organisations (HOs). Findings The QIMM consists of four dimensions of organisational maturity, namely, strategic, process, supply chain and philosophical maturity. These dimensions progress through six stages, namely, identification, ad-hoc, formal, process-driven, optimised enterprise and finally a way of life. The application of the QIMM by the QI managers revealed that the scope of QI and the breadth of the principles adopted by the QI managers and their HOs in New Zealand is limited. Practical implications The importance of QI in health systems cannot be overstated. The proposed QIMM can help HOs diagnose their current state and provide a guide to action achieving a desirable state of quality improvement maturity. This QIMM avoids reliance on any single QI methodology. HOs – using the QIMM – should retain full control over the process of selecting any QI methodology or may even cherry-pick principles to suit their needs as long as they understand and appreciate the true nature and scope of quality overstated. The proposed QIMM can help HOs diagnose their current state and provide a guide to action achieving a desirable state of quality improvement maturity. This QIMM avoids reliance on any single QI methodology. HOs – using the QIMM – should retain full control over the process of selecting any QI methodology or may even cherry-pick principles to suit their needs as long as they understand and appreciate the true nature and scope of quality. Originality/value This paper contributes new knowledge by presenting a maturity model with an integrated set of quality principles for HOs and their extended supply networks.

scholarly journals Data quality audit of a clinical quality registry: a generic framework and case study of the Australian and New Zealand Hip Fracture Registry

2019 ◽  
Vol 8 (3) ◽  
pp. e000490 ◽  
Author(s):  
Aidan Christopher Tan ◽  
Elizabeth Armstrong ◽  
Jacqueline Close ◽  
Ian Andrew Harris
Keyword(s):  
Quality Improvement ◽  
New Zealand ◽  
Hip Fracture ◽  
Data Quality ◽  
Data Abstraction ◽  
Data Set ◽  
Clinical Quality ◽  
Data Completeness ◽  
Quality Registry ◽  
Generic Framework

ObjectivesThe value of a clinical quality registry is contingent on the quality of its data. This study aims to pilot methodology for data quality audits of the Australian and New Zealand Hip Fracture Registry, a clinical quality registry of hip fracture clinical care and secondary fracture prevention.MethodsA data quality audit was performed by independently replicating the data collection and entry process for 163 randomly selected patient records from three contributing hospitals, and then comparing the replicated data set to the registry data set. Data agreement, as a proxy indicator of data accuracy, and data completeness were assessed.ResultsAn overall data agreement of 82.3% and overall data completeness of 95.6% were found, reflecting a moderate level of data accuracy and a very high level of data completeness. Half of all data disagreements were caused by information discrepancies, a quarter by missing discrepancies and a quarter by time, date and number discrepancies. Transcription discrepancies only accounted for 1 in every 50 data disagreements. The sources of inaccurate and incomplete data have been identified with the intention of implementing data quality improvement.ConclusionsRegular audits of data abstraction are necessary to improve data quality, assure data validity and reliability and guarantee the integrity and credibility of registry outputs. A generic framework and model for data quality audits of clinical quality registries is proposed, consisting of a three-step data abstraction audit, registry coverage audit and four-step data quality improvement process. Factors to consider for data abstraction audits include: central, remote or local implementation; single-stage or multistage random sampling; absolute, proportional, combination or alternative sample size calculation; data quality indicators; regular or ad hoc frequency; and qualitative assessment.

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