Are clinicians using routinely collected data to drive practice improvement? A cross-sectional survey

A pilot study examining the impact of a pragmatic process for improving the cultural responsiveness of non-Aboriginal alcohol and other drug treatment services using routinely collected data in Australia

Objective: Routine health data has the potential to identify changes in patient-related outcomes, in close to real time. This pilot project used routine data to explore and compare the impact of changes to cultural responsiveness on service use by Aboriginal and Torres Strait Islander (hereafter, Aboriginal) clients in Australia.Methods: The New South Wales Minimum Data Set (MDS) for alcohol and other drug use treatment services was provided for 11 services for a period of 30 months from March 2019 to September 2021 (four months prior to two years after the intervention; data were analysed between March 2022 to February 2023). Change in cultural responsiveness was assessed via practice audits of services at baseline and two years. The average change in audit rating was analysed using a linear mixed regression model. Generalised Linear Mixed Models were used to identify changes in service use by Aboriginal clients. Results: All 11 services showed increased audit scores at two years, with a statistically significant mean increase of 18.6 (out of 63 points; b = 18.32, 95% CI 12.42-24.22). No statistically significant pre-to post-changes were identified in: (1) the proportion of episodes delivered to Aboriginal versus non-Aboriginal clients (OR = 1.15, 95% CI = 0.94-1.40), (2) the number of episodes of care provided to Aboriginal clients per month (IRR = 1.01, 95% CI = 0.84-1.23), or (3) the proportion of episodes completed by Aboriginal clients (OR = 0.96, 95% CI = 0.82-1.13). Conclusions: The lack of statistically significant impact on service use outcomes using MDS contrasts to the improvements in cultural responsiveness, suggesting further work is needed to identify appropriate outcome measures. This may include patient-reported experience measures. This project showed that routine data has potential as an efficient method for measuring changes in patient-related outcomes in response to health services improvements.

The prototype of a frailty learning health system: The HARMONY Model

Introduction

Rapid translation of research findings into clinical practice through innovation is critical to improve health systems and patient outcomes. Access to efficient systems of learning underpinned with real-time data are the future of healthcare. This type of health system will decrease unwarranted clinical variation, accelerate rapid evidence translation, and improve overall healthcare quality.

Methods

This paper aims to describe The HARMONY model (acHieving dAta-dRiven quality iMprovement to enhance frailty Outcomes using a learNing health sYstem), a new frailty learning health system model of implementation science and practice improvement. The HARMONY model provides a prototype for clinical quality registry infrastructure and partnership within health care.

Results

The HARMONY model was applied to the Western Sydney Clinical Frailty Registry as the prototype exemplar. The model networks longitudinal frailty data into an accessible and useable format for learning. Creating local capability that networks current data infrastructures to translate and improve quality of care in real-time.

Conclusion

This prototype provides a model of registry data feedback and quality improvement processes in an inpatient aged care and rehabilitation hospital setting to help reduce clinical variation, enhance research translation capacity, and improve care quality.

Using data from mHealth apps to inform person-centred practice: A discussion paper

BACKGROUND

mHealth applications (apps) are tools that can enhance research by efficiently collecting and storing large amounts of data. However, data collection alone does not lead to change. Innovation and practice change occur through utilisation of evidence. The volume of data collected raises questions regarding utilisation of data by nurses and midwives, and how data from mHealth apps can be used to improve person-centred practice. There is limited empirical evidence and a lack of direction from global health authorities to guide nurses and midwives in this area.

AIM

To describe strategies for nurses and midwives that could enhance the effective use of data generated by mHealth apps to inform person-centred practice. The purpose of this paper is to stimulate reflection and generate actions for data utilisation when using mHealth apps in nursing research and practice.

METHODS

This discussion paper has been informed by current evidence, the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, and research experience as part of doctoral study.

FINDINGS

Before engaging in data collection using mHealth apps, nurses and midwives would benefit from considering the nature of the evidence collected, available technological infrastructure, and staff skill levels. When collecting data and interpreting results, use of a team approach supported by engaged leadership and external facilitation is invaluable. This provides support to operate apps, and more importantly use the data collected to inform person-centred practice.

CONCLUSIONS

This paper addresses the limited available evidence to guide nurses and midwives when using mHealth apps to collect and use data to inform practice change. It highlights the need for appropriate technology, external facilitative support, engaged leadership, and a team approach to collect meaningful evidence using mHealth apps. Clinicians, leaders, and researchers can apply the strategies provided to enhance the use of mHealth apps and ensure translation of evidence into practice.

How registry data are used to inform activities for stroke care quality improvement across 55 countries: A cross-sectional survey of Registry of Stroke Care Quality (RES-Q) hospitals

BACKGROUND AND PURPOSE

The Registry of Stroke Care Quality (RES-Q) is a worldwide quality improvement data platform that captures performance and quality measures, enabling standardized comparisons of hospital care. The aim of this study was to determine if, and how, RES-Q data are used to influence stroke quality improvement and identify the support and educational needs of clinicians using RES-Q data to improve stroke care.

METHODS

A cross-sectional self-administered online survey was administered (October 2021-February 2022). Participants were RES-Q hospital local coordinators responsible for stroke data collection. Descriptive statistics are presented.

RESULTS

Surveys were sent to 1463 hospitals in 74 countries; responses were received from 358 hospitals in 55 countries (response rate 25%). RES-Q data were used "always" or "often" to: develop quality improvement initiatives (n = 213, 60%); track stroke care quality over time (n = 207, 58%); improve local practice (n = 191, 53%); and benchmark against evidence-based policies, procedures and/or guidelines to identify practice gaps (n = 179, 50%). Formal training in the use of RES-Q tools and data were the most frequent support needs identified by respondents (n = 165, 46%). Over half "strongly agreed" or "agreed" that to support clinical practice change, education is needed on: (i) using data to identify evidence-practice gaps (n = 259, 72%) and change clinical practice (n = 263, 74%), and (ii) quality improvement science and methods (n = 255, 71%).

CONCLUSION

RES-Q data are used for monitoring stroke care performance. However, to facilitate their optimal use, effective quality improvement methods are needed. Educating staff in quality improvement science may develop competency and improve use of data in practice.

Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts

INTRODUCTION

eHealth data analytics is widely used in health care research. However, there is limited knowledge on the role of eHealth data analysis to inform continuing professional development (CPD). The aim of this study was to collate available research evidence on the use of eHealth data for the development of CPD programs and plans for medical practitioners.

METHODS

A scoping review was conducted using the six-stage Arksey and O'Malley Framework. A consultation exercise (stage 6) was performed with 15 international experts in the fields of learning and practice analytics to deepen the insights.

RESULTS

Scoping review. The literature searches identified 9876 articles published from January 2010 to May 2022. After screening and full-text review, a total of nine articles were deemed relevant for inclusion. The results provide varied-and at times partial or diverging-answers to the scoping review research questions. Consultation exercise. Research rigor, field of investigation, and developing the field were the three themes emerged from analysis. Participants validated the scoping review methodology and confirmed its results. Moreover, they provided a meta-analysis of the literature, a description of the current CPD ecosystem, and clear indications of what is and should be next for the field.

DISCUSSION

This study shows that there is no formal or well-established correlation between eHealth data and CPD planning and programming. Overall findings fill a gap in the literature and provide a basis for further investigation. More foundational work, multidisciplinary collaborations, and stakeholders' engagement are necessary to advance the use of eHealth data analysis for CPD purposes.

Improved quality of physiotherapy care in patients with Whiplash-Associated Disorders: Results based on 16 years of routinely collected data

Quality improvement is now a central tenet in physiotherapy care, and quality indicators (QIs), as measurable elements of care, have been applied to analyze and evaluate the quality of physiotherapy care over the past two decades. QIs, based on Donabedian's model of quality of care, provide a foundation for measuring (improvements in) quality of physiotherapy care, providing insight into the many remaining evidentiary gaps concerning diagnostics, prognostics and treatment, as well as patient-related outcome measures. In this overview we provide a synthesis of four recently published articles from our project group on the topic of quantitative measures of quality improvement in physiotherapy care, in this context specifically focused on patients with WAD in primary care physiotherapy. A set of process and outcome QIs (n = 28) was developed for patients with WAD and linked to a database consisting of routinely collected data (RCD) on patients with WAD collected over a 16-year period. The QIs were then embedded per step of the clinical reasoning process: (a) administration (n = 2); (b) history taking (n = 7); (c) objectives of examination (n = 1); (d) clinical examination (n = 5); (e) analysis and conclusion (n = 1); (f) treatment plan (n = 3); (g) treatment (n = 2); (h) evaluation (n = 5); and (i) discharge (n = 2). QIs were expressed as percentages, allowing target performance levels to be defined ≥70% or ≤30%, depending on whether the desired performance required an initially high or low QI score. Using RCD data on primary care patients with WAD (N = 810) and a set of QIs, we found that the quality of physiotherapy care has improved substantially over a 16-year period. This conclusion was based on QIs meeting predetermined performance targets of ≥70% or ≤30%. Twenty-three indicators met the target criterium of ≥70% and three indicators ≤30%. Our recommended set of QIs, embedded in a clinical reasoning process for patients with WAD, can now be used as a basis for the development of a validated QI set that effectively measures quality (improvement) of primary care physiotherapy in patients with WAD.

Using data analytics to enhance quality improvement projects

With the introduction of electronic health record systems in healthcare organisations, there is a significant opportunity within nursing, and as part of the multidisciplinary team, to access a wealth of data and use this to drive improvements in patient care and outcomes. Quality improvement has often involved the collection of data via manual audit, a time-consuming process with data fed back from small sample sizes over a short time period. In contrast, a data analytics approach enables data collection from larger sample sizes to be automated and for data to be presented in a way that is easy for staff to access and interpret. This article discusses the benefits and challenges of a data analytics approach as well as the resources required, the importance of stakeholder involvement, the setting and review of key performance indicators and how to optimise data presentation to achieve the greatest effect.