JMIR Publications

ABSTRACT

Background:

The health system collects a plethora of electronic health data that is a potentially rich source of information on the performance of health professionals and teams. There is a range of drivers for using electronic health data in this way including desire from health professionals themselves, potential improvements to care quality if clinicians can discover ways to improve their performance through data insights, improving the return on investment of electronic health records and other widely adopted clinical information systems, and policy developments in several jurisdictions expanding practice-based CPD requirements and opportunities for data use. Practice Analytics in Healthcare (PAH) is a term used to describe enquiry into how these diverse Electronic Health Data (EHD) sets support the health workforce engage in performance reflection, professional learning, and quality improvement. Over the last five years a major national program of work in the field of Practice Analytics has been undertaken in Australia investigating the viability of using electronic health data, including data from Patient Administrative Systems (PAS) and Patient Reported Measures (PRMs), to support medical practitioners reflect on their practice and engage in workplace-based CPD activities.

Objective:

This review was designed to synthesise key findings across a major program of work in Practice Analytics, describe implications for practice, and identify opportunities for future research and development.

Methods:

A modified integrative review of all selected research outputs (n=45) from the research program was undertaken to synthesise key findings across all projects. Key outputs from this program were analysed to distil key research insights, implications for practice, and recommendations for the future of the field of Practice Analytics.

Results:

The review analysed a total of 48 outputs from the Practice Analytics program. This analysis identified a range of contributions to the literature particularly related to how Artificial Intelligence can be used to structure EHD to understand health professionals’ performance and scaffold its feedback. Research outputs also identified policy implementation gaps and organisational and ecosystemic barriers to data-informed CPD, and investigated the ethical and legal implications of Practice Analytics.

Conclusions:

Repurposing routinely collected EHD to support health professionals understand their performance and reflect on their practice is possible. There are many ways to transform data into such insights. Despite this, realising the potential of EHD to support reflective practice requires overcoming many challenges including accessing high-quality data; ethical and equity concerns; broader conceptual, organisational, cultural and ecosystemic barriers; and best-practice approaches to designing data-driven reflective tools for the workforce. Findings from the Practice Analytics program highlighted that operational, organisational, regulatory, cultural and ecosystemic factors influence implementation of practice analytics programs. If data access issues can be overcome data from these systems can be transformed into meaningful indicators of performance that can help engage health professionals in data-driven learning.