The Alliance for Healthier Communities' journey to a learning health system in primary care

Introduction

The Alliance for Healthier Communities represents community-governed healthcare organizations in Ontario, Canada including Community Health Centres, which provide primary care to more disadvantaged populations.

Methods

In this experience report, we describe the Alliance's journey towards becoming a learning health system using examples for organizational culture, data and analytics, people and partnerships, client engagement, ethics and oversight, evaluation and dissemination, resources, identification and prioritization, and deliverables and impact.

Results

Many of the foundational elements for a learning health system were already in place at the Alliance including an integrated and accessible data platform. Leadership championed and embraced the movement towards a learning health system, which led to restructuring of the organization. This included role changes for data support personnel, better communication, and dissemination plans, strategies to engage clinicians and other front-line staff, restructuring of committees for more collaborative planning and prioritization of quality improvement and research initiatives, and the development of a new Practice-Based Learning Network for more opportunities to use the data for research and evaluation.

Conclusions

Next steps will focus on continued clinical engagement and partnerships as well as ongoing reflection on the transition and success of the learning health system work.

Can structured EHR data support clinical coding? A data mining approach

Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.

Built to last? The sustainability of health system improvements, interventions and change strategies: a study protocol for a systematic review

INTRODUCTION

The sustainability of healthcare interventions and change programmes is of increasing importance to researchers and healthcare stakeholders interested in creating sustainable health systems to cope with mounting stressors. The aim of this protocol is to extend earlier work and describe a systematic review to identify, synthesise and draw meaning from studies published within the last 5 years that measure the sustainability of interventions, improvement efforts and change strategies in the health system.

METHODS AND ANALYSIS

The protocol outlines a method by which to execute a rigorous systematic review. The design includes applying primary and secondary data collection techniques, consisting of a comprehensive database search complemented by contact with experts, and searching secondary databases and reference lists, using snowballing techniques. The review and analysis process will occur via an abstract review followed by a full-text screening process. The inclusion criteria include English-language, peer-reviewed, primary, empirical research articles published after 2011 in scholarly journals, for which the full text is available. No restrictions on location will be applied. The review that results from this protocol will synthesise and compare characteristics of the included studies. Ultimately, it is intended that this will help make it easier to identify and design sustainable interventions, improvement efforts and change strategies.

ETHICS AND DISSEMINATION

As no primary data were collected, ethical approval was not required. Results will be disseminated in conference presentations, peer-reviewed publications and among policymaker bodies interested in creating sustainable health systems.