Improving care for elderly patients living with polypharmacy: protocol for a pragmatic cluster randomized trial in community-based primary care practices in Canada.

BACKGROUND: Elders living with polypharmacy may be taking medications that do not benefit them. Polypharmacy can be associated with elevated risks of poor health, reduced quality of life, high care costs, and persistently complex care needs. While many medications could be problematic, this project targets medications that should be deprescribed for most elders and for which guidelines and evidence-based deprescribing tools are available. These are termed potentially inappropriate prescriptions (PIPs) and are as follows: proton pump inhibitors, benzodiazepines, antipsychotics, and sulfonylureas. Implementation strategies for deprescribing PIPs in complex older patient populations are needed. METHODS: This will be a pragmatic cluster randomized controlled trial in community-based primary care practices across Canada. Eligible practices provide comprehensive primary care and have at least one physician that consents to participate. Community-dwelling patients aged 65 years and older with ten or more unique medication prescriptions in the past year will be included. The objective is to assess whether the intervention reduces targeted PIPs for these patients compared with usual care. The intervention, Structured Process Informed by Data, Evidence and Research (SPIDER), is a collaboration between quality improvement (QI) and research programs. Primary care teams will form interprofessional Learning Collaboratives and work with QI coaches to review electronic medical record data provided by their regional Practice Based Research Networks (PBRNs), identify areas of improvement, and develop and implement changes. The study will be tested for feasibility in three PBRNs (Toronto, Montreal, and Edmonton) using prospective single-arm mixed methods. Findings will then guide a pragmatic cluster randomized controlled trial in five PBRNs (Calgary, Winnipeg, Ottawa, Montreal, and Halifax). Seven practices per PBRN will be recruited for each arm. The analysis will be by intention to treat. Ten percent of patients who have at least one PIP at baseline will be randomly selected to participate in the assessment of patient experience and self-reported outcomes. Qualitative methods will be used to explore patient and physician experience and evaluate SPIDER's processes. CONCLUSION: We are testing SPIDER in a primary care population with complex care needs. This could provide a widely applicable model for care improvement. TRIAL REGISTRATION: Clinicaltrials.gov NCT03689049 ; registered September 28, 2018.

Achieving quality primary care data: a description of the Canadian Primary Care Sentinel Surveillance Network data capture, extraction, and processing in Alberta.

INTRODUCTION: Electronic medical record (EMR) databases have become increasingly popular for secondary purposes, such as health research. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is the first and only pan-Canadian primary care EMR data repository, with de-identified health information for almost two million Canadians. Comprehensive and freely available documentation describing the data 'lifecycle' is important for assessing potential data quality issues and appropriate interpretation of research findings. Here, we describe the flow and transformation of CPCSSN data in the province of Alberta. APPROACH: In Alberta, the data originate from 54 publicly-funded primary care settings, including one community pediatric clinic, with 318 providers contributing de-identified EMR data for 410,951 patients (as of December 2018). Data extraction methods have been developed for five different EMR systems, and include both backend and automated frontend extractions. The raw EMR data are transformed according to specific rules, including trimming implausible values, converting values and free text to standard terminologies or classification systems, and structuring the data into a common CPCSSN format. Following local data extraction and processing, the data are transferred to a central repository and made available for research and disease surveillance. CONCLUSION: This paper aims to provide important contextual information to future CPCSSN data users.