Optimization of a Quality Improvement Tool for Cancer Diagnosis in Primary Care: Qualitative Study

Sophie Chima; Javiera Martinez-Gutierrez; Barbara Hunter; Jo-Anne Manski-Nankervis; Jon Emery

doi:10.2196/39277

Optimization of a Quality Improvement Tool for Cancer Diagnosis in Primary Care: Qualitative Study

JMIR Form Res. 2022 Aug 4;6(8):e39277. doi: 10.2196/39277.

Authors

Sophie Chima^{1

2}, Javiera Martinez-Gutierrez^{1

2

3}, Barbara Hunter², Jo-Anne Manski-Nankervis², Jon Emery^{1

2}

Affiliations

¹ Centre for Cancer Research, Victorian Comprehensive Cancer Centre, University of Melbourne, Melbourne, Australia.
² Department of General Practice, University of Melbourne, Melbourne, Australia.
³ Department of Family Medicine, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.

PMID: 35925656
PMCID: PMC9389376
DOI: 10.2196/39277

Abstract

Background: The most common route to a diagnosis of cancer is through primary care. Delays in diagnosing cancer occur when an opportunity to make a timely diagnosis is missed and is evidenced by patients visiting the general practitioner (GP) on multiple occasions before referral to a specialist. Tools that minimize prolonged diagnostic intervals and reduce missed opportunities to investigate patients for cancer are therefore a priority.

Objective: This study aims to explore the usefulness and feasibility of a novel quality improvement (QI) tool in which algorithms flag abnormal test results that may be indicative of undiagnosed cancer. This study allows for the optimization of the cancer recommendations before testing the efficacy in a randomized controlled trial.

Methods: GPs, practice nurses, practice managers, and consumers were recruited to participate in individual interviews or focus groups. Participants were purposively sampled as part of a pilot and feasibility study, in which primary care practices were receiving recommendations relating to the follow-up of abnormal test results for prostate-specific antigen, thrombocytosis, and iron-deficiency anemia. The Clinical Performance Feedback Intervention Theory (CP-FIT) was applied to the analysis using a thematic approach.

Results: A total of 17 interviews and 3 focus groups (n=18) were completed. Participant themes were mapped to CP-FIT across the constructs of context, recipient, and feedback variables. The key facilitators to use were alignment with workflow, recognized need, the perceived importance of the clinical topic, and the GPs' perception that the recommendations were within their control. Barriers to use included competing priorities, usability and complexity of the recommendations, and knowledge of the clinical topic. There was consistency between consumer and practitioner perspectives, reporting language concerns associated with the word cancer, the need for more patient-facing resources, and time constraints of the consultation to address patients' worries.

Conclusions: There was a recognized need for the QI tool to support the diagnosis of cancer in primary care, but barriers were identified that hindered the usability and actionability of the recommendations in practice. In response, the tool has been refined and is currently being evaluated as part of a randomized controlled trial. Successful and effective implementation of this QI tool could support the detection of patients at risk of undiagnosed cancer in primary care and assist in preventing unnecessary delays.

Keywords: Clinical Performance Feedback Intervention Theory; cancer; clinical decision support tool; diagnosis; feasibility; general practice; pilot; primary health care; quality improvement.

©Sophie Chima, Javiera Martinez-Gutierrez, Barbara Hunter, Jo-Anne Manski-Nankervis, Jon Emery. Originally published in JMIR Formative Research (https://formative.jmir.org), 04.08.2022.