An Electronic Clinical Decision Support System for the Management of Low Back Pain in Community Pharmacy: Development and Mixed Methods Feasibility Study

Aron Simon Downie; Mark Hancock; Christina Abdel Shaheed; Andrew J McLachlan; Ahmet Baki Kocaballi; Christopher M Williams; Zoe A Michaleff; Chris G Maher

doi:10.2196/17203

An Electronic Clinical Decision Support System for the Management of Low Back Pain in Community Pharmacy: Development and Mixed Methods Feasibility Study

JMIR Med Inform. 2020 May 11;8(5):e17203. doi: 10.2196/17203.

Authors

Aron Simon Downie^{1

2}, Mark Hancock³, Christina Abdel Shaheed¹, Andrew J McLachlan⁴, Ahmet Baki Kocaballi^{5

6}, Christopher M Williams⁷, Zoe A Michaleff^{1

8}, Chris G Maher¹

Affiliations

¹ Institute for Musculoskeletal Health, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia.
² Faculty of Science and Engineering, Macquarie University, Macquarie Park, Australia.
³ Faculty of Medicine, Health and Human Sciences, Macquarie University, Macquarie Park, Australia.
⁴ Sydney Pharmacy School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
⁵ Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, Australia.
⁶ Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
⁷ Hunter New England Population Health, Hunter New England Local Health District, Newcastle, Australia.
⁸ Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia.

PMID: 32390593
PMCID: PMC7248808
DOI: 10.2196/17203

Abstract

Background: People with low back pain (LBP) in the community often do not receive evidence-based advice and management. Community pharmacists can play an important role in supporting people with LBP as pharmacists are easily accessible to provide first-line care. However, previous research suggests that pharmacists may not consistently deliver advice that is concordant with guideline recommendations and may demonstrate difficulty determining which patients require prompt medical review. A clinical decision support system (CDSS) may enhance first-line care of LBP, but none exists to support the community pharmacist-client consultation.

Objective: This study aimed to develop a CDSS to guide first-line care of LBP in the community pharmacy setting and to evaluate the pharmacist-reported usability and acceptance of the prototype system.

Methods: A cross-platform Web app for the Apple iPad was developed in conjunction with academic and clinical experts using an iterative user-centered design process during interface design, clinical reasoning, program development, and evaluation. The CDSS was evaluated via one-to-one user-testing with 5 community pharmacists (5 case vignettes each). Data were collected via video recording, screen capture, survey instrument (system usability scale), and direct observation.

Results: Pharmacists' agreement with CDSS-generated self-care recommendations was 90% (18/20), with medicines recommendations was 100% (25/25), and with referral advice was 88% (22/25; total 70 recommendations). Pharmacists expressed uncertainty when screening for serious pathology in 40% (10/25) of cases. Pharmacists requested more direction from the CDSS in relation to automated prompts for user input and page navigation. Overall system usability was rated as excellent (mean score 92/100, SD 6.5; 90th percentile compared with similar systems), with acceptance rated as good to excellent.

Conclusions: A novel CDSS (high-fidelity prototype) to enhance pharmacist care of LBP was developed, underpinned by clinical practice guidelines and informed by a multidisciplinary team of experts. User-testing revealed a high level of usability and acceptance of the prototype system, with suggestions to improve interface prompts and information delivery. The small study sample limits the generalizability of the findings but offers important insights to inform the next stage of system development.

Keywords: community pharmacy; decision support systems, clinical; low back pain.

©Aron Simon Downie, Mark Hancock, Christina Abdel Shaheed, Andrew J McLachlan, Ahmet Baki Kocaballi, Christopher M Williams, Zoe A Michaleff, Chris G Maher. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 11.05.2020.