Department of Orthopedics and Physical Rehabilitation
Bioinformatics | Clinical Epidemiology | Epidemiology | Health Information Technology | Health Services Administration | Health Services Research | Musculoskeletal Diseases | Rehabilitation and Therapy | Translational Medical Research
INTRODUCTION: New informatics tools can transform evidence-based information to individualized predictive reports to serve shared decisions in clinic. We developed a web-based system to collect patient-reported outcomes (PROs) and medical risk factors and to compare responses to national registry data. The system generates predicted outcomes for individual patients and a report for use in clinic to support decisions. We present the report development, presentation, and early experience implementing this PRO-based, shared decision report for knee and hip arthritis patients seeking orthopedic evaluation.
METHODS: Iterative patient and clinician interviews defined report content and visual display. The web-system supports: (a) collection of PROs and risk data at home or in office, (b) automated statistical processing of responses compared to national data, (c) individualized estimates of likely pain relief and functional gain if surgery is elected, and (d) graphical reports to support shared decisions. The system was implemented at 12 sites with 26 surgeons in an ongoing cluster randomized trial.
RESULTS: Clinicians and patients recommended that pain and function as well as clinical risk factors (e.g., BMI, smoking) be presented to frame the discussion. Color and graphics support patient understanding. To date, 7891 patients completed the assessment before the visit and 56% consented to study participation. Reports were generated for 98% of patients and 68% of patients recalled reviewing the report with their surgeon.
CONCLUSIONS: Informatics solutions can generate timely, tailored office reports including PROs and predictive analytics. Patients successfully complete the pre-visit PRO assessments and clinicians and patients value the report to support shared surgical decisions.
Osteoarthritis, Patient-reported outcomes, Predictive analytics, Shared decision-making, Total joint replacement
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DOI of Published Version
Franklin PD, Zheng H, Bond C, Lavallee DC. Translating clinical and patient-reported data to tailored shared decision reports with predictive analytics for knee and hip arthritis. Qual Life Res. 2020 Jun 19. doi: 10.1007/s11136-020-02557-8. Epub ahead of print. PMID: 32562194. Link to article on publisher's site
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Franklin PD, Zheng H, Bond C, Lavallee DC. (2020). Translating clinical and patient-reported data to tailored shared decision reports with predictive analytics for knee and hip arthritis. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1007/s11136-020-02557-8. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/1810
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Bioinformatics Commons, Clinical Epidemiology Commons, Epidemiology Commons, Health Information Technology Commons, Health Services Administration Commons, Health Services Research Commons, Musculoskeletal Diseases Commons, Rehabilitation and Therapy Commons, Translational Medical Research Commons