Aims

Total joint replacement (TJR) is a high-cost, high-volume procedure that impacts patients’ quality of life. Informed decisions are important for patients facing TJR. The quality of information provided by websites regarding TJR is highly variable. We aimed to measure the quality of TJR information online.

Aims. While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. Methods. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”. Results. The mean accuracy and comprehensiveness scores were 4.26 (95% confidence interval (CI) 4.19 to 4.33) and 3.79 (95% CI 3.69 to 3.89), respectively. Out of all the responses, 59.2% (71/120; 95% CI 50.0% to 67.7%) were acceptable. ChatGPT was consistent when asked the same question twice, giving no significant difference in accuracy (t = 0.821; p = 0.415), comprehensiveness (t = 1.387; p = 0.171), acceptability (χ. 2. = 1.832; p = 0.176), and FKGL (t = 0.264; p = 0.793). There was a significantly lower FKGL (t = 2.204; p = 0.029) for easier responses (11.14; 95% CI 10.57 to 11.71) than original responses (12.15; 95% CI 11.45 to 12.85). Conclusion. ChatGPT answered THA and TKA patient questions with accuracy comparable to previous reports of websites, with adequate comprehensiveness, but with limited acceptability as the sole information source. ChatGPT has potential for answering patient questions about THA and TKA, but needs improvement. Cite this article: Bone Jt Open 2024;5(2):139–146

Aims

The primary aim was to assess the patient-perceived effect of restrictions imposed due to COVID-19 on rehabilitation following total hip arthroplasty (THA) and total knee arthroplasty (TKA). Secondary aims were to assess perceived restrictions, influence on mental health, and functional outcome compared to patients undergoing surgery without restriction.

Aims

This study aimed to develop a virtual clinic for the purpose of reducing face-to-face orthopaedic consultations.

Satisfaction with care is important to both patients and to those who pay for it. The Net Promoter Score (NPS), widely used in the service industries, has been introduced into the NHS as the ‘friends and family test’; an overarching measure of patient satisfaction. It assesses the likelihood of the patient recommending the healthcare received to another, and is seen as a discriminator of healthcare performance. We prospectively assessed 6186 individuals undergoing primary lower limb joint replacement at a single university hospital to determine the Net Promoter Score for joint replacements and to evaluate which factors contributed to the response.

Achieving pain relief (odds ratio (OR) 2.13, confidence interval (CI) 1.83 to 2.49), the meeting of pre-operative expectation (OR 2.57, CI 2.24 to 2.97), and the hospital experience (OR 2.33, CI 2.03 to 2.68) are the domains that explain whether a patient would recommend joint replacement services. These three factors, combined with the type of surgery undertaken (OR 2.31, CI 1.68 to 3.17), drove a predictive model that was able to explain 95% of the variation in the patient’s recommendation response. Though intuitively similar, this ‘recommendation’ metric was found to be materially different to satisfaction responses. The difference between THR (NPS 71) and TKR (NPS 49) suggests that no overarching score for a department should be used without an adjustment for case mix. However, the Net Promoter Score does measure a further important dimension to our existing metrics: the patient experience of healthcare delivery.

Cite this article: Bone Joint J 2014;96-B:622–8.