For Doctors in a Hurry
- Standard educational materials for total joint arthroplasty often lack the personalization required to address specific patient concerns effectively.
- The researchers conducted a prospective pilot study involving 32 patients undergoing primary total hip or knee arthroplasty surgery.
- 84.4 percent of users reported high comprehensibility of responses, while 75.0 percent felt more prepared for their procedure.
- The authors conclude that this artificial intelligence platform is a feasible and useful tool for delivering preoperative patient education.
- Future research must compare this tool against standard educational materials to determine its impact on actual clinical recovery outcomes.
Digital Integration in Perioperative Arthroplasty Education
The demand for total joint arthroplasty continues to rise, placing significant pressure on healthcare systems to optimize resource utilization and patient throughput [1]. While traditional educational materials aim to facilitate self-management, their impact on functional outcomes remains inconsistent, often failing to address the specific, real-time concerns of patients during the recovery phase [2]. Digital decision aids and digital twins (computational models that use individual patient data to simulate clinical outcomes) have begun to bridge this gap by providing personalized risk-benefit predictions that improve decision quality [3]. However, many mobile health interventions still face hurdles regarding the lack of high-quality evidence to support their routine clinical implementation [4]. Integrating patient-reported outcome measures (standardized tools used to collect data on a patient's perceived health status) into these digital platforms offers a pathway to more responsive, individualized perioperative care [5]. A new study now evaluates how a tailored artificial intelligence platform might address these persistent educational gaps in the arthroplasty population.
Researchers recently conducted a prospective, observational pilot study to evaluate the efficacy of a custom-tailored artificial intelligence (CTAI) platform, a digital tool designed to provide personalized responses to patient questions, in enhancing preoperative education for arthroplasty surgery. The study utilized a two-phase sequential design, beginning with a 10-patient a priori cohort (a preliminary group used to refine the intervention) followed by the implementation of the platform with 30 additional participants. All subjects were scheduled for primary total hip or knee arthroplasty, a population that often requires extensive perioperative counseling to manage expectations and postoperative protocols. Of the total enrollment, 32 patients completed the preoperative survey, representing an even distribution of 50.0% knee and 50.0% hip procedures.
Participants were granted access to the CTAI platform throughout the preoperative period and early postoperative recovery phases, allowing for continuous engagement as clinical needs evolved. To assess the impact of the tool, the authors administered structured surveys at the presurgical screening visit and again at the 6-week follow-up. The latter assessment employed a phase-specific postoperative instrument that retained core educational items while incorporating new recovery-focused domains relevant to the late subacute period, such as wound care and mobility milestones. The primary measures focused on feasibility, defined by platform access and engagement levels, and patient-reported experience. These experience metrics included the perceived usefulness of the tool, the understandability of responses, the perceived completeness of answers, and the patient's overall perceived preparedness or readiness for the surgical procedure.
Standard educational materials for total joint arthroplasty frequently exhibit deficiencies in readability and personalization, often failing to address the specific, idiosyncratic concerns of individual patients. To address these gaps, the researchers evaluated how patients utilized the custom-tailored artificial intelligence platform alongside traditional resources. While surgeon-provided educational materials remained the predominant information source for 84.4% of participants, the digital platform established itself as a significant secondary resource. Specifically, engagement with the custom-tailored artificial intelligence tool was the second most common information source, utilized by 56.3% of the cohort, suggesting that patients view these automated systems as a viable adjunct to physician-led instruction rather than a replacement for the surgical team.
Patient interaction with the technology was robust, as the majority of participants (84.8%) successfully engaged with the platform during the study period. The intensity of use varied among the 32 patients who completed the preoperative assessment: 59.4% of participants submitted between one and four queries, while a more active 25.1% of participants submitted five or more queries. These interactions were not limited to a single topic but spanned several critical areas of the surgical journey. The predominant query domains included perioperative logistics (22.8%), postoperative concerns (22.0%), and activity parameters (12.9%), indicating that patients primarily used the tool to navigate the practical and recovery-oriented aspects of their care, which are often the most time-consuming topics for clinical staff to address during office visits.
Impact on Patient Preparedness and Satisfaction
The researchers assessed the perceived utility of the custom-tailored artificial intelligence platform through structured surveys administered during the preoperative and postoperative periods. Among the participants, the platform was deemed clinically valuable, receiving a rating of 4 or higher on a 5-point scale, by 66.7% of users. This perceived utility was supported by the clarity of the information provided; specifically, 84.4% of users reported high comprehensibility of responses generated by the tool. For clinicians, these data suggest that the digital interface effectively translates complex perioperative instructions into a format that patients find both accessible and useful during their surgical journey, potentially reducing the cognitive load on patients during the stressful preoperative window.
Beyond simple understanding, the study measured the tool's ability to address specific patient anxieties and logistical questions. The findings indicated that 75.1% of users achieved satisfactory resolution of their surgical inquiries through the platform. This high rate of inquiry resolution likely contributed to the primary clinical outcome of the pilot: 75.0% of participants reported enhanced preparedness for their total hip or knee arthroplasty. By providing a reliable secondary source for information, the tool appears to mitigate the uncertainty often associated with major orthopedic procedures, potentially reducing the volume of basic logistical questions directed to the surgical team and allowing for more focused clinical consultations.
Patient satisfaction and the perceived reliability of the technology were further reflected in the participants' willingness to endorse the system to others. The researchers found that 80.4% of participants expressed an intention to recommend the platform to other patients undergoing similar procedures. For the practicing surgeon, these metrics indicate that integrating such a tool into the preoperative workflow may improve the patient experience and perceived readiness for surgery, though the researchers noted that larger comparative studies are required to determine if these subjective improvements translate into superior clinical outcomes or reduced postoperative complications.
Postoperative Variability and Future Directions
At the six-week postoperative follow-up, 24 of 32 participants (75.0%) completed the postoperative survey, providing insight into the platform's utility during the acute recovery phase. While 22 of 24 respondents (91.7%) reported successful platform access postoperatively, their level of engagement was notably heterogeneous (a term describing a wide and inconsistent range of patient behaviors). Specifically, 7 of 24 patients (29.2%) submitted zero questions during this period, whereas 5 of 24 participants (20.8%) submitted more than 10 queries. This variance suggests that while the digital infrastructure remains accessible after discharge, individual patient needs for supplemental information fluctuate significantly during the transition from hospital to home, perhaps reflecting varying levels of recovery complexity or health literacy.
The efficacy of the custom-tailored artificial intelligence in resolving postoperative concerns showed a distinct bimodal distribution (a statistical pattern where two different groups emerge within the same data set). For postoperative question resolution, 7 of 24 patients (29.2%) reported that all questions were answered clearly, assigning the tool a maximum score of 5. Conversely, an equal number of participants, 7 of 24 (29.2%), reported that the platform did not answer their questions, resulting in a score of 1. Despite this inconsistency in specific query resolution, the comprehensibility of general recovery instructions remained high; 14 of 24 respondents (58.3%) reported that the instructions were very easy to understand, assigning a score of 5 for this domain.
These preliminary findings demonstrate that the custom-tailored artificial intelligence platform is feasible to deploy as an efficacious adjunctive tool (a secondary resource used alongside primary clinical care) for preoperative total joint arthroplasty education. However, the researchers emphasize that larger comparative studies with appropriate control groups are needed to determine the platform's specific impact relative to standard education protocols. Future investigations must also evaluate downstream clinical outcomes, such as whether increased patient preparedness translates into reduced readmission rates, fewer unscheduled clinic contacts, or improved functional recovery scores following hip and knee replacement.
References
1. Entezari B, Koucheki R, Abbas A, et al. Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review.. Arthroplasty today. 2023. doi:10.1016/j.artd.2023.101116
2. Wu Z, Zhou R, Zhu Y, et al. Self-Management for Knee Osteoarthritis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Pain Research and Management. 2022. doi:10.1155/2022/2681240
3. Jayakumar P, Rathouz PJ, Lin E, et al. Shared decision making using digital twins in knee osteoarthritis care: a randomized clinical trial of an AI-enabled decision aid versus education alone on decision quality, physical function, and user experience. EClinicalMedicine. 2025. doi:10.1016/j.eclinm.2025.103545
4. Knight SR, Ng N, Tsanas A, McLean KA, Pagliari C, Harrison EM. Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review. npj Digital Medicine. 2021. doi:10.1038/s41746-021-00525-1
5. Bonsel JM, Itiola AJ, Huberts AS, Bonsel GJ, Penton H. The use of patient-reported outcome measures to improve patient-related outcomes – a systematic review. Health and Quality of Life Outcomes. 2024. doi:10.1186/s12955-024-02312-4
