Abstract
Background
Preoperative counseling may reduce postoperative opioid requirements; however, there is a paucity of randomized controlled trials (RCTs) demonstrating efficacy. The purpose of this study was to perform an interventional, telehealth-based RCT evaluating the effect of peri-operative counseling on quantity and duration of opioid consumption following primary total joint arthroplasty (TJA).
Methods
Participants were randomized into three groups: 1. Control group, no perioperative counseling; 2. Intervention group, preoperative educational video; 3. Intervention group, preoperative educational video and postoperative acceptance and commitment therapy (ACT). Opioid consumption was evaluated daily for 14 days and at 6 weeks postoperatively. Best-case and worse-case intention to treat analyses were performed to account for non-responses. Bonferroni corrections were applied.
Results
183 participants were analyzed (63 in Group 1, 55 in Group 2, and 65 in Group 3). At 2 weeks postoperatively, there was no difference in opioid consumption between Groups 1, 2, and 3 (p>0.05 for all). At 6 weeks postoperatively, Groups 2 and 3 had consumed significantly less opioids than Group 1 (p=0.04, p<0.001) (Table 1). Group 3 participants were less likely to obtain an opioid refill relative to Group 1 participants (p=0.04). Participants in groups 2 and 3 ceased opioid consumption a median of 6 days and 2 days sooner than Group 1, respectively (p<0.001, p=0.03) (Table 2).
Conclusion
Perioperative opioid counseling significantly decreases the quantity and duration of opioid consumption at 6 weeks following primary TJA.
Level of Evidence: I
Keywords: opioid, total hip arthroplasty, total knee arthroplasty, counseling
Introduction
As frequent providers of narcotic prescriptions, orthopedic surgeons are at the epicenter the opioid epidemic in the United States.1-4 Primary total joint arthroplasty (TJA) is one of the most common orthopedic procedures performed in the United States, with projections estimated to exceed 1 million cases annually by the year 2025.5 Patients undergoing primary TJA receive some of the greatest quantities of prescription opioids postoperatively relative to patients undergoing other major orthopedic procedures.4 Taken together, primary TJA patients represent a cohort in which small-scale changes in opioid consumption have the potential to make a large impact on the opioid epidemic.
Recent interventions aimed at patient counseling regarding opioid use in the perioperative period have been successful in reducing quantity and duration of postoperative opioid consumption after upper extremity surgery.1,6 Previous interventions have utilized in-person counseling by the treating surgeon, or staging showings of a pre-recorded video to patients during the preoperative work-up.1,6 However, as the COVID-19 epidemic continues, additional in-person interaction is becoming less practical and exploring novel strategies for patient communication in the perioperative period is necessary. Perioperative text-messaging has been demonstrated to be an effective tool in communicating with patients undergoing total joint arthroplasty.7 The purpose of this study was to examine the efficacy of perioperative patient counseling on reducing the quantity and duration of opioid consumption following primary TJA.
Methods
Trial Design
The study was a single-center, non-blinded randomized controlled trial (RCT) designed in compliance with Consolidated Standards of Reporting Trials (CONSORT) Group guidelines. The trial was approved by the University of Iowa Hospitals and Clinics Human SubjectsResearch Institutional Review Board (IRB 201805715) prior to study initiation. Written consent was obtained from all patients prior to study enrollment.
Recruitment and Enrollment
Prospective participants were identified at the time of their indication for primary total knee arthroplasty (TKA) or total hip arthroplasty (THA) between March 2019 and February 2020. Inclusion criteria were patients ≥18 years and ≤80 years of age, undergoing primary THA or TKA, English language proficiency, with a text-messaging compatible mobile phone. Exclusion criteria were patients younger than 18 years or older than 80 years of age, patients undergoing aseptic or septic revision THA or TKA, patients undergoing additional procedures during the study period, and those without text-messaging compatible mobile phones. Prospective participants deemed eligible for enrollment were approached following their clinic visit by a study coordinator. Enrollment in the study was strictly voluntary. After a thorough discussion of study policy and the risks and benefits of study enrollment, written informed consent for participation was obtained.
Power Analysis and Randomization
Prior to study initiation, an a priori power analysis was conducted based off of retrospective opioid consumption data collected from our institution and published previously.2 To detect at least a 30% reduction in quantity of opioid consumption (measured in morphine milliequivalents [MME]), 48 patients would be needed in each study group.
As part of the primary TJA care pathway at our institution, all patients undergoing primary TJA are required to attend the Total Joint Patient Education Class lead by orthopedic nurses. Once patients were enrolled in the study, they were randomized according to the date of their preoperative patient education class; randomization in this fashion was necessary as one of our interventions included showing an educational video on perioperative opioid use to all class participants. Patients were randomized into one of three study groups: Group 1 (“Control”), no perioperative counseling; Group 2 (“Video Only”), intervention group, viewing of a pre-recorded video during preoperative patient education class; Group 3 (“Video + ACT”), intervention group, viewing of a prerecorded video during preoperative patient education class and administration of acceptance and commitment (ACT) therapy via an automated text-messaging robot (Figure 1).
Figure 1.
CONSORT Flow Diagram depicting study thoroughfare for prospective and enrolled study participants. Pts – patients.
Interventions
All study participants underwent surgery with one of four fellowship trained adult reconstruction surgeons. Surgical approach and component selection were entirely at the discretion of the staff surgeon. Postoperatively, participants followed a standardized clinical pathway respective to their surgery (TKA vs THA). Opioids were prescribed as part of a multimodal pain management pathway in accordance with existing practice standards previously described by Holte et al.2
Study participants in the Video Only and Video + ACT groups screened a pre-recorded video during their preoperative patient education class. The video was developed by fellowship trained adult reconstruction surgeons within the practice and consisted of approximately 3 minutes of narration over slides aimed at illustrating correct use, alternatives, and dangers of opioid pain medications.
Participants in the Video + ACT group also received ACT via text messages beginning on postoperative day 1 for a total of 14 days. Administration of ACT via text messaging to patients following orthopedic surgery has previously been validated by Anthony et al.9 Content of text messages may be found in the Appendix, Table A1.
Study Outcomes and Data Collection
Primary study endpoints were quantity of opioid consumption (measured in MME) at 14 days postoperatively and 6 weeks postoperatively, as well as duration of opioid consumption, measured in days. Secondary study endpoints included the total number of opioid refills obtained in each group at 6 weeks postoperatively, change in Patient-Reported Outcomes Measurement Information System (PROMIS) Pain Intensity Scale v1.0 scores from preoperatively to 6 weeks postoperatively, as well as mean Visual Analog Scale (VAS) pain scores per 24-hour period measured on postoperative day 1 to postoperative day 14.
Quantity of opioid consumption and mean VAS pain scores of all study participants was measured using an automated text-messaging robot through the first 14 days postoperatively. Each day, the robot would query participants regarding their mean VAS pain score over the preceding 24 hours (Figure 2). Following this, the robot would query the participant regarding how many tablets of opioid pain medication they had consumed in the past 24 hours (Figure 2). Responses were saved on a secure, Health Insurance Portability and Accountability Act (HIPAA)-compliant server. Opioid consumption measured in tablet counts was manually converted into MME. Total quantity of opioid consumption at 6 weeks postoperatively was the sum of opioids consumed in the first 14 days plus any opioids obtained by the participant through refills. The total number of opioid refills obtained per participant in the 6-week postoperative period was also recorded.
Figure 2.
Example message exchange between automated robot and study participant for collection of VAS pain score and opioid consumption data.
Screenshot of an example message exchange between the Automated Text-Messaging Robot (text in grey bubble) and a study participant (text in green bubble).
Participants were considered to have ceased opioid at the time any of the following conditions was met: (1) participants reported consuming zero opioid tablets for the remainder of the first 14 days postoperatively and did not receive a subsequent opioid refill; (2) participants ceased responding to opioid consumption queries and the participant’s existing opioid prescription ran out (based on frequency and quantity prescribed, assuming participant taking opioid as frequently as possible allowed be prescription), and the participant did not receive A a subsequent opioid refill; (3) once all refill prescriptions ran out (based on frequency and quantity prescribed, assuming participant taking opioid as frequently as possible allowed by each respective prescription).
PROMIS Pain Intensity Scales v1.0 were administered in person by the study administrator at the time the participants were enrolled into the study as well as at the participant’s 6-week postoperative follow-up visit.
Demographic data collected included participant sex, age, a history of prolonged opioid use (defined as opioid use for longer than 6 months at any time point prior to their index surgery), and a history of any opioid use within 3 months of index surgery. Surgical data included type of surgery performed (primary TKA vs. primary THA).
Query Response Rates and Statistical Analysis
The overall participant response rate to all queries posed by the automated text-messaging robot was 66%. Response rates were not significantly different between study groups (Control, 66%; Video Only; 68%; Video + ACT, 63%; p>0.05 for all). To account for non-responses, differences in quantity of opioid consumption between groups was performed using intention to treat analyses using “best-case” and “worst-case” scenarios. In the best-case scenario, the absence of a response to a query regarding the quantity of opioid tablets consumed in the past 24 hours was recorded as the participant taking zero MME for that 24-hour period. In the worst-case scenario, the absence of a response to a query regarding the quantity of opioid tablets consumed in the past 24 hours was recorded as the participant taking the maximum amount of MME allowed by their respective prescriptions. For both best-case and worst-case scenarios, participants were considered to consume the entirety of any refill prescriptions that were written.
Quantity of opioid consumption between groups was evaluated for normality by Shapiro-Wilk tests and was found to be a non-normal distribution; non-parametric testing was used. Demographic variables were compared between groups using Chi-square and one-way analysis of variance (ANOVA) testing. Bonferroni corrections were applied to all p-values given the presence of multiple comparisons for respective each study variable. Analyses were completed using SAS statistical software v9.4 (SAS Institute, Inc., Cary, NC, USA).
Results
Eight-hundred and seventeen patients were assessed for study eligibility (Figure 1); 374 did not meet inclusion criteria and were excluded, while 213 patients declined to participate. 230 participants underwent randomization to one of three study groups: 85 to the Control group, 67 to the Video Only group, and 78 to the Video + ACT group. During the study, a hardware failure occurred with one of our servers, resulting in data loss for 47 patients in total (Figure 1); these patients were excluded from analysis. A total of 183 participants underwent analysis.
There was no difference in sex, surgery type, age, prevalence of prolonged opioid use, of prevalence of opioid use within 3 months of index surgery between the three study groups (p>0.05 for all) (Table 1).
Table 1.
Demographic Data, by Study Group
| Variable | Group | p-value | ||
|---|---|---|---|---|
| 1. Control | 2. Video Only | Video + ACT | ||
| Sex (n, % female) | 39 (62%) | 32 (58%) | 40 (62%) | 0.90 |
| Surgery (n, % THA) | 26 (41%) | 21 (38%) | 31 (47%) | 0.56 |
| Age (mean ± SD; years) | 59 ± 11 | 59 ± 11 | 58 ± 9 | Overall: 0.83 1v2: 0.98 2v3: 0.65 2v3: 0.56 |
| Prolonged Opioid Use > 60 mo. (n, %) | 0 | 0 | 0 | - |
| Opioid Use Within 3 mo. of Index Surgery (n, %) | 0 (14%) | 4 (7%) | 5 (8%) | 0.34 |
SD – standard deviation.
At 2 weeks postoperatively, in the best-case scenario, there was no difference in quantity of opioid consumption between all study groups (Table 2, Figure 3). Similarly, at 2 weeks postoperatively in the worst-case scenario, no difference in quantity of opioid consumption was observed between all study groups (Table 3, Figure 4). At 6 weeks postoperatively, in the best case-scenario, the Video Only and Video + ACT groups consumed a significantly lower quantity of opioids relative to the Control group (p=0.04, p<0.001, respectively) (Table 4, Figure 5). There was no difference in quantity of consumption between the Video Only and Video + ACT groups. In the worst-case scenario at 6 weeks postoperatively, the Video + ACT group consumed a significantly lower quantity of opioids relative to the Control group (p=0.04) (Table 5, Figure 6). There was no difference in quantity of consumption between the Video Only and Control groups or the Video Only and Video + ACT groups.
Table 2.
Quantity of Opioid Consumption at 2 Weeks Postoperatively, Best-Case Scenario
| Value | Group | p-value | p-value (corrected) | ||
|---|---|---|---|---|---|
| 1. Control | 2. Video Only | Video + ACT | |||
| Median | 192 | 113 | 90 | 1v2: 0.28 | 1v2: 0.56 |
| IQR | 60-308 | 8-308 | 15-248 | 1v3: 0.04* | 1v3: 0.15 |
| Min | 0 | 0 | 0 | 2v3: 0.47 | 2v3: 0.56 |
| Max | 690 | 623 | 694 | ||
Median, interquartile range (IQR), minimum (min), and maximum (max) values are reported in morphine milliequivalents (MME). * denotes statistical significance.
Figure 3.
Quantity of opioid consumption at 2 weeks postoperatively, best-case scenario.
Box and whisker plot of quanity of opioid consumption at 2 weeks postoperatively in the best-case scenario, organized by study group. BCS – best-case scenario; MME – morphine miliequivalents.
Table 3.
Quantity of Opioid Consumption at 2 Weeks Postoperatively, Worst-Case Scenario
| Value | Group | p-value | p-value (corrected) | ||
|---|---|---|---|---|---|
| 1. Control | 2. Video Only | Video + ACT | |||
| Median | 450 | 443 | 428 | 1v2: 0.13 | 1v2: 0.25 |
| IQR | 308-518 | 200-500 | 218-500 | 1v3: 0.04* | 1v3: 0.14 |
| Min | 0 | 0 | 0 | 2v3: 0.92 | 2v3: 0.92 |
| Max | 1238 | 950 | 960 | ||
Median, interquartile range (IQR), minimum (min), and maximum (max) values are reported in morphine milliequivalents (MME). * denotes statistical significance.
Figure 4.
Quantity of opioid consumption at 2 weeks postoperatively, worst-case scenario.
Box and whisker plot of quanity of opioid consumption at 2 weeks postoperatively in the worst-case scenario, organized by study group. WCS – worst-case scenario; MME – morphine miliequivalents.
Table 4.
Quantity of Opioid Consumption at 6 Weeks Postoperatively, Best-Case Scenario
| Value | Group | p-value | p-value (corrected) | ||
|---|---|---|---|---|---|
| 1. Control | 2. Video Only | Video + ACT | |||
| Median | 330 | 195 | 98 | 1v2: 0.02* | 1v2: 0.04* |
| IQR | 143-578 | 15-465 | 15-268 | 1v3: <0.001* | 1v3: <0.001* |
| Min | 0 | 0 | 0 | 2v3: 0.06 | 2v3: 0.06 |
| Max | 2025 | 1300 | 773 | ||
Median, interquartile range (IQR), minimum (min), and maximum (max) values are reported in morphine milliequivalents (MME). * denotes statistical significance.
Figure 5.
Quantity of opioid consumption at 6 weeks postoperatively at best-case scenario.
Box and whisker plot of quanity of opioid consumption at 6 weeks postoperatively in the best-case scenario, organized by study group. BCS – worst-case scenario; MME – morphine miliequivalents. * denotes statistical significance.
Table 5.
Quantity of Opioid Consumption at 6 Weeks Postoperatively, Worst-Case Scenario
| Value | Group | p-value | p-value (corrected) | ||
|---|---|---|---|---|---|
| 1. Control | 2. Video Only | Video + ACT | |||
| Median | 500 | 500 | 500 | 1v2: 0.03* | 1v2: 0.05 |
| IQR | 315-900 | 210-650 | 218-650 | 1v3: 0.01* | 1v3: 0.04* |
| Min | 0 | 0 | 0 | 2v3: 0.97 | 2v3: 0.97 |
| Max | 2190 | 1400 | 1560 | ||
Median, interquartile range (IQR), minimum (min), and maximum (max) values are reported in morphine milliequivalents (MME). * denotes statistical significance.
Figure 6.
Duration of opioid consumption.
Box and whisker plot of duration of opioid consumption, organized by study group. * denotes statistical significance.
Participants in the Video Only and Video + ACT groups consumed opioids for a median of 8 days and 12 days, respectively; median duration of consumption in the Control group was 14 days. Duration of opioid consumption was significantly shorter in participants in the Video Only and Video + ACT groups relative to participants in the Control group (p<0.001, p=0.03) (Table 6, Figure 7). There was no difference in duration of opioid consumption between Video Only and Video + ACT groups (p=0.38).
Table 6.
Duration of Opioid Consumption
| Value | Group | p-value | p-value (corrected) | ||
|---|---|---|---|---|---|
| 1. Control | 2. Video Only | Video + ACT | |||
| Median | 14 | 8 | 12 | 1v2: <0.001* | 1v2: <0.001* |
| IQR | 11-23 | 3-14 | 3-15 | 1v3: 0.01* | 1v3: 0.03* |
| Min | 1 | 0 | 0 | 2v3: 0.38 | 2v3: 0.38 |
| Max | 47 | 43 | 49 | ||
Median, interquartile range (IQR), minimum (min), and maximum (max) values are reported in morphine milliequivalents (MME). * denotes statistical significance.
A total of 28 participants (44%) in the Control group obtained an opioid refill compared to 16 participants (29%) in the Video Only group and 18 participants (27%) in the Video + ACT group. Participants in the Video + ACT group were significantly less likely to obtain an opioid refill relative to the Control group (Odds Ratio 0.48 [95% Confidence Interval 0.23-0.99); p=0.04) (Table 7). There was a statistically significant trend towards a decreasing likelihood of obtaining any opioid refill from the Control to Video Only group and the Video Only to the Video + ACT group (p=0.04) (Table 7).
Table 7.
Odds of Any Opioid Refill
| Group/Effect | n (%) Refills with ≥1 refill | Odds Ratio | 95% CI | p-value | |
|---|---|---|---|---|---|
| 1. Control | 28 (44%) | Referent | |||
| 2. Video Only | 16 (29%) | 0.51 | 0.24 | 1.10 | 0.09 |
| 3. Video + ACT | 18 (27%) | 0.48 | 0.23 | 0.99 | 0.04* |
| Trend | 0.69 | 0.47 | 0.99 | 0.04* | |
CI – confidence interval. * denotes statistical significance.
There was no difference in preoperative or 6-week postoperative PROMIS Pain Intensity Scale v1.0 scores between the Control, Video Only, and Video + ACT groups (p>0.05 for all). There was no difference in median VAS pain scores obtained in the first 14 days after surgery between the three study groups (p=0.12) (Table 8).
Table 8.
Median Postoperative VAS Pain Scores by Group and Postoperative Day
| Control | Video Only | Video + ACT | |
|---|---|---|---|
| POD 1 | 5 | 6 | 6 |
| POD 2 | 5 | 6 | 7 |
| POD 3 | 5 | 5 | 5 |
| POD 4 | 4 | 5 | 5 |
| POD 5 | 4 | 5 | 4 |
| POD 6 | 4 | 4 | 4 |
| POD 7 | 4 | 5 | 4 |
| POD 8 | 4 | 4 | 4 |
| POD 9 | 3 | 4 | 4 |
| POD 10 | 3 | 4 | 4 |
| POD 11 | 3 | 4 | 3 |
| POD 12 | 3 | 4 | 3 |
| POD 13 | 3 | 4 | 3 |
| POD 14 | 3 | 4 | 3 |
POD – postoperative day.
Discussion
Patients undergoing primary TKA and THA receive a large fraction of the opioid prescribed by orthopedic surgeons in the United States.4 As such, they are frequently a target group for potential interventions aimed at curbing prescription and consumption of opioid pain medications. Changes in practitioner prescribing practices have proven effective in decreasing the amount of opioid pain medication available to patients after primary TJA without adverse effects on pain control or patient-provider communication.2,10 Patient education regarding disposal of unused opioid pain medications significantly increases rates of proper opioid disposal.11 While perioperative counseling has proven successful in reducing quantity of opioid consumption following upper extremity orthopedic surgery, there is a paucity of data available regarding the potential effects of opioid counseling following primary TJA.1,6
The primary aim of the present study was to examine the efficacy of perioperative patient counseling on reducing the quantity and duration of opioid consumption following primary TJA. At 2 weeks postoperatively, we observed no difference in the quantity of opioid consumption between the Control, Video Only, and Video + ACT groups in best-case and worst-case scenarios. However, at 6 weeks postoperatively, participants in the Video Only and Video + ACT group consumed significantly less MMEs relative to the control group in the best-case scenario. Additionally, participants in the Video + ACT consumed less MME relative the control group in the worst-case scenario at 6 weeks postoperatively. The difference in opioid consumption in the Video Only versus Control group trended towards but did not reach significance in the worst-case scenario at 6 weeks postoperatively. Participants in intervention groups were also less likely to obtain an opioid refill relative to participants in the control group.
Alter et al.1 evaluated opioid consumption in the first three days after surgery in a prospective RCT in which patients undergoing mini-open carpal tunnel release were randomized to preoperative opioid counseling or a control group. The authors noted that patients who underwent preoperative counseling consumed less opioid medication on postoperative day 1; however, there was no difference in opioid consumption between counseling and control groups on postoperative day 2 or 3.1 Syed et al.6 evaluated opioid consumption in a cohort of patients undergoing arthroscopic rotator cuff repair, comparing consumption between patients who underwent preoperative opioid counseling in the form of a short video and a control group. They found no difference in total number of opioid pills consumed in the first 2 weeks after surgery in the counseling versus control groups.6 However, at 6 weeks postoperatively, the counseling group consumed significantly fewer opioid tablets relative to the control group.6 Taken together, these data suggest that the largest effect of perioperative opioid counseling may occur by decreasing the likelihood of a given patient obtaining an opioid refill. Anthony et al.9 evaluated the efficacy of ACT delivered by text-messaging to decrease quantity of opioid consumption following orthopedic trauma surgery. Patients who received ACT consumed 37% fewer opioid tablets relative to patients in the control group at 2 weeks after surgery.9 Notably, the patient cohort in this study was relatively heterogenous in regards to surgical procedure; as such, the expected duration of pain and opioid consumption may not be directly comparable to participants in the present study.
Participants in the Video Only and Video + ACT group ceased consumption of opioid medications significantly sooner than participants in the control group, with a median time to cessation on postoperative day 8, 12, and 14, respectively. While cessation of opioid a few days earlier may seem clinically insignificant, Shah et al.12 demonstrated a two-fold increased risk of chronic opioid use in patients whose initial episode of opioid consumption lasted longer than 8 days. Syed et al.6 noted that patients who received opioid counseling prior to arthroscopic rotator cuff repair were 6.8 times more likely to have ceased opioid consumption by their 3-month follow-up visit relative to patients with no opioid counseling.6 Dindo et al.13 provided ACT to a population of veterans undergoing orthopedic surgery and noted that veterans who received preoperative ACT ceased opioid consumption a median of 9 days sooner than veterans who received no therapy. Implementation of educational and therapy-based interventions regarding opioids in patients undergoing TJA has the potential to be a low-cost yet high-volume intervention that may decrease the likelihood of patients progressing to long-term opioid consumption.12
We observed no difference in PROMIS Pain Interference Scale v1.0 scores between groups preoperatively or at 6 weeks postoperatively. In a cohort of orthopedic trauma patients, Anthony et al.9 noted lower PROMIS Pain Interference Scale v1.0 scores at 2 weeks postoperatively in patients that received ACT relative to those who did not; however, the authors noted that the observed difference in scores may not have represented a clinically important difference. There was no difference in median VAS pain scores between the three study groups during the first 14 days postoperatively. Alter et al.1 noted no differences in mean VAS pain scores between preoperative counseling and control groups in the first 3 days following mini-open carpal tunnel release. Syed et al.6 noted a statistically significant decrease in VAS pain scores in the preoperative counseling group relative to the control group at 2 weeks and 6 weeks after arthroscopic rotator cuff repair; however, this difference was lost at 3 months follow-up. The authors postulated that preoperative opioid education may improve patient’s ability to cope with postoperative pain; the mechanism for this effect was not immediately clear.6 While ACT seemed to exhibit no effect on postoperative VAS pain scores in present study, prior studies have demonstrated ACT to decrease pain scores following orthopedic surgery by increasing pain acceptance.13
The present study has several limitations. This prospective RCT was non-blinded. Opioid consumption measured in the first 14 days postoperatively was self-reported, and may not be accurate. Response rates to queries posed by the automated text-messaging robot were much lower than previously reported response rates in a similar patient population.7 We were only able to account for opioids prescribed from our institution, and were not able to account for opioids consumed as a part of a separate prescription from outside our institution. This may be especially problematic as the trial was conducted at a tertiary referral center, where a significant number of patients have been cared for previously by outside institutions.
In conclusion, perioperative opioid counseling may decrease the quantity of opioids consumed at 6 weeks after primary TJA. A combination of a preoperative educational video and postoperative ACT was most effective, but an educational video alone was effective in some analyses. Perioperative opioid counseling decreases duration of postoperative opioid consumption following primary TJA. Implementation of these telehealth-based, low-cost interventions into primary TJA care pathways should be considered.
References
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