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Association of Digital Health Software with Clinical Outcomes and Complications Following Total Knee and Hip Arthroplasty: A Retrospective Comparative Study
*Corresponding author: Fernando Diaz Dilernia, Department of Orthopedics, Queen’s University, Ontario, Canada. fernandodiazdilernia@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Campagna N, Mann S, Campbell A, Wood G, Diaz Dilernia F. Association of a Digital Health Software with Clinical Outcomes and Complications Following Total Knee and Hip Arthroplasty: A Retrospective Comparative Study. J Orthoo. 2026;78:47-52. doi: 10.25259/JOO_89_2026
Abstract
Objectives:
Digital health platforms have emerged as a novel and promising tool in healthcare services. This study evaluates the impact of a digital platform on clinical outcomes in patients undergoing total knee arthroplasty (TKA) and total hip arthroplasty (THA) at a single academic institution between April 2023 and February 2025. We hypothesized that the use of a digital health platform would be associated with shorter length of stay (LOS) without increased 30-day emergency department (ED) utilization or readmissions.
Material and Methods:
A total of 1285 patients were analyzed, with 859 patients enrolled in the Seamless MD digital health platform (SMD cohort) and 426 not enrolled (NON-SMD cohort). Primary clinical outcomes included LOS, ED visits, and readmission rates. Multivariable regression analyses were performed to adjust for age, sex, body mass index, procedure type, and surgical setting.
Results:
After multivariable adjustment for baseline demographic and procedural variables, digital platform use was independently associated with shorter LOS (β = −0.92, 95% CI: confidence interval −1.30 to −0.54, p<0.0001), despite similar unadjusted LOS between cohorts (1.26 vs. 2.22, p=0.137). The 30-day ED visit rates were significantly reduced, with 136 (15.8%) in the SMD cohort and 89 (20.9%) in the NON-SMD cohort (p=0.025). However, on multivariable logistic regression, SMD use was not significantly associated with having ≥1 ED visit within 30 days (OR: odds ratio 0.77, 95% CI 0.55–1.09, p=0.14). 30-day readmission rates were similar between groups (3.6% vs. 4.5%, p=0.458). Integration of a digital health platform shows value in orthopedic care and was independently associated with shorter LOS, while maintaining patient safety and avoiding an increase in complications.
Conclusion:
This study suggests that broad application of digital health tools, such as SMD, may enhance perioperative care; however, further multi-center and prospective studies are needed to assess generalizability and causal impact.
Level of evidence:
III, retrospective comparative study
Keywords
Complications
Digital health platform
Length of stay
Total hip arthroplasty
Total knee arthroplasty
1. INTRODUCTION
Despite the overall success of total knee arthroplasty (TKA) and total hip arthroplasty (THA) procedures, postoperative challenges continue to impact patients and the healthcare system. Perioperative care focuses on pain management, mobility recovery, and risks of complications and infection.1 These adverse events worsen patient outcomes and strain an already overburdened healthcare system by increasing length of stay (LOS), emergency department (ED) visits, and 30-day readmissions after surgery.2 Another aspect of perioperative care is the patient’s role in managing their health and recovery, as preoperative education and active participation are key components of Enhanced Recovery After Surgery protocols.3 Preoperative education for patients undergoing TKA or THA is reported to lead to less pain, improved function, and reduced LOS.4 In the past 25 years, mobile health technologies have advanced rapidly, paving the way for digital health platforms to transform healthcare delivery and patient engagement.5 In orthopedic surgery, particularly total joint arthroplasty (TJA), these platforms have gained utility in recent years due to their potential to improve patient outcomes, enhance communication between patients and healthcare providers, and optimize care.6
Patient engagement technology (PET) provides personalized, interactive care plans to support patients throughout the perioperative stages. The mobile application incorporates patient education, remote monitoring, and facilitates communication between patients and providers. It aims to help patients actively participate in their own health management. Studies using PET across various surgical domains, including lung cancer7, colorectal8, and gynecologic oncology9, have reported promising outcomes. These studies show trends toward reduced LOS, fewer ED visits, and improved patient confidence. However, there is limited research on PET in orthopedic surgery.10 Optimizing perioperative care improves clinical outcomes, patient satisfaction, and healthcare resource utilization. Given the increasing volume and demand, identifying effective strategies to enhance care in patients undergoing TKA and THA is particularly important.11 We aim to provide additional evidence on the benefits of digital health platforms in patient care within TJA.
Therefore, this study aimed to address the following questions:
(1) Is the use of a digital health platform associated with shorter hospital LOS following primary TKA or THA?
(2) How does the use of a digital health platform impact 30-day post-discharge ED visits?
(3) Is there a decrease in readmissions or revision surgeries for patients using a digital health platform?
We hypothesized that perioperative use of the digital health platform, in addition to standard care pathways, would be associated with shorter LOS without increased postoperative complications in patients undergoing TJA compared with standard care pathways alone.
2. MATERIAL AND METHODS
2.1 Patients
Institutional Review Board approval was obtained before study initiation. This retrospective cohort study involved a chart review of patients who underwent primary TKA or THA between April 2023 and February 2025. Surgeries were performed by one of four arthroplasty surgeons. Patients were divided into two cohorts: those who used the SeamlessMD (Seamless Mobile Health Inc., Toronto, Canada) platform for perioperative care (SMD) and those who received standard perioperative care without using the platform (NON-SMD). All patients were invited to participate during the preoperative visit and were provided with a registration link. Informed consent was obtained electronically during registration. Patients in the SMD group accessed the platform via a smartphone, tablet, or web. Patients in the NON-SMD group either declined to participate or were unable to access the platform due to limited internet access. A total of 1285 patients were included in the study, with 859 in the SMD group and 426 in the NON-SMD group. Data were collected from inpatient and outpatient records; duplicates were removed.
2.2 Methods
All patients received standard perioperative care in accordance with institutional arthroplasty pathways, including routine preoperative assessment, perioperative education, discharge instructions, and standard postoperative follow-up. Patients in the SMD cohort received the same standard perioperative care, with the digital platform used as an adjunct. The digital care plans were tailored to the procedure type and surgical pathway.
The digital platform app is a customizable, interactive platform designed to educate, guide, and support patients through their perioperative care. The software replicates standardized preoperative and postoperative protocols tailored to the surgeons’ care pathways, delivering personalized care plans that include reminders, educational content, daily progress tracking, and scheduled health and satisfaction surveys. The app systematically presents educational content and information at predefined intervals, beginning at activation and continuing until 30 days postoperatively. Patient responses in the app can also trigger automated alerts on the platform (e.g., wound-check concerns, swelling, pain). These alerts are reviewed by the clinical care team, who follow up using standard clinical workflows, such as telephone contact or escalation to the surgical team when indicated.
2.3 Methods of assessment
Baseline characteristics (sex, age, body mass index (BMI), procedure type, and surgical setting) were compared between cohorts. Primary clinical outcomes were LOS, 30-day ED visits, and 30-day readmission rates. LOS analyses were stratified by procedure type and surgical setting. Multivariable regression analyses were performed as described below. The primary ED outcome was analyzed at the event level and included all ED visits occurring within 30 days post-discharge. Secondary outcome analyses included patient-level ED utilization, defined as the proportion of unique patients with at least one ED visit within 30 days. Additional secondary outcome analyses included the primary diagnosis responsible for readmission and revision operation rates due to infection upon readmission.
2.4 Statistical analysis
Categorical variables were analyzed using Chi-square tests to assess statistically significant differences in proportions. Continuous variables, including LOS, were compared using Mann-Whitney U-tests, as normality was not assumed. Multivariable linear regression was used to evaluate the association between SMD use and LOS, adjusting for age, sex, BMI, procedure type, and surgical setting. Because LOS data were right-skewed, a log-transformed LOS regression model was also performed as a sensitivity analysis. A multivariable logistic regression was used to evaluate the association between SMD use and the likelihood of at least one ED visit within 30 days, adjusting for the same covariates. Variables included in the multivariable models were selected a priori based on known clinical relevance. Regression coefficients are reported with 95% confidence intervals. Multicollinearity was assessed using variance inflation factors (VIF). There was no missing data. Statistical significance was set at p<0.05, and analyses were performed using GraphPad Prism 10.
3. RESULTS
Patients enrolled in the digital platform were slightly younger than those not enrolled, with a median age of 68 years in the SMD cohort and 70 years in the NON-SMD cohort (p=0.0021). Patient characteristics are shown in Table 1.
| Variable | Total (n=1285) | SMD (n=859) | NONSMD (n=426) | p- value |
|---|---|---|---|---|
| Age, years, median [IQR] | 68 [62, 75] | 68 [62, 74] | 70 [63, 76] | 0.0021* |
| BMI, median [IQR] | 31.5 [27.8, 36.6] | 31.5 [27.9, 36.4] | 31.4 [27.7, 37.0] | 0.768 |
| Sex, n (%) | 0.247 | |||
| Female | 693 (53.9) | 473 (55.1) | 220 (51.6) | |
| Male | 592 (46.1) | 386 (44.9) | 206 (48.4) | |
| Procedure, n (%) | 0.436 | |||
| Unilateral TKA | 770 (59.9) | 509 (59.3) | 261 (61.3) | |
| Unilateral THA | 464 (36.1) | 311 (36.2) | 153 (35.9) | |
| Bilateral TKA | 34 (2.7) | 27 (3.1) | 7 (1.6) | |
| Bilateral THA | 17 (1.3) | 12 (1.4) | 5 (1.2) | |
| Setting, n (%) | 0.201 | |||
| Inpatient | 1182 (92.0) | 796 (92.7) | 386 (90.6) | |
| Day surgery | 103 (8.0) | 63 (7.3) | 40 (9.4) |
BMI: Body mass index, TKA: Total knee arthroplasty, THA: Total hip arthroplasty, IQR: Interquartile range, SMD: SeamlessMD, Statistical significance of p-value is less than 0.05
3.1 Length of stay
The unadjusted overall LOS was not significantly different between the SMD and NON-SMD groups, with mean LOS values of 1.26 days (±2.02) and 2.22 days (±4.94), respectively (p=0.137) [Table 2]. LOS was stratified by procedure type (THA or TKA) and surgical setting (inpatient or day surgery). To account for anticipated same-day discharge pathways and avoid incorporating day-surgery and inpatient admissions within a single LOS analysis, LOS was analyzed separately for day-surgery and inpatient cases. LOS tended to be lower across several SMD subgroups. Statistically significant reductions in LOS in the SMD group were observed only for inpatient procedures overall (p=0.013). Patients undergoing THA had longer LOS than those undergoing TKA in the SMD and NONSMD cohorts. In contrast, LOS did not differ significantly in the day surgery setting (p=0.139). Multivariable regression analyses were used to evaluate the association between digital platform use and LOS while accounting for potential confounding by age, sex, BMI, procedure type, and surgical setting. After adjustment, SMD utilization was associated with a significant reduction in LOS (p<0.0001) [Table 2]. SMD use was independently associated with a 0.92-day shorter LOS (95% CI: confidence interval −1.30 to −0.54, p<0.0001). Increasing age and THA procedure type were associated with longer LOS, while a day-surgery setting was associated with shorter LOS. In the log-transformed LOS sensitivity analysis, SMD use remained significantly associated with shorter LOS (OR: odds ratio 0.83, 95% CI 0.75–0.92, p=0.0004).
| LOS (days, mean ± SD) | |||
| Variable | SMD | NON-SMD | p-value |
| Overall | 1.26 ± 2.02 | 2.22 ± 4.94 | 0.137 |
| THA | 1.50 ± 2.63 | 3.10 ± 6.91 | 0.142 |
| TKA | 1.12 ± 1.52 | 1.70 ± 3.17 | 0.295 |
| Inpatient | 1.33 ± 2.08 | 2.41 ± 5.15 | 0.013* |
| Day surgery | 0.41 ± 0.23 | 0.35 ± 0.15 | 0.139 |
| Multivariable linear regression (overall LOS, days) | |||
| Variable | β | 95% CI | p-value |
| SMD (vs. NON-SMD) | −0.92 | −1.30 to −0.54 | <0.0001* |
| Age (per year) | 0.037 | 0.019 to 0.056 | 0.0001* |
| Male (vs. female) | −0.25 | −0.61 to 0.11 | 0.17 |
| BMI (per unit) | 0.011 | −0.010 to 0.031 | 0.32 |
| THA (vs. TKA) | 0.68 | 0.32 to 1.05 | 0.0003* |
| Day surgery (vs. inpatient) | −1.12 | −1.77 to −0.46 | 0.0009* |
SD: Standard deviation, β: Regression coefficient, CI: Confidence interval, BMI: Body mass index, TKA: Total knee arthroplasty, THA: Total hip arthroplasty, IQR: Interquartile range, LOS: Increasing length of stay, SMD: SeamlessMD, Statistical significance of p-value is less than 0.05
3.2 Ed visits
The 30-day post-discharge ED visit rate was significantly lower in the SMD cohort, with 15.8% (n=136), than the NONSMD cohort, with 20.9% (n=89) (p=0.025) [Table 3]. After adjustment for age, sex, BMI, procedure type, and surgical setting, SMD use was not significantly associated with the likelihood of at least one ED visit within 30 days (OR 0.77, 95% CI 0.55–1.09, p=0.14) [Table 3]. When ED utilization was analyzed at the patient level, the proportion of patients with ≥1 ED visit within 30 days was not significantly different between groups [Table 3]. In total, 100 unique patients accounted for 136 total 30-day ED visits in the SMD cohort (11.6%), while 64 unique patients accounted for 89 total 30-day ED visits in the NON-SMD cohort (15.0%) (p=0.087).
| 30-day emergency department visits | |||
| Variable | SMD (n=859) | NON-SMD (n=426) | p-value |
| ED visits (event level), n (%) | 136 (15.8) | 89 (20.9) | 0.025* |
| Patients with ≥1 ED visit, n (%) | 100 (11.6) | 64 (15.0) | 0.087 |
| Multivariable logistic regression (≥1 ED visit within 30 days) | |||
| Variable | OR | 95% CI | p-value |
| SMD (vs. NON-SMD) | 0.77 | 0.55–1.09 | 0.14 |
| Age (per year) | 1.02 | 1.00–1.04 | 0.0019* |
| Male (vs. female) | 0.97 | 0.69–1.35 | 0.85 |
| BMI (per unit) | 1.01 | 0.98–1.03 | 0.49 |
| THA (vs. TKA) | 0.78 | 0.55–1.10 | 0.16 |
| Day surgery (vs. inpatient) | 0.96 | 0.51–1.80 | 0.89 |
The event-level rate counts all ED visits (including repeat visits) and was compared unadjusted; the logistic regression models the patient-level binary outcome (≥1 ED visit per patient). OR: Odds ratio, SD: Standard deviation, β: Regression coefficient, CI: Confidence interval, BMI: Body mass index, TKA: Total knee arthroplasty, THA: Total hip arthroplasty, IQR: Interquartile range, SMD: SeamlessMD, ED: Emergency department, SMD: SeamlessMD, Statistical significance of p-value is less than 0.05
3.3 Readmissions
There was no significant difference in 30-day readmission rates (3.6 vs. 4.5%, p=0.458) [Table 4]. Among readmitted patients, the most common diagnosis responsible for readmission in both groups was surgical site infection (SSI), occurring in 29.0% (n=9) of patients enrolled in the SMD group and 31.6% (n=6) of patients not enrolled (p=0.849) [Table 4]. On readmission, revision surgeries due to SSI occurred in 16.1% (n=5) patients in the SMD group compared to 26.3% (n=5) in the NON-SMD group (p=0.382).
| Variable | SMD | NON-SMD | p-value |
|---|---|---|---|
| 30-day readmission, n (%) | 31/859 (3.6) | 19/426 (4.5) | 0.458 |
| SSI as readmission diagnosis, n (%) | 9/31 (29.0) | 6/19 (31.6) | 0.849 |
| Revision for SSI, n (%) | 5/31 (16.1) | 5/19 (26.3) | 0.382 |
SSI: surgical site infection, SMD: SeamlessMD, Statistical significance of p-value is less than 0.05
4. DISCUSSION
This study evaluated the impact of a digital platform on clinical outcomes, specifically LOS, 30-day ED visits, and readmission rates in patients undergoing TKA or THA. Our results demonstrate that the perioperative integration of a digital platform was independently associated with shorter LOS after multivariable adjustment. ED visit rates were lower in the SMD cohort, although SMD was not independently associated with patient-level ED visit rates. Readmission rates were similar between cohorts. These findings highlight the platform's potential to optimize perioperative care and enhance resource allocation. The most meaningful unadjusted difference in LOS was observed among inpatient cases, where use of the digital platform was associated with a statistically significant reduction in hospital stay. This effect occurred without an increase in postoperative complications. These findings suggest that the platform may be more useful in inpatient pathways, helping to shorten hospital stays while maintaining the quality and safety of perioperative care.
Multiple factors may influence the timing of patient discharge following TJA, including patient demographics, comorbidities, surgical approach, and surgical scheduling. One potential factor contributing to the observed reduction in LOS is the preoperative education delivered via the digital platform. Structured preoperative education is associated with a reduced LOS for patients undergoing arthroplasty procedures.12 Beyond education, app-based symptom monitoring and automated guidance, paired with care-team follow-up when alerts are triggered, may help reduce uncertainty and concerns post-discharge by providing patients with access to guidance and support when needed. While the reduction in LOS is a notable outcome, its clinical significance is likely tied to improved patient confidence at discharge, rather than a direct influence of the digital platform on the physiological aspects of recovery.
The lower median age of the SMD cohort may have contributed to the observed reduction in LOS, as younger patients might recover more quickly and be discharged earlier.13 Furthermore, patients scheduled later in the day or week may face logistical barriers to discharge, such as timing constraints for assessments and clearance.14 The observed differences in LOS between TKA and THA also warrant consideration. TKA is typically associated with greater immediate postoperative pain and a longer LOS than THA.15 However, procedural scheduling plays a role in determining the feasibility of discharge. In our institution, TKAs tend to be scheduled earlier in the day to allow more time for postoperative pain management, mobilization, and discharge planning, whereas THAs are often booked for later. Thus, it reduces the likelihood of same-day discharge due to limited recovery time and the need for postoperative assessments. These scheduling differences may contribute to variations in LOS between procedures independently of patient factors. Beyond procedural differences, hospital-specific discharge pathways may influence LOS outcomes. At one of our hospitals, same-day discharge is the standard protocol. Patients undergoing surgery are generally healthier, typically undergo primary procedures, and the cases are usually less complex. In contrast, the General Hospital manages more complex arthroplasty cases, making same-day discharge less common. The established institutional care pathways at the same-day discharge center already optimize for early discharge, meaning digital health interventions may have less room to influence LOS in this setting.
The results of this study are similar to those reported by Chahal et al.10, who observed that the app usage was associated with reductions in both LOS and ED visits, but showed no significant differences in readmission. However, while Chahal et al. observed a significant decrease in overall LOS, our findings showed a reduction in LOS that did not reach statistical significance across the entire cohort.10 These differences may reflect variation in care pathways, surgical settings, or adherence to implementation. Literature evaluating the implementation of other PETs in arthroplasty demonstrates variable effects on LOS, ED visits, and readmissions, with some studies showing reductions and others showing no change.16,17 Success may depend on the education of nurses and other healthcare providers who play a role in patient care, to help reinforce recovery protocols, support discharge, address concerns early, and guide patients toward alternative resources before seeking emergency care.
5. LIMITATIONS
This study has limitations inherent to its retrospective design. Cohort classification was based on platform enrollment status before outcome analysis; however, selection and confounding biases remain a concern. The NON-SMD group self-selected, as patients either declined to use the platform or lacked internet access, potentially introducing selection or volunteer bias. This may explain the age difference between the groups, as younger patients might be more likely to engage with the technology. Although statistical control methods were used to account for differences between the groups, residual confounding cannot be entirely ruled out. Patient-reported satisfaction and PROM data were collected only within the digital platform and were unavailable for the NON-SMD cohort, with low response rates. These measures were therefore not formally analyzed and warrant future prospective study. The present study evaluated digital platform enrollment rather than actual patient engagement with the application; therefore, the relationship between the degree of platform use and clinical outcomes could not be assessed. The single-center nature of the study may restrict the generalizability of the results. A multi-institutional study may provide insight into the broader effects of digital platforms in different healthcare settings. A randomized controlled trial would strengthen the study design by reducing bias and improving causal inference. However, achieving appropriate double-blinding in this context may be challenging because app use requires patient cooperation.
6. CONCLUSION
This study demonstrates that the use of a digital health platform was independently associated with shorter hospital LOS after adjustment for baseline and procedural variables, without increasing early postoperative complications following primary total knee and hip arthroplasty. Although preliminary, these findings support the use of digital health platforms as an adjunct to established perioperative care pathways while maintaining patient safety. Broader implementation and further prospective multicenter studies are warranted to assess generalizability and better define the causal effect, as the use of digital health platforms could improve the delivery of care in orthopedic surgery.
Authors’ contributions:
NC: Formal analysis, investigation, data curation, writing, original draft preparation, review, and editing. SM, AC and GW: Methodology, resources, and project administration. FDD: Conceptualization, methodology, formal analysis, investigation, resources, data curation, writing, original draft preparation, review and editing, supervision, project administration. All authors have read and agreed to the final version of the manuscript.
Data availability statement:
The data presented in this study are available from the corresponding author upon reasonable request and subject to institutional ethics approval.
Ethical approval:
The research/study was approved by the Institutional Review Board at Queen’s University Health Sciences and the affiliated teaching hospitals' research ethics board, number 6046200, dated March 17, 2026.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understand that the patient's names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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