Follow-up nonadherence is common in the orthopaedic trauma clinic and creates challenges for multiple players in the healthcare field. Orthopaedic trauma surgeons are unable to make adjustments to postoperative treatment plans, trauma outcomes research is subject to bias, and patients are at higher risk for poor outcomes and more likely to present to the emergency department for treatment of postoperative complications. These behaviors also increase resource utilization and cost of care, and subsequently, limit the quality of future healthcare delivery. Early anticipation and identification of trauma patients at risk for low clinic adherence is one method by which healthcare providers can address this issue before it materializes. The purpose of this study was to identify risk factors for follow-up nonadherence in orthopaedic trauma patients, and then to use these risk factors to build a predictive model that will assist healthcare providers in identifying at-risk patients prior to hospital discharge.
The results of our study demonstrate that there is an array of sociodemographic and clinical factors associated with follow-up nonadherence (Table 2, 3, and 4). We found that higher DCI scores, more severe injury, lack of insurance, government insurance, lower education levels, financial and relationship instability, lack of transportation and lack of PCP were significantly associated with lower clinic follow-up adherence (Tables 1, 2 and 3). There was also a significant association between low clinic adherence and ED visits within the 90-day post-operative period, indicating that this could be an area for quality improvement (p < 0.01, Table 3). Additionally, our multivariate logistic regression model using clinically applicable parameters including “Distressed” or “At Risk” DCI levels, insurance status, education level, gender, and PCP availability demonstrated that lack of private insurance status significantly increased the odds of patient follow-up nonadherence by 2-fold (OR = 2.0, CI = 1.05–3.99, p = 0.04, Table 5). “Distressed or “At Risk” DCI scores and high school or lower education levels also demonstrated similar predictive trends, though were not statistically significant (Table 5). Moreover, the prediction model yielded a maximum possible risk score of 8 (Table 5). Patients with low clinic adherence (< 0.75) had a mean score of 4.0 ± 1.6, while patients with high clinic adherence (≥ 0.75) had a mean score of 3.1 ± 1.75. The difference was statistically significant (p < 0.01), suggesting that this could be a viable predictive tool of patients at-risk for follow-up nonadherence, and thus, valuable in coordinating post-discharge care efforts.
Though the literature on risk factors for follow-up nonadherence in orthopaedic trauma is limited, some studies have reported similar results as those shown here. In 2014, Whiting et al. reported insurance status and injury complex severity as significant risk factors for nonadherence in the orthopaedic trauma clinic with the first follow-up appointment(9). Studies by Zelle et al. and ten Berg and Ring also found male gender, uninsured or governmentally insured patients, and single patients to be significantly at-risk for follow-up nonadherence at 1- and 6-month follow-up visits after injury(10, 12). Contrary to what we were able to show here, Whiting et al. and Zelle et al. both reported smoking status as a statistically significant risk factor for loss to follow-up. Specifically, Zelle et al. noted that illicit drug abuse significantly increased risk for follow-up nonadherence at any time point, not only at 6 months(9, 10). Notably, tobacco and illicit drug use have previously been correlated with lower socioeconomic status and lower levels of education – which were two factors we found significantly associated with loss to follow-up in this investigation(25, 26). In any case, all of these studies agree that risk factors for follow-up nonadherence are invaluable data points and can allow for healthcare teams to identify patients at high risk for follow-up nonadherence. Our prediction tool can help automate this identification process, and subsequently, allow for healthcare providers to design and implement strategies to improve follow-up in these populations.
Our risk prediction model scores patient’s risk for lack of follow-up on a scale from 0 to 8 based upon a set of risk factors(24). On average, scores greater than or equal to 4.0 were shown to be associated with a lower clinic adherence fraction, and thus, identify patients who would benefit from a more targeted treatment approach prior to and following hospital discharge. These approaches could include extended care efforts such as virtual visits, mobile emergency medical services, and mobile x-ray services, which already exist at our institution. Though our risk prediction model requires validation in future studies, we envision this tool as a valuable component of a social work discharge plan. The impact this simple intervention could have extends far beyond the principal benefit of improved follow-up. Patients will have better outcomes as presentation to clinic will allow the orthopaedic surgeon to appropriately adjust postoperative treatment plans and recognize and treat complications early. Additionally, orthopaedic researchers will be able to conduct clinical trials with less bias, and therefore, be able to identify avenues for continued innovation in the field. Lastly, the healthcare system will be subject to far less financial stress as increased clinic adherence will decrease emergency department utilization, decrease subsequent hospital readmissions, and decrease overall patient cost of care. We believe this will lead to improved quality of healthcare delivery and a better patient experience.
This study has several limitations. First, given our sample size, it is possible that our study may have been underpowered to detect clinically meaningful differences in some variables, such as tobacco and illicit drug use, which have been shown in other studies to correlate significantly with poor follow up(9, 10). Further, this was a single-center study and may not be generalizable to other urban or rural trauma centers. Larger, multicentered studies are necessary to validate and improve our model. Lastly, we are not able to ascertain why patients missed clinic visits, such as scheduling errors, follow-up at another institution, and even death. This subjects our study to potential information bias and could mean that our true adherence rate was higher than what is presented here.