Despite differences in the structure of health care delivery systems, health care spending continues to outpace gross domestic product (GDP) and average wage globally. This highlights the stark... Show moreDespite differences in the structure of health care delivery systems, health care spending continues to outpace gross domestic product (GDP) and average wage globally. This highlights the stark reality that health systems today are – in many cases – financially unsustainable. Further, most health care services today are paid for via a fee-for-service or payment for each service provided mechanism, which does not ensure a focus on optimal health outcomes for patients; this includes clinical outcomes most important to them, such as function, pain, andquality of life. As such, bold reforms are needed to better align incentives to “bend” the cost curve and to ensure high-quality health care and the best possible patient outcomes. This dissertation includes scientific studies that highlight how the core principles of value-based health care, which focuses on maximizing the outcomes achieved per dollar spent, may be able to begin to address some of the issues plaguing our strained health care delivery systems globally, including within orthopaedic surgery. Show less
Background: It is well documented that routinely collected patient sociodemographic characteristics (such as race and insurance type) and geography-based social determinants of health (SDoH)... Show moreBackground: It is well documented that routinely collected patient sociodemographic characteristics (such as race and insurance type) and geography-based social determinants of health (SDoH) measures (for example, the Area Deprivation Index) are associated with health disparities, including symptom severity at presentation. However, the association of patient-level SDoH factors (such as housing status) on musculoskeletal health disparities is not as well documented. Such insight might help with the development of more-targeted interventions to help address health disparities in orthopaedic surgery. Questions/purposes: (1) What percentage of patients presenting for new patient visits in an orthopaedic surgery clinic who were unemployed but seeking work reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, reported trouble paying for medications, and/or had no current housing? (2) Accounting for traditional sociodemographic factors and patient-level SDoH measures, what factors are associated with poorer patient-reported outcome physical health scores at presentation? (3) Accounting for traditional sociodemographic factor patient-level SDoH measures, what factors are associated with poorer patient-reported outcome mental health scores at presentation? Methods: New patient encounters at one Level 1 trauma center clinic visit from March 2018 to December 2020 were identified. Included patients had to meet two criteria: they had completed the Patient-Reported Outcome Measure Information System (PROMIS) Global-10 at their new orthopaedic surgery clinic encounter as part of routine clinical care, and they had visited their primary care physician and completed a series of specific SDoH questions. The SDoH questionnaire was developed in our institution to improve data that drive interventions to address health disparities as part of our accountable care organization work. Over the study period, the SDoH questionnaire was only distributed at primary care provider visits. The SDoH questions focused on transportation, housing, employment, and ability to pay for medications. Because we do not have a way to determine how many patients had both primary care provider office visits and new orthopaedic surgery clinic visits over the study period, we were unable to determine how many patients could have been included; however, 9057 patients were evaluated in this cross-sectional study. The mean age was 61 +/- 15 years, and most patients self-reported being of White race (83% [7561 of 9057]). Approximately half the patient sample had commercial insurance (46% [4167 of 9057]). To get a better sense of how this study cohort compared with the overall patient population seen at the participating center during the time in question, we reviewed all new patient clinic encounters (n = 135,223). The demographic information between the full patient sample and our study subgroup appeared similar. Using our study cohort, two multivariable linear regression models were created to determine which traditional metrics (for example, self-reported race or insurance type) and patient-specific SDoH factors (for example, lack of reliable transportation) were associated with worse physical and mental health symptoms (that is, lower PROMIS scores) at new patient encounters. The variance inflation factor was used to assess for multicollinearity. For all analyses, p values < 0.05 designated statistical significance. The concept of minimum clinically important difference (MCID) was used to assess clinical importance.Regression coefficients represent the projected change in PROMIS physical or mental health symptom scores (that is, the dependent variable in our regression analyses) accounting for the other included variables. Thus, a regression coefficient for a given variable at or above a known MCID value suggests a clinical difference between those patients with and without the presence of that given characteristic. In this manuscript, regression coefficients at or above 4.2 (or at and below -4.2) for PROMIS Global Physical Health and at or above 5.1 (or at and below -5.1) for PROMIS Global Mental Health were considered clinically relevant. Results: Among the included patients, 8% (685 of 9057) were unemployed but seeking work, 4% (399 of 9057) reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, 4% (328 of 9057) reported trouble paying for medications, and 2% (181 of 9057) had no current housing. Lack of reliable transportation to attend doctor visits or pick up medications (beta = -4.52 [95% CI -5.45 to -3.59]; p < 0.001), trouble paying for medications (beta = -4.55 [95% CI -5.55 to -3.54]; p < 0.001), Medicaid insurance (beta = -5.81 [95% CI -6.41 to -5.20]; p < 0.001), and workers compensation insurance (beta = -5.99 [95% CI -7.65 to -4.34]; p < 0.001) were associated with clinically worse function at presentation. Trouble paying for medications (beta = -6.01 [95% CI -7.10 to -4.92]; p < 0.001), Medicaid insurance (beta = -5.35 [95% CI -6.00 to -4.69]; p < 0.001), and workers compensation (beta = -6.07 [95% CI -7.86 to -4.28]; p < 0.001) were associated with clinically worse mental health at presentation. Conclusion: Although transportation issues and financial hardship were found to be associated with worse presenting physical function and mental health, Medicaid and workers compensation insurance remained associated with worse presenting physical function and mental health as well even after controlling for these more detailed, patient-level SDoH factors. Because of that, interventions to decrease health disparities should focus on not only sociodemographic variables (for example, insurance type) but also tangible patient-specific SDoH characteristics. For example, this may include giving patients taxi vouchers or ride-sharing credits to attend clinic visits for patients demonstrating such a need, initiating financial assistance programs for necessary medications, and/or identifying and connecting certain patient groups with social support services early on in the care cycle. Show less
Background: Patient-reported outcome measures (PROMs) can help predict clinical outcomes and improve shared clinical decision-making discussions. There remains a paucity of research assessing how... Show moreBackground: Patient-reported outcome measures (PROMs) can help predict clinical outcomes and improve shared clinical decision-making discussions. There remains a paucity of research assessing how the use of PROMs may drive improved patient experience and patient activation. Methods: New foot and ankle patients completed PROMIS physical function (PF), pain interference (PI), and depression assessments. Patients were then randomized to viewing and discussing their PROMIS scores with their surgeon or not. Following the clinic visit, patients completed a series of Clinician & Group Survey-Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) questions and the Patient Activation Measure (PAM). Responses to the CG-CAHPS questions and PAM were compared between the 2 groups and after clustering on surgeon. Potential interaction effects by social deprivation were also explored. Results: After enrolling patients but removing those lost to follow-up or with missing data, 97 and 116 patients remained in the intervention control cohorts, respectively. No difference was found in CG-CAHPS responses nor PAM scores between the 2 groups (P > .05). All surgeons were highly rated by all patients. When clustered by surgeon, intervention subjects were less likely to indicate "top box" scores for the understanding domain of the CG-CAHPS question (OR 0.51, P < .001) and had decreased odds of high patient activation compared to control subjects (OR 0.67; P = .005). Among the most socially disadvantaged patients, there was no difference in control and intervention subjects in their likelihood of having high patient activation (P = .09). Conclusion: Highly rated foot and ankle surgeons who show and discuss PROM results may not improve patient experience or activation and may, in fact, decrease understanding or patient activation in select populations. Future work is needed to determine when PROM discussions are most beneficial and how best to present PROMs data, as we suspect that how the information was presented-and not the use of PROMs-resulted in our findings. Health literacy tools and/or communication training may better engage different patient groups regarding PROMs. Show less
Introduction: The COVID-19 pandemic severely impacted musculoskeletal care. To better triage the notable backlog of patients, we assessed whether a digital medical history (DMH), a summary of... Show moreIntroduction: The COVID-19 pandemic severely impacted musculoskeletal care. To better triage the notable backlog of patients, we assessed whether a digital medical history (DMH), a summary of health information and concerns completed by the patient prior to a clinic visit, could be routinely collected and utilised.Methods: We analysed 640 patients using a rapid cycle, semi-randomised A/B testing approach. Four rapid cycles of different randomised interventions were conducted across five unique patient groups. Descriptive statistics were used to report DMH completion rates by cycle/patient group and intervention. Multivariable logistic regression was used to determine whether age or anatomic injury location was associated DMH completion.Ethical Approval: N/A (Quality Improvement Project)Results: Across all patients, the DMH completion rate was 48% (307/640). Phone calls were time consuming and resource intensive without an increased completion rate. The highest rate of DMH completion was among patients who were referred and called the clinic themselves (78% of patients [63 out of 81 patients]). Across all patients, increasing age (odds ratio [OR]: 0.985 (95% CI: 0.976-0.995), p = 0.002), patients with back concerns (OR: 0.395 (95% CI: 0.234-0.666), p = 0.001), and patients with non-specific/other musculoskeletal concerns (OR: 0.331 (95% CI: 0.176-0.623), p = 0.001) were associated with decreased odds of DMH completion.Discussion and Conclusion: DMHs can be valuable in helping triage orthopaedic patients in resource-strapped settings, times of crisis, or as we transition towards value-based health care delivery. However, further work is needed to continue to increase the completion rate about 50%. Show less
Background The goal of bundled payments-lump monetary sums designed to cover the full set of services needed to provide care for a condition or medical event-is to provide a reimbursement structure... Show moreBackground The goal of bundled payments-lump monetary sums designed to cover the full set of services needed to provide care for a condition or medical event-is to provide a reimbursement structure that incentivizes improved value for patients. There is concern that such a payment mechanism may lead to patient screening and denying or providing orthopaedic care to patients based on the number and severity of comorbid conditions present associated with complications after surgery. Currently, however, there is no clear consensus about whether such an association exists. Questions/purposes In this systematic review, we asked: (1) Is the implementation of a bundled payment model associated with a change in the sociodemographic characteristics of patients undergoing an orthopaedic procedure? (2) Is the implementation of a bundled payment model associated with a change in the comorbidities and/or case-complexity characteristics of patients undergoing an orthopaedic procedure? (3) Is the implementation of a bundled payment model associated with a change in the recent use of healthcare resources characteristics of patients undergoing an orthopaedic procedure? Methods This systematic review was registered in PROSPERO before data collection (CRD42020189416). Our systematic review included scientific manuscripts published in MEDLINE, Embase, Web of Science, Econlit, Policyfile, and Google Scholar through March 2020. Of the 30 studies undergoing full-text review, 20 were excluded because they did not evaluate the outcome of interest (patient selection) (n = 8); were editorial, commentary, or review articles (n = 5); did not evaluate the appropriate intervention (introduction of a bundled payment program) (n = 4); or assessed the wrong patient population (not orthopaedic surgery patients) (n = 3). This led to 10 studies included in this systematic review. For each study, patient factors analyzed in the included studies were grouped into the following three categories: sociodemographics, comorbidities and/or case complexity, or recent use of healthcare resources characteristics. Next, each patient factor falling into one of these three categories was examined to evaluate for changes from before to after implementation of a bundled payment initiative. In most cases, studies utilized a difference-in-difference (DID) statistical technique to assess for changes. Determination of whether the bundled payment initiative required mandatory participation or not was also noted. Scientific quality using the Adapted Newcastle-Ottawa Scale had a median (range) score of 8 (7 to 8; highest possible score: 9), and the quality of the total body of evidence for each patient characteristic group was found to be low using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) tool. We could not assess the likelihood of publication using funnel plots because of the variation of patient factors analyzed in each study and the heterogeneity of data precluded a meta-analysis. Results Of the nine included studies that reported on the sociodemographic characteristics of patients selected for care, seven showed no change with the implementation of bundled payments, and two demonstrated a difference. Most notably, the studies identified a decrease in the percentage of patients undergoing an orthopaedic operative intervention who were dual-eligible (range DID estimate -0.4% [95% CI -0.75% to -0.1%]; p < 0.05 to DID estimate -1.0% [95% CI -1.7% to -0.2%]; p = 0.01), which means they qualified for both Medicare and Medicaid insurance coverage.Of the 10 included studies that reported on comorbidities and case-complexity characteristics, six reported no change in such characteristics with the implementation of bundled payments, and four studies noted differences. Most notably, one study showed a decrease in the number of treated patients with disabilities (DID estimate -0.6% [95% CI -0.97% to -0.18%]; p < 0.05) compared with before bundled payment implementation, while another demonstrated a lower number of Elixhauser comorbidities for those treated as part of a bundled payment program (before: score of 0-1 in 63.6%, 2-3 in 27.9%, > 3 in 8.5% versus after: score of 0-1 in 50.1%, 2-3 in 38.7%, > 3 in 11.2%; p = 0.033). Of the three included studies that reported on the recent use of healthcare resources of patients, one study found no difference in the use of healthcare resources with the implementation of bundled payments, and two studies did find differences. Both studies found a decrease in patients undergoing operative management who recently received care at a skilled nursing facility (range DID estimate -0.50% [95% CI -1.0% to 0.0%]; p = 0.04 to DID estimate: -0.53% [95% CI -0.96% to -0.10%]; p = 0.01), while one of the studies also found a decrease in patients undergoing operative management who recently received care at an acute care hospital (DID estimate -0.8% [95% CI -1.6% to -0.1%]; p = 0.03) or as part of home healthcare (DID estimate -1.3% [95% CI -2.0% to -0.6%]; p < 0.001). Conclusion In six of 10 studies in which differences in patient characteristics were detected among those undergoing operative orthopaedic intervention once a bundled payment program was initiated, the effect was found to be minimal (approximately 1% or less). However, our findings still suggest some level of adverse patient selection, potentially worsening health inequities when considered on a large scale. It is also possible that our findings reflect better care, whereby the financial incentives lead to fewer patients with a high risk of complications undergoing surgical intervention and vice versa for patients with a low risk of complications postoperatively. However, this is a fine line, and it may also be that patients with a high risk of complications postoperatively are not being offered surgery enough, while patients at low risk of complications postoperatively are being offered surgery too frequently. Evaluation of the longer-term effect of these preliminary bundled payment programs on patient selection is warranted to determine whether adverse patient selection changes over time as health systems and orthopaedic surgeons become accustomed to such reimbursement models. Show less
Study Design. Retrospective, observational study. Objective. To determine the association of patient socioeconomic disadvantage, insurance type, and other characteristics on presenting symptom... Show moreStudy Design. Retrospective, observational study. Objective. To determine the association of patient socioeconomic disadvantage, insurance type, and other characteristics on presenting symptom severity in patients with isolated lumbar disc herniation. Summary of Background Data. Little is known of the impact of socioeconomic disadvantage and other patient characteristics on the level of self-reported symptom severity when patients first seek care for lumbar disc herniation. Methods. Between April 2015 and December 2018, 734 patients newly presenting for isolated lumbar disc herniation who completed the Patient-Reported Outcomes Measurement Information System Physical Function (PF), Pain Interference (PI), and Depression Computer Adaptive Tests (CATs) were identified. Socioeconomic disadvantage was determined using the Area Deprivation Index, a validated measure of socioeconomic disadvantage at the census block group level (0-100, 100 = highest socioeconomic disadvantage). Bivariate analyses were used. Multivariable linear regression was used to determine if there was an association between socioeconomic disadvantage, insurance type, and other patient factors and presenting patient-reported health status. Results. Significant differences in age, insurance type, self-reported race, marital status, and county of residence were appreciated when comparing patient characteristics by socioeconomic disadvantage levels (all comparisons, P < 0.01). In addition, significant differences in age, insurance type, marital status, and county of residence were appreciated when comparing patient characteristics by self-reported race (all comparisons, P < 0.01). Being in the most socioeconomically disadvantaged cohort was associated with worse presenting Patient-Reported Outcomes Measurement Information System scores (Physical Function: beta = -3.27 (95% confidence interval [CI]: -4.89 to -1.45), P < 0.001; Pain Interference: beta = 3.20 (95% CI: 1.58-4.83), P < 0.001; Depression: beta = 3.31 (95% CI: 1.08-5.55), P = 0.004. Conclusion. The most socioeconomically disadvantaged patients with symptomatic lumbar disc herniations present with worse functional limitations, pain levels, and depressive symptoms as compared to patients from the least socioeconomically disadvantaged cohort when accounting for other key patient factors. Show less
