It Is a Brave New World: Alternative Payment Models and Value Creation in Total Joint Arthroplasty: Creating Value for TJR, Quality and Cost-Effectiveness Programs

Analysis of Centralized Efficiency Improvement Practices in Australian Public Health Systems

Aim

Analysis of centralized efficiency improvement practices in Australian public health systems.

Introduction

Public health systems seek to maximize outcomes generated for resources used through efficiency improvement (EI) in response to funding and demand pressures. Despite this focus, evidence for EI approaches at the whole-of-system level is lacking in the literature. There is an urgent need for evidence-based approaches to centralized EI to address these pressures. This study aims to address this gap by answering the research question "How is EI conceptualized and managed by central public health system management entities in Australia?".

Material and Methods

Document analysis was selected due to its suitability for systematically searching and appraising health system documentation, with this study following Altheide's approach focusing on whole-of-system strategic plan and management framework documents originating from Australian public health organizations.

Results

Conceptualization of efficiency varied substantially with no consistent definition identified, however common attributes included resource use, management, service and delivery. Forty-two of 43 documents contained approaches associated with improving efficiency at the whole of system level.

Discussion

While no comprehensive framework for centralized EI was evident, we identified nine core approaches which together characterize centralized EI. Together these approaches represent a comprehensive evidence-based approach to EI at the whole of system level.

Conclusion

The approaches to whole-of-system EI identified in this study are likely to be highly transferable across health systems internationally with approaches including strategic priority setting, incentivization, performance support, use of EI evidence, digital enablement and workforce capability development.

From volume to value: Improving peri-operative elective pathways through a roadmap from fast-track orthopedic surgery

Healthcare institutions face the pressure generated by modern medicine and society, in terms of increasing expectations and financial constraints. Chronic patients need multidisciplinary care pathways to preserve their wellbeing across the entire journey.The orthopaedic community has been particularly receptive in testing solutions to align good clinical outcomes and financial sustainability, given the increase in elective procedures provided among aging populations to alleviate pain and reduce disability. Fast-track (FT) total joint arthroplasty (TJA) and bundled payments (BPs) offer relevant examples both from the clinical and the financial perspective; however, they have not been evaluated in combination yet.The aim of this manuscript is to provide a road map to improve the value of high-volume, multidisciplinary elective procedures, with potential applications in a vast number of surgical specialties, (1) based on an integrated financial budget per episode of care (the BP), (2) building on lessons from a review of the literature on FT TJA.Although clinical outcomes vary from procedure to procedure, the coordination between the single treatments and providers involved across the patient journey; the commitment of patients and relatives; and the systematic adoption of patient-reported outcomes; can add further value for the benefit of patients, healthcare funders and providers, once essential clinical, financial and administrative conditions are guaranteed.

The Examination of the Benefits of the Usage of Barbed, Knotless Suture in Capsule Repair During Total Knee Arthroplasty: A Prospective, Double-Blind, Randomized Controlled Study

Introduction

In today's world, high-cost procedures are being examined, and alternative procedures are being developed. In this context, one frequently examined procedure is total knee replacement.

Purpose

This study aims to examine the three different closure techniques used in total knee replacement.

Methods

This study is a prospective randomized controlled study. Two hundred participants who underwent total knee replacement surgery, were included in the study. Participants were randomly divided into three groups. Arthrotomy was performed using a medial parapatellar approach with a midline incision. Standard femoral and tibial cuts were followed by the implantation of a Smith and Nephew genesis II implant for all participants. Complications, joint range of motion, pain scores, certain movement degrees, and functional scores were investigated.

Results

Pre-op and post-op range of motion, knee society score, oxford knee score, certain movement degree values have shown no significant difference. Visual analogue scale values were different significantly between the groups. There is a statistical difference between the range of motion, knee society score, oxford knee score, certain movement degree and visual analogue scale values in repeated measurements. The most common complication was a hematoma. This was observed most frequently in the continuous vicryl suture group. The closure time in the Barbed group was significantly lower than in the other groups.

Discussion

Treatment for total knee replacement is a heavy economic burden. Health systems and hospitals are under pressure. The results obtained in our study show that there is no superiority of one closure technique over the other.

Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty

Background

Machine learning (ML) methods have shown promise in the development of patient-specific predictive models prior to surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters following primary total knee arthroplasty (TKA).

Methods

Data from the Nationwide Inpatient Sample was used to identify patients undergoing TKA during 2016-2017. Four distinct ML models predictive of mortality, length of stay (LOS), and discharge disposition were developed and validated using 15 predictive patient and hospital-specific factors. Area under the curve of the receiver operating characteristic curve (AUCROC) and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed.

Results

A total of 305,577 patients were included. For mortality, the XGBoost, neural network (NN), and LSVM models all had excellent responsiveness during validation, while random forest (RF) had fair responsiveness. For predicting LOS, all four models had poor responsiveness. For the discharge disposition outcome, the LSVM, NN, and XGBoost models had good responsiveness, while the RF model had poor responsiveness. LSVM and XGBoost had the highest responsiveness for predicting discharge disposition with an AUCROC of 0.747.

Discussion

The ML models tested demonstrated a range of poor to excellent responsiveness and accuracy in the prediction of the assessed metrics, with considerable variability noted in the predictive precision between the models. The continued development of ML models should be encouraged, with eventual integration into clinical practice in order to inform patient discussions, management decision making, and health policy.

Supporting efficiency improvement in public health systems: a rapid evidence synthesis

BACKGROUND

Public health systems internationally are under pressure to meet increasing demand for healthcare in the context of increasing financial resource constraint. There is therefore a need to maximise health outcomes achieved with public healthcare expenditure. This paper aims to establish and synthesize the contemporary evidence base for approaches taken at a system management level to improve efficiency.

METHODS

Rapid Evidence Assessment (REA) methodology was employed. A search strategy was developed and applied (PUBMED, MEDLINE) returning 5,377 unique titles. 172 full-text articles were screened to determine relevance with 82 publications included in the final review. Data regarding country, study design, key findings and approaches to efficiency improvement were extracted and a narrative synthesis produced. Publications covering health systems from developed countries were included.

RESULTS

Identified study designs included policy reviews, qualitative reviews, mixed methods reviews, systematic reviews, literature reviews, retrospective analyses, scoping reviews, narrative papers, regression analyses and opinion papers. While findings revealed no comprehensive frameworks for system-wide efficiency improvement, a range of specific centrally led improvement approaches were identified. Elements associated with success in current approaches included dedicated central functions to drive system-wide efficiency improvement, managing efficiency in tandem with quality and value, and inclusive stakeholder engagement.

CONCLUSIONS

The requirement for public health systems to improve efficiency is likely to continue to increase. Reactive cost-cutting measures and short-term initiatives aimed only at reducing expenditure are unlikely to deliver sustainable efficiency improvement. By providing dedicated central system-wide efficiency improvement support, public health system management entities can deliver improved financial, health service and stakeholder outcomes.

Enhanced Recovery After Surgery Protocols in Lower Extremity Joint Arthroplasty: Using Observational Data to Identify the Optimal Combination of Components

BACKGROUND

Enhanced recovery after surgery (ERAS) protocols are increasingly used in orthopedic surgery. Data are lacking on which combinations of ERAS components are (1) the most commonly used and (2) the most effective in terms of outcomes.

METHODS

This retrospective cohort study utilized claims data (Premier Healthcare, n = 1,539,432 total joint arthroplasties, 2006-2016). Eight ERAS components were defined: (A) regional anesthesia, (B) multimodal analgesia, (C) tranexamic acid, (D) antiemetics on day of surgery, (E) early physical therapy, and avoidance of (F) urinary catheters, (G) patient-controlled analgesia, and (H) drains. Outcomes were length of stay, "any complication," and hospitalization cost. Mixed-effects models measured associations between the most common ERAS combinations and outcomes. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported.

RESULTS

In 2006-2012 and 2013-2016, the most common ERAS combinations were B/D/E/F/G/H (20%, n = 172,397) and B/C/D/E/F/G/H (17%, n = 120,266), respectively. The only difference between the most commonly used ERAS combinations over the years is the addition of C (addition of tranexamic acid to the protocol). The most pronounced beneficial effects in 2006-2012 were seen for combination A/B/D/E/F/G/H (6% of cases vs less prevalent ERAS combinations) for the outcome of "any complication" (OR 0.87, CI 0.83-0.91, P < .0001). In 2013-2016, the strongest effects were seen for combination B/C/D/E/F/G/H (17% of cases) also for the outcome of "any complication" (OR 0.86, CI 0.83-0.89, P < .0001). Relatively minor differences existed between ERAS protocols for the other outcomes.

CONCLUSION

Despite varying ERAS protocols, maximum benefits in terms of complication reduction differed minimally. Further study may elucidate the balance between an increasing number of ERAS components and incremental benefits realized.

LEVEL OF EVIDENCE

III.

Readmission, Complication, and Disposition Calculators in Total Joint Arthroplasty: A Systemic Review

BACKGROUND

Predictive tools are useful adjuncts in surgical planning. They help guide patient selection, candidacy for inpatient vs outpatient surgery, and discharge disposition as well as predict the probability of readmissions and complications after total joint arthroplasty (TJA). Surgeons may find it difficult due to significant variation among risk calculators to decide which tool is best suited for a specific patient for optimal decision-based care. Our aim is to perform a systematic review of the literature to determine the existing post-TJA readmission calculators and compare the specific elements that comprise their formula. Second, we intend to evaluate the pros and cons of each calculator.

METHODS

Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols protocol, we conducted a systematic search through 3 major databases for publications addressing TJA risk stratification tools for readmission, discharge disposition, and early complications. We excluded those manuscripts that were not comprehensive for hips and knees, did not list discharge, readmission or complication as the primary outcome, or were published outside the North America.

RESULTS

Ten publications met our criteria and were compared on their sourced data, variable types, and overall algorithm quality. Seven of these were generated with single institution data and 3 from large administrative datasets. Three tools determined readmission risk, 5 calculated discharge disposition, and 2 predicted early complications. Only 4 prediction tools were validated by external studies. Seven studies utilized preoperative data points in their risk equations while 3 utilized intraoperative or postsurgical data to delineate risk.

CONCLUSION

The extensive variation among TJA risk calculators underscores the need for tools with more individualized stratification capabilities and verification. The transition to outpatient and same-day discharge TJA may preclude or change the need for many of these calculators. Further studies are needed to develop more streamlined risk calculator tools that predict readmission and surgical complications.

Introducing enhanced recovery after surgery in a high-volume orthopaedic hospital: a health technology assessment

BACKGROUND

The number of patients undergoing joint arthroplasty is increasing worldwide. An Enhanced Recovery After Surgery (ERAS) pathway for hip and knee arthroplasty was introduced in an Italian high-volume research hospital in March 2018.

METHODS

The aim of this mixed methods observational study is to perform a health technology assessment (HTA) of the ERAS pathway, considering 938 procedures performed after its implementation, by means of a hospital-based approach derived from the EUnetHTA (European Network for Health Technology Assessment) Core Model. The assessment process is based on dimensions of general relevance, safety, efficacy, effectiveness, economic and financial impact, equity, legal aspects, social and ethical impact, and organizational impact. A narrative review of the literature helped to identify general relevance, safety and efficacy factors, and a set of relevant sub-dimensions submitted to the evaluation of the professionals who use the technology through a 7-item Likert Scale. The economic and financial impact of the ERAS pathway on the hospital budget was supported by quantitative data collected from internal or national registries, employing economic modelling strategies to identify the amount of resources required to implement it.

RESULTS

The relevance of technology under assessment is recognized worldwide. A number of studies show accelerated pathways to dominate conventional approaches on pain reduction, functional recovery, prevention of complications, improvements in tolerability and quality of life, including fragile or vulnerable patients. Qualitative surveys on clinical and functional outcomes confirm most of these benefits. The ERAS pathway is associated with a reduced length of stay in comparison with the Italian hospitalization average for the same procedures, despite the poor spread of the pathway within the country may generate postcode inequalities. The economic analyses show how the resources invested in training activities are largely depreciated by benefits once the technology is permanently introduced, which may generate hospital cost savings of up to 2054,123.44 € per year.

CONCLUSIONS

Galeazzi Hospital's ERAS pathway for hip and knee arthroplasty results preferable to traditional approaches following most of the HTA dimensions, and offers room for further improvement. The more comparable practices are shared, the before this potential improvement can be identified and addressed.

High-Volume Arthroplasty Centers Demonstrate Higher Composite Quality Scores and Enhanced Value: Perspective on Higher-Volume Hospitals Performing Arthroplasty from 2001 to 2011

BACKGROUND

In recent years, there has been a move toward value-based health care. Value is generally defined as outcome divided by cost; however, it is not clear exactly how to define and measure outcomes. In this study, we utilized the Nationwide Inpatient Sample (NIS) to determine how hospital volume and other factors affect quality for patients undergoing total hip and knee arthroplasty.

METHODS

Using the NIS of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ), we conducted a retrospective study of all total hip and total knee arthroplasties performed from 2001 to 2011. We identified all procedure and outcome variables using the International Classification of Diseases, Ninth Revision (ICD-9) billing codes. Patients were grouped into quartiles based on the corresponding hospital's procedure volume. The quality measurement for each hospitalization was binary, with perfect inpatient care reflecting a favorable result for all of the following outcomes of interest: death, sepsis, postoperative infection, thromboembolic events, venous thrombosis, hematoma, blood transfusion, and length of stay below average. The Perfect Inpatient Care Index (PICI) was then calculated for each hospital. The PICI was defined as the number of hospitalizations with no unfavorable outcomes divided by total volume of arthroplasty. Value was measured as the PICI divided by the mean total charges. Multivariable nested regression was used to determine variables that predict perfect inpatient care.

RESULTS

From 2001 to 2011, the NIS database reported 1,651,354 total hip or total knee arthroplasties. Hospital arthroplasty volume ranged from 0 to 11,758 procedures. Overall, hospital PICI scores increased as arthroplasty volume increased. In multivariable nested regression analysis, procedure volume (odds ratio [OR] for the highest quartile compared with the lowest quartile, 2.116 [95% confidence interval (CI), 1.883 to 2.378]) and lower patient acuity (OR, 2.450 [95% CI, 2.429 to 2.472]) were independently associated with better PICI scores. Value increased as hospital procedure volume increased.

CONCLUSIONS

Hospital procedure volume varied widely. Although small differences were seen in individual outcome measures, composite scores (PICI) and value were substantially better at hospitals that had higher procedure volume and in lower-acuity patients.

LEVEL OF EVIDENCE

Economic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Impact of liposomal bupivacaine on opioid use, hospital length of stay, discharge status, and hospitalization costs in patients undergoing total hip arthroplasty

Aims: Effective postsurgical analgesia hastens recovery, reduces hospital length of stay (LOS), and decreases hospitalization costs for total hip arthroplasty (THA). Improving these outcomes is critical for value-based surgical bundled payment programs such as the Medicare Comprehensive Care for Joint Replacement and similar programs for commercial insurance providers. This study compared clinical outcomes and hospitalization costs for patients undergoing THA with and without liposomal bupivacaine (LB).Materials and methods: This retrospective, comparative cohort study used data from the Premier Healthcare Database from the 10 hospitals with highest use of LB for THA from January 2011 through April 2017. A cohort undergoing THA with LB at those hospitals was compared with a propensity-score matched cohort at those hospitals who had THA without LB. Descriptive, univariate, and multivariable analyses compared post-surgical inpatient opioid consumption, hospital LOS, discharge status, same-hospital readmissions, and total hospitalization costs. Analyses were performed using the Pearson Chi-square test (categorical variables) and Wilcoxon or Student t-test (continuous variables).Results: For patients with Medicare (with LB, n = 3622; without LB, n = 3610) and commercial insurance (with LB, n = 2648; without LB, n = 2709), use of LB was associated with lower post-surgical inpatient opioid consumption (105 and 81 mg, respectively; p < 0.0001), a 0.7-day shorter LOS (p < 0.0001), a 1.6-1.7-fold increased likelihood of home discharge (p < 0.0001), and no increase in readmissions (p ≥ 0.103). Total hospitalization costs were $561 lower with LB in the Medicare population (p < 0.0001) and $41 higher with LB in the commercial population (p = 0.7697).Limitations: Hospitalization costs were estimated from the hospital chargemaster. Findings from these 10 hospitals may not represent other US hospitals.Conclusions: At select hospitals, THA with LB was associated with reduced post-surgical inpatient opioid consumption, shorter hospital LOS, increased likelihood of home discharge, and lower hospitalization costs. Post-surgical pain management with LB may help hospitals in value-based bundled payment programs.

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model

BACKGROUND

The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity.

METHODS

Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a risk-based PSPM.

RESULTS

The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively.

CONCLUSION

Our deep learning model demonstrated "learning" with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity.

Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model

OBJECTIVES

With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level factors that may not be optimized preoperatively due to acuity of care. The objectives of this study are to (1) develop a supervised naive Bayes machine-learning algorithm using preoperative patient data to predict length of stay and cost after hip fracture and (2) propose a patient-specific payment model to project reimbursements based on patient comorbidities.

METHODS

Using the New York Statewide Planning and Research Cooperative System database, we studied 98,562 Medicare patients who underwent operative management for hip fracture from 2009 to 2016. A naive Bayes machine-learning model was built using age, sex, ethnicity, race, type of admission, risk of mortality, and severity of illness as predictive inputs.

RESULTS

Accuracy was demonstrated at 76.5% and 79.0% for length of stay and cost, respectively. Performance was 88% for length of stay and 89% for cost. Model error analysis showed increasing model error with increasing risk of mortality, which thus increased the risk-adjusted payment for each risk of mortality.

CONCLUSIONS

Our naive Bayes machine-learning algorithm provided excellent accuracy and responsiveness in the prediction of length of stay and cost of an episode of care for hip fracture using preoperative variables. This model demonstrates that the cost of delivery of hip fracture care is dependent on largely nonmodifiable patient-specific factors, likely making bundled care an implausible payment model for hip fractures.

2018 John Charnley Award: Analysis of US Hip Replacement Bundled Payments: Physician-initiated Episodes Outperform Hospital-initiated Episodes

BACKGROUND

The Centers for Medicare & Medicaid Services (CMS) launched the Bundled Payment for Care Improvement (BPCI) initiative in 2013 to create incentives to improve outcomes and reduce costs in various clinical settings, including total hip arthroplasty (THA). This study seeks to quantify BPCI initiative outcomes for THA and to determine the optimal party (for example, hospital versus physician group practice [PGP]) to manage the program.

QUESTIONS/PURPOSES

(1) Is BPCI associated with lower 90-day payments, readmissions, or mortality for elective THA? (2) Is there a difference in 90-day payments, readmissions, or mortality between episodes initiated by PGPs and episodes initiated by hospitals for elective THA? (3) Is BPCI associated with reduced total Elixhauser comorbidity index or age for elective THA?

METHODS

We performed a retrospective analysis on the CMS Limited Data Set on all Medicare primary elective THAs without a major comorbidity performed in the United States (except Maryland) between January 2013 and March 2016, totaling more than USD 7.1 billion in expenditures. Episodes were grouped into hospital-run BPCI (n = 42,922), PGP-run BPCI (n = 44,662), and THA performed outside of BPCI (n = 284,002). All Medicare Part A payments were calculated over a 90-day period after surgery and adjusted for inflation and regional variation. For each episode, age, sex, race, geographic location, background trend, and Elixhauser comorbidities were determined to control for major confounding variables. Total payments, readmissions, and mortality were compared among the groups with logistic regression.

RESULTS

When controlling for demographics, background trend, geographic variation, and total Elixhauser comorbidities in elective Diagnosis-Related Group 470 THA episodes, BPCI was associated with a 4.44% (95% confidence interval [CI], -4.58% to -4.30%; p < 0.001) payment decrease for all participants (USD 1244 decrease from a baseline of USD 18,802); additionally, odds ratios (ORs) for 90-day mortality and readmissions were unchanged. PGP groups showed a 4.81% decrease in payments (95% CI, -5.01% to -4.61%; p < 0.001) after enrolling in BPCI (USD 1335 decrease from a baseline of USD 17,841). Hospital groups showed a 4.04% decrease in payments (95% CI, -4.24% to 3.84%; p < 0.01) after enrolling in BPCI (USD 1138 decrease from a baseline of USD 19,799). The decrease in payments of PGP-run episodes was greater compared with hospital-run episodes. ORs for 90-day mortality and readmission remained unchanged after BPCI for PGP- and hospital-run BPCI programs. Patient age and mean Elixhauser comorbidity index did not change after BPCI for PGP-run, hospital-run, or overall BPCI episodes.

CONCLUSIONS

Even when controlling for decreasing costs in traditional fee-for-service care, BPCI is associated with payment reduction with no change in adverse events, and this is not because of the selection of younger patients or those with fewer comorbidities. Furthermore, physician group practices were associated with greater payment reduction than hospital programs with no difference in readmission or mortality from baseline for either. Physicians may be a more logical group than hospitals to manage payment reduction in future healthcare reform.

LEVEL OF EVIDENCE

Level II, economic and decision analysis.

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model

BACKGROUND

Value-based and patient-specific care represent 2 critical areas of focus that have yet to be fully reconciled by today's bundled care model. Using a predictive naïve Bayesian model, the objectives of this study were (1) to develop a machine-learning algorithm using preoperative big data to predict length of stay (LOS) and inpatient costs after primary total knee arthroplasty (TKA) and (2) to propose a tiered patient-specific payment model that reflects patient complexity for reimbursement.

METHODS

Using 141,446 patients undergoing primary TKA from an administrative database from 2009 to 2016, a Bayesian model was created and trained to forecast LOS and cost. Algorithm performance was determined using the area under the receiver operating characteristic curve and the percent accuracy. A proposed risk-based patient-specific payment model was derived based on outputs.

RESULTS

The machine-learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate a high degree of validity with an area under the receiver operating characteristic curve of 0.7822 and 0.7382 for LOS and cost. As patient complexity increased, cost add-ons increased in tiers of 3%, 10%, and 15% for moderate, major, and extreme mortality risks, respectively.

CONCLUSION

Our machine-learning algorithm derived from an administrative database demonstrated excellent validity in predicting LOS and costs before primary TKA and has broad value-based applications, including a risk-based patient-specific payment model.