Reimbursement of Lumbar Decompression at an Orthopedic Specialty Hospital Versus Tertiary Referral Center

One year patient reported outcomes after single-level lumbar fusion at orthopedic specialty hospital compared to tertiary referral center

BACKGROUND CONTEXT

Lumbar spinal fusion is an increasingly common operation to treat symptoms related to degenerative disorders of the spine including radiculopathy and pain. As the volume of spine surgeries grows, it is becoming increasingly common for procedures to take place in nontertiary care centers, including orthopaedic specialty hospitals (OSH). While previous research demonstrates that surgical outcomes at an OSH are noninferior to those at a tertiary referral center (TRC), the implications of this difference on patient-reported outcome measures (PROMs) have not been sufficiently assessed.

PURPOSE

The objectives of this study were (1) to determine if changes in patient reported outcome measures (PROMs) after elective lumbar spinal fusion surgery differ between patients who undergo surgery at an orthopedic specialty hospital (OSH) and those who undergo surgery at a tertiary referral center (TRC) and (2) to characterize differences in short-term outcomes between hospitals.

STUDY DESIGN

Retrospective cohort study.

PATIENT SAMPLE

Adult patients (≥18 years old) who underwent primary, elective single-level posterior lumbar decompression and fusion between January 2014 and December 2021 at a tertiary referral center or an orthopaedic specialty hospital.

OUTCOME MEASURES

PROMs: Oswestry Disability Index (ODI), Short-form 12 (SF12) Mental Component Summary (MCS); SF12 Physical Component Summary (PCS); Visual Analogue Back and Leg (VAS Back/Leg) METHODS: PROMs were collected preoperatively, 6 months after surgery, and 1 year after surgery. Six-month and 1-year delta PROM values were calculated by subtracting the preoperative PROM score from the 6-month or 1-year score, respectively. Multivariable linear regression analyses were conducted to assess the independent effect of hospital location on postoperative PROM scores.

RESULTS

A total of 288 patients were identified as part of the study cohort including 205 patients who underwent surgery at the tertiary hospital and 83 patients who underwent surgery at the OSH. OSH patients had shorter length of stay (1.57±0.72 vs. 3.28±1.32, p<.001), however there was no difference in discharge disposition or 90-day readmission rates between hospitals (p>.05). At 6 months, having surgery at the specialty hospital was associated with higher PCS (estimate=2.96, confidence interval: 0.21-5.71, p=.035). At 1-year postoperatively, the location of surgery no longer demonstrated significant associations with PROM scores. Preoperative PROM scores demonstrated significant associations with 6-month and 1-year scores for each PROM (p<.05) except VAS leg at 6 months postoperatively.

CONCLUSION

To our knowledge, this is one of the largest studies investigating PROMs at OSH versus TRCs for single-level lumbar fusions. We demonstrated that at 1-year follow-up, there is not a significant difference in PROM improvement between patients who undergo surgery at a TRC and patients who do so at an OSH.

Assessment of a Private Payer Bundled Payment Model for Lumbar Decompression Surgery

INTRODUCTION

Although bundled payment models are well-established in Medicare-aged individuals, private insurers are now developing bundled payment plans. The role of these plans in spine surgery has not been evaluated. Our objective was to analyze the performance of a private insurance bundled payment program for lumbar decompression and microdiskectomy.

METHODS

A retrospective review was conducted of all lumbar decompressions in a private payer bundled payment model at a single institution from October 2018 to December 2020. 120-day episode of care cost data were collected and reported as net profit or loss regarding set target prices. A stepwise multivariable linear regression model was developed to measure the effect of patient and surgical factors on net surplus or deficit.

RESULTS

Overall, 151 of 468 (32.2%) resulted in a deficit. Older patients (58.6 vs. 50.9 years, P < 0.001) with diabetes (25.2% vs. 13.9%, P = 0.004), hypertension (38.4% vs. 28.4%, P = 0.038), heart disease (13.9% vs. 7.57%, P = 0.030), and hyperlipidemia (51.7% vs. 35.6%, P = 0.001) were more likely to experience a loss. Surgically, decompression of more levels (1.91 vs. 1.19, P < 0.001), posterior lumbar decompression (86.8% vs. 56.5%, P < 0.001), and performing surgery at a tertiary hospital (84.8% vs. 70.3%, P < 0.001) were more likely to result in loss. All readmissions resulted in a loss (4.64% vs. 0.0%, P < 0.001). On multivariable regression, microdiskectomy (β: $2,398, P = 0.012) and surgery in a specialty hospital (β: $1,729, P = 0.096) or ambulatory surgery center (β: $3,534, P = 0.055) were associated with cost savings. Increasing number of levels, longer length of stay, active smoking, and history of cancer, dementia, or congestive heart failure were all associated with degree of deficit.

CONCLUSIONS

Preoperatively optimizing comorbidities and using risk stratification to identify those patients who may safely undergo surgery at a facility other than an inpatient hospital may help increase cost savings in a bundled payment model of working-age and Medicare-age individuals.

Risk stratification of patients undergoing outpatient lumbar decompression surgery

BACKGROUND CONTEXT

Reimbursement has slowly transitioned from a fee-for-service model to a bundled payment model after introduction of the United States Centers for Medicare and Medicaid Services bundled payment program. To minimize healthcare costs, some surgeons are trying to minimize healthcare expenditures by transitioning appropriately selected lumbar decompression patients to outpatient procedure centers.

PURPOSE

To prepare a risk stratification calculator based on machine learning algorithms to improve surgeon's preoperative predictive capability of determining whether a patient undergoing lumbar decompression will meet inpatient vs. outpatient criteria. Inpatient criteria was defined as any overnight hospital stay.

STUDY DESIGN/SETTING

Retrospective single-institution cohort.

PATIENT SAMPLE

A total of 1656 patients undergoing primary lumbar decompression.

OUTCOME MEASURES

Postoperative outcomes analyzed for inclusion into the risk calculator included length of stay.

METHODS

Patients were split 80-20 into a training model and a predictive model. This resulted in 1,325 patients in the training model and 331 into the predictive model. A logistic regression analysis ensured proper variable inclusion into the model. C-statistics were used to understand model effectiveness. An odds ratio and nomogram were created once the optimal model was identified.

RESULTS

A total of 1,656 patients were included in our cohort with 1,078 dischared on day of surgery and 578 patients spending ≥ 1 midnight in the hospital. Our model determined older patients (OR=1.06, p<.001) with a higher BMI (OR=1.04, p<0.001), higher back pain (OR=1.06, p=.019), increasing American Society of Anesthesiologists (ASA) score (OR=1.39, p=.012), and patients with more levels decompressed (OR=3.66, p<0.001) all had increased risks of staying overnight. Patients who were female (OR=0.59, p=.009) and those with private insurance (OR=0.64, p=.023) were less likely to be admitted overnight. Further, weighted scores based on training data were then created and patients with a cumulative score over 118 points had a 82.9% likelihood of overnight. Analysis of the 331 patients in the test data demonstrated using a cut-off of 118 points accurately predicted 64.8% of patients meeting inpatient criteria compared to 23.0% meeting outpatient criteria (p<0.001). Area under the curve analysis showed a score greater than 118 predicted admission 81.4% of the time. The algorithm was incorporated into an open access digital application available here: https://rothmanstatisticscalculators.shinyapps.io/Inpatient_Calculator/?_ga=2.171493472.1789252330.1671633274-469992803.1671633274 CONCLUSIONS: Utilizing machine-learning algorithms we created a highly reliable predictive calculator to determine if patients undergoing outpatient lumbar decompression would require admission. Patients who were younger, had lower BMI, lower preoperative back pain, lower ASA score, less levels decompressed, private insurance, lived with someone at home, and with minimal comorbidities were ideal candidates for outpatient surgery.