Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Importance: In 2014, Maryland implemented the all-payer model, a distinct hospital funding policy that applied caps on annual hospital expenditures and mandated reductions in avoidable complications. Expansion of this model to other states is currently being considered; therefore, it is important to evaluate whether Maryland's all-payer model is achieving the desired goals among surgical patients, who are an at-risk population for most potentially preventable complications.

Objective: To examine the association between the implementation of Maryland's all-payer model and the incidence of avoidable complications and resource use among adult surgical patients.

Design, Setting, And Participants: This comparative effectiveness study used hospital discharge records from the Healthcare Cost and Utilization Project state inpatient databases to conduct a difference-in-differences analysis comparing the incidence of avoidable complications and the intensity of health resource use before and after implementation of the all-payer model in Maryland. The analytical sample included 2 983 411 adult patients who received coronary artery bypass grafting (CABG), carotid endarterectomy (CEA), spinal fusion, hip or knee arthroplasty, hysterectomy, or cesarean delivery between January 1, 2008, and December 31, 2016, at acute care hospitals in Maryland (intervention state) and New York, New Jersey, and Rhode Island (control states). Data analysis was conducted from July 2019 to July 2021.

Exposures: All-payer model.

Main Outcomes And Measures: Complications (infectious, cardiovascular, respiratory, kidney, coagulation, and wound) and health resource use (ie, hospital charges).

Results: Of 2 983 411 total patients in the analytical sample, 525 262 patients were from Maryland and 2 458 149 were from control states. Across Maryland and the control states, there were statistically significant but not clinically relevant differences in the preintervention period with regard to patient age (mean [SD], 49.7 [19.0] years vs 48.9 [19.3] years, respectively; P < .001), sex (22.7% male vs 21.4% male; P < .001), and race (0.3% vs 0.4% American Indian, 2.8% vs 4.5% Asian or Pacific Islander, 25.9% vs 12.7% Black, 4.7% vs 11.9% Hispanic, and 63.5% vs 63.4% White; P < .001). After implementation of the all-payer model in Maryland, significantly lower rates of avoidable complications were found among patients who underwent CABG (-11.3%; 95% CI, -13.8% to -8.7%; P < .001), CEA (-1.6%; 95% CI, -2.9% to -0.3%; P = .02), hip arthroplasty (-0.8%; 95% CI, -1.0% to -0.5%; P < .001), knee arthroplasty (-0.4%; 95% CI, -0.7% to -0.1%; P = .01), and cesarean delivery (-1.0%; 95% CI, -1.3% to -0.7%; P < .001). In addition, there were significantly lower increases in index hospital costs in Maryland among patients who underwent CABG (-$6236; 95% CI, -$7320 to -$5151; P < .001), CEA (-$730; 95% CI, -$1367 to -$94; P = .03), spinal fusion (-$3253; 95% CI, -$3879 to -$2627; P < .001), hip arthroplasty (-$328; 95% CI, -$634 to -$21; P = .04), knee arthroplasty (-$415; 95% CI, -$643 to -$187; P < .001), cesarean delivery (-$300; 95% CI, -$380 to -$220; P < .001), and hysterectomy (-$745; 95% CI, -$974 to -$517; P < .001). Significant changes in patient mix consistent with a younger population (eg, a shift toward private/commercial insurance for orthopedic procedures, such as spinal fusion [4.3%; 95% CI, 3.4%-5.2%; P < .001] and knee arthroplasty [1.6%; 95% CI, 1.0%-2.3%; P < .001]) and a lower comorbidity burden across surgical procedures (eg, CABG: -0.7% [95% CI, -0.1% to -0.5%; P < .001]; hip arthroplasty: -3.0% [95% CI, -3.6% to -2.3%; P < .001]) were also observed.

Conclusions And Relevance: In this study, patients who underwent common surgical procedures had significantly fewer avoidable complications and lower hospital costs, as measured against the rate of increase throughout the study, after implementation of the all-payer model in Maryland. These findings may be associated with changes in the patient mix.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463941PMC
http://dx.doi.org/10.1001/jamanetworkopen.2021.26619DOI Listing

Publication Analysis

Top Keywords

all-payer model
16
avoidable complications
12
control states
12
implementation maryland's
8
maryland's all-payer
8
incidence avoidable
8
health resource
8
analytical sample
8
model
6
maryland
5

Similar Publications

Objective: Children with medical complexity (CMC) may bypass emergency departments (EDs) close to home to seek care at hospitals with more specialized pediatric services. However, few studies have examined ED choice for CMC or how this differs by rurality. This work describes rural-urban differences in ED care and bypass patterns, examines associations between ED bypass and visit outcomes, and identifies factors associated with ED bypass.

View Article and Find Full Text PDF

Background: The overlapping epidemics of opioid use disorder (OUD) and HIV present a critical public health challenge. Although people with OUD frequently engage with healthcare settings, uptake of HIV prevention services such as pre-exposure prophylaxis (PrEP) remains low. Integrating HIV prevention into routine OUD care could reduce new infections, but scalable, evidence-based strategies are lacking.

View Article and Find Full Text PDF

Objectives: Incorporating social determinants of health to identify distinct pediatric asthma patient groups can help stratify populations by their risk of adverse events, improving targeted outreach and care.

Methods: Insurance claims and enrollment data from the Arkansas All-Payer Claims Database identified 22 169 children aged 5-18 years with an asthma diagnosis in 2018 and continuous Medicaid enrollment in 2018 and 2019. The clustering approach used information on comorbid conditions, asthma controller medication intensity, total controller and reliever medications filled, zip code-level Child Opportunity Index, and rural-urban classification.

View Article and Find Full Text PDF

Background: Amid the opioid epidemic in the United States, hepatitis C virus (HCV) infections are rising, with one-third of individuals with infection unaware due to the asymptomatic nature. This study aimed to develop and validate a machine learning (ML)-based algorithm to screen individuals at high risk of HCV infection.

Methods: We conducted prognostic modeling using the 2016-2023 OneFlorida+ database of all-payer electronic health records.

View Article and Find Full Text PDF

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem.

View Article and Find Full Text PDF