Abstract:BackgroundVery frequent outpatient emergency department (ED) use—so called “superutilization”—at the state level is not well-studied. To address this gap, we examined frequent ED utilization in the largest state Medicaid population to date.MethodsUsing Texas Medicaid (the third largest in the USA) claims data, we examined the variability in expenditures, sociodemographics, comorbidities, and persistence across seven levels of ED utilization/year (i.e., 1, 2, 3–4, 5–6, 7–9, 10–14, and ≥ 15 visits). We classifie… Show more
“…The size of the high utilizer population varies from 1% to 7% of the overall population for different years and model parameter settings. In terms of population size identified, the proportion is similar to count-based methods [25]. Fig.…”
Section: Methodsmentioning
confidence: 85%
“…Inpatient acute care hospital and emergency department (ED) are two important types of health care services. Because inpatient acute care hospital costs are generally an order of magnitude higher than ED costs [25], to better analyze the variations in these two types of services, we stratify the adjustment model by these settings. For this purpose, in each setting, the study population is restricted to those who had nonzero expenditures in the corresponding setting.…”
Background
High utilizers receive great attention in health care research because they have a largely disproportionate spending. Existing analyses usually identify high utilizers with an empirical threshold on the number of health care visits or associated expenditures. However, such count-and-cost based criteria might not be best for identifying impactable high utilizers.
Methods
We propose an approach to identify impactable high utilizers using residuals from regression-based health care utilization risk adjustment models to analyze the variations in health care expenditures. We develop linear and tree-based models to best adjust per-member per-month health care cost by clinical and socioeconomic risk factors using a large administrative claims dataset from a state public insurance program.
Results
The risk adjustment models identify a group of patients with high residuals whose demographics and categorization of comorbidities are similar to other patients but who have a significant amount of unexplained health care utilization. Deeper analysis of the essential hypertension cohort and chronic kidney disease cohort shows these variations in expenditures could be within individual ICD-9-CM codes and from different mixtures of ICD-9-CM codes. Additionally, correlation analysis with 3M™ Potentially Preventable Events (PPE) software shows that a portion of this utilization may be preventable. In addition, the high utilizers persist from year to year.
Conclusions
After risk adjustment, patients with higher than expected expenditures (high residuals) are associated with more potentially preventable events. These residuals are temporally consistent and hence may be useful in identifying and intervening impactable high utilizers.
“…The size of the high utilizer population varies from 1% to 7% of the overall population for different years and model parameter settings. In terms of population size identified, the proportion is similar to count-based methods [25]. Fig.…”
Section: Methodsmentioning
confidence: 85%
“…Inpatient acute care hospital and emergency department (ED) are two important types of health care services. Because inpatient acute care hospital costs are generally an order of magnitude higher than ED costs [25], to better analyze the variations in these two types of services, we stratify the adjustment model by these settings. For this purpose, in each setting, the study population is restricted to those who had nonzero expenditures in the corresponding setting.…”
Background
High utilizers receive great attention in health care research because they have a largely disproportionate spending. Existing analyses usually identify high utilizers with an empirical threshold on the number of health care visits or associated expenditures. However, such count-and-cost based criteria might not be best for identifying impactable high utilizers.
Methods
We propose an approach to identify impactable high utilizers using residuals from regression-based health care utilization risk adjustment models to analyze the variations in health care expenditures. We develop linear and tree-based models to best adjust per-member per-month health care cost by clinical and socioeconomic risk factors using a large administrative claims dataset from a state public insurance program.
Results
The risk adjustment models identify a group of patients with high residuals whose demographics and categorization of comorbidities are similar to other patients but who have a significant amount of unexplained health care utilization. Deeper analysis of the essential hypertension cohort and chronic kidney disease cohort shows these variations in expenditures could be within individual ICD-9-CM codes and from different mixtures of ICD-9-CM codes. Additionally, correlation analysis with 3M™ Potentially Preventable Events (PPE) software shows that a portion of this utilization may be preventable. In addition, the high utilizers persist from year to year.
Conclusions
After risk adjustment, patients with higher than expected expenditures (high residuals) are associated with more potentially preventable events. These residuals are temporally consistent and hence may be useful in identifying and intervening impactable high utilizers.
“…There is no consistent definition of super utilizers; however, the lowest common threshold in the literature is patients with four or more ED visits in a 12 month period [ [19] , [20] , [21] ]. These individuals account for a disproportionate number of hospital visits and healthcare costs [ 19 , [21] , [22] , [23] ]. Numerous studies have examined patient characteristics [ 19 , [21] , [22] , [23] , [24] , [25] ], patient complaints [ 24 ], super utilizer behavior classification [ 20 ], and appropriateness of care for these patients [ 26 , 27 ].…”
Background
Despite the trend of rising Emergency Department (ED) visits over the past decade, researchers have observed drastic declines in number of ED visits due to the COVID-19 pandemic. The purpose of the current study was to examine the impact of the COVID-19 pandemic and governor mandated Stay at Home Order on ED super utilizers.
Methods
This was a retrospective chart review of patients presenting to the 12 emergency departments of the Franciscan Mission of Our Lady Hospital System in Louisiana between January 1, 2018 and December 31, 2020. Patients who were 18 years of age or older and had four ED visits within a one-year period (2018, 2019, or 2020) were classified as super-utilizers. We examined number and category of visits for the baseline period (January 2018 – March 2020), the governor's Stay at Home Order, and the subsequent Reopening Phases through December 31, 2020.
Results
The number of visits by super utilizers decreased by over 16% when the Stay at Home Order was issued. The average number of visits per week rose from 1010.63 during the Stay at Home Order to 1198.09 after the Stay at Home Order was lifted, but they did not return to Pre-COVID levels of approximately 1400 visits per week in 2018 and 2019. When categories of visits were examined, this trend was found for emergent visits (
p
< 0.001) and visits related to injuries (p < 0.001). Non-emergent visits declined during the Stay at Home Order compared to the baseline period (p < 0.001), and did not increase significantly during reopening compared to the Stay at Home Order (
p
= 0.87). There were no changes in number of visits for psychiatric purposes, alcohol use, or drug use during the pandemic.
Conclusions
Significant declines in emergent visits raise concerns that individuals who needed ED treatment did not seek it due to COVID-19. However, the finding that super utilizers with non-emergent visits continued to visit the ED less after the Stay at Home Order was lifted raises questions for future research that may inform policy and interventions for inappropriate ED use.
“…HCHN patients have shown a high degree of persistence in medical expenditures reflected in administrative claims data [ 2 , 13 ], although some recent studies have concluded that the high utilization may be temporary and not persistent [ 14 ]. The purpose of this study is to better understand these temporal patterns and to apply machine learning-based models to predict expenditures.…”
BackgroundThis paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.ResultsWe systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance.ConclusionsThis study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
