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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 7 of 7 Research Studies DisplayedAswani MS, Roberts ET
Social risk adjustment in the hospital readmission reduction program: pitfalls of peer grouping, measurement challenges, and potential solutions.
The objective of this study was to investigate the limitations of peer grouping and associated challenges in the measurement of social risk in Medicare's Hospital Readmission Reduction Program (HRRP). Public data on hospitals in the HRRP were used to examine the relationship between hospital dual share and readmission rates within peer groups as well as changes in hospital peer group assignments, readmission rates, and penalties, and the relationship between state Medicaid eligibility rules and peer groups. The findings indicated that peer grouping is limited in the extent to which it accounts for differences in hospitals' patient populations. The authors concluded that problems arise from the construction of peer groups and the measure of social risk used to define them.
AHRQ-funded; HS026727.
Citation: Aswani MS, Roberts ET .
Social risk adjustment in the hospital readmission reduction program: pitfalls of peer grouping, measurement challenges, and potential solutions.
Health Serv Res 2023 Feb; 58(1):51-59. doi: 10.1111/1475-6773.13969..
Keywords: Hospital Readmissions, Hospitals, Risk
Ye S, Hiura G, Fleck E
Hospital readmissions after implementation of a discharge care program for patients with COVID-19 illness.
The surge of coronavirus 2019 (COVID-19) hospitalizations in New York City required rapid discharges to maintain hospital capacity. The objective of this study was to determine whether lenient provisional discharge guidelines with remote monitoring after discharge resulted in safe discharges home for patients hospitalized with COVID-19 illness. The investigators found that lenient discharge criteria in conjunction with remote monitoring after discharge were associated with a rate of early readmissions after COVID-related hospitalizations that was comparable to the rate of readmissions after other reasons for hospitalization before the COVID pandemic.
AHRQ-funded; HS024262; HS025198.
Citation: Ye S, Hiura G, Fleck E .
Hospital readmissions after implementation of a discharge care program for patients with COVID-19 illness.
J Gen Intern Med 2021 Mar;36(3):722-29. doi: 10.1007/s11606-020-06340-w..
Keywords: COVID-19, Hospital Discharge, Hospital Readmissions, Hospitals, Public Health, Hospitalization, Risk
Marafino BJ, Schuler A, Liu VX
Predicting preventable hospital readmissions with causal machine learning.
This study’s goal was to assess the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention called the Transitions Program, which used electronic health records from Kaiser Permanent Northern California (KPNC). A total of 1,539,285 index hospitalizations meeting the inclusion criteria and occurring between June 2010 and December 2010 at 21 KPNC hospitals were analyzed. There was substantial heterogeneity in patients’ response to the intervention, with patients at somewhat lower risk appearing to have the largest predicted effects. The estimates appeared to be well calibrated. The results did suggest a mismatch between risk and treatment effects.
AHRQ-funded; HS022192.
Citation: Marafino BJ, Schuler A, Liu VX .
Predicting preventable hospital readmissions with causal machine learning.
Health Serv Res 2020 Dec;55(6):993-1002. doi: 10.1111/1475-6773.13586..
Keywords: Hospital Readmissions, Hospitals, Clinical Decision Support (CDS), Risk
Saleh SN, Makam AN, Halm EA,
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). In this study, the investigators assessed how well a previously validated 30-day EHR-based readmission model predicted 7-day readmissions and compared differences in strength of predictors. They suggested that improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
AHRQ-funded; HS022418.
Citation: Saleh SN, Makam AN, Halm EA, .
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
BMC Med Inform Decis Mak 2020 Sep 15;20(1):227. doi: 10.1186/s12911-020-01248-1..
Keywords: Hospital Readmissions, Hospitals, Risk, Transitions of Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Horwitz LI, Bernheim SM, Ross JS
Hospital characteristics associated with risk-standardized readmission rates.
This national study using Medicare data examined the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR). Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence.
AHRQ-funded; HS022882.
Citation: Horwitz LI, Bernheim SM, Ross JS .
Hospital characteristics associated with risk-standardized readmission rates.
Med Care 2017 May;55(5):528-34. doi: 10.1097/mlr.0000000000000713.
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Keywords: Hospitals, Hospital Readmissions, Medicaid, Risk, Quality of Care
McLeod L, Flynn J, Erickson M
Variation in 60-day readmission for surgical-site infections (SSIs) and reoperation following spinal fusion operations for neuromuscular scoliosis.
The purpose of this study was to examine variation in hospital performance based on risk-standardized 60-day readmission rates for surgical-site infection (SSIs) and reoperation across 39 US Children's Hospitals. It found that reoperations were associated with an SSI in 70 percent of cases. Across hospitals, SSI and reoperation rates ranged from 1 percent to 11 percent and 1 percent to 12 percent, respectively.
AHRQ-funded; HS022198.
Citation: McLeod L, Flynn J, Erickson M .
Variation in 60-day readmission for surgical-site infections (SSIs) and reoperation following spinal fusion operations for neuromuscular scoliosis.
J Pediatr Orthop 2016 Sep;36(6):634-9. doi: 10.1097/bpo.0000000000000495.
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Keywords: Children/Adolescents, Surgery, Healthcare-Associated Infections (HAIs), Injuries and Wounds, Adverse Events, Hospital Readmissions, Hospitals, Risk
Donnelly JP, Hohmann SF, Wang HE
Unplanned readmissions after hospitalization for severe sepsis at academic medical center-affiliated hospitals.
The researchers sought to characterize 7- and 30-day readmission rates following hospital admission for severe sepsis as well as institutional variations in readmission. They concluded that severe sepsis readmission places a substantial burden on the healthcare system, with one in 15 and one in five severe sepsis discharges readmitted within 7 and 30 days, respectively.
AHRQ-funded; HS013852.
Citation: Donnelly JP, Hohmann SF, Wang HE .
Unplanned readmissions after hospitalization for severe sepsis at academic medical center-affiliated hospitals.
Crit Care Med 2015 Sep;43(9):1916-27. doi: 10.1097/ccm.0000000000001147..
Keywords: Hospital Readmissions, Hospitals, Risk, Sepsis