Risk Adjustment Tools for Learning Health Systems: A Comparison of Dx<scp>CG</scp> and <scp>CMS</scp>‐<scp>HCC</scp> V21

Upadhyay, Anjali; Cowgill, Elizabeth; Stefos, Theodore; Moran, Eileen; Asch, Steven M.; Almenoff, Peter L.

doi:10.1111/1475-6773.12454

Cited by 86 publications

(103 citation statements)

References 22 publications

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“…It has become increasingly valuable in considering the allocation of funds to providers, as it helps quantify patients’ clinical needs and predict their expected resource use and costs accordingly (Management Decision and Research Center (MDRC) ). For this study, we used Nosos risk scores ( Nosos is the Greek word for chronic disease), which were developed in VHA in order for researchers and managers to adjust for the sociodemographic and clinical characteristics of Veterans when determining their annual concurrent and/or prospective expected total VHA costs (Wagner et al ,c). The Nosos risk model expands on the Centers for Medicare and Medicaid Services (CMS) risk adjustment model, designed to adjust capitated payments for Medicare Advantage plans.…”

Section: Methodsmentioning

confidence: 99%

Differences in Risk Scores of Veterans Receiving Community Care Purchased by the Veterans Health Administration

Rosen¹,

Pettey

Shwartz³

et al. 2018

Health Services Research

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Objective To assess differences in risk (measured by expected costs associated with sociodemographic and clinical profiles) between Veterans receiving outpatient services through two community care (CC) programs: the Fee program (“Fee”) and the Veterans Choice Program (“Choice”). Data Sources/Study Setting Administrative data from VHA's Corporate Data Warehouse in fiscal years (FY) 2014–2015. Study Design We compared the clinical characteristics of Veterans across three groups (Fee only, Choice only, and Fee & Choice). We classified Veterans into risk groups based on Nosos risk scores and examined the relationship between type of outpatient utilization and risk within each CC group. We also examined changes in utilization of VHA and CC in FY14–FY15. We used chi‐square tests, t tests, and ANOVAs to identify significant differences between CC groups. Principal Findings Of the 1,400,977 Veterans using CC in FY15, 91.4 percent were Fee‐only users, 4.4 percent Choice‐only users, and 4.2 percent Fee & Choice users. Mean concurrent risk scores were higher for Fee only and Fee & Choice (1.9, SD = 2.7; 1.8, SD = 2.2) compared to Choice‐only users (1.0, SD = 1.2) (p < .0001). Most CC users were “dual users” of both VHA and CC in FY14–FY15. Conclusions As care transitions from VHA to CC, VHA should consider how best to coordinate care with community providers to reduce duplication of efforts, improve handoffs, and achieve the best outcomes for Veterans.

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Section: Methodsmentioning

confidence: 99%

Differences in Risk Scores of Veterans Receiving Community Care Purchased by the Veterans Health Administration

Rosen¹,

Pettey

Shwartz³

et al. 2018

Health Services Research

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“…We calculated the propensity score for each patient to undergo frequent surveillance using logistic regression and all covariates listed in Table 1 [16][17][18] (for details on covariates, see Supporting Methods). Data analyses were performed from April 2018 to February 2019.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…We then performed propensity score-adjusted analyses to assess differences in outcomes between patients undergoing frequent versus recommended surveillance. We calculated the propensity score for each patient to undergo frequent surveillance using logistic regression and all covariates listed in Table 1 [16][17][18] (for details on covariates, see Supporting Methods). We Cancer September 15, 2019 then adjusted all subsequent analyses using this propensity score as a covariate.…”

Section: Statistical Analysesmentioning

confidence: 99%

The impact of frequent cystoscopy on surgical care and cancer outcomes among patients with low‐risk, non–muscle‐invasive bladder cancer

Schroeck

Lynch

et al. 2019

Cancer

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BACKGROUND:Surveillance recommendations for patients with low-risk, non-muscle-invasive bladder cancer (NMIBC) are based on limited evidence. The objective of this study was to add to the evidence by assessing outcomes after frequent versus recommended cystoscopic surveillance. METHODS: This was a retrospective cohort study of patients diagnosed with low-risk (low-grade Ta (AJCC)) NMIBC from 2005 to 2011 with follow-up through 2014 from the Department of Veterans Affairs. Patients were classified as having undergone frequent versus recommended cystoscopic surveillance (>3 vs 1-3 cystoscopies in the first 2 years after diagnosis). By using propensity score-adjusted models, the authors estimated the impact of frequent cystoscopy on the number of transurethral resections, the number of resections without cancer in the specimen, and the risk of progression to muscle-invasive cancer or bladder cancer death. RESULTS: Among 1042 patients, 798 (77%) had more frequent cystoscopy than recommended. In adjusted analyses, the frequent cystoscopy group had twice as many transurethral resections (55 vs 26 per 100 person-years; P < .001) and more than 3 times as many resections without cancer in the specimen (5.7 vs 1.6 per 100 person-years; P < .001). Frequent cystoscopy was not associated with time to progression or bladder cancer death (3% at 5 years in both groups; P = .990). CONCLUSIONS: Frequent cystoscopy among patients with low-risk NMIBC was associated with twice as many transurethral resections and did not decrease the risk for bladder cancer progression or death, supporting current guidelines. Cancer 2019;125:3147-3154.

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“…We categorized patients as urban (RUCA 1.0 or 1.1) or other (rural and highly rural, RUCA > 1.1) and aggregated as the proportion of rural patients at each clinic. To adjust for comorbidity, we used the Nosos risk score, a modified version of the Centers for Medicare and Medicaid (CMS) Hierarchical Condition Categories version 21 risk scores, which predicts expected health care costs based upon demographic and ICD‐9 or ICD‐10 diagnoses . The number of patients in each clinic in each year was extracted from the CDW and then standardized to unit variance within each year.…”

Section: Methodsmentioning

confidence: 99%

“…To adjust for comorbidity, we used the Nosos risk score, a modified version of the Centers for Medicare and Medicaid (CMS) Hierarchical Condition Categories version 21 risk scores, which predicts expected health care costs based upon demographic and ICD-9 or ICD-10 diagnoses. 17,18 The number of patients in each clinic in each year was extracted from the CDW and then standardized to unit variance within each year. ∑ 160 j=1 j is the mean timely care success rate across the 160 clinics in our analysis; t is the average trend over time in the timely care success rate across the 160 clinics; X j[t] is a matrix of average patient characteristics, average risk, and standardized number of patients in clinic j in year t; and j[t] is a random variable that represents the deviation in the timely care success rate in year t in clinic j from the mean timely care success rate in clinic j.…”

Section: Covariatesmentioning

confidence: 99%

Development of a novel metric of timely care access to primary care services

Batten

Augustine

Nelson

et al. 2020

Health Services Research

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Objective To develop a model for identifying clinic performance at fulfilling next‐day and walk‐in requests after adjusting for patient demographics and risk. Data Source Using Department of Veterans Affairs (VA) administrative data from 160 VA primary care clinics from 2014 to 2017. Study Design Using a retrospective cohort design, we applied Bayesian hierarchical regression models to predict provision of timely care, with clinic‐level random intercept and slope while adjusting for patient demographics and risk status. Timely care was defined as the provision of an appointment within 48 hours of any patient requesting the clinicʼs next available appointment or walking in to receive care. Data Collection/Extraction Methods We extracted 1 841 210 timely care requests from 613 263 patients. Principal Findings Across 160 primary care clinics, requests for timely care were fulfilled 86 percent of the time (range 83 percent‐88 percent). Our model of timely care fit the data well, with a Bayesian R2 of .8. Over the four years of observation, we identified 25 clinics (16 percent) that were either struggling or excelling at providing timely care. Conclusion Statistical models of timely care allow for identification of clinics in need of improvement after adjusting for patient demographics and risk status. VA primary care clinics fulfilled 86 percent of timely care requests.

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Risk Adjustment Tools for Learning Health Systems: A Comparison of DxCG and CMS‐HCC V21

Cited by 86 publications

References 22 publications

Differences in Risk Scores of Veterans Receiving Community Care Purchased by the Veterans Health Administration

Differences in Risk Scores of Veterans Receiving Community Care Purchased by the Veterans Health Administration

The impact of frequent cystoscopy on surgical care and cancer outcomes among patients with low‐risk, non–muscle‐invasive bladder cancer

Development of a novel metric of timely care access to primary care services

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