Diagnostic, Pharmacy-Based, and Self-Reported Health Measures in Risk Equalization Models

Stam, Pieter J. A.; Vliet, René C.J.A. van; Ven, Wynand P.M.M. van de

doi:10.1097/mlr.0b013e3181d559b4

Cited by 32 publications

(31 citation statements)

References 18 publications

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“…Extensive literature demonstrates a link between various diagnosisbased and prior utilization-based measures of complexity, acute care utilization, and costs. [19][20][21][22][23][29][30][31][32] Generally, these approaches use administrative claims/billing, and less commonly, electronic medical record data to create risk groupings that predict future utilization and costs. [33][34][35] More limited literature demonstrates that adding outpatient utilization information and psychosocial factors improve predictive models for acute care utilization.…”

Section: Discussionmentioning

confidence: 99%

“…Current quantitative methods for identifying the complex patients at highest risk for suboptimal future clinical quality and utilization outcomes rely primarily on diagnosis-based and utilization-based algorithms to predict future utilization. [13][14][15][16][17][18][19][20][21][22][23] These tools miss clinical characteristics that are not present in billing data and may not capture non-clinical contributors to patient complexity.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

et al. 2015

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BACKGROUND: Improving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems. OBJECTIVE: The purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP). DESIGN: Using a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP. PARTICIPANTS: 143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY RESULTS: There was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p<0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c≥9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL≥130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDCcomplex patients had higher rates (p<0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS). CONCLUSION: Our measure for estimated physiciandefined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

et al. 2015

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“…A c c e p t e d M a n u s c r i p t Initial studies found that demographics alone have very low predictive power [10]. The introduction of diagnosis cost group/hierarchical conditions categories (DCG/HCC) greatly improves the predictive power compared to models that use sex and age alone as independent variables [24].…”

Section: Discussionmentioning

confidence: 99%

“…Others are based on diagnoses: Clinical Risk groups (CRGs) [6,7], Adjusted Clinical Groups (ACGs) [8] and Diagnostic Cost Groups, (DCG). Finally there are the mixed models that combine both -Diagnostic Cost Groups, (DCG/Rx) [9] and Adjusted Clinical Groups (ACG/Rx) [10].The usefulness of these models is especially relevant for integrated health systems such as National Health Systems. These tools described above also allow stratification of the population according to morbidity, which permits patients with the greatest risk to benefit from case management programmes.…”

Section: Introductionmentioning

confidence: 99%

Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups

et al. 2014

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“…Some models combining medicine code and diagnoses, Diagnostic Cost Groups, DCG/Rx [14] and Adjusted Clinical Groups ACG/Rx [15,16], have proven their efficacy, but need diagnoses accuracy information that is not always available.…”

Section: Adjusting Pharmaceutical Expenditure Backgroundmentioning

confidence: 99%

Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions

et al. 2011

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ElsevierVivas Consuelo, DJJ.; Guadalajara Olmeda, MN.; Barrachina Martínez, I.; Poza Plaza, EDL. (2011). Explaining primary health care pharmacy expenditure using classification of medications for chronic conditions. Health Policy. 103(1):9-15. doi:10.1016/j.healthpol.2011.08.014. 1Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions. David Vivas*, Natividad Guadalajara*, Isabel Barrachina*, José-Luis Trillo**, Ruth Usó** and Elena de-la-Poza*. AbstractBackground: The Valencian Autonomous Community (Spain) has implemented a scheme of purchasing services with the participation of public and private providers. Five districts are managed using PublicPrivate Partnership. The financing model is capitation and inter-centre invoice. The pharmaceutical benefits are not included in the per capita assignment.Objectives: Modeling and explaining pharmacy expenditure using electronic prescriptions drug data.Methods: A database of electronic prescription corresponding to 625,246 patients between Nov 2008 and Oct 2009 was used to run four linear models that explain the pharmaceutical expenditures. We take as dependent variable the neperian log of total pharmacy annual cost per patient in the primary health setting. The independent variables used combine demographics with revised classification in 18 chronic conditions obtained from the anatomical therapeutic chemical classification index (ATC).Results: The retrospective model selected included: gender, pharmaceutical co-payment status and 8 dummy variables for the number of chronic conditions of each patient from 1 to 8 or more. The goodness-of-fit achieved is measured in R 2 of 57%.Conclusions: These models must be considered in the current capitation system for pharmaceutical budgeting in a primary care setting established at regional level, as is the case in the Valencian Autonomous Community. The use of diagnostics and information regarding hospital encounters appears to be a complementary option for refining models of capitation of pharmaceutical and total health expenditure.

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Diagnostic, Pharmacy-Based, and Self-Reported Health Measures in Risk Equalization Models

Abstract: It is concluded that the self-reported health measures make an independent contribution to forecasting health care expenditures, even if the prediction model already includes diagnostic and pharmacy-based information currently used in Dutch risk equalization models.

Cited by 32 publications

References 18 publications

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups

Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions

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