Risk Stratification in Primary Care : Value-Based Contributions of Provider Adjudication
© 2021. Society of General Internal Medicine..
BACKGROUND: In primary care risk stratification, automated algorithms do not consider the same factors as providers. The process of adjudication, in which providers review and adjust algorithm-derived risk scores, may improve the prediction of adverse outcomes.
OBJECTIVE: We assessed the patient factors that influenced provider adjudication behavior and evaluated the performance of an adjudicated risk model against a commercial algorithm.
DESIGN: (1) Structured interviews with primary care providers (PCP) and multivariable regression analysis and (2) receiver operating characteristic curves (ROC) with sensitivity analyses.
PARTICIPANTS: Primary care patients aged 18 years and older with an adjudicated risk score. APPROACH AND MAIN MEASURES: (1) Themes from structured interviews and discrete variables associated with provider adjudication behavior; (2) comparison of concordance statistics and sensitivities between risk models.
KEY RESULTS: 47,940 patients were adjudicated by PCPs in 2018. Interviews revealed that, in adjudication, providers consider disease severity, presence of self-management skills, behavioral health, and whether a risk score is actionable. Provider up-scoring from the algorithmic risk score was significantly associated with patient male sex (OR 1.24, CI 1.15-1.34), age > 65 (OR 2.55, CI 2.24-2.91), Black race (1.26, CI 1.02-1.55), polypharmacy >10 medications (OR 4.87, CI 4.27-5.56), a positive depression screen (OR 1.57, CI 1.43-1.72), and hemoglobin A1c >9 (OR 1.89, CI 1.52-2.33). Overall, the adjudicated risk model performed better than the commercial algorithm for all outcomes: ED visits (c-statistic 0.689 vs. 0.684, p < 0.01), hospital admissions (c-statistic 0.663 vs. 0.649, p < 0.01), and death (c-statistic 0.753 vs. 0.721, p < 0.01). When limited to males or seniors, the adjudicated models displayed either improved or non-inferior performance compared to the commercial model.
CONCLUSIONS: Provider adjudication of risk stratification improves model performance because providers have a personal understanding of their patients and are able to apply their training to clinical decision-making.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Journal of general internal medicine - 37(2022), 3 vom: 07. Feb., Seite 601-607 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ricci, Brian C [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 11.03.2022 Date Revised 02.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11606-021-06896-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM326483985 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021. Society of General Internal Medicine. | ||
| 520 | |a BACKGROUND: In primary care risk stratification, automated algorithms do not consider the same factors as providers. The process of adjudication, in which providers review and adjust algorithm-derived risk scores, may improve the prediction of adverse outcomes | ||
| 520 | |a OBJECTIVE: We assessed the patient factors that influenced provider adjudication behavior and evaluated the performance of an adjudicated risk model against a commercial algorithm | ||
| 520 | |a DESIGN: (1) Structured interviews with primary care providers (PCP) and multivariable regression analysis and (2) receiver operating characteristic curves (ROC) with sensitivity analyses | ||
| 520 | |a PARTICIPANTS: Primary care patients aged 18 years and older with an adjudicated risk score. APPROACH AND MAIN MEASURES: (1) Themes from structured interviews and discrete variables associated with provider adjudication behavior; (2) comparison of concordance statistics and sensitivities between risk models | ||
| 520 | |a KEY RESULTS: 47,940 patients were adjudicated by PCPs in 2018. Interviews revealed that, in adjudication, providers consider disease severity, presence of self-management skills, behavioral health, and whether a risk score is actionable. Provider up-scoring from the algorithmic risk score was significantly associated with patient male sex (OR 1.24, CI 1.15-1.34), age > 65 (OR 2.55, CI 2.24-2.91), Black race (1.26, CI 1.02-1.55), polypharmacy >10 medications (OR 4.87, CI 4.27-5.56), a positive depression screen (OR 1.57, CI 1.43-1.72), and hemoglobin A1c >9 (OR 1.89, CI 1.52-2.33). Overall, the adjudicated risk model performed better than the commercial algorithm for all outcomes: ED visits (c-statistic 0.689 vs. 0.684, p < 0.01), hospital admissions (c-statistic 0.663 vs. 0.649, p < 0.01), and death (c-statistic 0.753 vs. 0.721, p < 0.01). When limited to males or seniors, the adjudicated models displayed either improved or non-inferior performance compared to the commercial model | ||
| 520 | |a CONCLUSIONS: Provider adjudication of risk stratification improves model performance because providers have a personal understanding of their patients and are able to apply their training to clinical decision-making | ||
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