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September 2020 Inferring a consensus problem list using penalized multistage models for ordered data
Philip S. Boonstra, John C. Krauss
Ann. Appl. Stat. 14(3): 1557-1580 (September 2020). DOI: 10.1214/20-AOAS1361

Abstract

A patient’s medical problem list describes his or her current health status and aids in the coordination and transfer of care between providers. Because a problem list is generated once and then subsequently modified or updated, what is not usually observable is the provider-effect. That is, to what extent does a patient’s problem in the electronic medical record actually reflect a consensus communication of that patient’s current health status? To that end, we report on and analyze a unique interview-based design in which multiple medical providers independently generate problem lists for each of three patient case abstracts of varying clinical difficulty. Due to the uniqueness of both our data and the scientific objectives of our analysis, we apply and extend so-called multistage models for ordered lists and equip the models with variable selection penalties to induce sparsity. Each problem has a corresponding nonnegative parameter estimate, interpreted as a relative log-odds ratio, with larger values suggesting greater importance and zero values suggesting unimportant problems. We use these fitted penalized models to quantify and report the extent of consensus. We conduct a simulation study to evaluate the performance of our methodology and then analyze the motivating problem list data. For the three case abstracts, the proportions of problems with model-estimated nonzero log-odds ratios were $10/28$, $16/47$ and $13/30$. Physicians exhibited consensus on the highest ranked problems in the first and last case abstracts but agreement quickly deteriorated; in contrast, physicians broadly disagreed on the relevant problems for the middle—and most difficult—case abstract.

Citation

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Philip S. Boonstra. John C. Krauss. "Inferring a consensus problem list using penalized multistage models for ordered data." Ann. Appl. Stat. 14 (3) 1557 - 1580, September 2020. https://doi.org/10.1214/20-AOAS1361

Information

Received: 1 October 2019; Revised: 1 June 2020; Published: September 2020
First available in Project Euclid: 18 September 2020

MathSciNet: MR4152146
Digital Object Identifier: 10.1214/20-AOAS1361

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 3 • September 2020
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