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, among other things. 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 non-negative 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. For the three case abstracts, the proportions of problems with model-estimated non-zero 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 deteriorates; in contrast, physicians broadly disagreed on the relevant problems for the middle and most difficult case abstract.
Disciplines
Biostatistics | Health and Medical Administration | Medical Education | Statistical Models
Suggested Citation
Boonstra, Philip S. and Krauss, John C., "Inferring a consensus problem list using penalized multistage models for ordered data" (October 2019). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 126.
https://biostats.bepress.com/umichbiostat/paper126
Boonstra Revision 042320
Previous Versions
Included in
Biostatistics Commons, Health and Medical Administration Commons, Medical Education Commons, Statistical Models Commons