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Inferring a consensus problem list using penalized multistage models for ordered data.

Philip S Boonstra1, John C Krauss2

  • 1Department of Biostatistics, University of Michigan, USA.

The Annals of Applied Statistics
|August 9, 2021
PubMed
Summary
This summary is machine-generated.

Physician agreement on patient medical problem lists varies by case complexity. Consensus was found on key issues in simpler cases but decreased significantly in more complex scenarios, highlighting the provider effect in electronic health records.

Keywords:
L0 penaltyconditional multinomialranked listsvariable selection

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Area of Science:

  • Medical Informatics
  • Health Services Research
  • Statistical Modeling

Background:

  • Patient medical problem lists are crucial for care coordination and transfer.
  • The influence of individual providers (provider-effect) on problem list generation is not well understood.
  • Assessing consensus in problem list generation is vital for accurate patient health status communication.

Purpose of the Study:

  • To quantify the extent of consensus among medical providers when generating patient problem lists.
  • To analyze the impact of clinical case difficulty on provider agreement.
  • To evaluate a novel statistical approach for analyzing ordered list data.

Main Methods:

  • Utilized a unique interview-based design with multiple providers independently creating problem lists for three distinct patient case abstracts.
  • Applied and extended multistage models for ordered lists, incorporating variable selection penalties for sparsity.
  • Interpreted parameter estimates as relative log-odds ratios to quantify problem importance and consensus.

Main Results:

  • The proportions of problems with non-zero model-estimated log-odds ratios were 10/28, 16/47, and 13/30 across the three case abstracts.
  • Physicians reached consensus on the highest-ranked problems in the first and third (less difficult) case abstracts.
  • Agreement among physicians significantly deteriorated for the most difficult, middle case abstract, indicating broad disagreement.

Conclusions:

  • Provider consensus on medical problem lists is not uniform and is influenced by clinical case complexity.
  • The developed penalized multistage models effectively quantify consensus and identify key problems.
  • Understanding and addressing the provider-effect is essential for improving the accuracy and reliability of electronic medical record problem lists.