PubMed Health⌕ Search

PubMed · 10136821

Case-mix adjustment, claims data quality, and physician profiling.

Abstract

On the surface, there are many ways in which case mix may be considered, but the key to any successful system often lies below the surface, in the way in which the available data feed the systems, and in the operational environment in which the output is to be used. This article will review terminology used in case-mix adjustment and profiling techniques, highlight some dangerous (and avoidable) data pitfalls, and emphasize reasonable goals that can be achieved through the use of case-mix adjusted profiling.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N A Perkins. 1994. Case-mix adjustment, claims data quality, and physician profiling.. https://pubmed.ncbi.nlm.nih.gov/10136821/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗