Predicting diagnostic coding in hospitals: individual level effects of price incentives

Abstract

The purpose of this paper is to test if implicit price incentives influence the diagnostic coding of hospital discharges. We estimate if the probability of being coded as a complicated patient was related to a specific price incentive. This paper tests empirically if upcoding can be linked to shifts in patient composition through proxy measures such as age composition, length of stay, readmission rates, mortality- and morbidity of patients. Data about inpatient episodes in Norway in all specialized hospitals in the years 1999–2012 were collected, N = 11 065 330. We examined incentives present in part of the hospital funding system. First, we analyse trends in the proxy measures of diagnostic upcoding: can hospital behavioural changes be seen over time with regards to age composition, readmission rates, length of stay, comorbidity and mortality? Secondly, we examine specific patient groups to see if variations in the price incentive are related to probability of being coded as complicated. In the first years (1999–2003) there was an observed increase in the share of episodes coded as complicated, while the level has become more stable in the years 2004–2012. The analysis showed some indications of upcoding. However, we found no evidence of widespread upcoding fuelled by implicit price incentive, as other issues such as patient characteristics seem to be more important than the price differences. This study adds to previous research by testing individual level predictions. The added value of such analysis is to have better case mix control. We observe the presence of price effects even at individual level.

Bias due to re-used databases: Coding in hospital for extremely vulnerable patients

“…0.0005, Delta-method). This upcoding behaviour deriving from direct financial incentive is consistent with the existing literature on the topic ( [18]- [20]).…”

Methodologies to Measure Upcoding in Provider Settings: A Scoping Review

“…Dafny implemented this model at both the DRG-year level and the DRGhospital-year level, using administrative claims data on inpatient admissions (Dafny, 2005). Six other studies, including all four that examined changes in DRG weights (or the equivalent), used the same overall modeling setup as the Dafny article (Barros and Braun, 2017;Januleviciute et al, 2016;Milcent, 2021;Anthun, Bjørngaard, and Magnussen, 2017;Anthun, 2022;Cook and Averett, 2020). All studies used repeated cross-sectional analyses, with consistent findings that increases in spread were associated with increases in the frequency of the DRG with the highest complexity.…”

Do budget constraints limit access to health care? Evidence from PCI treatments in Hungary

“…1 It must be noted that price increases might not only lead to increases in quantity or quality of services, but can also trigger upcoding (Shin, 2019 ). Health systems dominated by public providers can also be prone to upcoding, as demonstrated in, for example, France (Milcent, 2021 ), Portugal (Barros & Braun, 2017 ) or Norway (Anthun, 2022 ; Januleviciute et al, 2016 ), however, the effect seems to be moderate.…”