Related Experiment Video
Updated: Oct 17, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Predicting diagnostic coding in hospitals: individual level effects of price incentives
1Department of Health Research, SINTEF Digital, Trondheim, Norway. kjartan.s.anthun@sintef.no.
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.
More Related Videos
06:16Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Related Concept Videos
Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
Health Information Technology and Healthcare Information System
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Receiver Operating Characteristic Plot
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.