Development of a validation algorithm for 'present on admission' flagging

Abstract

BackgroundThe use of routine hospital data for understanding patterns of adverse outcomes has been limited in the past by the fact that pre-existing and post-admission conditions have been indistinguishable. The use of a 'Present on Admission' (or POA) indicator to distinguish pre-existing or co-morbid conditions from those arising during the episode of care has been advocated in the US for many years as a tool to support quality assurance activities and improve the accuracy of risk adjustment methodologies. The USA, Australia and Canada now all assign a flag to indicate the timing of onset of diagnoses. For quality improvement purposes, it is the 'not-POA' diagnoses (that is, those acquired in hospital) that are of interest.MethodsOur objective was to develop an algorithm for assessing the validity of assignment of 'not-POA' flags. We undertook expert review of the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM) to identify conditions that could not be plausibly hospital-acquired. The resulting computer algorithm was tested against all diagnoses flagged as complications in the Victorian (Australia) Admitted Episodes Dataset, 2005/06. Measures reported include rates of appropriate assignment of the new Australian 'Condition Onset' flag by ICD chapter, and patterns of invalid flagging.ResultsOf 18,418 diagnosis codes reviewed, 93.4% (n = 17,195) reflected agreement on status for flagging by at least 2 of 3 reviewers (including 64.4% unanimous agreement; Fleiss' Kappa: 0.61). In tests of the new algorithm, 96.14% of all hospital-acquired diagnosis codes flagged were found to be valid in the Victorian records analysed. A lower proportion of individual codes was judged to be acceptably flagged (76.2%), but this reflected a high proportion of codes used <5 times in the data set (789/1035 invalid codes).ConclusionAn indicator variable about the timing of occurrence of diagnoses can greatly expand the use of routinely coded data for hospital quality improvement programmes. The data-cleaning instrument developed and tested here can help guide coding practice in those health systems considering this change in hospital coding. The algorithm embodies principles for development of coding standards and coder education that would result in improved data validity for routine use of non-POA information.

Accuracy of surgical complication rate estimation using ICD-10 codes

“…On a practical level, an electronic ‘flag’ built into ICD‐10 classification systems can be recommended, so that the coder can identify a ‘complication’ already present on admission. Such a flagging option is available in the USA, Canada and Australia. This improves coding accuracy without the requirement for significant financial investment or training, thereby enhancing the value of inexpensive complication reports based on routinely collected data.…”

A classification of hospital‐acquired diagnoses for use with routine hospital data

“…We applied a recently developed computerised algorithm 22 to remove codes judged ineligible for the hospital‐acquired flag (ie, congenital or chronic conditions). On this basis, 14 898 diagnoses (2.9%) were removed.…”

How different are complications that affect the older adult inpatient?

“…Chronic and long-term conditions are not eligible for flagging as “hospital acquired” and were removed from further analysis using a data-cleaning algorithm 17. The clinical consequences of a hospital-acquired diagnosis can result in multiple codes being assigned to a single clinical admission.…”