Questions on validity of International Classification of Diseases-coded diagnoses

Surján, György

doi:10.1016/s1386-5056(98)00171-3

Cited by 77 publications

(62 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Whereas diseases, or pathologic conditions that affect the normal functioning of the body, are abstract (e.g., arteriosclerosis), diagnoses are more concrete, as they may be symptom-based (e.g., angina) or a series of facts that are not in themselves diseases (e.g., post-angiography). 32 This is a fundamental problem of translating one "language" that is relevant for clinical care to another "language" of standardized disease classifications that is important for epidemiology and health care planning. Neither physicians nor coders can solve this issue at an individual I know it's really hard to do, and it doesn't seem to ever happen.…”

Section: Discussionmentioning

confidence: 99%

Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study

2017

View full text Add to dashboard Cite

Background: Professional coding specialists ("coders") are experts at translating patient chart information into alphanumerical codes, which are then widely used in research and health policy decision-making. Coders rely solely on documentation by health care providers to complete this task. We aimed to explore physician-related barriers to coding that results in high-quality administrative data. Methods:In a qualitative study conducted from December 2015 to March 2016, we recruited 28 coders who worked in health care facilities in Alberta using purposive and snowball sampling. Semistructured interviews were conducted, audio-recorded and transcribed. The interviews delved into coder training, work environment, documentation and coding standards. Thematic content analysis of transcripts was performed by 2 study investigators through line-by-line coding and constant comparison, after which the codes were collated into themes. Results:Five themes emerged regarding physician-related barriers in coding of high-quality administrative data: 1) coders are limited in their ability to add to, modify or interpret physician documentation, which supersedes all other chart documentation, 2) physician documentation is incomplete and nonspecific, 3) chart information tends to be replete with errors and discrepancies, 4) physicians and coders use different terminology to describe clinical diagnoses and 5) there is a communication divide between coders and physicians, such that questions and issues regarding physician documentation cannot be reconciled.Interpretation: Physicians play a major role in influencing the quality of administrative data. There is a need for physicians to advocate for culture change in physicians' attitudes toward coders and chart documentation, in recognition of the importance of accurate chart information. Abstract ResearchSee related commentary by Nicholls and colleagues at

show abstract

Section: Discussionmentioning

confidence: 99%

Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study

2017

View full text Add to dashboard Cite

show abstract

“…The data set also does not contain information on patients who were not hospitalised. Within the data, diseases were recorded in the ICD-9-CM classification scheme format and potential errors for using the ICD classification scheme at entry, and disease coding in general have been noted (Surján, 1999). However, Medicare claims data have been shown to be both accurate (Zhang et al, 1999;Hennessy et al, 2007) and sensitive (Cooper et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Cancer metastasis networks and the prediction of progression patterns

et al. 2009

View full text Add to dashboard Cite

BACKGROUND: Metastasis patterns in cancer vary both spatially and temporally. Network modelling may allow the incorporation of the temporal dimension in the analysis of these patterns. METHODS: We used Medicare claims of 2 265 167 elderly patients aged X65 years to study the large-scale clinical pattern of metastases. We introduce the concept of a cancer metastasis network, in which nodes represent the primary cancer site and the sites of subsequent metastases, connected by links that measure the strength of co-occurrence. RESULTS: These cancer metastasis networks capture both temporal and subtle relational information, the dynamics of which differ between cancer types. Using these networks as entities on which the metastatic disease of individual patients may evolve, we show that they may be used, for certain cancer types, to make retrograde predictions of a primary cancer type given a sequence of metastases, as well as anterograde predictions of future sites of metastasis. CONCLUSION: Improvements over traditional techniques show that such a network-based modelling approach may be suitable for studying metastasis patterns.

show abstract

“…Reasons to code medical documents include data reduction, standardization, quality control, being able to compare individual cases, and making data available for research. In general, medical coding is considered a difficult and timeconsuming task [15,4,9,19]. Ever since the introduction of formal coding systems, attempts have been made to automate the coding process [16,20,11].…”

Section: Related Workmentioning

confidence: 99%

An Experiment in Automatic Classification of Pathological Reports

Zwaan

Sang

Rijke

2007

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Abstract. Medical reports are predominantly written in natural language; as such they are not computer-accessible. A common way to make medical narrative accessible to automated systems is by assigning 'computer-understandable' keywords from a controlled vocabulary. Experts usually perform this task by hand. In this paper, we investigate methods to support or automate this type of medical classification. We report on experiments using the PALGA data set, a collection of 14 million pathological reports, each of which has been classified by a domain expert. We describe methods for automatically categorizing the documents in this data set in an accurate way. In order to evaluate the proposed automatic classification approaches, we compare their output with that of two additional human annotators. While the automatic system performs well in comparison with humans, the inconsistencies within the annotated data constrain the maximum attainable performance.

show abstract

Questions on validity of International Classification of Diseases-coded diagnoses

Cited by 77 publications

References 25 publications

Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study

Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study

Cancer metastasis networks and the prediction of progression patterns

An Experiment in Automatic Classification of Pathological Reports

Contact Info

Product

Resources

About