Validation of diagnostic codes within medical services claims

Wilchesky, Machelle; Tamblyn, Robyn; Huang, Allen

doi:10.1016/s0895-4356(03)00246-4

Cited by 401 publications

(332 citation statements)

References 45 publications

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“…Second, the use of Medicare billing data to obtain key patient information (e.g., dosing, weight), although shown to be highly reliable (42)(43)(44), deserves further investigation to establish its accuracy. Patient body weight, assessed in our study at 30 d, may have undergone change in the months after transplantation, and these weights were not included in these analyses.…”

Section: Discussionmentioning

confidence: 99%

Influence of Early Posttransplantation Prednisone and Calcineurin Inhibitor Dosages on the Incidence of New-Onset Diabetes

Burroughs¹,

Lentine²,

Takemoto³

et al. 2007

Clinical Journal of the American Society of Nephrology

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Section: Discussionmentioning

confidence: 99%

Influence of Early Posttransplantation Prednisone and Calcineurin Inhibitor Dosages on the Incidence of New-Onset Diabetes

Burroughs¹,

Lentine²,

Takemoto³

et al. 2007

Clinical Journal of the American Society of Nephrology

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“…Methods for determining HF presence varied across studies, especially when chart review was used as the reference standard. Seven studies accepted HF diagnosis documented in the chart (12,(16)(17)(18)(19)(20)(21) and four studies used documented HF diagnosis or a combination of clinical judgement based on information in the chart (eg, test results, diagnostics) (22)(23)(24)(25). Eight studies (15,(25)(26)(27)(28)(29)(30)(31) ascertained HF presence based on diagnostic criteria used by scoring systems from Framingham, Carlson, Boston, the National Health and Nutrition Examination Survey (NHANES), the New York Heart Association (NYHA) or the European Society of Cardiology (ESC).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Twenty-one studies validated hospital discharge data, three studies (9,14,21) examined physician claims data and two studies (8,21) examined ambulatory care data. One study (14) linked multiple administrative data sources (facility, physician claims and pharmacy).…”

Section: Literature Reviewmentioning

confidence: 99%

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Administrative data have high variation in validity for recording heart failure

Quach

Blais

Quan

2010

Canadian Journal of Cardiology

127

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H eart failure (HF) has been widely studied because of its high mortality and morbidity rates (1-4). Studies on HF have relied on multiple data sources such as surveys, disease registries, hospital charts and administrative data. Of these sources, administrative data have been increasingly used for health services utilization and outcome evaluation.Administrative data result from implementing health care delivery, enrolling members into health insurance plans and reimbursing health care providers for services (5). The types of administrative data depend on the health care system and the funding structure. For example, Canada has a nationally funded health care system that collects hospital discharge data at a national level, while insurance registry, physician services and emergency room visit information is collected at a provincial level. Although these data are not intended for research, they are widely used in surveillance and outcome studies. Therefore, the validity of the research results rely on the quality of the data. Data quality is critical for interpreting variation across studies from different geographical areas.The purpose of the present study was to systematically review the literature pertaining to the validity of administrative data for recording HF and apply the International Classification of Diseases (ICD) HF definitions to Canadian hospital discharge abstract data to explore the prevalence of HF across ICD code definitions. METHODSLiterature review Search strategy: Only English language articles that compared the validity of ICD codes in administrative databases (eg, physician claims data, hospital discharge) with a reference standard (eg, chart review, registry, survey, case notes) were included. The literature search focused on articles published from 1990 to 2008. To identify peerreviewed journal articles, both the Ovid MEDLINE database and EMBASE were searched. Because validation studies were not indexed consistently with standard MeSH terms, the following key search terms were used: 'admin data', 'administrative data', 'population surveillance', 'heart failure', 'congestive heart failure', 'hospital data', 'physician claims', 'claims', 'hospital discharge', 'validation' and 'registries'. Next, Boolean operators were used to join search terms within the database search engines to locate articles. The reference lists from these articles were scanned to identify any additional articles that met heAlth outcomes/public policy ©2010 Pulsus Group Inc. All rights reserved Twenty-one studies validated hospital discharge abstract data; three studies validated physician claims and two studies validated ambulatory care data. Eighteen studies reported sensitivity (range 29% to 89%). Specificity and negative predictive value were greater than 70% across 17 studies. Nineteen studies reported positive predictive values (range 12% to 100%). Ten studies reported kappa values (range 0.39 to 0.84).

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“…58 A recent comprehensive study has shown that in the HCU databases, sensitivity was only small or moderate, whereas specificity was usually ≥95% (Table 3). 58,59 In general, a high specificity may be expected when diagnoses are based on hospital records, whereas performance is likely to be worse when diagnoses are based on out-of-hospital identification of diseases (eg, by general practitioners or specialists).…”

Section: Outcome Misclassificationmentioning

confidence: 99%