Dataset Information

Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia.

ABSTRACT:

Introduction

Formal dementia ascertainment with research criteria is resource-intensive, prompting the growing use of alternative approaches. Our objective was to illustrate the potential bias and implications for study conclusions introduced through the use of alternate dementia ascertainment approaches.

Methods

We compared dementia prevalence and risk factor associations obtained using criterion-standard dementia diagnoses to those obtained using algorithmic or Medicare-based dementia ascertainment in participants of the baseline visit of the Aging, Demographics, and Memory Study (ADAMS), a Health and Retirement Study (HRS) sub-study.

Results

Estimates of dementia prevalence derived using algorithmic or Medicare-based ascertainment differ substantially from those obtained using criterion-standard ascertainment. Use of algorithmic or Medicare-based dementia ascertainment can, but does not always, lead to risk factor associations that substantially differ from those obtained using criterion-standard ascertainment.

Discussion/conclusions

Absolute estimates of dementia prevalence should rely on samples with formal dementia ascertainment. The use of multiple algorithms is recommended for risk factor studies when formal dementia ascertainment is not available.

SUBMITTER: Power MC

PROVIDER: S-EPMC7725984 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia.

Power Melinda C MC Gianattasio Kan Z KZ Ciarleglio Adam A

Neuroepidemiology 20201019 6

<h4>Introduction</h4>Formal dementia ascertainment with research criteria is resource-intensive, prompting the growing use of alternative approaches. Our objective was to illustrate the potential bias and implications for study conclusions introduced through the use of alternate dementia ascertainment approaches.<h4>Methods</h4>We compared dementia prevalence and risk factor associations obtained using criterion-standard dementia diagnoses to those obtained using algorithmic or Medicare-based de ...[more]

PMID: 33075766

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Project description:BackgroundWith increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data.ObjectiveIn our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness.MethodsWe develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis-specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes).ResultsA typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset.ConclusionWe propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

Project description:ObjectivesAdministrative claims data sets are often used for emergency care research and policy investigations of healthcare resource utilization, acute care practices, and evaluation of quality improvement interventions. Despite the high profile of emergency department (ED) visits in analyses using administrative claims, little work has evaluated the degree to which existing definitions based on claims data accurately captures conventionally defined hospital-based ED services. We sought to construct an operational definition for ED visitation using a comprehensive Medicare data set and to compare this definition to existing operational definitions used by researchers and policymakers.MethodsWe examined four operational definitions of an ED visit commonly used by researchers and policymakers using a 20% sample of the 2012 Medicare Chronic Condition Warehouse (CCW) data set. The CCW data set included all Part A (hospital) and Part B (hospital outpatient, physician) claims for a nationally representative sample of continuously enrolled Medicare fee-for-services beneficiaries. Three definitions were based on published research or existing quality metrics including: 1) provider claims-based definition, 2) facility claims-based definition, and 3) CMS Research Data Assistance Center (ResDAC) definition. In addition, we developed a fourth operational definition (Yale definition) that sought to incorporate additional coding rules for identifying ED visits. We report levels of agreement and disagreement among the four definitions.ResultsOf 10,717,786 beneficiaries included in the sample data set, 22% had evidence of ED use during the study year under any of the ED visit definitions. The definition using provider claims identified a total of 4,199,148 ED visits, the facility definition 4,795,057 visits, the ResDAC definition 5,278,980 ED visits, and the Yale definition 5,192,235 ED visits. The Yale definition identified a statistically different (p < 0.05) collection of ED visits than all other definitions including 17% more ED visits than the provider definition and 2% fewer visits than the ResDAC definition. Differences in ED visitation counts between each definition occurred for several reasons including the inclusion of critical care or observation services in the ED, discrepancies between facility and provider billing regulations, and operational decisions of each definition.ConclusionCurrent operational definitions of ED visitation using administrative claims produce different estimates of ED visitation based on the underlying assumptions applied to billing data and data set availability. Future analyses using administrative claims data should seek to validate specific definitions and inform the development of a consistent, consensus ED visitation definitions to standardize research reporting and the interpretation of policy interventions.

Dataset Information

Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia.

Introduction

Methods

Results

Discussion/conclusions

Publications

Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia.

Similar Datasets

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