Linking individual medicare health claims data with work-life claims and other administrative data

SummaryObjective: The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays.Methods: We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database.Results: Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented.Conclusion: The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.Citation: Edlinger D, Sauter SK, Rinner C, Neuhofer LM, Wolzt M, Grossmann W, Endel G, Gall W. JADE: A tool for medical researchers to explore adverse drug events using health claims data. Appl Clin Inf 2014; 5: 621–629http://dx.doi.org/10.4338/ACI-2014-04-RA-0036

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Health Claims Data as a Strategy and Tool in Disease Management

Journal of Ambulatory Care Management ◽

10.1097/00004479-200104000-00008 ◽

2001 ◽

Vol 24 (2) ◽

pp. 69-85 ◽

Cited By ~ 8

Author(s):

Heidi Solz ◽

Kevin Gilbert

Keyword(s):

Disease Management ◽

Claims Data ◽

Health Claims ◽

Health Claims Data

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Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data

Clinical Epidemiology ◽

10.2147/clep.s23690 ◽

2011 ◽

pp. 259 ◽

Cited By ~ 2

Author(s):

Fionna Mowat ◽

Lau ◽

Whyte ◽

Kelsh ◽

Legg ◽

...

Keyword(s):

Electronic Medical Records ◽

Outcomes Research ◽

Medical Records ◽

Claims Data ◽

Health Claims ◽

Patient Registry ◽

Health Claims Data

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An Explainable Multi-Modal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data

10.1101/2020.11.07.20227454 ◽

2020 ◽

Author(s):

Thomas Linden ◽

Johann de Jong ◽

Chao Lu ◽

Victor Kiri ◽

Kathrin Haeffs ◽

...

Keyword(s):

Neural Network ◽

Network Architecture ◽

Claims Data ◽

Health Claims ◽

Superior Performance ◽

Patient Specific ◽

Administrative Claims ◽

Theoretic Approach ◽

Neural Network Architecture ◽

Health Claims Data

1AbstractEpilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological co-morbidities (e.g. anxiety, migraine, stroke, etc.). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for the individual epilepsy patient.In this work we use inpatient and outpatient administrative health claims data of around 19,500 US epilepsy patients. We suggest a dedicated multi-modal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model - DeepLORI) to predict the time dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods Moreover, we show that DeepLORI based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in the light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.

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