title: Using 'big data' to validate claims made in the pharmaceutical approval process.
abstract: Big Data in the healthcare setting refers to the storage, assimilation, and analysis of large quantities of information regarding patient care. These data can be collected and stored in a wide variety of ways including electronic medical records collected at the patient bedside, or through medical records that are coded and passed to insurance companies for reimbursement. When these data are processed it is possible to validate claims as a part of the regulatory review process regarding the anticipated performance of medications and devices. In order to analyze properly claims by manufacturers and others, there is a need to express claims in terms that are testable in a timeframe that is useful and meaningful to formulary committees. Claims for the comparative benefits and costs, including budget impact, of products and devices need to be expressed in measurable terms, ideally in the context of submission or validation protocols. Claims should be either consistent with accessible Big Data or able to support observational studies where Big Data identifies target populations. Protocols should identify, in disaggregated terms, key variables that would lead to direct or proxy validation. Once these variables are identified, Big Data can be used to query massive quantities of data in the validation process. Research can be passive or active in nature. Passive, where the data are collected retrospectively; active where the researcher is prospectively looking for indicators of co-morbid conditions, side-effects or adverse events, testing these indicators to determine if claims are within desired ranges set forth by the manufacturer. Additionally, Big Data can be used to assess the effectiveness of therapy through health insurance records. This, for example, could indicate that disease or co-morbid conditions cease to be treated. Understanding the basic strengths and weaknesses of Big Data in the claim validation process provides a glimpse of the value that this research can provide to industry. Big Data can support a research agenda that focuses on the process of claims validation to support formulary submissions as well as inputs to ongoing disease area and therapeutic class reviews.
authors.affiliation: a Healthcore Inc. , Wilmington , DE USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Wasser
authors.affiliation: a Healthcore Inc. , Wilmington , DE USA.
authors.forename: Kevin
authors.initials: K
authors.lastname: Haynes
authors.affiliation: a Healthcore Inc. , Wilmington , DE USA.
authors.forename: John
authors.initials: J
authors.lastname: Barron
authors.affiliation: a Healthcore Inc. , Wilmington , DE USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Cziraky
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D063025:Health Information Management / Q000379:methods / Q000458:organization & administration / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review / Q000592:standards / Q000706:statistics & numerical data*; D007356:Insurance, Pharmaceutical Services / Q000706:statistics & numerical data*; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data*; D011358:Product Surveillance, Postmarketing / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data*; D054928:Validation Studies as Topic
keywords: Bias; Big data; Claims analysis; Outcomes research; Validity