12/4/2023 0 Comments Hospital compare data setsThe addition of this metadata dictionary allows FAE to merge multiple tables into one file for analysis. Dictionaries are metadata in that their purpose is to help normalize and explain the larger dataset. Meta-thinking is when a person thinks about their own thinking patterns. When transforming the data, FAE is able to look up the meaning of these column headers and measure identifiers to standardize the data and create one merged dataset.ĭictionaries are a form of metadata, data that helps us to explain and process other data. The same type of dictionary is used to normalize measure identifiers. ![]() When FAE sees a synonym, the dictionaries make it possible to translate that synonym into its canonical value. Column headers “End_Date”, “Measure End Date”, “FUH_Measure_End_Date”, and “Flu_Season_End_Date” are all synonyms to the canonical value “end_date”. Dictionaries are data structures that allow us to connect multiple synonyms for one canonical or standardized value. ![]() In order to normalize column headers and measure identifiers, FAE uses dictionaries. This post focuses on the transformation step, in which we normalize or standardize the data format, while considering the fact that column and row orientation, headers, and measure identifiers are all inconsistent from file to file. The majority of this process, known as ETL, is completed by the Freedman Analytical Engine (FAE). health system.In the previous blog post, I explained the general ETL process for CMS quality data.Īnalysis of quality data from the Centers for Medicare and Medicaid Services (CMS), just like all payer claims data, population health management data, and other data sources, requires the data to be extracted, transformed, and loaded. The brief is available on the Peterson-KFF Health System Tracker, an online information hub dedicated to monitoring and assessing the performance of the U.S. The authors note that the available hospital data does not always clearly indicate the market in which a payer is operating thus, an analysis of variation in prices by insurer market segment is not possible for most hospitals examined. For example, the price of a lower back MRI at a hospital in New Mexico ranged from $221 to $2,142 depending on the payer. hospitals, the brief finds significant variation in the price of common services. Using payer-negotiated rates from ten U.S. ![]() While the new price transparency data does not yet support price comparison across hospitals, it could in some cases facilitate analysis of price variation within a hospital. Many hospital machine-readable files are inconsistently formatted and leave out key information, including the full range of payers and plans in a given region. For example, some hospitals include professional fees (e.g., for physician services) in the posted prices, other hospitals do not, and still others do not specify either way. ![]() Many of the hospitals included in the analysis define and describe prices differently. Only 35 of the 102 hospitals included in the analysis provide some payer-negotiated rates accessible to the public in a machine-readable file only 3 provide payer-negotiated rates via consumer tools.Įven when hospitals are compliant, the lack of data standardization makes it difficult to compare prices across facilities. Using data collected from large hospitals in all 50 states and the District of Columbia, the analysis finds limited compliance with the new federal rule. To be compliant, hospitals must post payer-specific negotiated rates for medical services and products in two formats on their websites: in a machine-readable file that insurers, employers, health care providers, and other stakeholders can use to compare prices across providers, and in a consumer-friendly tool that allows patients to shop for lower-priced care. The federal rule, which went into effect on January 1, 2021, aims to lift the veil on how much health plans pay hospitals for health services. In spite of a new price transparency rule that requires hospitals to publish the prices of common health services, comparing prices across hospitals remains challenging due to limited compliance with the law and a lack of standardization in the available data, a new KFF analysis finds.
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