ABOUT DATA WAREHOUSE

About

Operational data is a key asset to the organization, but sometimes such data is generally not utilized to its full potential. Thus, the fundamental goal of the data warehouse is to enable user's appropriate access to a homogenized and comprehensive view of the organization, supporting forecasting and decision-making processes at the enterprise level. An additional goal of the data warehouse is to achieve information consistency. By bringing data from different data sources into a centralized repository, the data warehouse provides a homogenized view of the organization's data.


DWH is designed to keep all these things in mind. It is designed in a way that maximum can be utilized from the organization data. It utilizes data by showing it in three layers. The top layer is designed to keep in mind the high level authorities or policy makers(CEO, CFO, President, Vice President etc) that are interested in yearly data for all the locations. Second layer is for middle level authorities(General manager, Plant manager, Regional manager, Divisional manager etc) which are interested in one particular only for period of time. Third layer is for first level authorities(Office manager, Shift supervisor, Department manager etc) that are interested in one division of one location only for period of time.


DWH uses consistent data so whatever be the drill down level, data will be same. Inconsistent data may create a situation of confusion so consistency is necessarily maintained.


Features

The major benefits of maintaining a data warehouse are summarized below :


•  Empowers decision-makers by enabling direct access to accurate, consistent, and non-volatile information and by offering decision-support services that will facilitate the analysis and interpretation of Institutional data.
•  Redirects costly personnel hours from data gathering, matching and consolidating to data analysis. Reduces the need for information consumers to replicate data and maintain redundant tracking systems.
•  Promotes the no walls culture. The information sharing will lower the walls among all administrative and academic units, creating alliances and connecting people to each other and the Institute as a whole.
•  Improves data quality over time. Broader access to information will help better to define what types of information we need on hand for effective decision-making. This will identify areas for improvement in data gathering functions and data entry.
•  Enhances organizational effectiveness by extending and expanding the use of management information across the organization. The extensive use of management information will eventually promote the analytical culture that places value on information and analysis.
•  Improves integrity and conformity of organization-wide information. As more people in organization use the data, error will be identified and corrected thereby improving data integrity over time.
•  Improves access to the historical data. Historical data will be available to the broader campus community for ad hoc access and analytical processing.
•  Provides direct support to Institute executives and the organizations in collection, analysis, interpretation, and provision of Institute information.


Full Screenshot Tour

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  • Home Page

    data warehouse is to enable user's appropriate access to a homogenized and comprehensive view of the organization, supporting forecasting and decision-making processes at the enterprise level.

  • Cash Collection

    Monthly cash collection provides information of cash collected monthly and average of collected cash for 24 months

  • Cash Collection

    Cash collection daily shows collected cash for a month of particular year on daily basis on different grouping like payor, location, region.

  • Monthly Summary Report

    Monthly summaries provides monthly report of last three year for adjustment, billed volume, charges, collection % as billed, collection per patient, open AR, % open AR, average collection or charges per visit.

  • AR Distribution

    AR distribution show how company data is managed according to days bucket, how much data is remaining.

  • Billed Volume

    Billed volume shows last 12 months report of total number of generated voucher by location, payor, region etc criteria.

  • Collection Per Visit

    Monthly collection per visit shows data of collection for per visit by location, payor, region etc criteria.

  • Collection Percentage

    Collection percentage show cash collected in percentage form by location, payor, region etc criteria

  • Days In AR

    Days in AR shows average number of days for last 12 months for account receivables to be pending from their bill date/service date so that we can easily analyze for how many days our cash was/is pending.

  • E&M

    E&M shows monthly e&m level on basis of new and existing patient in chart form.

  • Open AR

    Open AR shows cash for last 12 months for account receivables to be pending from their bill date/service date for different criteria.

  • RVU

    Relative value unit used to find ranking of doctors to give increment and other facility accordingly.

  • Source of Collection

    Source of collection shows collected cash by payor for a reporting period.

  • Cash Flow Forecasting

    Cash flow forecasting provides functionality to forecast how much growth can be by changing charges and other functions.

  • Charge Lag analysis

    Charge lag analysis show average number of days of last 12 months for charge lag pending from their bill date/service date for different criteria.

  • Coding Pattern Analysis

    Coding pattern analysis show analysis/comparison of charges on basis of procedure code/test on different criteria.

  • GCR Analysis

    GCR analysis shows gross charges rate analysis for adjustment, charges, payment and ar balance for last 12 months.

  • Payer Mix Charges

    Payer mix charges shows quarterly charges data for last 12 months for all payer in percentage form.

  • Production Variance

    Production variance shows variation for cash collection, billed volume, Charges by number of visit for particular time period.

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