What is data warehousing in healthcare?
Jul 13, 2018 · The Health Catalyst Data Operating System (DOS™) is a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform. DOS serves as the …
How to choose the right warehouse for your healthcare data?
The technology that houses a traditional data warehouse is designed to manage transactional data that is highly dominated by numerical information. When textual, non transactional information is. encountered, the classical data warehouse technology of today is simply at a …
What are the challenges facing the healthcare data warehouse?
Oct 18, 2013 · October 18, 2013 - The data warehouse is often seen as the holy grail of analytics tools. A single, centralized, normalized data set containing every piece of information being used to produce reports leaves your organization with clean, vetted data uninhibited by silos or the …
What is prescriptive analytics in a data warehouse?
A data warehouse is distinct from a data mart, which is designed to focus on a single type of data or single business problem or report. Data marts are often subsets of data warehouses. 1 …
Is data warehouse a technology project?
It is essential to understand that a data warehousing project is not a technology project, even though it seems that the technology aspects of such projects may be overwhelming. Unless a data warehouse fits into the business and clinical goals of a healthcare organization, it can not be a success. Once it is decided to develop a data warehouse and the organization has completed the planning and data design, the organization must ensure that its technology infrastructure is adequate to support this venture.
What is data warehouse?
data warehouse is an example of the journey that data takes, when combined with context, to become information. Prior to application of context it is just a collection of numbers and letters, bits and bytes. Yet information is still not enough to enable an organization to learn from and act based on what they have collected. Dutch physicist Heike Kamerlingh Onnes who won the Nobel Prize in 1913 for having discovered superconductivity, knew even then what that missing ingredient was to take information to the next step, as indicated by his motto inscribed on a sign posted at the entrance to his laboratory: “Through measurement comes knowledge.” The ability to use accurate data and timely information to objectively measure and, therefore, proactively manage clinical outcomes and business processes demonstrates the value of a data warehouse.
Is privacy a new issue in healthcare?
Issues of privacy are nothing new to healthcare, yet the aggregation of vast amounts of patient data found in a data warehouse – especially when combined with tools specifically designed to mine such data – presents even more challenges for participating organizations in their roles as stewards of the information.
What is secondary use of information?
Secondary use of information is also a growing concern. Patient identifiable information is ultimately owned by the patient and while informed consent may have been obtained initially, the usefulness of information contained in a data warehouse grows concurrently and proportionately with the length of time that that data is maintained and as the volume of data in the warehouse grows. Such vast amounts of information are not often found elsewhere and may be attractive to those who may wish to use it for other purposes. Not only might such secondary usage not have been approved by the patient initially, there are many ethical and moral issues regarding such use of data that have arisen in the recent past that may also have a bearing on how an individual may wish their data to be used. In addition,
What is data integrity?
Data integrity is perhaps the most critical success factor for a successful data warehouse. Data available for analysis and reporting must be transformed so that it conforms to the established data model, and it must be cleansed so that it is free of duplicates and ambiguous or incorrect information. This is accomplished through the use of an Extract, Transform and Load (ETL) tool (or set of tools). ETL is an application that operates against a set of definitions to normalize the data. For example, identical data reported in different formats are mapped to the same form (Doctor, Dr., Dr, dr. and dr are all mapped to Dr). Such mapping is usually performed using temporary or staging tables.
What is data dictionary?
of which method is used, a data dictionary that defines the organization of the data in the warehouse and documents its contents is also essential. All commercial database products have data dictionary components. It is convenient as part of the schema design process to address additional elements that you might want in the data dictionary for your analytic data. These might include relationships among specific data elements (to enable or optimize certain types of analysis or reporting), information on the origin of the data, information on the transformation process and others. The data dictionary makes it possible for analytic and reporting applications to more correctly use the data that is available.
What are HIPAA regulations?
HIPAA regulations restrict the use and disclosure of patient identifiable information by health centers without patient authorization to outside entities (such as an HCCN). State privacy laws, which vary from jurisdiction to jurisdiction, may further restrict the use and disclosure of patient identifiable information. Moreover, legal responsibility for maintaining confidentiality and accounting for such uses and disclosures, and the potential liability if such laws are not complied with, falls upon the health centers participating in a data warehouse initiative. The fact that an HCCN may include health centers across state lines is an important consideration as privacy laws in the strictest participating state should then be observed in matters relating to the data warehouse to ensure compliance.
What is electronic storage in healthcare?
Electronic storage of healthcare data, including individual-level risk factors for both infectious and other diseases, is increasing. These data can be integrated at hospital, regional and national levels. Data sources that contain risk factor and outcome information for a wide range of conditions offer the potential for efficient epidemiological analysis of multiple diseases. Opportunities may also arise for monitoring healthcare processes. Integrating diverse data sources presents epidemiological, practical, and ethical challenges. For example, diagnostic criteria, outcome definitions, and ascertainment methods may differ across the data sources. Data volumes may be very large, requiring sophisticated computing technology. Given the large populations involved, perhaps the most challenging aspect is how informed consent can be obtained for the development of integrated databases, particularly when it is not easy to demonstrate their potential. In this article, we discuss some of the ups and downs of recent projects as well as the potential of data warehousing for antimicrobial resistance monitoring.
What is healthcare epidemiology?
Healthcare epidemiology, a branch of epidemiology concerned with the detection, control, and prevention of adverse events in the health economy, has gained prominence in recent years.1 This is attributable partly to a desire to learn more about the determinants of morbidity, mortality and cost in modern healthcare, and partly to an expectation that continuous quality monitoring and benchmarking can be built into efficient management systems.2
What is the responsibility of a data warehouse?
While a major part of a data warehouse’s responsibility is to simplify your business data, most of the work that will have to be done on your part is inputting the raw data.
What are the pros and cons of DW?
One of the pros and cons of your DW is its ability to consistently update. This is great for the business owner who wants the best and latest features, however these upgrades don’t usually come cheap.
What are the drawbacks of data warehouses?
Using data warehouses also poses some drawbacks, some of them are: 1 Throughout his life the data warehouses can suppose high costs. The data warehouse is not usually static. Maintenance costs are high. 2 Data warehouses may become obsolete relatively soon. 3 Sometimes, before a request for information, they return suboptimal information, which also represents a loss for the organization. 4 There is often a thin line between data warehouses and operational systems. It is necessary to determine which functionalities of these can be exploited and which ones should be implemented in the data warehouse, it would be costly to implement unnecessary operations or to stop implementing any that will be needed. 5 It is not very useful for making decisions in real time due to the long processing time it may require. In any case, the tendency of current products (together with hardware advances) is to solve this problem by converting the disadvantage into an advantage. 6 It requires continuous cleaning, transformation and data integration. 7 Maintenance. 8 In an implementation process, difficulties may be encountered in relation to the different objectives that an organization intends. 9 Once implemented, it can be difficult to add new data sources. 10 They require a review of the data model, objects, transactions and in addition to storage. 11 They have a complex and multidisciplinary design. 12 They require a restructuring of the operational systems.
How does a data warehouse work?
Data warehouses can work together and, therefore, increase the operational value of business applications, especially customer relationship management. Provides key information for business decision making. Improves the quality of decisions made. Especially useful for the medium and long term.
What is data warehouse?
In the context of computing, a data warehouse is a collection of data aimed at a specific area (company, organization, etc.), integrated, non – volatile and variable over time, which helps decision making in the entity in which it is used . It is used for reporting and data analysis 1 and is considered a fundamental component ...
What is the function of a data warehouse?
Function of a data warehouse. In a data warehouse what is wanted is to contain data that are necessary or useful for an organization , that is, that is used as a repository of data to later transform them into useful information for the user. A data warehouse must deliver the correct information to the right people at the right time and in ...
Why is it important to use a data warehouse?
Some of them are: Data warehouses make access to a wide variety of data easier for end users.
Is a data warehouse static?
The data warehouse is not usually static. Maintenance costs are high. Data warehouses may become obsolete relatively soon. Sometimes, before a request for information, they return suboptimal information, which also represents a loss for the organization.
Is obsolescence a risk?
On the other hand, obsolescence is another risk, since it can come too soon. And there are cases in which the effectiveness of data warehouses does not occur as we would like. There are times when the response to a query provides little and little information, which is not very useful for a complete report.
Can you move data from a trusted data zone into an exploration zone?
Anyone can decide to move data from the raw, trusted, or refined data zones into the exploration zone. Here, data from all these zones can be morphed for private use. Once information has been vetted, it is promoted for broader use in the refined data zone.
What is enterprise data model?
The enterprise data model approach (Figure 1) to data warehouse design is a top-down approach that most analytics vendors advocate for today. The goal of this approach is modeling the perfect database from the start—determining, in advance, everything you’d like to be able to analyze to improve outcomes, safety, and patient satisfaction, and then structuring the database accordingly.
What is raw data zone?
In the raw data zone, data is moved in its native format without transformation or binding to any business rules. Typically, the only organization or structure added in this layer is outlining what data came from what source system—Health Catalyst calls these areas source marts. Although all data starts in the raw data zone, it’s too vast of a landscape for less technical users. Typical users include ETL developers, data stewards, data analysts, and data scientists, who are defined by their ability to derive new knowledge and insights amid vast amounts of data. This user base tends to be small and spends a lot of time sifting through data, then pushing it into other zones.
What is refined data?
Refined data is used by a broad group of people, but is not yet blessed by everyone in the organization.
What is the Health Catalyst approach?
Health Catalyst believes that a methodology of binding data at the right time is the right approach (sometimes early, sometimes late, and sometimes in between). Adopting a methodology that restricts your flexibility in binding early or late limits your ability to be successful with your analytic efforts.