Standards and Policy
A Standards and policy program is set to collect standards across the organizations’ departments and teams, review these standards against existing corporate standards and approve or make changes to them. For a 30-Day Hospital Readmissions goal set, all existing and new standards will be checked to ensure they will help with the execution of the goal. The team in charge of reviewing standards and policy can also be in charge of redefining the data strategy to align with the readmission goals. They help provide support for weak projects and bring them up to par with other projects in the enterprise landscape.
Data Quality Initiatives
A data quality program takes on the charge of finding correcting, monitoring, and fixing data quality and data quality issues in a healthcare organization. Data quality programs usually have profiling software and these software profiles the data, groups them, matches similar data and reduces redundancies.
Data quality initiatives also lead to the creating of master data management projects that have a bigger approach to data quality issues including issues that arise with big data. Master data management defines the master data sets and gives a 360-degree view of domains such as customer or Vendor. So for 30-Day Hospital Readmissions, master data management will set a data standard for the patient from the first day of readmission and start tracking even before they start receiving treatment.
The data storage and tracking goes on throughout the patients stay and so if the patient fails the 30-day readmission mark, their data history can shed light on why. This data tracking also leads a pattern to be formed and this pattern can help doctors and nurses predict when a patient is going to fail the 30-day readmission benchmark and then they can start putting things and extra effort in place to make sure it does not happen.
Data Security and Privacy
With any talk of data comes the talk of data security. Any healthcare organization that is serious about their data must put data security measures in place. Every healthcare organization should have compliance and regulatory systems in place. These compliance systems should include proper access passes for different level staff and management, information system controls, data privacy procedures, proper clearance structures etc.
This is particularly important and needed for sensitive data. In a healthcare organization, almost all data is sensitive data from patient’s medical records, to their financial information like insurance provider, credit card information, next of kin information etc. This kind of data in the wrong hands could be used for malicious purposes like identity theft and financial theft.
The right kind of data architecture will improve the operational efficiency of a healthcare organization. Data architecture usually includes components like Data Modelling, Master Data modeling, Service Oriented Architecture etc. Data architecture could also include programs built to promote the use of dat warehouses, data marts, and futuristic reporting.
Data Aggregation for Data Integration
Data aggregation refers to the process of taking data from multiple sources and multiple systems and interesting all of it into one system. For an organization that runs several systems, a data analyst might want to generate a report which uses data from all of them. An aggregation system makes that possible by taking all the data and aggregating it into one platform for easy assess and for clearer data representation. Data aggregation also helps to integrate data from different time periods. So a day old data will be put in its proper place in a system next to much older data and a data integration system will make sure there is no clash.
Without an aggregation system, data analysts will have to integrate the systems manually and this could cause delay, waste of time and resources and inefficiencies as there will be more room for human error. For example, a healthcare organization can build an integration app (or buy a software program built for solely this purpose) which queries the various systems. This app after querying the various systems merges the data and then produces a report.
This way a healthcare organization avoids having a separate database with different reporting formats. A data integration system is valuable because it allows a healthcare organization to attract and process data from multiple systems.
If there is a perfect example of an organization that has multiple systems it will be healthcare organization. Different departments, labs, clinics, etc data is always changing hands and treatment of a patient could cut across different systems. Using a data aggregation system means that the data is up to date at the time that you need it, does not get replicated, and can be processed or merged to produce the dataset you want.
The usefulness of the data aggregation system knows no bounds. Have you ever heard of API? Well, a data aggregation system can be used for creating orchestration APIs to modernize and aggregate legacy systems. So for healthcare organizations with older data systems, they do not have to worry about compatibility with new systems.
Another use of an aggregation system is for creating reports or dashboards which similarly have to pull data from multiple systems and create an experience with that data. A healthcare organization may have systems that they use for compliance or auditing purposes which need to have related data from multiple systems. The aggregation pattern is helpful in ensuring that a healthcare organization’s compliance data lives in one system but can be the amalgamation of relevant data from multiple systems.