Data Aggregation In Healthcare Industry

With healthcare industry, you will get data everywhere. It is frequently split among different information systems like laboratory systems, electronic health records, radiology systems, and spreadsheets, databases, adjudicated claims and much more, which can be compatible.

April 8, 2019

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With healthcare industry, you will get data everywhere. It is frequently split among different information systems like laboratory systems, electronic health records, radiology systems, and spreadsheets, databases, adjudicated claims and much more, which can be compatible.

Generally, the data ownership can be siloed at the departmental level or team, hindering the organization’s ability to validate and standardize it. In this situation, it becomes easy to select data to give support for different viewpoints. However, this “healthcare data shopping” offers no strategic insights into what’s happening in the organization and stops leadership from making data-driven decisions.

Data Aggregation for Healthcare

Data Aggregation for Healthcare is the initial step towards solving the problem and making the “sole resource of truth,” which can be utilized to make decision-making in the organization strategically. Providing data to the level of the enterprise is required for “analytical modeling” or other “data analytics for healthcare.”

Data Aggregation in Healthcare at the Enterprise Level

A cross-functional and enterprise-level team approach for healthcare analytics.
Automate and streamline data collection with reporting efforts.
Build analytics like an organizational source, not any departmental source.
Identify standards for the data elements and sources.
Optimize the usage of current vendor, IT, and technology sources.
Select a platform, which can manage different data resources.
Set up ownership of the data quality management beyond IT.
Use of data authority in healthcare, which offers multi-disciplinary rights over tools, data, and procedures.

Using Data Aggregation in Healthcare for Breaking the Language Barriers between Roles

A by-product of compartmentalized data is an organization’s lack of consistent definitions. If one department calculates a newborn’s weight in pounds and the other uses kilograms, you can’t compare the data easily or use it outside the department.

For the healthcare network having multiple hospitals, duration of staying is a dominant example. Any nurse manager within the given hospital can define the duration length as the real days every patient is in the hospital. In contrast, finance is more expected to determine the length of staying by calculation.

Initially, this may look like a fine distinction. Nevertheless, the length of staying in hospitals with maternity wards is expected to be lesser because the majority of newborns are quickly discharged. Services like a psychiatric or trauma service will twist higher without considering the kind of patients being cared for. Data aggregation for healthcare helps standardization across different enterprises, which leads to insights into the giant picture.

Do you want to take data at an enterprise level? Contact 3i Data Scraping for Data Aggregation in healthcare and see how healthcare analytics can aggregate health data.