ied in a de-identified manner into a standard system such as
Informatics for Integrating Biology and the Bedside (i2b2).
Then, these systems are connected together and establish a
network, such as Shared Health Research Informatics Network, which is a network of i2b2 implementations.
There are also systems providing interoperability among
multiple data-generating systems within an organization,
and others that provide interoperability among organizations that belong to a certain group or geographic location. The former are positioned in-between data-generating
systems and data warehouses, because they would have to
rely on real-time data for day-to-day operations. These are
usually systems that are built in-house and are referred to
as an organization’s interface engine. Since these serve systems from different vendors, they have to transfer data in a
standard manner, such as by using Fast Healthcare Interoperability Resources (FHIR).
6 Health information exchanges—interoperability systems that connect different organizations—are also based on a standardized manner of data
transfer, such as a Continuity of Care Record or Continuity
of Care Document.
Health data is also used by Decision Support Systems
(DSS), whether they are clinical, administrative, or otherwise. Similar to interoperability systems, some DSS depend
on real-time data and others require only retrospective data,
but depend on the results of DA/BI systems. Therefore, the
former have to interface with data-generating systems and
the latter would perform above the DA/BI systems as shown
in Figure 4.
Implications, Challenges of the Health Data Lifecycle
HIIM professionals have an important role in all stages of the
health data lifecycle. Their impact on the data-generating systems is vital for data accuracy, precision, consistency, and timeliness. Therefore, there are several data management implications
for HIIM professionals dealing with these information systems:
There is a broad spectrum of systems and different types
of data that goes beyond this limited perception. It is essential to have a comprehensive understanding of health
data to harness its power and develop strategies to overcome business challenges.
Challenges with data. Harnessing the power of data
has its own challenges, given that data comes from disparate sources in different formats, such as radiology
data as images, physician notes as unstructured text,
precision medicine, and patient-generated data in very
large volumes. It is important to recognize that some
of the challenges with data is not unique to healthcare.
Therefore, healthcare can adopt data best practices
from other industries.
Real-time feedback into operational systems. As DA/
BI and reporting and visualization systems mature, the
next challenge becomes feeding the insight gained from
higher-level systems back into the operational systems
in real time, bringing new insights to end users. This
requires the ability to develop applications that can na-
Figure 2: Data-Generating Systems in Healthcare