DescriptionThis section covers the basic components involved i

Description
This section covers the basic components involved in
processing, storage, data input and output, and connectivity.
Also covered is the software systems and connectivity between
users and devices.
Course Topics
Basic Hardware
Network
Input/Output
Interface and security
Database
HIPAA
Hardware Basics
Mainframe – one large computer
Server – Program or devices that connects multiple computers
(distributed processing)
PC – one “small” computer
Mainframe
Operating system: Propriety (IBM, HP, Cray), UNIX/LINUX,
several command systems
Storage: Tandem, Non-Stop, RAID 5
Terminals and devices

Servers
Operating systems: Windows, LINUX, MacOS, coordination
between devices
Storage: Cisco, Dell, HP, Fijitsu, file servers, blade servers
Network server and routers
Desktop computing
Workstation (WOWs and COWs), laptops, tablets
Fat client v. thin client
ASP model – application service provider
Peer to peer
Network Connectivity
Network
Size: LAN, PAN, WAN, MAN
Protocols:
Network routing: Ethernet
Internet: TCP, HTTP
Wireless Network: Wi-Fi, Bluetooth, LTE
Copper and fiber optics
Types of Networks
Closed network
Users – direct access or modem
VPN – connection from a public to a private
Business to business – protocol or encryption
Open network
Cloud storage and cloud computing
Internet

Input and Output Devices
Input devices:
Keyboard and mouse
Scanners and cameras
Voice to text
Barcode readers
Sensors and automated input (monitoring, dispensing, infusion,
robotic devices)
Output devices:
Screens and Printers
Robotics
Interfaces
Connection in network
Device to Device
System to system: Lab – Radiology
EHR to other organizations, government, insurance companies
HL7
HL7 segments and fields
Sending and receiving systems need to be coordinated on the
fields
Example
MSH|^~&|EPIC|EPICADT|SMS|SMSADT|199912271408|CHA
RRIS|ADT^A04|1817457|D|2.5|
PID||0493575^^^2^ID
1|454721||DOE^JOHN^^^^|DOE^JOHN^^^^|19480203|M||B|254
MYSTREET

AVE^^MYTOWNÔH^44123ÛSA||(216)1234567|||M|NON|400
003403~1129086|
NK1||ROE^MARIE^^^^|SPO||(216)123-4567||EC|||||||||||||||||||||||||||
PV1||O|168 ~219~C~PMA^^^^^^^^^||||277ÂLLEN
MYLASTNAME^BONNIE^^^^||||||||||
||2688684|||||||||||||||||||||||||199912271408||||||002376853
Internal security
Access to system
Secure workstations
Passwords
Authentication
Firewall
Database Basics
Flat file
Relational database
Use of data dictionaries
Object oriented database
HIPAA Regulation
HIPAA - Medical focus and reimbursement focus
Creation of standards
Creation of privacy - PHI (policy and informatics)
Creation of security - creation of user and organization security
Creation of recovery - backups and recovery
Retention and release of information

1 This course reviews the topics that are included in the
concepts and skills in understanding and managing healthcare
data standards and interoperability.
2 The way that data standards are maintained and sustained
within the electronic health record is how the data is mapped
and stored. Data architecture is the foundation of the
specification and requirements that are needed for data
modeling. Where the data is stored and how it’s arranged builds
the strategy for creating good data standards and allows
interoperability. This module looks at the approach to creating
standards from modeling and design to mapping, leveraging
master data management and reference files, master files, and
dictionary files to allow data to be used across healthcare
information technology. This module also covers how data
mapping and modeling impact security, life cycle, and disaster
recovery and how the approaches to metadata are used to
organize data for data mining.
3 The objectives in this module focus on data standards and how
storage, mapping, routing, and how the data is arranged, impact
interoperability and integration with healthcare information
technology. The role of data architecture through modeling and
mapping becomes the foundation for many of the functions in
the EHR, both for the healthcare of patients and system
operations.
4 One of the biggest impacts on data standards in healthcare
information systems is the system architecture. The architecture
is a master plan of the data, including the physical and
electronic layout and rules that govern the flow of data within a
system. When electronic health records first started, the scope
of system architecture was confined to a simple database that
included orders and results. The scope has increased so that

several applications and databases support CPOE, computerized
physician order entry. Adding patient information management,
revenue management, ambulatory scheduling, ancillary systems,
and hospital operations, has created a diverse and complicated
architecture of the healthcare enterprise. The first step in
architecture is modeling, understanding the data value including
type, use, and flow within the healthcare system. Since most
healthcare information systems are an aggregation of many
smaller systems, data modeling becomes increasingly important
in managing this complexity to maintai n standardization. It is
this standardization that allows integration between system and
healthcare interoperability to occur, no matter the secured
format. A data architect is a role that manages the master plan,
but this role is divided between individuals and departments,
some of which never communicate with each other. We find that
much of the modeling and design of the enterprise architecture
is established in the individual application and programs that
make up the healthcare information system. Each different
system has its ways of managing healthcare information and has
its own rules for organizing data in a database. For each
program, the architect is the healthcare application vendor. The
vendor will define how the data will be stored in their
application, however in some cases, the healthcare organization
has some control over the type of data being stored. For
example, one of the fields in an EHR is weight. This can be
expressed in pounds or kilos, depending on what the
organization decides is the standard. In addition to the
programmatic data master plan, there also needs to be a data
plan on how data is shared between applications and across
physical devices. Weight may be information for the lab
information system and the master plan sharing the weight
between the EHR and LIS needs to be defined. This is typically
part of the system operations department as this would be
passed from one system to another by interface, network, or
internet routing. The complexity of defining standards by
creating and managing a healthcare enterprise architecture is

complex and vital and must always be done in a secure
exchange format such as HL7 or FHIR.
5 One of the ways to manage data standards is through master
data management. Master data management is identifying
identity data and referent data to create a single consistent point
of reference. When we reviewed the electronic medical records,
the typical point of reference for patient identity is the medical
record number. Some organizations have variations on this
name, but all have a single point of reference that all the
demographic data links to. There are rules and standards
regarding this number, such as numeric or alphanumeric,
acceptable length, or the presences of a check digit but all
applications that use this as a standard have rules that maintain
the integrity of this standard. Also, the concept that is included
in identity data and referent data is the use of dictionary tables
or lookup tables to maintain data standards. Before entering a
new patient, staff should search for them by date of birth and
other identifying factors and compare possible matches to avoid
2 records for 1 patient. Duplicate patients can create fraud and
abuse situations, inaccurate patient reporting, and other medical
authentication concerns within a practice. An example of a data
dictionary in the electronic health record is the chargemaster.
The chargemaster is a database that includes all relevant data
about all hospital charges including insurance and employer
information and may be linked to other tables such as order
entry or medication master file. Chargemasters are consistently
priced, and may include allowables per payer contracts to help
organizations and posters know what the appropriate amount to
collect would be. This allows the system to have a code or value
that is linked to a set of other standard values so that data
integrity is maintained. The role of master data management
becomes an important rule that governs data management.
Another type of data that is critical to establish data standards

within a healthcare system is metadata management. Metadata is
data about data and is being used increasingly in data analytics
and reporting. One way that metadata is used is to monitor
activities and timestamps for nursing management. In the days
of paper records, nurses were audited by the nurse manager to
verify they documented patient progress at defined time
intervals. Now electronic health records include a timestamp
when clinical documentation occurs, and this metadata can be
used to programmatically to audit nursing and physician chart
documentation. Metadata is also added to data when extracted to
a data warehouse to allow cataloging and indexing of data. This
includes when a claim is released to the clearinghouse and
payer, and when a response and payment is returned.
Interoperability is using more metadata and standards to define
metadata is becoming a great part of the data architecture. This
means that managing data architecture is becoming more
complex and the rules to set up new or modified applications
need a process to manage this outcome. Data governance is the
procedures and policies that maintain and enforce data
standards with the healthcare information system. The best data
governance for enterprise architecture has a couple of
characteristics. It includes all departments that interact with the
data and follows a defined set of rules for data integrity across
the system. When approved and implemented, an effective
enterprise architecture governance documents the new
architecture. The goal of maintaining standards is
interoperability and integration. Interoperability is the ability of
applications and programs to share information across the
system and integration is being able to combine information
from many sources into one unified view. Both interoperability
and integration require data standardization to work.
6 HITECH was created in 2009 to further secure patient
information methods and encouraged the use of EHRs. With this
in mind, data architecture is possible and is modeling healthcare

information. There are three different views or data models.
Each model represents how the data is stored and the
relationship between data items. Each model has a specific
purpose. The conceptual model defines what the system
contains. For example, a radiology information system contains
textual patient data and radiology picture files. The logical
model includes how the system is implemented and the data
stored within its application. This includes the programs and
rules to use and create data standards within the radiology
system. The physical data model is the models of how the
information is shared with the other system resources. The
physical model would include the method of connection and
routing for a radiological picture to be shared and viewed
within a CPOE system. This means that different maps get
created for each application and a system-wide map gets created
that shows the flow and traffic through the network, from the
source to the destinations. Standards in mapping may include
networking within an organization of electronic data
interchange between organizations. These connections all need
to be defined to establish standards from one system to another
and sometimes involve the transformation of data between
systems, a process typically found in interface engines. The
security of these methods follow the CIA triad- Confidentiality,
Integrity and Availability. During this process, the network map
of the data location and flow needs to be built and maintained
by the data architect to evaluate new applications or programs,
or by data governance for auditing. There are software programs
that can create data maps, based on maps or schema produced
by vendor or network engineers. Some of these software
programs can also create a data map based on system activity
and can be used to monitor system performance.
7 Two factors that define the data mapping is where the data is
stored and how it is organized in the application or program.

Some simple programs use flat files to store information. A flat
file is a simple text file that contains only numbers, letters, or
symbols and does not contain information about other files. A
common flat file in healthcare is called an ASCII file. ASCII
stands for American Standard Code for Information Interchange
where each 7-bit binary combination stands for a letter, number,
or symbol and is the basis for a text file. A tree file or
hierarchical file had a parent file with multiple children files;
however, it is more common to find a relational database where
the values of one table can join to a value on another table. The
relational database management system is more common and
relies on structured values. Relational databases in healthcare
created a means to provide improved data analytics but the
integration between databases meant the data values had to be
standardized. For example, if the radiology database could
create medical record numbers and the electronic health records
also created medical record numbers, there was the danger of a
mismatch of patient to their specific healthcare data. If the
radiology system creating a 10-digit number and the EHR only
stored 8-digit numbers, this would also be a problem. Using
master data management as a way to identify and enforce data
standards required understanding the data needed in each
database. Remember, not all healthcare data is structured data.
Recall the video about structured and unstructured data.
Pictures, documents, images, graphs, and transcribed text are all
examples of unstructured data. One approach to handle this was
a non-relational database to link non-tabular data to other data.
Approaches that link documents, graphs, and key pairs are ways
that databases can be used to organize healthcare data. Another
way to organize data is to extract it to a data warehouse. Data
warehouses can be a specific subset of the data found in an EHR
and can be transformed to manage data standards. Patient data
that can be linked back to a patient include their insurance
policy information, phone number, identifying patient
information, charges per visit, orders and results for imaging
and labs, and many others. Another use for a data warehouse is

to add metadata to be able to analyze the data that is extracted.
Data architecture and mapping are important to create network
and server strategies for system performance. Many practices
utilize a PM software on their network, but it is linked to a
server that they may or may not own. If a healthcare practice is
using another company’s server, is important to make sure their
data is secure, and it is on the vendor to have enough private
server capacity for the different client’s capacity that is needed.
The physical data map shows the flow of data between
applications and programs and allows the network engineers to
define the best path of data flow. The goal of network engineers
is to increase the speed of transmission of data packets and
reduce the number of data collisions. This can be done by static
or dynamic routing. Static routing is to set up the routing of
data between applications to take one defined pathway. The
advantage is the data packets stick to their path and cause less
traffic and data collision in other parts of the system. The
downside is that a static system can't take a less used route, it
has to stick to the defined path. A system with dynamic routing
will take different routes to get to the target program. Dynamic
routing can avoid traffic backup but may take a longer route to
get there. This balance between static and dynamic routing is
visualized through data modeling and mapping and is important
to understand to reach desired results.
8 Some of the processes in system operations affect data
standards. Three areas that are impacted by data standards are
security, data lifecycle, and disaster recovery. Cybersecurity
within an electronic health record is defined by HIPAA, which
defines the standards that patient data must be protected for
privacy, but also contain standards so that information can be
transmitted between the organization, such as the hospital and
Medicare for insurance reimbursement. The mapping in data
architectures needs to be able to define the data types that are
consistent between the healthcare organization and the

insurance provider so that an 837 transaction, an interface-
specific for sending hospitals, and reimburse data to replace
paper forms matches the data between the sending and receiving
systems. Another type of mapping that needs to be defined is
the type of access and encryption to allow authorized users to
access patient data. These functions are supported by data
standards and modeling and mapping efforts of data architects.
We had also covered the need for new programs and
applications to have up to date data maps. These mapping
programs can provide system performance metrics and can also
manage the software data life cycle or SDLC. Programs or
applications that are becoming out of date can be analyzed for
ways to extend or replace functionality and produce a set of
proposed requirements of application or program replacements.
Another system operation function is disaster recovery, the
activities to backup and restore vital system information. In the
data center project, I worked on, we analyzed available data and
the corresponding metadata for performance, frequency or
review, and amount of updated information; I was able to
propose disaster recovery processes for vital systems. Click the
check mark to learn extended information on data standards.
9 The goal of using data standards is to allow interoperability,
for the system to share information across the system. Sharing
information allows the system not to have to write a whole
record in multiple places but can use data from one source in
another. Interoperability also works with master data
management to maintain system integrity. If multiple sources of
the same information are used, then it's possible to create an out
of sync condition, where the same data source is represented by
two different values. The architecture strategy to focus on one
source of truth keeps the data consistent. The ability to
integrate data is to use data from multiple sources to one
combined view. Metadata is data that can be added to healthcare
data to provide additional analytic capabilities. One of the use s

of metadata is to manage "big data" or the overwhelming
amount of data points that is common in many large-scale
electronic health records. When there are too many data points,
in some cases it's harder to analyze the data. Data mining is a
technique to filter large collections of data to be able to analyze
trends or patterns. By adding metadata programmatically to
classes of information, techniques like indexing, sorting, and
chunking can be used. An example is the age and/or DOB of the
patient, time is a continuous type of data, but you wanted to
analyze the data by categories, you could add metadata that
created chunks, like boomers, generation x, and millennials,
three ranges of age groups. You could do something similar
with patient zip codes or the chief complaint/ reason for visit.
These approaches to how data is stored and mapped allows data
architects the ability to manage data using standards and can
create important reporting capabilities to higher-level
management when they are working on marketing campaigns or
trying to gauge what type of employees are needed for their
practice. Make sure you complete the readings for this week to
understand data standards and interoperability.
This week, you learned that healthcare organizations must track
patient data, audit trails, data mining purposes and all must be
done securely. The attached document focuses on a BI report
that provides data mining. Explain the security or lack thereof,
of this data. This will be counted as an analysis assignment, but
you will be writing and providing learned concepts on the data
from a sleep center BI report.

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