153
Aspects of Ambient Assisted Living and Its Applications
Stage 2: Data Provisioning
Analysts need data from multiple source systems throughout the organization to produce
meaningful insights. For example, an analyst assisting a team of clinicians on a quality
improvement issue needs a variety of data from multiple source systems:
• EMR data (for clinical observations and lab results)
• Billing data (for identifying cohorts using diagnosis and procedure codes, charges,
and revenue)
• Cost data (for determining the improvements’ impact on margins)
• Patient satisfaction data
Aggregating data manually—pulling all of the data into one location in a common format and ensuring datasets are linked to each other (through common linkable identifiers
such as patient and provider identifiers)—is extremely time-consuming. It also makes data
more susceptible to errors. There are more effective ways to gather data.
Stage 3: Data Analysis
The data analysis starts after the appropriate data have been captured, pulled into a single
place, and tied together. The analysis process consists of several parts:
• Data quality evaluation: Analysts need to understand the data by taking time to
evaluate the same. They also need to note their method of evaluation with the
reference when they share their findings with the audience.
• Data discovery: Before attempting to answer a specific question, analysts should take
time to explore the data and look for meaningful oddities and trends. It is a critical
Data analysis
• Integrate data
• Discover new information
in the data (data mining)
• Evaluate data quality
Data provisioning
• Move data from
transactional systems
into the EDW
• Build visualization for
use by clinicians
Data capture
• Acquire key data
elements
• Acquire data quality
• Integrate data capture
into operational
window
FIGURE 8.12
IoT analytics.
Aspects of Ambient Assisted Living and Its Applications
Stage 2: Data Provisioning
Analysts need data from multiple source systems throughout the organization to produce
meaningful insights. For example, an analyst assisting a team of clinicians on a quality
improvement issue needs a variety of data from multiple source systems:
• EMR data (for clinical observations and lab results)
• Billing data (for identifying cohorts using diagnosis and procedure codes, charges,
and revenue)
• Cost data (for determining the improvements’ impact on margins)
• Patient satisfaction data
Aggregating data manually—pulling all of the data into one location in a common format and ensuring datasets are linked to each other (through common linkable identifiers
such as patient and provider identifiers)—is extremely time-consuming. It also makes data
more susceptible to errors. There are more effective ways to gather data.
Stage 3: Data Analysis
The data analysis starts after the appropriate data have been captured, pulled into a single
place, and tied together. The analysis process consists of several parts:
• Data quality evaluation: Analysts need to understand the data by taking time to
evaluate the same. They also need to note their method of evaluation with the
reference when they share their findings with the audience.
• Data discovery: Before attempting to answer a specific question, analysts should take
time to explore the data and look for meaningful oddities and trends. It is a critical
Data analysis
• Integrate data
• Discover new information
in the data (data mining)
• Evaluate data quality
Data provisioning
• Move data from
transactional systems
into the EDW
• Build visualization for
use by clinicians
Data capture
• Acquire key data
elements
• Acquire data quality
• Integrate data capture
into operational
window
FIGURE 8.12
IoT analytics.