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Aspects of Ambient Assisted Living and Its Applications
8.8.2 Electronic Health Record System
Implementing an electronic health record (EHR) is clearly a necessary step toward datadriven care delivery, and an EHR system alone is insufficient to enable an enterprise-wide,
consistent view of data from multiple sources. The conversion of clinical data from paper
to an electronic format is a necessary step—it allows for the use of data to improve care.
However, without a way of organizing all sources—clinical, financial, patient satisfaction,
and administrative data—into a single source of truth, a health care organization is unable
to harness the analytic power of the data. Some EHR vendors are beginning to come
out with data warehouse offerings that run on top of the EHR’s transactional database.
However, these data warehouses still have the limitation that they don’t aggregate data
from a variety of external sources—because the vendor can’t or won’t. Some EHR vendors
are becoming willing to integrate some external data, but they are years behind analytics
vendors in terms of their performance.
8.8.3 Independent Data Marts in Different Databases
Independent data marts that live in different databases throughout a health system provide limited analytics capabilities because they can only deliver little sources of truth from
the different soiled systems. Take, for example, the ADT (admission, discharge, transfer)
information that lives in the EMR. When there’s a need to analyze the ADT information
and the role it plays on costs, analysts move the data over to the costing system with the
independent data mart model. Requests like this one can happen over and over for many
different types of scenarios, which ends up becoming time-consuming. It also slows down
the entire system as analysts repetitiously bombard the system with requests for each new
use case.
8.9 Ambient Intelligence for AAL
The research on Aml was initiated by European Commission in 2001 (I.A. Group 2001).
Aml is characterized by sensitivity, responsivity, adaptability, transparency, ubiquity and
intelligence. It’s interaction with other technologies is shown in Figure 8.13.
The project PERSONA named after PERceptive Space prOmoting iNdependent Aging
was developed with a scalable open standard platform to building wired range of applications in AAL. It has an efficient infrastructure with self-organizing middleware technology having extensibility of components or device ensembles. The components of this
system communicate with each other based on the distributed coordination strategies
using PERSONA middleware. It has different types of communication buses: input, output, context, and service buses that enable interoperability with different components
using middleware. The middleware of PERSONA was implemented using the OSGi platform with communication through UPnp, Bluetooth, and R-OSGi. In this model, various reasoning algorithms are adopted for modeling, activity prediction and recognition,
decision-making, and spatial temporal reasoning (Cook et al. 2009).
Modeling—it deals with the design that separates the computing and user interaction.
This can customize the Aml software toward user requirement. These models have capability of recognizing the changes and adopting itself based on the changes in the patterns.
Aspects of Ambient Assisted Living and Its Applications
8.8.2 Electronic Health Record System
Implementing an electronic health record (EHR) is clearly a necessary step toward datadriven care delivery, and an EHR system alone is insufficient to enable an enterprise-wide,
consistent view of data from multiple sources. The conversion of clinical data from paper
to an electronic format is a necessary step—it allows for the use of data to improve care.
However, without a way of organizing all sources—clinical, financial, patient satisfaction,
and administrative data—into a single source of truth, a health care organization is unable
to harness the analytic power of the data. Some EHR vendors are beginning to come
out with data warehouse offerings that run on top of the EHR’s transactional database.
However, these data warehouses still have the limitation that they don’t aggregate data
from a variety of external sources—because the vendor can’t or won’t. Some EHR vendors
are becoming willing to integrate some external data, but they are years behind analytics
vendors in terms of their performance.
8.8.3 Independent Data Marts in Different Databases
Independent data marts that live in different databases throughout a health system provide limited analytics capabilities because they can only deliver little sources of truth from
the different soiled systems. Take, for example, the ADT (admission, discharge, transfer)
information that lives in the EMR. When there’s a need to analyze the ADT information
and the role it plays on costs, analysts move the data over to the costing system with the
independent data mart model. Requests like this one can happen over and over for many
different types of scenarios, which ends up becoming time-consuming. It also slows down
the entire system as analysts repetitiously bombard the system with requests for each new
use case.
8.9 Ambient Intelligence for AAL
The research on Aml was initiated by European Commission in 2001 (I.A. Group 2001).
Aml is characterized by sensitivity, responsivity, adaptability, transparency, ubiquity and
intelligence. It’s interaction with other technologies is shown in Figure 8.13.
The project PERSONA named after PERceptive Space prOmoting iNdependent Aging
was developed with a scalable open standard platform to building wired range of applications in AAL. It has an efficient infrastructure with self-organizing middleware technology having extensibility of components or device ensembles. The components of this
system communicate with each other based on the distributed coordination strategies
using PERSONA middleware. It has different types of communication buses: input, output, context, and service buses that enable interoperability with different components
using middleware. The middleware of PERSONA was implemented using the OSGi platform with communication through UPnp, Bluetooth, and R-OSGi. In this model, various reasoning algorithms are adopted for modeling, activity prediction and recognition,
decision-making, and spatial temporal reasoning (Cook et al. 2009).
Modeling—it deals with the design that separates the computing and user interaction.
This can customize the Aml software toward user requirement. These models have capability of recognizing the changes and adopting itself based on the changes in the patterns.