Health Care IT infrastructure: A Software
Engineering Perspective.
Samuel Kasimalla under Supervision of Dr Box for CSC 532 Term paper
ABSTRACT: Health care infrastructure is an important topic under general consideration. With
many new diseases and outbreaks, health care always remains to be one of the biggest industries
in terms of expenditure and market size. Health care is again such a field in which negligence
can be very costly. This requires that all the implementation should be utmost reliable.
Software engineering comes into play when the reliability factor is needed to be considered. For
a software to be reliable the efficiency, planning, execution should be perfectly engineered.
INTRODUCTION
The research problem chosen for the paper
is loosely based on software engineering
principles on distributed systems. More and
more systems use a centralized data base
with nodes in varied geographical locations.
So we need high speed networks capable of
transferring huge amount of data that is
required by data miners. Maintaining the
reliability is absolutely essential, but other
factors should also be considered in the
software engineering principles. This paper
would touch upon different aspects of
software engineering and management in the
field of Health care. Some of them are
1.
2.
3.
4.
5.
6.
Planning and Implementation a
project
Phases of implementing a health care
based large project.
Frameworks
Cloud based approach
Data Mining in Health care.
Privacy preserving sharing of
information
7.
High performance computing for
data mining
PLANNING AND IMPLEMENTATION A
PROJECT
Hospitals and nations are allocating
significant amount of resources for
introducing new technology. “Cost
accounting analysis is a multivariate
function that includes determining the
amount, based upon a strategic plan and
financial resources, of funding to be
allocated annually for medical equipment
acquisition and replacement” [8].
The process of selecting and acquiring
medical technology has not been well
coordinated in most hospitals until recent
times (Sprague, 1988). This is true with
most countries. The focus of the thing
should be to first identify its goals, secondly
select and define priorities, and finally
allocate there sources, although limited, with
which we have to attain those goals [8].
During the initial planning process
percentage of resources should be allocated
instead of the actual values. The actual
figures are subject to change with the time.
Health care related projects can be
expensive but there are always alternate
ways to provide for them.
medical field is expensive. So it is
absolutely necessary for project
management techniques to be implemented.
As given by [8]. A good planning package
should
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
Provides for guiding strategy for
allocation of limited resources
Maximizes the value provided by
resources invested in medical
technology
Identifies and evaluates
technological opportunities or threats
Optimizes prioritization in systems
integration, facility preparation and
staff planning
Meets or exceeds standards of care
Reduces operating costs
Reduces risk exposure.
There exists a relationship between the
methods and information. Methods help to
take decisions regarding the management of
the medical technology. Medical technology
is used in the complex environment of the
healthcare delivery system, which includes
the variances among users, applications and
cultures from one hospital to another [8]
PHASES OF IMPLEMENTING A
HEALTH CARE BASED LARGE
PROJECT.
This section is based on the summary of the
following paper. It is a noted fact that
compared to other industries the R&D and
capital required in a healthcare, biotech or
[9],[9]
One of the suggestions of the mentioned
paper is to have a project management
Office
The typical features of a project
management office as given by [10] includes
(i)
(ii)
(iii)
(iv)
Tracking reporting and information
sharing
Repository of project performance
Setting Standards
Project management improvement
efforts
(v)
(vi)
(vii)
Coaching, training internal
consulting
Source of project management talent
Project cricis response team
[9], [9]
Case study:
A public insurance scheme in the state of
Andhra Pradesh India, phase wise
implementation. Aarogyasri is a Health
Insurance Scheme that IS modeled for the
benefit of the poor, to bear the health care
costs of families below the poverty line for
identified diseases. This scheme was
introduced with the guideline that public
insurance schemes should be targeted at
large scale and fatal illnesses and the benefit
in the basic care should be done through free
screening and outpatient consultation [11].
1. Aarogyamithras(Facilitator services)
2. Round the clock Call Centre with Toll
free help line.
3. Health Camps conducted by network
hospitals.
4. Follow up by elaborate field mechanism.
5. End-to-end cashless packages.
6. Services of RAMCO (Rajiv Aarogyasri
Medical Coordinator) and AMCCO
(Aarogyasri Medical Camp Coordinator)
in the network hospitals.
7. CUG (Closed User Group) connectivity
to all the field staff, RAMCO and
AAMCO.
8. Placement of Aarogyasri kiosk with
Network connectivity.
9. Robust IT based solution, capturing
patient details right from the reporting to
the claim settlement and follow up.
10. Social auditing through feedback letter
from the beneficiary and Prajapatham
programme.
Aarogyasri to date has screened more than
3.1 million patients in the rural areas and
essential drugs were supplied. Further 1.018
million patients were given access to
outpatient consultation. The government
health system combined with this program
Aarogyasri is able to meet the all the health
requirements of population in the state. The
scheme is managed through effective use of
Information Technology based solution
which is unique to the scheme in reaching
out to the beneficiary. The scheme has many
special features to its credit to reach the
beneficiary and guide them to use the
services without having to pay out of their
pocket as given by the government website
[11], the following are its salient features
The project was implemented in 3 phases,
number of associate hospitals under the
scheme 5 per district in the first phase. After
successful installation and implementation,
phase 2 and phase 3 were implemented.
agents give an opportunity to create fully
blown and accessorized decision support
services for healthcare personnel. The use of
smart agents for implementation of a
distributed Agent based Data Mining
Infrastructure that provides a range of
healthcare based decision-support
systems[6].
1.
Agent-based DM Info-structure (ADMI)
FRAMEWORKS
Distributed Data Mining From
Heterogeneous Healthcare Data
Repositories: Towards an Intelligent AgentBased Framework, the mentioned paper [6].
It presents a model with a smart agent based
framework for knowledge mining in a
distributed healthcare system consisting of
many diverse healthcare data centers. Datacentered knowledge mining, especially from
many diverse data repositories, is a
painstaking process and imposes significant
operational constraints on destination users.
The independence, interactive and smart
The proposed multi Agent-Based Data
Mining Info-Structure (ADMI),
“responsible for the generation of datamediated diagnostic-support and strategic
services, takes advantage of a multi-agent
architecture which features the
amalgamation of various types of intelligent
agents, each responsible for an independent
one who collects user specification for a
data mining service.
This collection of user specification is done
through a web based interface.
Data Collection agent: The job of a data
collection agent is to fetch related data from
multiple health care repositories.
task” [6] .
Data mining agent: Data mining agent is the
one that performs and manages the entire
data mining process. Services generation
agent: The services generation agent obtains
the results from data mining agent and uses
it for decision support.
As given by the reference paper such an
agent-federation is designed to service four
functional components—
2.
(i) end-user interface;
(ii) remote data access network;
(iii)data mining engine; and
(iv) diagnostic-support and strategic
services. [6]
A brief overview of the constituent agents
and their functionalities according to the
reference paper is summarized as follows:
Interface agent: The interface agent is the
CLOUD BASED APPROACH
Cloud computing is the delivery of
computing as a service rather than a product,
whereby shared resources, software, and
information are provided to computers and
other devices as a utility (like the electricity
grid) over a network (typically the Internet)
[12].
Cloud computing provides computation,
software, data access, and storage services
that do not require end-user knowledge of
the physical location and configuration of
the system that delivers the services.
Parallels to this concept can be drawn with
the electricity grid, wherein end-users
consume power without needing to
understand the component devices or
infrastructure required to provide the service
[12]. A Cloud Computing Framework for
Real-time Rural and Remote Service of
Critical Care[12].In a distributed system
every node does not have the memory or the
computational capacity to maintain all of the
systems
3.
DATA MINING
Data Mining (the analysis step of the
Knowledge Discovery in Databases process,
or KDD), a relatively young and
interdisciplinary field of computer science,
is the process of discovering new patterns
from large data sets involving methods from
statistics and artificial intelligence but
also database management. In contrast to
machine learning, the emphasis lies on the
discovery of previously unknown patterns as
opposed to generalizing known patterns to
new data[12].
software, programming languages,
4.
PRIVACY PRESERVING
SHARING OF INFORMATION
algorithms and computational techniques.
HPC technologies are the tools and systems
used to implement and create high
performance computing systems. Recently,
HPC systems have shifted from
supercomputing to computing clusters and
grids. Because of the need of networking in
clusters and grids, High Performance
Computing Technologies are being
promoted by the use of a collapsed network
backbone, because the collapsed backbone
architecture is simple to troubleshoot and
upgrades can be applied to a single router as
opposed to multiple ones.
Various privacy preserving models in this
field have been proposed, Distributed
clustering based on sampling local density
estimates [13], Secure multi-party
computation made simple[14], Privacypreserving k-means clustering over
vertically partitioned data[15], Privacy
preserving association rule mining in
vertically partitioned data [16], Secure set
intersection cardinality with application to
association rule mining [17], Privacy
Preserving Data Mining, Privacy-preserving
data mining [18].
5.
HIGH PERFORMANCE
COMPUTING FOR DATA MINING
HPC integrates systems administration
(including network and security knowledge)
and parallel programming into a
multidisciplinary field that combines digital
electronics, computer architecture, system
Scalable Data Mining with Log Based
Consistency DSM for High Performance
Distributed Computing Log Based
Consistency Mechanism.
11073 DIM.Mustafa Yuksel and Asuman
Dogac
[3] Towards a Framework for Health
Information Systems Evaluation Maryati
Mohd. Yusof, Ray J. Paul, Lampros K.
Stergioulas
[4] Research and Implementation of
Transmitting and Interchanging Medical
Information based on HL7
Xiaoqi LU, Yu GU*, Lidong YANG,
Weitao JIA, Lei Wang
[5] Development of Data Authenticity
Verification System in Regional Health
Information Network
ZHOU Tian-shu, LI Jing-song , ZHANG
Xiao-guang, HU Zhen, YU Hai-yan, CHEN
Huan
[6] Distributed Data Mining From
Heterogeneous Healthcare Data
Repositories: Towards an Intelligent AgentBased FrameworkSyed Zahid Hassan Zaidi
Syed Sibte Raza Abidi Selvakumar
Manickam
REFERENCES
[1] Privacy Preserving Distributed Learning
Clustering Of HealthCare Data Using
Cryptography ProtocolsAhmed M.
Elmisery, Huaiguo Fu
[2] Interoperability of Medical Device
Information andthe Clinical Applications:
An HL7 RMIM basedon the ISO/IEEE
[7] Design and Implementation of
Interoperable Medical Information System
Based on SOA ZHANG Xiao-guang, LI
Jing-song, ZHOU Tian-shu, YANG Yibing,CHEN Yun-qi, XUE Wan-guo, ZHAO
Jun-ping
[8] Medical Technology Management: From
Planning to Application
[10]
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp
=&arnumber=4599745
[11]https://www.aarogyasri.org
[12] Wikipedia
[13] M. Klusch, et al., "Distributed
clustering based on samplinglocal density
estimates," presented at the Proceedings of
the18th international joint conference on
Artificial intelligence, Acapulco, Mexico,
2003.
[14] U. Maurer, "Secure multi-party
computation made simple,"Discrete Appl.
Math., vol. 154, pp. 370-381, 2006.
[15] J. Vaidya and C. Clifton, "Privacypreserving k-means clustering over
vertically partitioned data," presented at the
Proceedings of the ninth ACM SIGKDD
international conference on Knowledge
discovery and data mining, Washington,
D.C., 2003.
[15] J. Vaidya and C. Clifton, "Privacy
preserving association rule mining in
vertically partitioned data," presented at the
Proceedings of the eighth ACM SIGKDD
international conference on Knowledge
discovery and data mining, Edmonton,
Alberta, Canada, 2002.}
[16] J. Vaidya and C. Clifton, "Secure set
intersection cardinality with application to
association rule mining," J. Comput. Secur.,
vol. 13, pp. 593-622, 2005.
[17] Y. Lindell and B. Pinkas, "Privacy
Preserving Data Mining,"presented at the
Proceedings of the 20th Annual
International Cryptology Conference on
Advances in Cryptology, 2000.
[18] R. Agrawal and R. Srikant, "Privacypreserving data mining," SIGMOD Rec.,
vol. 29, pp. 439-450, 2000.
All figures in the paper are extracted from
the above papers mentioned.