Download Data Mining techniques and Age Care Industry

Risk Assessment Platform for Age Care domain- New grounds for Data Mining
By Raghavi Yarasi
Contents
Table of Contents ..................................................................................... Error! Bookmark not defined.
Abstract ................................................................................................................................................... 3
Introduction ............................................................................................................................................ 4
Literature Review .................................................................................................................................... 5
Research Methodologies ...................................................................................................................... 15
Nature of Data in Age Care Domain...................................................................................................... 18
Application of Data Mining Methods .................................................................................................... 19
Case Study ............................................................................................................................................. 20
Conclusion ............................................................................................................................................. 21
References ............................................................................................................................................ 22
Abstract
IT innovations and reforms in the age care sector have leaded a path way to the
implementation of Electronic Health Record Systems and e-Business systems. This leads to
capturing of data electronically. Applying data mining (DM) techniques on this data can
benefit all the parties involved in this sector. This trend of using the available data in new
dimensions will lead to the design of better strategies for developing services by the age
care providers. These techniques will not only benefit the age care providers - but also help
government in setting up/revising the funding types for age care sector.
Introduction
Australian population is aging ( Australian Bureau of Statistics 2011). In order to provide
better services to the growing population in this sector, sophisticated systems and methods
are required by the industry. The industry has started capturing the data electronically in
various areas a decade ago(YU, 2012). This voluminous data has not been processed or
analysed to make best use of it. Data mining is increasingly becoming popular in the IT world
and is used intensively in various sectors. Age care sector is one of the areas that has not
been explored seriously for the application of Data Mining Techniques
This article is an attempt of exploring data mining techniques on the data that is
electronically available in age care domain. The directions and applications of DM concepts
are still to be explored. The last section of this literature review discusses five new
applications of DM concepts.
Literature Review
LITERATURE REVIEW
Tr ends in Population
Growth
Required services and IT
applications
Data Mining
Methodology
Study of DM techniques
in Health care Industry
Trends in Population Growth
The Australian Bureau of statics for the year 2013 mentions that one in 4 of Australians will
be aged over 65 and over by 2056. Below is the a figure showing the age structure of
Australia in the past and future.
Figure 1: Ageing population statistics
Source : Australian Bureau of Statistics
This increase in the ageing population influences the health related services currently
available. This also puts a lot of demand on the care that will be required by the growing
aging population.
By 2020 more number of older people will be living alone due to increased divorce rates,
smaller families and fewer old people living with their children. The current immigrants will
start aging by 2020. These immigrants are from diverse cultural backgrounds and linguistics
( Mayer Foundation. 2004). With these wide requirements the expectations from the age
care service provider’s increase.
Future Service Requirements and Financial Implications
The above mentioned trend in the growing aged population derives the fact that the
existing aged care services will not be able to deliver the quantity and quality of services we
will need over the next two decades. Starting from 2020 the demand for aged care will
accelerate with the increase in number of older people. This increase in the older
population is not directly proportional to the work force required by the industry. As a result
there will be a scarce in the services. Modelling work done by The Allen Consulting Group
estimates that by 2020 the costs of age care services will increase by 60%. These costs will
increase as we create solutions to the significant problems we already face.
The Age Pension provides the bare essentials. The fact that the proportion of people
reaching retirement age, coupled with increased life expectation calls for steps that will
pump money into future for providing subsides by the government . Government will also
need to work on new strategies that will help boost up the revenue for the coming future
(AMP 2009). As a result a greater importance is imposed on the necessity to save for
retirement. This planning for the future retirement both by the individuals and government
will help to afford for the services.
Current Services
Australian Health and Welfare website lists the following type of cares currently available to
the clients of age care industry
Table 1: Care Types
Care Type
Categories
Low Level Care
Community Age Care
High Level Care
Permanent residential age Care
Residential Age Care
Respite Care
This research is being conducted on the residential age care. So the next set of literature
review concentrates on the residential age care providers
Information Technology and Residential Age Care
The available data in aged care sector and the increasing demands shows that there needs
a change that would take the advantage of the current data to improve the quality of life of
aged Australians in future( KPMG . 2009). A study of current trends and predictions based
on the available data will provide better insight into the problems and requirements that
could arise in the future. This calls for data mining principles to be applied for the data
available in this domain. There are already systems in place that are collecting and storing
the data. There is a need to further identify the important parameters so that the data
generation points can be clearly identified. Data marts could then be created for data
analysis purposes. To further support this Hovenga mentions Residential aged care is largely
considered a ‘green field’ with regard to Information Technology(Hovenga, 2007). Systems
that already exist usually have their own system architectures, which results in a lack of
interoperability within the aged care sector as a whole. The provision, administration and
funding of aged care consist of a complex and varied set of arrangements which requires an
IT infrastructure that meets the needs of many stakeholders including nurses and personal
carers in the aged care residential sector. These health workers must be able to comply with
contemporary best practice, and meet all quality and reporting requirements. In this
scenario, the implementation of Electronic Health Records (EHRs) is a key strategy for
improving the quality, safety and efficiency of residential aged care delivery. The openEHR
approach (http://www.openEHR.org) is one of the most recognised approaches for EHR
systems. The definition and use of aged care openEHR Archetypes (clinical models
representing semantic constructs) can contribute to interoperability of EHRs as well as
various health information systems. Current IT Applications
The adoption and use of ICT in the aged care sector is largely a ‘green field’as mentioned
earlier. (Young, 2006) gives an example of an EHR project related to aged care is Health
elink, the first pilot of an Electronic Health Record for New South Wales. This has initially
been made available for people aged 65 years or more and located in certain regions of
NSW (NSW Health Website, 2006).With regard to further current implementations that are
indirectly related to the implementation of Electronic Health Records in the aged care
sector, the Aged Care Association Australia lists a number of software products at their
website (http://agedcareassociation.com.au).
In this context of various software products being available in the domain - capturing the
data electronically is possible in most of the areas. There is no industry specific research for
data mining in this area. The section below in the literature review covers the data mining
methodology adapted for this research followed by the data mining applications. Most of
the information has been extracted for the Data Mining applications in health care so that
they can be implied to the age care domain.
Introduction to Data Mining Methodologies
Data Mining is a relatively new methodology and technology that came into prominence in
late 20th century(Trybula, 1997). It is aimed to identify useful and understandable corelations in the data (Chung, 1999). Data mining techniques can be broadly classified based
on what they can do, namely description and visualization; association and clustering; and
classification and estimation, which is predictive modelling. Description and visualization can
contribute greatly towards understanding a data set, especially a large one, and detecting
hidden patterns in data, especially complicated data containing complex and non-linear
interactions.
CRISP-DM (CRoss-Industry Standard Process for Data Mining) has been adapted for the
Data Mining process in the current context. (Shearer 2000) CRISP-DM is an industry-, tool-,
and application-neutral model. This model encourages best practices and offers
organizations the structure needed to realize better, faster results from data mining.

CRISP-DM organizes the data mining process into six phases:

Business Understanding

Data Understanding

Data Preparation

Modelling

Evaluation

Deployment
These phases help organizations understand the data mining process and provide a road
map to follow while planning and carrying out a data mining project.
Figure 2:Phases of CRISP-DM [Source : (Shearer 2000)]
Phase One: Business Understanding
The business understanding Phase involves following key steps

Determining business objectives

Assessing the situation

Determining the data mining goals

Producing the project plan
Phase Two: Data Understanding
The data understanding Phase includes the following steps:

Collect the Initial Data

Describing the Data

Explore the Data

Verify the Data Quality
Phase Three: Data Preparation
The five steps in data preparation are

The selection of data

The cleansing of data

The construction of data

The integration of data

The formatting of data
Phase Four: Modelling
Modelling steps include

The selection of the modelling technique

The generation of test design

The creation of models

The assessment of models
Phase Five: Evaluation
The key steps here are

The evaluation of results

The process review

Determination of next steps.
Phase Six: Deployment
The key steps in this Phase are

Plan deployment

Plan monitoring

Maintenance

The production of the final report

Review of the project.
Data Mining techniques and Age Care Industry
Data can be a great asset to age care organizations, but they have to be first transformed
into information. As there are not enough studies about data mining in age care we will first
examine data mining applications in health care and extend them to age care domain.
An important factor motivating the use of data mining applications in healthcare is the
realization that data mining can generate information that is very useful to all parties
involved in the health care industry. Data mining applications also can benefit health care
providers, by identifying effective service delivery pattern and best practices.
A research in health care industry mentions that with the Balanced Budget Act of 1997, the
Centres for Medicare and Medicaid Services must implement a prospective payment system
based on classifying patients into case-mix groups, using empirical evidence that resource
use within each case-mix group is relatively constant. CMS has used data mining to develop
a prospective payment system for inpatient rehabilitation(Relles,2002) . If we extend this
concept to the age care domain, we could have a system that could look into the health
records of the age care clients and group them based on their conditions so that they can be
located in a same location and the care workers can be appropriately rostered to provide
services more effectively.
(Silver M)Blue Cross has been implementing data mining initiatives to improve outcomes
and reduce expenditures through better disease management. For instance, it uses
emergency department and hospitalization claims data, pharmaceutical records, and
physician interviews to identify unknown asthmatics and develop
appropriateinterventions.1 Data mining also can be used to identify and understand highcost patients.
To aid healthcare management, data mining applications can be developed to better
identify and track chronic disease states and high-risk patients, design appropriate
interventions, and reduce the risky situations that could be life threating to the age care
clients. For example, to develop better diagnosis and treatment protocols, the Arkansas
Data Network looks at readmission and resource utilization and compares its data with
current scientific literature to determine the best treatment options, thus using evidence to
support medical care.( Kincade,1998) Also, the Group Health Cooperative stratifies its
patient populations by demographic characteristics and medical conditions to determine
which groups use the most resources, enabling it to develop programs to help educate
these populations and prevent or manage their conditions.1 Group Health Cooperative has
been involved in several data mining efforts to give better healthcare at lower costs. In the
Seton Medical Center, data mining is used to decrease patient length-of-stay, avoid clinical
complications, develop best practices, improve patient outcomes, and provide information
to physicians—all to maintain and improve the quality of healthcare(Dakins,2001).
Data mining can be used to analyse massive volume of data and statistics to search for
patterns that might indicate an attack by bio-terrorists(Piazza,2002). The Lightweight
Epidemiological Advanced Detection Emergency Response System (LEADERS) is one such
effort. In the past, LEADERS has uncovered several disease outbreaks. Data mining also can
be used for hospital infection control12 or as an automated early-warning system in the
event of epidemics. A syndromic system, based on patterns of symptoms, is likely to be
more efficient and effective than a traditional system that is based on diagnosis. An early
warning of the global spread of the SARS virus is an example of the usefulness of a
syndromic system based on data mining(Brewin,2003).
While customer relationship management is a core approach in managing interactions
between commercial organizations—typically banks and retailers—and their customers, it is
no less important in a healthcare context. Customer interactions may occur through call
centers, physicians’ offices, billing departments, inpatient settings, and ambulatory care
settings. As in the case of commercial organizations, data mining applications can be
developed in the healthcare industry to determine the preferences, usage patterns, and
current and future needs of individuals to improve their level of satisfaction. (Biafore, 1999)
These applications also can be used to predict other products that a healthcare customer is
likely to purchase, whether a patient is likely to comply with prescribed treatment or
whether preventive care is likely to produce a significant reduction in future utilization.
(Koh ,?? )Through the use of data mining, Customer Potential Management Corp. has
developed a Consumer Healthcare Utilization Index that provides an indication of an
individual’s propensity to use specific healthcare services, defined by 25 major diagnostic
categories, selected diagnostic related groups or specific medical service
areas(Paddison,2000). This index, based on millions of healthcare transactions of several
million patients, can identify patients who can benefit most from specific healthcare
services, encourage patients who most need specific care to access it, and continually refine
the channels and messages used to reach appropriate audiences for improved health and
long-term patient relationships and loyalty. The index has been used by OSF Saint Joseph
Medical Centre to get the right messages and services to the most appropriate patients at
strategic times. The end result is more effective and efficient communications as well as
increased revenue. (Paddison,2000) Miller has suggested that the data mining of patient
survey data can help set reasonable expectations about waiting times, reveal possible ways
to improve service, and provide knowledge about what patients want from their healthcare
providers. Also, Hallick(Hallick,2001) has suggested that CRM in healthcare can help
promote disease education, prevention, and wellness services. (Kolar,2001) (Veletsos,2003)
also have reported that Florida Hospital has used data mining to segment Medicare patients
as well as develop commercial applications that enable credit scoring, debt collection, and
analysis of financial data. Rafalski(Rafalski,2002) has studied Sinai Health System’s use of
data mining for healthcare marketing and CRM. Lastly, pharmaceutical companies can
benefit from healthcare CRM and data mining, too. By tracking which physicians prescribe
which drugs and for what purposes, pharmaceutical companies can decide whom to target,
show what is the least expensive or most effective treatment plan for an ailment, help
identify physicians whose practices are suited to specific clinical trials (for example,
physicians who treat a large number of a specific group of patients), and map the course of
an epidemic to support pharmaceutical salespersons, physicians, and
patients.(Brannigan,1999 ) Pharmaceutical companies can also apply data mining to huge
masses of genomic data to predict how a patient’s genetic makeup determines his or her
response to a drug therapy. (Thompson,2000)
(Koh)Data mining applications that attempt to detect fraud and abuse often establish norms
and then identify unusual or abnormal patterns of claims by physicians, laboratories, clinics,
or others. Among other things, these applications can highlight inappropriate prescriptions
or referrals and fraudulent insurance and medical claims. For example, the Utah Bureau of
Medicaid Fraud has mined the mass of data generated by millions of prescriptions,
operations and treatment courses to identify unusual patterns and uncover fraud.( Milley,
2000)
Limitations of Data Mining
Data mining applications can greatly benefit the healthcare industry. However, they are not
without limitations. Healthcare data mining can be limited by the accessibility of data,
because the raw inputs for data mining often exist in different settings and systems, such as
administration, clinics, laboratories and more. Hence, the data have to be collected and
integrated before data mining can be done. While several authors and researchers have
suggested that a data warehouse be built before data mining is attempted, that can be a
costly and time-consuming project. On a positive note, a data warehouse has been
successfully built by Intermountain Health Care from five different sources— a clinical data
repository, acute care case-mix system, laboratory information system, ambulatory case-mix
system, and health plans database—and used to find and implement better evidence-based
clinical solutions. Oakley29 has suggested a distributed network topology instead of a data
warehouse for more efficient data mining, and Friedman and Pliskin30 have documented a
case study of Maccabi Healthcare Services using existing databases to guide subsequent
data mining. Secondly, other data problems may arise. These include missing, corrupted,
inconsistent, or non-standardized data, such as pieces of information recorded in different
formats in different data sources. In particular, the lack of a standard clinical vocabulary is a
serious hindrance to data mining. (Cios,2002) have argued that data problems in healthcare
are the result of the volume, complexity and heterogeneity of medical data and their poor
mathematical characterization and non-canonical form. Further, there may be ethical, legal
and social issues, such as data ownership and privacy issues, related to healthcare data. The
quality of data mining results and applications depends on the quality of data. (Chopoorian
,2001)Thirdly, a sufficiently exhaustive mining of data will certainly yield patterns of some
kind that are a product of random fluctuations.( Hand, 1998) This is especially true for large
data sets with many variables. Hence, many interesting or significant patterns and
relationships found in data mining may not be useful. Murray34 and Hand33 have warned
against using data mining for data dredging or fishing, which is randomly trawling through
data in the hope of identifying patterns. Fourthly, the successful application of data mining
requires knowledge of the domain area as well as in data mining methodology and tools.
Without a sufficient knowledge of data mining, the user may not be aware of or be able to
avoid the pitfalls of data mining(Brannigan,1999) . Collectively, the data mining team should
possess domain knowledge, statistical and research expertise, and IT and data mining
knowledge and skills. Finally, healthcare organizations developing data mining applications
also must make a substantial investment of resources, particularly time, effort, and money.
Data mining projects can fail for a variety of reasons, such as lack of management support,
unrealistic user expectations, poor project management, inadequate data mining expertise,
and more. Data mining requires intensive planning and technological preparation work. In
addition, physicians and executives have to be convinced of the usefulness of data mining
and be willing to change work processes. Further, all parties involved in the data mining
effort have to collaborate and cooperate(Gillespie,2000).
Research Methodologies
The age care industry can benefit greatly from data mining applications. The objective of
this section is to explore relevant Data Mining applications by providing a detailed
description of the Research Methodology that is considered for this study
This research has been conducted by studying and working on the software applications for
2 years in one of the leading service provider in Age Care Industry. Experimental Research
Method is used for the current research.
(Clarker, 2005) Experimental Research is defined as a process in which researchers try to
isolate and control every relevant condition which determines the events investigate, so as
to observe the effects when the conditions are manipulated.
The experimental research methodology is further classified into the following four types
Pre-experimental: unreliable assumptions are made despite the lack of control over
variables
True experimental: rigorous check of the identical nature of groups before testing the
influence of a variable on a sample of them under controlled circumstances
Quasi-experimental: not all conditions of true experimental design can be fulfilled but the
shortcomings are identified
Correlation and ex post facto: correlation looks for cause and effect relationships between
two sets of data whereas ex post facto reverse experimentation interprets the cause of
phenomenon observing its effects
Based on the above definitions the current research can be further classified as a quasiexperimental type.
First we will look at the structure of the applications in age care domain. In the context of
this research, the applications in aged care domain( Age Care e-connect website) are
broadly classified into five different categories [This classification does not include the
applications used for internal operations – like mailing services, intranets, messaging
systems, IT Service Management systems and so on]
Business Financial Applications(BFA) Applications used for the financial purposes –
Accounting Systems, Budgeting Systems, Funding Maintenance Systems
Payroll and HR Systems (PHRS):The payroll systems , HR Systems
Rostering and Attendance Applications(RAA) These applications are used for rostering and
managing the attendance of the care workers
Client Record Systems (CRS)These systems are used to maintain the information about the
client. These systems when implemented efficiently can get transformed into electronic
health recording ( eHR ) systems.
Web Applications ( WA)The web sites about the organization, web sites for marketing.
Figure 3 : IT Applications used in Aged Care
In this section the research method is combined with the concept of CRISP-DM phases to
provide the details about the research method used for the entire process.
There are many IT applications currently used in by an age care service provider. In most of
the situations the applications are used without interfacing with the other systems – as a
result there are discrepancies between the data. For Ex : The Rostering systems are not
interfaced with the payroll systems as a result most of the employee data is entered
manually which could lead to manual error. This causes inconsistency and manual correction
when processing the pay roll. Also the rostering applications don’t interact with the budget
applications – due to which there is no way to identify if the rostering is over or under the
budget.
Figure 3 shows that there are electronic data collection points of most of the information
used in a age care service organization. But this data is not sythesized to explore new
grounds for better opportunities.
Business is constantly looking for the following information:

Objective 1 :
Best possible usage of IT applications by reducing manual intervention when dealing
with more than one application. This reduces manual errors .
The above figure shows that most of the information is driven by the payroll system.
If we could integrate all the applications with pay d financial applications we could
achieve this objective

Objective 2 :
Interface all the applications with the financial applications to identify the areas in
the organizations that are running over the budget. This can be achieved by deriving
the data from the budgeting application and combine with the data from other
applications.

Objective 3:
Use the clinical records and records of a client to find patterns that would help
identify risks to the client, so that preventive measures can be undertaken.

Objective 4:
The churning rate and the reasons for the client switching to a different service
provider can be identified to retain the existing customers.

Objective 5:
Increase the new customer acquisition which is achieved by analysing the data
from CRS and WA applications.
Nature of Data in Age Care Domain
<TODO>
Application of Data Mining Methods
<TODO>
Case Study
<TODO>
Conclusion
Data mining applications in age care can have tremendous potential and usefulness.
However, the success of age care data mining hinges on the availability of clean age care
data. In this respect, it is critical that the age care industry consider how data can be better
captured, stored, prepared, and mined. Possible directions include the standardization of
the vocabulary in the domain and the sharing of data across organizations to enhance the
benefits of age care data mining applications. Further, as age care data are not limited to
just quantitative data, such as notes from the registered nursed or electronic records
related to the clients, it is necessary to also explore the use of text mining to expand the
scope and nature of what age care data mining can currently do. In particular, it is useful to
be able to integrate data from various sources.
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