Data Quality As Crime And Crime

Data Quality As Crime And Crime
In our continuing state of shrinking government operating budgets, crime scientists and crime
analysts need to consider the interrelatedness of spatial and temporal shifts in crime patterns when
creating, tracking, and handling crime hot spots. Many studies indicate that crimes are clustered at
the neighborhood level, but the entire neighborhood is rarely (if ever) criminogenic and only
specific parts of neighborhoods contain high concentrations of crime
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
The study of crime traditionally involved disciples such as psychology and sociology (George,
1978) but crime has always had an inherent geographic quality as crimes will always be linked to a
geographical location (Chainey and Radcliff, ... Show more content on Helpwriting.net ...
Then finally looking at the study area of the District of Columbia (Washington D.C.), identifying
why this area is one of Americas problem crime areas.
2.2 History of GIS and Crime The use of GIS in crime has been around for centuries with Dent
(2000) tracing the mapping of crime back to 1829, when Adriano Balbi and André Michel Guerry
created choropleth maps showing the relationship between violent and property crimes with
education levels. As time went on sociologists from the Chicago School, Shaw and McKay (1931)
began to map crimes by their XY coordinates to show the geographic location to understand the
importance of the crime location. In the 1980's the reduction in price of computers meant it became
more cost effective for GIS Applications (Longley et al, 2011). With the introduction of the new GIS
technologies, the ability to use police records within the GIS applications allowed for crime and
intelligence analysis (Radcliff, 2004). These days the advancements in technology and the
reductions in cost has resulted in GIS applications moving from the backroom computer analysis
tool to be used by almost every discipline, from criminology to healthcare, natural resources to
economics. These advancements have not just been in the applications but also in the science behind
them. This has allowed for more advanced analysis, which use well known mathematical models
within their calculations, such as spatial statistics and the use of the Getis–Ordi
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Design Of Audit Trails And Data Quality Monitoring Programs
Recommend elements included in the design of audit trails and data quality monitoring programs
Audit trails are a set of guidelines that are developed for purposes of maintaining a record of all the
activities of the system and the application that are done by the system users. Importantly, audit
trails are highly used in the process of detecting any form of security violations in the system,
performance issues, and any flaws in the applications. Some of the key elements of audit trails
include original source documents, transaction history database, and safe storage capabilities. For
purposes of making sure that the healthcare data is safe, there are a number of policies that have
been developed to make audit trials more efficient and effective. In this, some of the policies that
have been developed include the network access for third parties, records management and security–
networked devices. The network access for third parties policy tries to make an explanation of the
conditions under which the third parties accessing the healthcare facilities are allowed to access the
information contained in the database. The records management policy on the other hand tries to
offer an explanation of the records management requirements that may include the procedures of
records retention and disposal. Additionally, he policy of security–networked devices tries to offer
an explanation of all the responsibilities that are given to the different data users in making sure that
all

Quality And Data Management Essay
Data management, statistical analysis & quality assurance Data collection The data and the values of
the sensitivity scores would be collected at the general dental practice by the trained dentists who
will report to the second investigator responsible for the overall collection of the data. Direct patient
examination would be carried out at base line, immediately, 3, 6 and 9 months post application using
visual analogue scale for tactile stimuli response and Schiff cold air sensitivity scale for standard
cold air blast. Case report forms (CRF) would be given to the investigators for better understanding
and optimum care would be maintained to avoid giving any information which can lead to bias for
example the treatment carried out etc. Data storage: Research data would be documented in the
papers to begin with which would be regularly updated on to the computer by the data manager who
will be responsible for managing and storage of the data .An assistant would be provided to him on
his request of needed Data will be checked at follow–ups and will be collected by the principal
investigator .It will then inserted in to the software by the data manager with the help of the assistant
f necessary who will completely blinded of the procedure again by providing minimum information
required to avoid bias. The main office of the surgery will be accommodating the computer where
all the data would be inserted. No one else other than the principal investigator himself and the

Data Quality Management : The Business Processes That...
Data Quality Management: The business processes that ensure the integrity of an organization 's
data during collection, application (including aggregation), warehousing, and analysis. While the
healthcare industry still has quite a journey ahead in order to reach the robust goal of national
healthcare data standards, the following initiatives are a step in the right direction for data exchange
and interoperability: Continuity of Care Document (CCD), Clinical Documentation Architecture
(CDA) Data Elements for Emergency Department Systems (DEEDS) Uniform Hospital Discharge
Data Set (UHDDS) Minimum Data Set (MDS) for long–term care ICD–10–CM/PCS, Systemized
Nomenclature of Medicine–Clinical Terms (SNOMED CT), Logical Observation Identifiers Names
and Codes (LOINC). Data Quality Measurement: A quality measure is a mechanism to assign a
quantity to quality of care by comparison to a criterion. Quality measurements typically focus on
structures or processes of care that have a demonstrated relationship to positive health outcomes and
are under the control of the healthcare system. This is evidenced by the many initiatives to capture
quality/performance measurement data, including: The Joint Commission Core Measures Outcomes
and Assessment Information Set (OASIS) for home health care National Committee for Quality
Assurance 's (NCQA) Health Plan Employer Data and Information Set (HEDIS) Meaningful Use–
defined core and menu sets These data sets will be used within

Principles Of Data Quality Management
Principles of Data Quality
There are many principles for the data quality that ensure the data quality for the data entered to a
database. The most significant principles for the data quality include:
1– The Vision
2– The Policy
3– The Strategy
4– The collector has primary responsibility
5– User responsibility
6– Consistency
7– Transparency
8– Outliers
The Vision
It is very important for the big organization to get a high vision for their data and its quality
especially when the same data will be shared with other organization, companies or users. In the
vision the managers should focus on the resources that will use to build the data like the software,
like the database software and its capabilities, and the hardware like the computers and the routers
and other hardware equipment.
The Policy
As well as the vision, the organization should have a policy to implement its vision for the database,
which make the organization think to improve their database to reach its vision. Policy help the
organization to be more obvious about its goals with focusing on reducing costs, improving data
quality, improving customer service and relations, and improving the decision–making process.
The Strategy
The organizations should have a good strategy to manage their database and data entry process.
Therefore, the organizations need to improve a strong strategy for capturing and checking data. The
good strategy must include some clear goals for the short, intermediate, and long terms, which

Data Management And Quality Control Essay
Data management and quality control
To ensure internal validity of the study, we employed trained research assistants who at the time of
the study were registrars in psychiatry as research assistants. They had also received 2 weeks
training on the research proctocol. The interviewers had equal level of training and have been
involved in similar studies. A 3 days of debriefing and review of all protocols was carried out prior
to the commencement of the study.
All questionnaires were fully anonymized and reviewed for completeness and all data went through
extensive data–cleaning process. All data were electronically stored and had regular backups.
Regular meetings were held with all members of the research team, during which conflicting and
unclear issues were discussed and rectified Data Analysis
The data obtained was cleaned and entered into statistical package for social sciences, version 16.0
Software (SPSS 16), which was also used for data analyses. The sociodemographic characteristics of
participants in the two at baseline were compared using Chi2 statistic. The association between
sociodemographic characteristics and ASSIST score between the two groups was also determined
using independent t test.
Also, associations between an additional DSM IV axis I diagnosis (dual diagnosis), chronic general
medical conditions and tobacco abstinence at 6 months were explored using Chi2 statistics and
binary regression analysis.
Treatment Effects
In order to determine the

Wireless Sensor Networks : Data Quality For Better...
Abstract– An outlier is a data value which is significantly deviated from the remaining data. In
Wireless sensor networks (WSNs) outliers are the major issues that affect the inherent
characteristics, such as flexibility, maintenance costs and scalability. The sources of an outliers
include noise, errors, events and malicious attacks on the network. In this paper, we proposed a
Compressive sensing algorithm (also known as compressive sensing, compressive sampling, or
sparse sampling) to detect outliers images obtain from wireless sensors. The objective of this
proposed method is to obtain an outlier degree in images through wireless sensors which provides
the data quality for better selection process. CS theory ... Show more content on Helpwriting.net ...
The ideal wireless sensor is networked and scalable, consumes very little power, is smart and
software programmable, efficient for fast data acquisition, reliable and accurate over the long term,
costs little to purchase and install, and requires no real maintenance.
i. Architecture of Wireless Sensor Network
1. The tiny sensors are deployed all over the implemented background in WSNs.
2. The Networks are usually comprised of few sinks and large quantity of sensor nodes.
3. Sensor nodes are ordered into clusters.
4. Each node has a corresponding cluster header.
5. Each sensor node can sense different parameters such as temperature, smoke and relative
humidity.
6. Nodes location details can be obtained by equipment such as Global Positioning System (GPS) or
Bluetooth.
Figure 1: Block diagram of a Wireless Sensor Network
Sensor nodes can be used for continuous sensing, event detection, event ID, location sensing, and
local control of sensors. We classify the applications in military, environment, health, home and
many commercial areas. It is possible to expand this classification with more categories such as
space exploration, chemical process industries and disaster relief. Most of the sensor network
routing techniques and sensing tasks require the knowledge of location with high accuracy without
losing information. Thus, it is important that a sensor node has a location finding system. A
mobilizer may sometimes be

Statistics And Its Impact On The Quality Of Data
Statistics is defined as a branch of mathematics used to analyze, explain summarize as well as
interpret what we observe– in order to make sense or meaning of our observations. Every day in life
we encounter information that originates from diverse forms and ways. This means that to make this
information to have sense, there is the need to use statistics. However, due to its focus and empirical
on applications, statistics is classically considered a distinctive mathematical science rather than a
branch of mathematics (Chance et al, 2005). Thus, some tasks a statistician use are less
mathematical; for instance, making sure data collection is carried out in a manner that yields valid
conclusions, reporting results, or coding data in ways understandable to the users. Statistics is
known to improve the quality of data by fashioning specific survey samples and experiment designs.
It also offers tools used to predict and forecasting the use of data as well as statistical models. It is
applicable in many academic fields that include business, government, social and natural sciences.
Descriptive statistics are entirely used to describe the sample under study. They are used basically to
describe the fundamental characteristics of a given data. They offer simple summaries concerning
the measures and the samples. When utilized together with simple graphics study, they form the
heart of practically each quantitative study of data. This means that descriptive statistics utilized
both

Evaluating The Quality Improvement Initiative And...
The Evaluation Plan Many different conceptual models exist for evaluating a process. An evaluation
is a necessary step to determine how well a process is working and if the targets are being met. The
measurements in a quality improvement project are important to assess where the organization
stands with the project, and to determine success of the project (Sadeghi, Barzi, Mikhail, & Shabot,
2013). There are both financial performance metrics and quality performance metrics that are used
in healthcare to determine success. The purpose of this paper is to propose on outline for evaluating
the quality improvement initiative and financial implications, along with giving a description of
specific metrics. A recommendation will be discussed as to how the organization can represent the
data related to the quality improvement issue for ongoing monitoring. Also, there will be an
explanation of how the organization can create an integrated view of performance that links finance
and quality.
Methods for Evaluating
To improve the quality, safety, efficiency, and effectiveness of patient care, applying research and
evidence–based practice is necessary. In the Institute of Medicine's report, Keeping Patient Safe:
Transforming the Work Environment of Nurses, there is an emphasis on adequate nurse staffing
(Hickey & Brosnan, 2012). Therefore, the quality improvement initiative is to focus on closing the
gap between the core staffing and actual staffing in a six–week schedule.

How Statistics Is Important For The Quality Of Data
Statistics is well–defined as a division of mathematics and is most often used to explain, analyze,
and summarize as well as interpret what we perceive– in order to make sense or meaning of our
interpretations. Every day we encounter statistics that originate from various forms and behaviors.
Which means that to in order for all of the information gathered to make sense, there would
realistically be no need for the use of statistics. Conversely, due to its focus and pragmatic on
applications, statistics is characteristically considered a distinctive mathematical science rather than
a division of mathematics (Chance et al, 2005). Therefore, some measures a statistician utilizes are
less mathematical; for example, ensuring data collection is conceded in a manner that yields valid
conclusions, recording results, or coding data in behaviors understandable to the users. Statistics has
been recognized to improve the quality of data by forming specific survey samples and experiment
strategies. It also offers tools used to forecast the use of data as well as statistical models. It is
appropriate in many academic arenas that consist of business, social and natural sciences, and
government, to list a few.
Descriptive statistics are wholly used to define the sample under study; they are used essentially to
describe the fundamental characteristics of a prearranged data. Descriptive statistics offer modest
summaries concerning the measures as well as the samples. When applied in

Water Quality Data ( Fixed Interval Sampling )
Water quality data (fixed–interval sample) collected bi–monthly from 1999–2008 and monthly from
2009–2013 for all 18 monitoring sites within the Reedy Fork and Buffalo Creek basins over a 15–
year period was obtained from The City of Greensboro Stormwater Division, North Carolina. The
sampled data were grouped in ranges of years from 1999–2002, 2003–2008, 2009–2010 and 2011–
2013 so as to obtain a detailed analysis on the data. The sampling sites in the study area were
numbered for simplicity of result presentation. Sites 1 to 6 were located at the highly sub–urban and
agricultural area and sites 7 to 18 were located in the highly urbanized area of Greensboro. This sites
include; Bluff Run (1), Fleming (2), Friendship Church Rd (3), Old Oak Ridge Rd. (4), Pleasant
Ridge (5) and Battleground Ave. (6), are located in the Reedy Fork Creek basin (Figure 1). Whereas
Aycock (7), North Church St. (8), Fieldcrest Dr. (9), McConnell (10), Merritt Dr. (11), 16th St. (12),
Randleman Rd (13), Rankin Mills Rd. (14) West JJ (15), White St. (16), Mcleansville (17), and
Summit Ave. (18) sites are located in the Buffalo Creek basin. Twelve water quality parameters were
selected for statistical analysis (Total suspended solids (TSS, mg/L), total Kjeldahl nitrogen (TKN,
m/L), chemical oxygen demand (COD, mg/L), biochemical oxygen demand (BOD5, mg/L), total
dissolved solids (TDS, mg/L), total Phosphorus (TPhos, mg/L),Turbidity (TURB, NTU) nitrite
nitrogen (NO2–N, mg/L), nitrate nitrogen (NO3–N,

Data Quality Analysis : Analysis Of A Data Stored Data
Stored data may be retrieved as part of a patient record for review and update, or to undertake
analysis across a broader dataset – particularly in the case of research datasets or as part of ongoing
data quality reviews. The processing requirements for each of these needs are very different. For an
individual patient record the volume of data to be transferred is relatively small. Where large
volumes of data are to be analysed, it may be possible to copy all the data from the storage location
to a local PC or server for analysis, however it is much more efficient to leave the data in situ and
have servers undertake the analysis in the data centre. This means that alongside storage hosting
considerations there also needs to be ... Show more content on Helpwriting.net ...
Having better, more accurate data opens the way for improved decision support based upon machine
learning – as we will see later. Storage Management and Compliance Management of very large
datasets requires specialist expertise – each different area briefly described in this section is a
technical specialisation and whilst there are a limited number of people with expertise in more than
one of these areas, there are not deep technical specialists with capabilities in all areas discussed.
This means expert management of very large data stores requires a team of specialists and in large
organisations teams of specialists in each area. Such expertise is expensive to acquire, retain,
develop and manage on an ongoing basis, so there is sense in sharing that expertise across as many
datasets and users as possible. Alternatively, compromises are made and lesser levels of expertise
are utilised with smaller datasets or compromises made with regards to the robustness and rigour of
the data storage. There is an analogy with libraries – very large national libraries have more
specialists than a local library with a smaller collection of books. Large datasets in a live
environment require ongoing management with daily maintenance tasks and oversight. This
management will replace failed components,

Monitoring Quality Data
How a Facility Evaluates and Monitors Quality Data
Yadira Garcia
University of the Incarnate Word Online
How a Facility Evaluates and Monitors Quality Data
The purpose of this paper is to discuss the methods used by a local health care facility, Southwest
General Hospital, to evaluate and monitor healthcare quality data. Quality measurement in health
care is the process of using data to evaluate the performance of health care providers against
recognized quality standards (FamiliesUSA, 2014). The measuring of quality plays a vital role in the
creating, maintaining, and managing of the data that this healthcare facility aims in focusing on
quality of health care.
Quality measures are set in place by Southwest General Hospital (SGH) to assess the delivery of its
health care. These measurement systems allow the hospital access to tools that enhance the
efficiency and delivery of patient care. One system that SGH uses to promote quality is the use of
computerized physician order entry (CPOE) system which reduces medication errors. Another way
Southwest General promotes the measurement of quality data is by becoming the first local hospital
to use a high–tech system to track staff in promoting hygiene. Karen Barnhart RN, Director of
Quality states, "Our staff members clip a small device onto their badges which then interfaces ...
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(DNV). Creating, maintaining, and managing quality health information plays a vital role in
achieving DNV accreditation for this facility. Measuring quality of this facility requires that an
entire hospital is dedicated to meeting the needs its patients and achieving the goals required to
initiate and maintain accreditations. As a result of its hard work and dedication, Southwest General
Hospital has earned numerous distinctions for quality, including accreditations which include Chest
Pain, Stroke, Bariatric Surgery, and Wound

Case Study: What Can Be Done About Data Quality?
What was the impact of data quality problems on the companies described in this case study? What
management, organization, and technology factors caused these problems? BT Group had data
quality issues within the product inventory and customer billing databases, resulting in poor
efficiency of the system. The case doesn't really give the reason that these databases contained
inaccurate data. However, one can assume that the errors were caused by lack of structure within the
organization at data collection points. Emerson Process Management, had a data warehouse that was
collecting data from transaction processing systems across the world. The inaccurate data was
caused by assuming that all members of the global sales team would be ... Show more content on
Helpwriting.net ...
Discuss how this statement applies to the companies described in this case study. I think each of the
companies in the case study came to the realization that they data quality was not simply a technical
problem. Each company had multiple business lines with inconsistent processes of collecting data.
BT Group came to the realization that too much time and effort was being spent correcting data on a
regular basis. To institute a change in the quality of data collected, the company set measurable
goals that would show the success of those changes. These goals were set within each business
group to emphasize the necessity of contribution. The end result was a substantial savings in time
and money, as well as an improved data collection process. Emerson realized that the data being
collected was inconsistent from across the world. The designers of the data warehouse made the
assumption that all data collection processes would be the same. However, the absorption of
acquired companies as well as cultural differences from locations around the world, created multiple
systems for data collection and entry. Once the realization was made that the data warehouse was
full of inaccurate information, it was obvious that a new system would need to be put in place.
Cintas had a similar experience with the multiple business units that operate under the same name. A
customer could appear in multiple databases, yet not be

The High Quality Data Gathering System Essay
Measured and Monitored In China, as well as other countries, including the United States,
controlling the rising cost medical care while providing high–quality services (value for money
spent) is an arduous task in advancing the cause of imparting universal healthcare coverage to a
country's population (Tang, Tao, & Bekedam, 2012). This quest is particularly problematic in China
with respect to quality, due to a lack of appropriate systematic monitoring which may be considered
essential for any type of improvement (Ma, Lu, & Quan, 2008). As China has no reliable clinical
quality data gathering system, the current extent to which the quality of medical services is
measured and monitored consists of graded public hospitals divided into levels based on the inherent
resources available to patients (Xu, Liu, Shu, Yang, & Liang, (2015). In terms of measuring and
monitoring cost control, China has begun to monitor and evaluate pharmaceutical drugs brought into
all of the public hospitals, as this is a significant source of the country's health care expenditure, and
reportedly obtains some measurement data through the collection of information from national
health services surveys (Tang, Tao, & Bekedam, 2012), which are only instituted on an infrequent
five–year basis (The Commonwealth Fund, 2016, p.35). In the United States, measuring and
monitoring the quality of care has been reported to be lacking, given the immensity of this country's
health care sector (Schuster, McGlynn, &

High Quality Data Quality Analysis
3.1 Introduction
High–quality data facilitate a precise analysis and the resulting statistics. Hence, high–quality data
assist the organization to increase its business value. This chapter demonstrates the concept of data
quality, the effects of inaccurate data, and the factors that cause low–quality data.
3.2 Data Quality
The executive and the top management in organizations seek comprehensive reports and dashboards
to enable them to understand on going processes and facilitate the decision– making that improves
their business. However, the decision–making process may be influenced by various factors. Data
quality is a critical factor because when the quality of the data is inferior, poor decisions could be
made [39]. In addition, data ... Show more content on Helpwriting.net ...
Hence, several parties share the responsibility for the quality of data. In addition, J. E. Olson (2003)
observed that the poor data quality resulted from the rapid growth of information system technology
and the prompt evolution in system implementation and frequent changes that complicate and
hamper the quality control process [44]. According to C. Boulton (2016), 57.5% of poor data are
caused by users, followed by 47% caused by data migration and integration, which usually lead to
gaps or duplicate information, and 43.5% caused by changes to source systems (Figure 5) [45].
Figure 5: Causes of poor data quality [45]
Jack E. Olson (2003) defined data quality as data that are linked to their fit for use. In other words,
high data quality is obtained when the data fulfill the requirements and the criteria of its intended
usage. In contrast, poor quality results when the data do not fulfil their requirements [44].
3.3 Dimensions of Data Quality
In previous research, data quality was divided into two main categories: intrinsic and contextual. In
the intrinsic category, value resides within the data, which refers to objective attributes. In the
contextual category, the attributes of the data mainly depend on the context in which the data are
present, used, as well as the situation or problem. Data in the contextual category include the
dimensions of relevance and believability [39],

Manual vs. Automated Statistical Process Control: Food...
Israel Ortega–Ramos
The Prime Example
Our recent visit to a food packaging plant in New Jersey highlighted the inconsistent results of
statistical process control routinely faced by Quality Control Managers. Product weight readings
were taken from the manufacturing floor, entered into an Excel spreadsheet and analyzed. The
results produced no predictable under or over filling trend despite the fact that the same people used
the same scales at the same time of day. The problem is simple and fundamental. Human error is an
inevitable part of the process of collecting statistical data. This is consistently overlooked in
companies that utilize manual SPC[1] (statistical process control) for their manufactured goods. To
ensure the ... Show more content on Helpwriting.net ...
The scale will then calculate the statistical data after the last product is placed on the scale and store
this data in a password–protected memory for collection by the Quality Manager. This statistical
data can then be sent wirelessly to a spreadsheet, printed on a label to accompany the sampled
product, or simply viewed on the scale interface. The flow diagram below shows the improved SPC
process.
Companies can also utilize various connectivity and software options that can integrate filling
machines to automated SPC scale systems. This means that fill volumes based on trends calculated
by the scale can be adjusted via an automated system. Quality Control Managers and Plant
Managers can also connect all the SPC scale systems in a factory via a central control computer that
will provide easy access to "real–time" data. Integrating an automated SPC Scale System into a
manufacturing environment will have the following advantages over the older manual SPC systems:
Upgrading outdated manual SPC processes is the first step to improve overall quality, efficiency,
and trace ability. This can be accomplished with as little as $5,000 in capital investment. Quality
Control Managers and Plant managers have to take a hard look at how their product samples are
being weighed and how these measurements are turned into results that can improve production line
efficiency. It is now time for

Is Data Mining A Valuable Asset? Essay
Chapter 1
INTRODUCTION
1.1 Background
It is a reputable fact that we are in an information technology motivated society, where knowledge is
a priceless asset to any individual, organization or government. Companies are provided with
massive amount of information in daily basis, and there is the desire for them to concentrate on
improving these data so as to get the most essential and useful information in their data warehouses.
The urge for a technology to help solve this task for information has been on the study and
development front for quite a few years now. Data in the real world is dirty such as incomplete
lacking of attributes value, lacking certain attributes of interest, or containing only aggregate data
also dirty data is also called noisy data that containing errors and outliers and the data also is
inconsistent that containing discrepancy in codes or names.
Data mining is a new technology which could be used in extracting valuable information from data
warehouses and databases of companies and governments. It involves the extraction of hidden
information from some raw data. It helps in detecting inconsistency in data and predicting future
patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm
and framework, and the automated analysis provided by this algorithm and framework go ahead of
evaluation in dataset to providing solid evidences that human experts would not have been able to
detect due to the fact that they

The High Quality Data Gathering System Essay
In China, as well as other countries, including the United States, controlling the rising cost medical
care while providing high–quality services (value for money spent) is an arduous task in advancing
the cause of imparting universal healthcare coverage to a country's population (Tang, Tao, &
Bekedam, 2012). This quest is particularly problematic in China with respect to quality, due to a
lack of appropriate systematic monitoring which may be considered essential for any type of
improvement (Ma, Lu, & Quan, 2008). As China has no reliable clinical quality data gathering
system, the current extent to which the quality of medical services is measured and monitored
consists of graded public hospitals divided into levels based on the inherent resources available to
patients (Xu, Liu, Shu, Yang, & Liang, (2015). In terms of measuring and monitoring cost control,
China has begun to monitor and evaluate pharmaceutical drugs brought into all of the public
hospitals, as this is a significant source of the country's health care expenditure, and reportedly
obtains some measurement data through the collection of information from national health services
surveys (Tang, Tao, & Bekedam, 2012), which are only instituted on an infrequent five–year basis
(The Commonwealth Fund, 2016, p.35). In the United States, measuring and monitoring the quality
of care has been reported to be lacking, given the immensity of this country's health care sector
(Schuster, McGlynn, & Brook, 2005). Despite

Quality Improvement Data
Quality improvement data: Using QI instruments to address high readmission rates and medication
errors Keeping track of quality improvement data is essential for all organizations: not simply for
for–profit entities, but also for not–for–profit organizations such as hospitals. Patient readmission
rates and medication errors are serious issues at many healthcare organizations. Using quality
improvement instruments such as a fishbone (Ishikawa) diagram: which can "identify many possible
causes for an effect or problem and sorts ideas into useful categories" and a Pareto chart, which
"shows on a bar graph which factors are more significant" can help pinpoint the root causes of
chronic problems that seem to arise from a multitude of factors (Cause analysis tools, 2013, ASQ).
"The standard benchmark used by the Centers for Medicare & Medicaid Services (CMS) is the 30–
day readmission rate. Rates at the 80th percentile or lower are considered optimal by CMS"¦A
hospital's readmission rate is calculated by dividing the total number of patients readmitted within
seven days of discharge by the total number of hospital discharges." (Readmission rates, 2013,
Mayo Clinic). Keeping readmission rates low is essential for the hospital to demonstrate a
commitment to providing quality care at low cost and "a high readmission rate also can result in
reduced Medicare reimbursements" as well as reflect negatively upon the hospital's PR when such
reports are released (Can hospital

Company Analysis : Pb And The Erp Re Engineering Project
1 Introduction
This chapter aims to provide an overview of the thesis topic including: an introduction to the
problem Pitney Bowes (PB) want to solve and the related business topic; the company summary of
PB and the ERP re–engineering project which they are undergoing; the objectives and scope as well
as the structure of this thesis project. The problem definition will show its importance and rationale
to this topic as well as PB. The company summary will provide contextual and valuable background.
The objectives and scope will illustrate the key deliverables considering the limitation of resource
and time duration of this project as well as how they will be acquired.
1.1 Introduction to the problem
In the recent decades, Information technology (IT) has become an imperative part of business for
most companies, especially international corporations. It is quite difficult for an international
corporation to operate without a mature IT system to collect and organise data and information.
With the development of IT, systems such as SAP have enabled companies to collect, store and
utilise many times more data than they used to be. It means companies now have to deal with huge
amount of data, which indicates that more data quality problems are more likely to occur. With their
increasingly dependency upon the IT systems or databases to support business process and decision
making, the number of errors in stored data and organisational impact of these errors are likely to
increase.

Measuring Data Quality Business Value Essay
3.5 Data Quality Business Value:
According to Umar at al. (1999), (as cited in Haug., & Arjborn., 2011 ) there are many big
organizations losing millions of dollars annually because of Data Quality related problems. They
lose this amount in terms of revenue opportunities and failure to address customer issues in a timely
manner. In addition; poor Data Quality is one of the biggest reasons for failure of critical
information projects. So, here there are some major benefits of using proper Data qualities which are
below:
Deliver high–quality data
Deliver high quality data for a range of enterprise initiatives including business intelligence,
applications consolidation and retirement, and master data management.
Reduce time and cost
Data Quality uses to implement Client relationship management (CRM), data warehouse, data
governance, and other strategic IT initiatives and maximize the return on investments.
Help improve customer service Data Quality helps to improve customer service and to identify a
company 's most profitable customers.
Provide business intelligence for research, fraud detection, and planning
Data Quality provides business intelligence for research, fraud detection, and planning. Better Data
Quality leads to better analysis in research, fraud detection and in the part of planning.
For example, HSBC Bank used good Data Quality to manage relationship between organization and
customers. After using new technology, this organization manages data three

Documentation For Quality Data Preparation ( Federal...
Introduction
This assignment utilizes one of the file sets in the FAA database, Aircraft Series, to demonstrate a
proposed process for quality data preparation (Federal Aviation Administration – Data Downloads,
n.d.). This documentation includes a process overview, a description of the data files, two attempts
at cleaning the data, and validation and standardization of the resulting content. A short discussion
of data integrity, data validation, data governance, and documentation follows including
recommendations to overcome the challenges encountered.
Process Overview
Per the advice of Robin Hunt's video tutorial, the exercise began with a notional workflow diagram
(2015). After two attempts, the succeeding process consolidated into a series of four phases, each
described in the following sections. The complete diagram appears below. Figure 1. Assignment
Workflow Diagram Select Data
The FAA database provided the source data. The data cleaning process required downloading two
text formatted files in the subject area of Accident/Incident data: the source data file and a document
describing the layout of the source data file. The website download process appeared similar to the
figure below:
Figure 2. FAA Data Download Web Page
On first inspection, the downloaded source data file appeared to only contain text characters similar
to as shown below. Figure 3. Aircraft Series Source Data File
The data contained 4959 rows (as measured by Notepad ++) and

Reaction, Recommendation, Conclusion Paper
Corrective and Preventive Actions (CAPA) * Inspectional Objectives * Decision Flow Chart *
Narrative * Medical Device Reporting * Inspectional Objectives * Decision Flow Chart * Narrative
* Corrections & Removals * Inspectional Objectives * Decision Flow Chart * Narrative *
Medical Device Tracking * Inspectional Objectives * Decision Flow Chart * Narrative
Corrective and Preventive Actions (CAPA)
Inspectional Objectives
1. Verify that CAPA system procedure(s) that address the requirements of the quality system
regulation have been defined and documented.
2. Determine if appropriate sources of product and quality ... Show more content on Helpwriting.net
...
Once you have gained a knowledge of the firm's corrective and preventive action procedure, begin
with determining if the firm has a system for the identification and input of quality data into the
CAPA subsystem. Such data includes information regarding product and quality problems (and
potential problems) that may require corrective and/or preventive action.
2. Determine if appropriate sources of product and quality problems have been identified. Confirm
that data from these sources are analyzed to identify existing product and quality problems that may
require corrective action.
The firm should have methods and procedures to input product or quality problems into the CAPA
subsystem. Product and quality problems should be analyzed to identify product and quality
problems that may require corrective action.
The firm should routinely analyze quality data regarding product and quality problems. This
analysis should include data and information from all acceptance activities, complaints, service, and
returned product records. Determine if the firm is capturing and analyzing data from acceptance
activities relating to component, in–process and finished device testing. Information obtained
subsequent to distribution, which includes complaints, service activities and returned products, as
well as information relating to concessions (quality and nonconforming products), quality records,
and other sources of quality data should also be captured

Securing the Quality of Data
2.3 Securing the quality of data
Adopting explicit evaluation criteria increases the transparency of the research and provides the
researcher with the means to highlight the strengths and limitations of that particular research
(Eriksson, P., & Kovalainen, A., 2008). According to Eriksson, P., & Kovalainen, A. (2008), one of
the reasons that lead to a poor–quality research is when a qualitative research is assessed with the
help of evaluation criteria adopted from quantitative research and vice–versa. Conducting a
quantitative research requires gathering data from a large number of samples whereas in qualitative
research, generally, data is collected from a relatively smaller number of participants and the focus
is on understanding the participants social world in depth through probing, asking questions and
case studies. When conducting a qualitative research, the question shouldn't be "How many
interviews do I need to do to get my theory accepted"? According to Eriksson, P., & Kovalainen, A.
(2008), by asking this question, the logic of quantitative acceptability enters into qualitative
research. As mentioned earlier, assessing a qualitative research with the evaluation criteria adopted
from quantitative research leads to a poor quality research and vice–versa. Instead, when evaluating
qualitative research as a good–quality research, the focus should be on the materials obtained from
the interviews, quality of the interviews and the logic through which a researcher makes

Ops 571 Statistical Process Control
Chase, Jacobs and Aquilano pose questions such as, "How many paint defects are there in the finish
of a car? [and] Have we improved our painting process by installing a new sprayer?" These
questions are meant to investigate and apply different techniques that we can use to improve the
quality of life. Quality control not only applies to manufacturing techniques, it can also be applied to
everyday life. This discussion will focus on a specific method of quality control called statistical
process control that will ensure my morning process is effective.
One method of quality control can be pursued through process control procedures like statistical
process control or SPC. SPC "involves testing a random sample of output from a process to ... Show
more content on Helpwriting.net ...
The more data that is available the stronger your confidence intervals are.
UCL = p + z Sp
UCL = p + 3Sp
UCL = .08333 + 3(.05050) = .23483
LCL = p – z Sp
LCL = p – 3Sp
LCL = .08333 − 3(.05050) = –.06817
In the control chart, the data from the sample stays in between the controls. This means that my
process in the morning is working properly and is effective. Now, it is important to look to the future
trends in order to predict seasonal factors. "A seasonal factor is the amount of correction needed in a
time series to adjust for the season of the year." (Chase, Jacobs & Anquilano, 533) Seasonal
factors may affect the samples by taking into consideration factor based on seasons or time periods.
The alarm clock that is used to wake me up in the morning is not dependent on any factors of time
or season.
Statistical process control is one way to control quality and make sure goals are attained. Statistical
methods show that the samples taken can create visual representations that conclude my alarm clock
is an effective method to starting my morning process. This ensures that it is operating at its fullest
potential.
REFERENCES
Chase, R. B., Jacobs, F. R., Aquilano, N.J. Operations management for competitive advantage (11th
ed). New York: McGraw Hill/Irwin.

Green Jr. K, Toms L, Stinson T. STATISTICAL PROCESS CONTROL APPLIED WITHIN AN
EDUCATION SERVICES ENVIRONMENT. Academy Of Educational Leadership Journal [serial
online]. June 2012;16

Quality Improvement Is Defined “As Systematic, Data-Guided
Quality improvement is defined "as systematic, data–guided activities designed to bring about
immediate improvement in health care delivery in particular settings"(Lynn et al., 2007, p 667) by
utilising the Model for Improvement, which is not a replacement for one already used by the
organisation. Instead, the model of improvement accelerates improvement of health care process and
proved to be successful. It consists of two parts such as the three questions and the Plan–Do–Study–
Act (PDSA) cycle of rapid change tool to determine if the change is an improvement (Institute for
Healthcare Improvement, 2017).
Jane (pseudonym) is a student nurse in the first week of practicum placement in an aged residential
care facility. Jane has ... Show more content on Helpwriting.net ...
All of these are not only unavoidable but also impact a person's ability to interact with family, health
professionals and health care providers. This also, impacts their ability to take an active part in the
decision–making of their care limiting participation in therapy, counselling and education. There
was no data or resources available at the residential care facility on communication aids to support
those with communication problems. Jane added activity facilitators, nurses, student nurses and
health care assistants to the team to represent the interdisciplinary aspects of the communication
issue. She also recruited a family member of one of the residents on a hospital wing to provide their
perspective.
Jane then applied the Plan–Do–Study– Act (PDSA) cycle of rapid change tool or process to her
project on q–cards aid tool. In the "Plan" part of the cycle, Jane talked to the activity facilitator
regarding the communication aids available in the rest home facility for those who have aphasia,
dysphasia or dysarthria. She found that even though the activity facilitator uses cards for people with
dementia in the dementia unit; it is more a picture recognition rather than supporting
communication. Furthermore, Jane spoke to the clinical manager regarding an idea to use the q–
cards as a means to support

Collect Quality Data
Emergence of Tools and Methods to Collect Quality Data While it is easy to decide that quality
needs attention in emergency medicine the first question when reviewing something is of course,
how? Tools and methods must be developed when investigating anything in order to collect
information. The Centers for Medicare and Medicaid Services (CMS) describes quality measures as
tools that help us measure or quantify healthcare processes, outcomes, patient perceptions, and
organizational structure and/or systems associated with the ability to provide high–quality health
care and/or that relate to one or more quality goals for health care (CMS, 2015). In order for quality
measures to be used, however, how is that data collected? According to CMS ... Show more content
on Helpwriting.net ...
The AHRQ has developed many tools in order to measure quality in healthcare. To truly grasp the
history and evolution of research and findings of quality measurement and healthcare, their
databases should be explored. On their website, www.ahrq.gov, you can find reports, research, and
fact sheets to update you on the most up to date quality requirements and measurement tools and
outcomes of their use in healthcare. The AHRQ uses evidence based reports that they collect with
their work done through Evidence–based Practice Centers (EPC's). Their basis always going back to
their key quality indicators, used by health care organizations everywhere. Their mission statement
reads, "The Agency for Healthcare Research and Quality's (AHRQ) mission is to improve the
quality, safety, efficiency, and effectiveness of health care for all Americans" (U.S. Department of
Health and Human Services, 2015). The work of the AHRQ helps set the standards in quality
measurement tools and their

Data Mining of Chemical Analysis for White Wine Quality
Background Wine was once viewed as a luxury good, but now it is increasingly enjoyed by a wider
range of consumers. According to the different qualities, the prices of wines are quite different. So
when the wine sellers buy wines from wine makers, it's important for them to understand the wine
quality, which is in some degrees affected by some chemical attributes. When wine sellers get the
wine samples, it makes difference for them to accurately classify or predict the wine quality and this
will differentiate their profits. So our goal is to model the wine quality based on physicochemical
tests and give the reference for wine sellers to select high, moderate and low qualities of wines. We
download wine quality data set that is the white ... Show more content on Helpwriting.net ...
Since our goal is to make model to give reference to the 3 categories, we can re–defined the
categories into 3 other than 7 and in this way, we expected to gain more reasonable results and give
wine sellers more accurate models to support their decisions to purchase wines from the wine
makers. 3.1 Clustering and redefine data set Considering that clustering's goal is to put objects that
are "similar" together in a cluster, this matches our goal to make three quality categories. So we
decided to use clustering first to explore if the categories can be parted. Since the XLMiner just can
run no more than 4000 records for clustering, we need to reduce our data set size to 4000. First, we
eliminated the data outside of 3 stand deviation range; then we found that the quality 5 has about
1800 records which took nearly a half of all records which spread 7 qualities, so we randomly
selected 80% of the quality 5 records and quality 5's dominant effect would be reduced. In this way
the new data set was determined. After the new data set was decided, we created a new output
variable–new quality–and the values are 1, 2, and 3, each representing low quality, mid–range
quality and high quality. The details are showed as the tables below: Wine with quality 3, 4, 5 | Wine
with quality 6 | Wine with quality 7, 8, 9 | Low quality wine | Mid–range quality wine | High quality
wine | 1271 observations |

SWOT Analysis: Identifying Quality and Shortcomings Essay
SWOT investigation is a fundamental, clear model that gives guidance and serves as a premise for
the advancement of promoting arrangements. It finishes this by surveying an associations qualities
(what an association can do) and shortcomings (what an association can't do) notwithstanding open
doors (potential ideal conditions for an association) and dangers (potential unfavorable conditions
for an association). SWOT investigation is a paramount venture in arranging and its esteem is
regularly disparaged in spite of the effortlessness in creation. The part of SWOT dissection is to take
the data from the natural investigation and separate it into inward issues (qualities and
shortcomings) and outer issues (open doors and dangers). When this ... Show more content on
Helpwriting.net ...
Furthermore, a concentrate on a company's qualities in promoting is advancement is vital to expand
mindfulness in ranges that a firm exceeds expectations in. This technique not just inspires a positive
reaction inside the personalities of the shopper, however pushes the shortcomings further from the
choice making methodology (Promoting Procedure, 1998).
Shortcomings ought to likewise be considered from an interior and outside perspective. It is
essential that posting of a company's shortcomings is truthful so they may be overcome as fast as
would be prudent. Deferring the disclosure of shortcomings that as of now exist inside an
organization will just further damage the firm. A decently created posting of shortcomings ought to
have the capacity to answer a couple of inquiries. What could be moved forward? What is carried
out crudely? What ought to be evaded (PMI, 1999)?
The part of the inward parcel of SWOT is to figure out where assets are accessible or needing so that
qualities and shortcomings could be distinguished. From this, the advertising chief can then create
showcasing systems that match these qualities with open doors and accordingly make new
capacities, which will then be a piece of ensuing SWOT investigation. In the meantime, the
administrator can create techniques to defeat the association's shortcomings, or discover approaches
to minimize the negative impacts of these shortcomings (Showcasing Method,

Data Management, Data And Information Quality For Big Data?
type of data, and it has a massive amount of processing power, and can handle a boundless number
of jobs or tasks. Data Management, Data ingestion, Warehouse, and ETL provides features for
effective management and data warehousing for data managing as a valuable resource. The Stream
computing features pulls streams of data and then streams it back out as a single flow and then
processes that data. Analytics/ Machine Learning features advanced analytics and machine learning.
Content Management which features document management and comprehensive content lifecycle.
Integration features the integration of big data from any sources with ease. Data governance which
is a compliance solution to protect the data and comprehensive security, and ... Show more content
on Helpwriting.net ...
Making it a friendlier drag–and–drop graphical interface that would automatically generate the
fundamental Hadoop code. The Talend tool includes components for leading Apache Hadoop
software's like HDFS, HBase, Hive, Pig, and Sqoop.
Talend's Hadoop–leveraging big data quality functionality has made it possible for data quality
management across an organization or business entire enterprise. Talend Big Data Platform
distributes data quality features that include Data profiling, Data standardization, matching and
cleansing, Data enrichment, Reporting and real–time monitoring, and Data governance and
stewardship (Big Data Quality: Talend Hadoop Data Quality & Management 2017).
The challenges of data quality and data quality assessment
High–quality data are the precondition for guaranteeing, using big data and analyzing. Big data has
a quality that faces many challenges. The characteristics of big data are the three Vs Variety,
Velocity, and Volume, as explained in the what is big data section of the paper Variety of data
indicates that big data has a different kind of data types, and with this diverse division puts the data
into unstructured data or structured data. These data need a much higher data processing capability.
Velocity is the data that is being formed at and unbelieve amount of speed and it must be dealt in an
organizational and timely manner. Volume is the tremendous volume

Pharmaceutical Validation Tool For Quality Management
Pharmaceutical Validation tool for Quality Management
Abstract
Pharmaceutical validations is part of CGMP regulations by which one can build quality attributes of
pharmaceutical specification, i.e. safety, efficacy, purity, in pharmaceutical products. It assures that
the process follow for the manufacturing of pharmaceutical products is well controlled and
monitored at its critical parameters for consistently producing the quality products.
The present review describes the importance of validation in pharmaceutical industry, its
requirement for approval of new drug application by the various regulatory agencies. Furthermore it
highlights the current guidance on process validation by USFDA, EMA.
Key Words: Validation, Quality assurance, critical parameters, NDA, USFDA, EMA, Validation
Guidelines
Contents
Introduction 2
History of Pharmaceutical validation 2
Approaches to Process Validation 4
Stages of process validation according to life cycle approach 4
Stage I: Process Design 4
Stage II: Process Qualification 4
Stage III: Continued Process Verification 5
Features of USFDA Process validation guidance 2011 5
Conclusion 6
References 6
Introduction
Quality is concept applicable at everywhere from business to have successful life. Everyone wants
to buy the quality products and want to live the quality life as per the standards determined by them
for it. As concern with pharmaceutical industries, they are built for to bring the quality in health of
human and animal beings.

Case Study : Finding Quality Data
Finding Quality Data
To stay competitive in today's market, "companies are using big data analytics to understand and
engage customers in a way that inspires greater loyalty" (Rackey, 2015). Better understanding our
customers will result in opportunities for increased sales through up–selling and cross–selling while
also improving customer satisfaction by catering to customer's needs more efficiently. Developing a
BI program using data, technology, analytics, and human knowledge allows us to transform data
into useful BI solutions.
The first step in gaining this business intelligence is to locate appropriate customer data sources
within the organization. The quality of the data must be confirmed using data profiling and data
quality ... Show more content on Helpwriting.net ...
Completeness is characterized by the presence, absence, and meaning of null values in the data
tables (Batini & Scannapieca, 2006). Uniqueness refers to the data item recorded without
duplication. The dimension of timeliness measures how data represent reality from a particular point
in time. Data consistency shows that a data item is the same in multiple data sets or databases.
Validity refers to data that conforms to the correct syntax for its definition (DAMA UK, 2013). Once
the data quality assessment is completed, proof of concept can be developed.
Proof of concept
A Proof of Concept (POC) is used to demonstrate the design idea using only a small part of a
complete system. This system will be used to discover the factors that help influence a customer's
purchases using existing data such as customer sales history, initial purchases, discounts and other
data. Simple queries can be effective in showing the customer's paths to their purchases. The results
of these queries will help predict future purchases of existing customers, uncover up–sell and cross–
sell opportunities, and possibly target new customers. The first step of the POC is to build a scaled–
down environment similar to the actual environment for testing the program using separate, reserved
resources for a predetermined number of days. The POC should be carefully documented showing
configuration, installation, and test results. This documentation can then

Classification-Based Data Mining Approach for Quality...
Classification–Based Data Mining Approach For Quality Control In Wine Production
GUIDED BY: | | SUBMITTED BY: | Jayshri Patel | | Hardik Barfiwala |
INDEX
Sr No | Title | Page No. | 1 | Introduction Wine Production | | 2 | Objectives | | 3 | Introduction To
Dataset | | 4 | Pre–Processing | | 5 | Statistics Used In Algorithms | | 6 | Algorithms Applied On
Dataset | | 7 | Comparison Of Applied Algorithm | | 8 | Applying Testing Dataset | | 9 | Achievements |
|
1. INTRODUCTION TO WINE PRODUCTION
* Wine industry is currently growing well in the market since the last decade. However, the quality
factor in wine has become the main issue ... Show more content on Helpwriting.net ...
* Free Sulfur Dioxide : Amount of Free Sulfur Dioxide present in wine. (In mg per liter)
* Total Sulfur Dioxide : Amount of free and combined sulfur dioxide present in wine. (In mg per
liter) Used mainly as preservative in wine process.
* Density : The density of wine is close to that of water, dry wine is less and sweet wine is higher.
(In kg per liter)
* PH : Measures the quantity of acids present, the strength of the acids, and the effects of minerals
and other ingredients in the wine. (In values)
* Sulphates : Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg
per liter)
* Alcohol : Amount of Alcohol present in wine. (In percentage)
* Output variable (based on sensory data)
* Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8

4. PRE–PROCESSING
* Pre–processing Of Data Preprocessing of the dataset is carried out before mining the data to
remove the different lacks of the information in the data source. Following different process are
carried out in the preprocessing reasons to make the dataset ready to perform classification process.
* Data in the real world is dirty because of the following reason.
* Incomplete : Lacking attribute values, lacking certain attributes of interest, or containing

Quality Data Characteristics
Discuss some characteristics of quality data.
Precisely some of the characteristics are generally based on the main levels of quality assurance
such as with accuracy, accessibility, comprehensiveness, consistency.
Data accuracy– means that it must be correct at all time, error free.
Data accessibility– means should be easy to get a hold of at all time.
Data comprehensiveness– means that all data must be updated and completed.
Data consistency– means that all information within the document must be a reliable source of data.
Discuss how gaps (or breaches) are predicted and handled.
Most organizations with company data files must predict that it is possible that someone would
possibly hack into their files and cause a potential data breach. ... Show more content on
Helpwriting.net ...
This data system for HIPAA is constantly amended based on changes due to civil right laws or legal
laws suits that have made ground breaking changes in health care today. The law has allowed it to be
possible to maintain the privacy rights for all patients with written consent and is continuing to be
debated each year.
Discuss Web 2.0 and its primary features.
In a nut shell the web 2.0 is the way the information is shared, stored, created, displayed,
manipulated, and distributed how it effects the internet.
Review each of the following Web 2.0 tools (you will need to create a free account to use Practice
Fusion). Are these tools useful? What are the benefits or drawbacks of using tools such as these?
Share your observations with the class.
Typically, the tools used from web 2.0 have brilliantly displayed how easy it is to maneuver around
the Practice fusion website. Initially, the setup process required that an access code was used to
setup the account which clearly meant that there were security safe guards that needed to be passed
through first. Overall, this website is an easy part to setting up ordering prescriptions, billing
insurance companies, and setting up patient electronic medical

Data Preparation And Quality Of Data Essay
Introduction Data gathering methods are often loosely controlled, resulting in out–of–range values
(e.g., Income: –100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), missing
values, etc. Analyzing data that has not been carefully screened for such problems can produce
misleading results. Thus, the representation and quality of data is first and foremost before running
an analysis. If there is much irrelevant and redundant information present or noisy and unreliable
data, then knowledge discovery during the training phase is more difficult. Data preparation and
filtering steps can take considerable amount of processing time. Data pre–processing includes
cleaning, normalization, transformation, feature extraction and selection, etc. The product of data
pre–processing is the final training set. Data Pre–processing Methods Raw data is highly susceptible
to noise, missing values, and inconsistency. In order to help improve the quality of the data and,
consequently of the results, raw data is pre–processed. Data preprocessing is one of the most critical
steps in data analysis which deals with the preparation and transformation of the initial dataset. Data
preprocessing methods are divided into following categories:  Data Cleaning  Data Integration 
Data Transformation  Data Reduction Data Cleaning Data that is to be analyzed can be incomplete
(lacking attribute values or certain attributes of interest, or containing only aggregate data), noisy

Recommend Elements In The Design Of Audit Trails And Data...
Part 1: Recommend elements included in the design of audit trails and data quality monitoring
programs With today's advancement in technology, most hospitals have developed a data security
plan to ensure that patient data is being handled correctly and is only viewed by authorized
personnel. Hospitals can keep unauthorized personnel from viewing patient information by setting
up individual passwords (Wager, Lee, & Glaser, 2013) only allowing those employees to view the
patient's information for them to complete their job task. When an employee is entering information
into the system, it needs to be in real time as much as possible to keep human errors from occurring
and for a correction to be made there will need to be a note attached to ... Show more content on
Helpwriting.net ...
We also need a more secure software to keep those who are not authorized from accessing the
system. One of the ways we can achieve this is by making sure the software that we have is only
accessible by the staff from audit department and not our normal hospital employees (Loshin, 2011).
The software also needs to be designed that once the data has been retrieved for an audit, it can no
longer be changed unless it is approved through the chain of command.
Part 3: Recommend device selection based on workflow, ergonomic and human factors The current
system that we have is not effective for the staff and needs to be upgraded. When choosing a system
for the hospital, we need to make sure it meets the privacy for our patients and is effective for our
staff. When our staff is treating our patients, we need to be mindful of the placement of computers in
patient rooms so staff can easily access them and bring them to a comfortable level for them to
work. These computers need to have the security screen protection covers placed over them so
visitors cannot see the screen as the staff is documenting. I also think we should install the
fingerprint identification to allow fast and easy access to the system, so the staff is able to complete
real time charting and in the event of an emergency they are not trying to type out their password
and forgetting, having this device installed will also ensure that patients are not able to can access to
the computer (Maksimov & Kalkis, 2016).

The Role of the Leader in Evaluating Data to Improve...
The Role of the Leader in Evaluating Data to Improve Quality and Safety
Mary Slaton
Walden University
Leadership Competencies in Nursing and Health Care
NURS 4021–9
Dr. Merilyn Long
May 17, 2013
The Role of the Leader in Evaluating Data to Improve Quality and Safety
Quality and Safety has been recognized as important issues in creating the delivery of effective and
responsive health care. To improve Quality and Safety the leader must analyze data and interpret the
information to develop a system for clinical performance by motivating, supervising, and develop a
problem solving approach to deal with system of medical errors. The purpose of this paper is to
inform the reader of the role of the leader in evaluating data to improve ... Show more content on
Helpwriting.net ...
The data shows that ninety four percent of the falls reported were patients prescribed diuretics.
Diuretics have the capacity to cause dehydration by fluid volume depletion, increase the urge to
void, potentially causes dizziness and cause postural hypotension therefore increasing the risk of
falls in all patients but is increased in older women (Lim, 2009). Telemetry unit have more patients
with arrhythmias and other cardiac related problems and are often prescribed diuretic,
anticoagulants, antihypertensive and other cardiac medications, which can increase the risk of falls
(Carey, 2001).
Quality Improvement Plan The quality management process involves review of the data that tracks
activities and outcomes. Six sigma is a quality management program that focuses on the patient, the
data collected provides evidence of the results and the emphasis is on the processes used within the
system (Sullivan, 2013). A risk management plan would identify risk for injuries, accidents and
financial losses, it would review the monitoring system, analyze the data and identify ways to
eliminate or reduce the risks. A continuous quality improvement plan for fall prevention needs to be
established because falls are the leading cause of fatal and nonfatal

Quality Improvement Data Analysis
Use of Quality Improvement Data Quality improvement data is followed in all health care setting.
The use of Cerner EHRs allows data to be obtained from patient charts to analyze core measures.
According to the joint commission influenza and pneumococcal vaccinations measures should be
addressed in all hospital in–patients (The Joint Commission, 2015). Data can be retrieved on those
patient who were diagnosed with pneumonia to determine if they received the vaccinations for
pneumonia and influenza. As well as tracking the time frame between diagnosis and treatment, and
additionally the patient outcome. This is captured through the documentation of the clinical staff.
The information can be analyzed to determine the quality improvement changes that need to be
implemented to improve patient outcomes. Another core measure that is tracked utilizing Cerner
EHRs system is the collection of data related to tobacco use (The Joint Commission, 2015). The
system will prompt ... Show more content on Helpwriting.net ...
Cerner offers Skybox storage for the storage of patient information. It has an unlimited storage
capacity and the data is uploaded once and then available in the Cloud at anytime and location. Data
is located at the hospital site and at Cerner data center locations. This allows for file replication in
the event of data loss or corruption. Military grade encryption is utilized with continuous intrusion
monitoring (Cerner, 2015). Security standards are also built into the system to meet HIPAA
standard. HIPAA training must be completed by each new employee and a signature must be obtain
that the employee will follow HIPAA guidelines. Access to patient information is only given if it is
pertains to their hired position. The hospital must develop HIPAA policies that are updated annually.
User specific logins and passwords are utilized to sign into the system and they need to be changed
at set

Data Quality As Crime And Crime

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