PUBH 8545 Big Data and Health Care Research Paper With advances in medical-records management and the ability to store massive amounts of data, opportuniti

PUBH 8545 Big Data and Health Care Research Paper With advances in medical-records management and the ability to store massive amounts of data, opportunities for analyzing health information have grown in almost unimaginable proportions in the past decade. “Simple” databases with thousands of fields now seem quaint and almost irrelevant, as “big data” appears on the horizon as the emerging answer to many health-related data questions.

However, the concept of big data has as many detractors as supporters, and arguments range from big data being the answer to reducing costs and improving health care, to big data potentially leading to discrimination and total government or corporate control.

For this Discussion, you will investigate how big data have been applied to health care, and you will consider the potential use of big data by medium and small local health jurisdictions.

To prepare:

Select a published article on big data and health care from the Walden Library that discusses trends, potential use, or issues related to using big data in health care.

Post analysis of the article you selected. Include the following in your post:

A brief description of the published article you selected, including:
research question,
dataset used, and
source of the data.
An explanation of your position on the use of big data for health care. Do you think that analyzing and using big data in health care can provide crucial answers and lead to great innovation and breakthroughs?
To what extent do you think that privacy and data security concerns and the potential misuse of data outweigh the potential advantages? Justify your position.
Using examples, describe how local health authorities can use secondary data sources in their jurisdictions.

Support your post based on the Learning Resources and current literature. Use APA formatting for your Discussion and to cite your resources.

Resources

Lee, C., Famoye, F., Shelden, B., & Brown, A. (n.d.a). Data manipulation. SPSS On-Line Training Workshop. Retrieved from http://calcnet.mth.cmich.edu/org/spss/data_manipulation.htm

Hellerstein, J. M. (2008). Quantitative data cleaning for large databases. Retrieved from http://db.cs.berkeley.edu/jmh/papers/cleaning-unec… Guidelines for Responsible �
Data Management in �
Scientific Research �
Developed by:
Funded by:
Office of Research Integrity �
US Department of Health and Human Services �
About the Course �
Data management is one of the essential areas of responsible conduct of
research, as outlined by the Office of Research Integrity. This educational
course will educate new investigators about conducting responsible data
management in scientific research. Researchers who are considering
submitting a federal grant or contract for the first time can also benefit from
this introductory course on data management, as can other research team
members. The course includes background information about the topic, best
practice guidelines, various learning features, and a resource section.
Learning Objectives
After taking the course, learners will be able to
• Understand the general rules of appropriate data management in
accordance with responsible conduct of research
• Understand how to define roles and responsibilities of research staff
regarding data management
• Develop and implement a communication plan for dealing with data
management issues among the research team
• Utilize the information featured in the course to implement a system for
conducting responsible data management
Online Version
This course was previously available on the Internet at
http://www.RCREducation.com. The website is not active at this time.
1
Meghan B. Coulehan, MPH
Jonathan F. Wells, BA
Development of this website
was funded by the Office of
Research Integrity (ORI)
Responsible Conduct of
Research Resource
Development Program.
Feel free to contact us with
comments or questions. You
can reach Project Director,
Meghan Coulehan, at
coulehan@clinicaltools.com.
Introduction
Data management is one of the core areas addressed by the Office of
Research Integrity (ORI) in its responsible conduct of research initiative (see
links in sidebar). This important, multifaceted issue affects all health
researchers and deserves extra attention and diligence.
Oversight of data management represents a significant investment of time and
effort by the Principal Investigator (PI) of a research project. For oversight to
be thorough and correct, PIs must understand the basic concepts of data
management and ensure that every member of the research project team is
involved in the planning, implementation, and maintenance of data
management policies and procedures.
Data management is one of
9 core areas addressed by
the Office of Research
Integrity’s responsible
conduct of research
initiative.
To learn more about the ORI
or the responsible conduct in
research initiative, check out
the following links:
2
•
US Department of Health
and Human Services’ ORI
website
[http://www.ori.dhhs.gov/
]
•
ORI’s Introduction to the
Responsible Conduct of
Research
[http://www.ori.dhhs.gov/
documents/rcrintro.pdf]
Overview: Concepts of Data Management
Before starting a new scientific research project, the PI and research team
must address issues related to data management, including the following:
Key Concept
How It Relates to Responsible
Conduct of Research
Data Ownership
This pertains to who has the legal rights to the data
and who retains the data after the project is
completed, including the PI’s right to transfer data
between institutions.
Data Collection
This pertains to collecting project data in a
consistent, systematic manner (i.e., reliability) and
establishing an ongoing system for evaluating and
recording changes to the project protocol (i.e.,
validity).
Data Storage
This concerns the amount of data that should be
stored — enough so that project results can be
reconstructed.
Data Protection
This relates to protecting written and electronic data
from physical damage and protecting data integrity,
including damage from tampering or theft.
Data Retention
This refers to the length of time one needs to keep
the project data according to the sponsor’s or
funder’s guidelines. It also includes secure
destruction of data.
Data Analysis
This pertains to how raw data are chosen,
evaluated, and interpreted into meaningful and
significant conclusions that other researchers and
the public can understand and use.
Data Sharing
This concerns how project data and research results
are disseminated to other researchers and the
general public, and when data should not be
shared.
Data Reporting
This pertains to the publication of conclusive
findings, both positive and negative, after the
project is completed.
(Steneck, 2004)
The pages that follow will provide more in-depth descriptions of each of these
terms and will explain how each one relates to the responsible conduct of
research.
3
It is important for
researchers to understand
how data management
issues relate to the
responsible conduct of
research.
You can print out the
worksheet version of this
page to share with your
entire research team. This
worksheet is included at the
end of the document.
Think Ahead Quiz: What Are Data? �
True or False: In scientific research, only the information and observations that are
made as part of scientific inquiry are considered data.
__True
__False
Answer: False. In fact, data also include the materials, products, procedures, and other data sources that are part of
the research project. Essentially, data are considered to be anything and everything that informs the way in which
individuals are able to understand and to process their world. Read on to learn more.
Defining Data
Before reviewing the concepts of data management, the term data should be
defined. The Merriam-Webster Dictionary (2005) defines data as “factual
information (as measurements or statistics) used as a basis for reasoning,
discussion, or calculation.”
According to this definition, some examples of types
of medical research data would include the
following:
•
•
•
•
Patient survey responses
White blood cell counts
Core temperature readings
Metabolism rates
However, data can also refer to any observations
that are made — such as a patient’s symptoms or a
population’s health habits.
Other Forms of Data
Data are not only the information and observations made as part of scientific
inquiry but also the materials, the means, and the products of that inquiry
(these are sometimes called data sources). In other words, data can also
include the following:
•
•
•
•
•
Tissue samples
Specially designed primers
Patient questionnaires
Interviews
Customized online content
4
Data are any information or
observations that are
associated with a particular
project, including
experimental specimens,
technologies, and products
related to the inquiry.
Case Vignette: Data Ownership �
Dr. Smith works at The University and is the Principal Investigator on a large research
project that is funded by the National Institutes of Health (NIH). However, while Dr.
Smith wrote the original grant proposal, he does very little day-to-day work on the
project. Instead, the Research Director, Betsy, oversees all aspects of the project,
including staff supervision and all data management activities. In addition, Betsy has
been lead author on several publications about the project’s research findings.
Who owns the project and its data?
__ The PI, Dr. Smith
__ The Research Director, Betsy
__ The University
__ The National Institutes of Health
__ No one person or organization
Answer: The University. Despite the PI’s and the Research Director’s work on the project, the sponsoring institution
typically maintains ownership of a project’s data as long as the PI submitted the grant through that institution and is
employed by them. However within the sponsoring institution, a PI is generally granted stewardship over the project
data; he/she may control the course, publication, and copyright of any research, subject to institutional review. Read
on to learn more about data ownership.
5
Data Ownership
Understanding data ownership, who can possess data, and who can publish
books or articles about it are often complicated issues, related to questions of
project funding, affiliations, and the sources and forms of the research itself.
For federally funded research, ownership of data involves at least 3 different
entities: the sponsoring institution, the funding agency, and the PI. In many
cases, the institution/organization owns the project data, but the PI and the
funding agency have “rights” to access and use the data. Usually the PI has
physical custody of the data on behalf of the organization. However, these rules
vary by institution and depending on the funding source. Some general
guidelines are presented below:
1. The Sponsoring Institution, e.g., a university or a research firm
Most often, the sponsoring institution/organization maintains ownership of a
project’s data as long as the PI is employed by that institution. The institution
often controls all funding or the disbursement of government funding;
consequently, it is also responsible for ensuring that funded research is
conducted responsibly and ethically. Within the sponsoring institution, a PI is
granted stewardship over the project data; the PI may control the course,
publication, and copyright of any research, subject to institutional review.
2. The Funding Agency, e.g., NIH or the Centers for Disease Control and
Prevention (CDC)
Many research projects are funded by federal government agencies,
philanthropic organizations, or private industries. These agencies often have
specific stipulations for how data will be retained and disseminated: for
example, they decide whether to publish the project’s results or market a
resulting product, rather than the PI. The PI and institution should understand
his or her funding agency’s regulations regarding a research project and the
data it produces. Note that requirements for federal grants may be different
than government contracts (discussed further on the next page).
3. The Principal Investigator
In addition to being the steward of a project’s data, a PI may retain some
ownership of the data. In small businesses, it is assumed that rights and
ownership of data remain with the business itself or with the funding agency,
unless otherwise stipulated. In academic institutions, however, PIs are
sometimes allowed to take their research and its data with them if they change
research institutions. Many universities have offices and policies in place to
ensure that such a transfer of data respects both the rights of the researcher
and those of the institution(s).
(USDHHS, 1990)
Subjects’ Rights to Ownership
It is also important to consider data ownership from the perspective of
individuals who suggest research ideas and/or participate in the research. Some
research subjects are expressing a desire for partial ownership or control of
research in which they have participated. For instance, in 2 recent court cases,
the defense contended that research institutions had improperly benefited in
extending their study’s implications beyond any consent that the participating
subjects had given. (See sidebar for links to read more.) Since human subjects
are often sources for data that may be otherwise unavailable to researchers, it
is important to consider study participants’ beneficence and dignity in relation to
the project’s progress and goals.
6
Data ownership refers to the
control and rights over the
data as well as data
management and use.
Ownership of research is a
complex issue that involves
the PI, the sponsoring
institution, the funding
agency, and any
participating human
subjects.
The Bayh-Dole Act of 1980
allows universities to obtain
patents for inventions made
with federal funding and to
work directly with industry to
commercialize these
products. If you would like to
learn more about the act’s
development and results thus
far, follow this link to learn
more about the Bayh-Dole
Act.[http://www.ucop.edu/
ott/bayh.html]
If you would like to learn more
about the difference between
government contracts and
government grants, follow this
link to learn about government
funding through the NIH.
[http://grants.nih.gov/grants/
funding/contracts_vs_grants.
Htm]
If you would like to learn more
about how research subjects
have challenged data ownership
and their own role in research,
read the article “Who Owns
Your Genes?” from the New
York Times.
[http://www.nytimes.com/librar
y/national/science/health/05150
0hth-aids-gene.html]
Pop up Page: Grants Versus Contracts �
Much of scientific research financing from federal agencies, such as the Food and Drug Administration (FDA) or the
NIH, is in the form of grants. For instance, 95% of awards that are made through NIH’s Small Business Innovation
Research (SBIR) program are grants, and the remaining 5% are contracts. So, what is the difference between
government grants and contracts?
Government Grants
Government grants can be described as assistance funding. Grants are usually awarded to research projects that are
deemed to be “good science,” i.e., projects that increase our understanding of new or established theories or that
further research. With a grant, the PI retains control over the scope of the research and makes decisions about how
the funding will be spent.
Government Contracts
Government contracts can be described as procurement funding: that is, the government is providing money in order
to acquire a product, property, or service. Like a contractual agreement between a buyer and a seller, governmentcontracted research is often subject to strict regulations, requirements, and expectations. For instance, the PI must
coordinate project goals and decisions with the funding agency, which assigns a project officer to oversee the project
and to make sure that the agency’s goals are being met. Funding may be distributed in installments, contingent upon
the funder’s satisfaction with project progress reports. Also, the data typically belong to the funding agency, unless
otherwise stipulated in the initial contract.
Think Ahead Quiz: Data Collection
Data that are collected as part of a scientific research project ultimately
prove or disprove the PI’s hypotheses and justify a body of research to the
public at large. Which statement is true about data collection in scientific
research?
__ Ensuring validity of the data is the key to successful research. �
__ Ensuring reliability of the data is the key to successful research. �
__ Ensuring reliability and validity are equally important. �
__ Data collection is actually not a key part of scientific research, since many researchers use previously collected �
data. �
Answer: Ensuring reliability and validity are equally important. Ensuring reliability and validity of the data are equally �
important during data collection. When data collection is carried out according to these 2 rules, researchers will be �
able to accurately assess, replicate, and disseminate their results. Read on to learn more. �
7
Data Collection
Data collection refers not only to what information is recorded and how it is
recorded, but also to how a particular research project is designed. Although
data collection methodology varies by project, the aim of successful data
collection should always be to uphold the integrity of the project, the
institution, and the researchers involved.
Data collection may seem tedious or
repetitive, but the data produced in
research ultimately prove or disprove
hypotheses and justify or counter a body of
research. In addition, thorough data
collection accomplishes the following:
•
Enables those involved in the
research to more accurately
analyze and assess their work
•
Allows independent researchers to
replicate the process and evaluate
results
•
Impresses upon research team members the importance of data �
management �
•
Details the rationale behind a research project
•
Provides justification to sponsors for expenditures and project decisions
•
Yields reliable and valid results, and hypothesis testing
Data collection provides the
information necessary to
develop and justify
research.
A successful project collects
reliable and valid data.
You can print out the
worksheet version of this
page to help track your data
collection activities. This
worksheet is included at the
end of the document.
8
Collecting Reliable Data �
Data collection guidelines and methodologies should be carefully developed
before the research begins. The researchers must determine what sort of data
will be collected and how this data will be analyzed. For data to be considered
reliable, data collection should occur consistently and systematically
throughout the course of the project.
The Importance of Planning for Data Collection
Team members who will collect data should be thoroughly trained to ensure
consistency in data collection. By collecting data in a well-planned, systematic
manner, team members will be able to answer any question about a project,
including the following:
•
•
•
•
•
•
The purpose behind the research
The particular methodologies chosen
The implementation of these methodologies
How data that were collected and analyzed
If unexpected results or significant errors were encountered
The implications of the research and future directions
A clear and comprehensive account of a project and its purpose and direction
make it much easier for research to be disseminated, understood, and evaluated
by other members of the scientific community.
9
Data collection is reliable
when it is employed in a
consistent and
comprehensive manner
throughout the course of a
project.
Thorough data collection
enables research team
members to answer any
question about a project.
For most research projects,
data collection procedures
are usually described briefly
in grant or contract
proposals. However,
researchers should take the
time to further define each
element of data collection,
including specific
methodologies and plans for
analysis, after receiving
funding but before starting
the project.
Case Vignette: Collecting Valid Data �
Part of the data collection methodology for Dr. Smith’s study includes distributing a 12-page
self-administered questionnaire to participants; they must fill out and initial each page of the
questionnaire to confirm completion.
One day on his way home from conducting an interview with a subject, the Research
Assistant, Joel, needed to write directions for a friend and he reached in his bag and grabbed
the first piece of paper that he could find. Joel accidentally ripped the back page off of one of
the completed questionnaires to write the directions, which he then gave to his friend. He
didn’t realize this until a few hours later, when he was reviewing the data that he had
collected that day.
Joel thought that he remembered the participant’s answers on the last page of the survey,
since they were mostly demographic questions.
What should Joel do?
__ Staple on a new page and fill out the subject’s responses, since he remembers them.
__ Contact the subject and ask her to complete the last page of the questionnaire again.
__ Omit the participant’s questionnaire from the study, his/her partial data is invalid.
__ Just pretend like he doesn’t know what happened to the last page.
Answer: Omit the participant’s questionnaire from the study, his/her partial data is invalid. This is Joel’s best option if he were to attempt to collect the data again from the subject, the subject would be responding in a different time
and mood than when the original interview occurred. As part of responsible data management, honesty about the
mishap is the best way to maintain the validity of the data and to clarify that the data were not tampered with or
falsified in any way. Read on to learn more about collecting valid data.
10
Collecting Valid Data
Collecting valid data ensures that when research is evaluated it will be deemed
good science — meaning that the research is …
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