DATA SHARING, DISSEMINATION AND USE. HND2 Complect.pdf

DATA SHARING/DISSEMINATION
AND USE
HIM 425
HIGHER NATIOAL DIPLOMA COURSE IN
HEALTH INFORMATION MANAGEMENT,
DEPARTMENT OF MANAGEMENT STUDIES,
KADUNA SCHOOL OF HEALTH INFORMATION
MANAGEMENT (KSHIM), KADUNA STATE,
NIGERIA.
By: Engr. Ochu Ibrahim A.
FIRST CONTACT

INTRODUCTORY CLASS
I believe in the Chinese philosophy of
knowledge which states that:
What I Read, I Forget;
What I Am Told, I Remember;
What I Do, I Understand.

COURSE OUTLINE AND
MATERIALS
 Course Outline: Consult Course
Curriculum (HIM 425)
 Materials: Data Sharing or
Dissemination and Use Lecture
note.
 Lecture Model: Slides and
Projector.

COURSE STRUCTURE
 3 Hours Contact: 1 Hour for Lectures and 2Hours for
Practical.
 All the lectures and Reading Materials will be at your
disposal (Mostly Electronic/Soft Copy)
 As we progresses into the semester, we will expolre the
practical aspect of the course.
 To pass the course, you need to have atleast 70%
attendance as its essential in obtaining your certificate and
going for NYSC if possible.
 CA conduct will be agreed by the class. This will be 20% of
your course work.
 20% for Practical Work
 60% for Exam as usual
 The exam will be 7Q, answer 5Q only (But this may change if
any circumstances arises)
ARE WE READY?

BRAIN TEASER
You’re escaping a labyrinth and there
are Three exits.
ExitAleads to an inferno.
Exit B leads to an assassin.
Exit C leads to a lion that hasn’t eaten in
3 years.
Which exit would you pick to survive?

ANSWER TO BRAIN TEASER
Exit C.
If a lion hasn’t eaten in 3 years, it
had definitely starved to death.

DATA
SHARING/DISSEMINATIO
N AND USE
By the end of this lecture we will be
able to:
1. Define Data Dissemination,
2. Explain various sources of existing data in
health institution that can be shared,
3.Describe how data extraction is
carried out.

What is Data Sharing or Data
Dissemination?
 Data Dissemination or
Sharing is the
distribution or
transmitting of
statistical, or any other
data to end users.

CLASS ACTIVITY
DISCUSSION
Mention the various sources and
ways YOU think data can be
shared or disseminated in your
place of work.

SOURCES OF EXISTING DATA IN
HEALTH THAT CAN BE SHARED.
Under Special Data
Dissemination Standard,
the formats are divided into
TWO categories:
“HARDCOPY” and
“ELECTRONIC” publications.

SOURCES OF EXISTING DATA IN
HEALTH THAT CAN BE SHARED.
1. Administrative Data
2. Patient Medical Records
3. Patient Surveys
4. Standard Clinical Data
NOTE
You will all agree with me that the various
existing data sources above can be
categorize into the two main categories

ADMINISTRATIVE DATA
In the course of providing and paying for
care, organizations generates administrative
data on the characteristics of the population
they serve as well as their use of services
and charges for those services, often at the
level of individual users. The data is
gathered from claims, encounter,
enrollment, and providers systems.
Common data elements includes type of
service, number of units (e.g. days of
service), diagnosis and procedure codes for
clinical services, location of services , and
amount billed and amount reimbursed.

ADVANTAGES OF
ADMINISTRATIVE DATA
1. Available electronically
2. Less expensive that obtaining
medical record data
3. Available for an entire
population of patient and
across payers.
4. Fairly uniform (and improving
coding systems and
practices).

CHALLENGES OF
ADMINISTRATIVE DATA
1. Limited clinical information.
2. Questionable accuracy for
public reporting because the
primary purpose is billing.
3. Completeness
4. Timeliness.

PATIENT MEDICAL RECORDS
A medical record is the
documentation of a patient’s
medical history and care. The
advent of electronic medical
records has increased the
accessibility of patients’ files.
Wider use of electronic medical
record system is expected to
improve the ease and cost of using
this information for quality
measurement and reporting.

ADVANTAGES OF PATIENT
MEDICAL RECORDS
1. Rich in clinical detail.
2. Viewed by providers as
credible.

CHALLENGES OF MEDICAL
RECORDS
1. The cost, complexity, and time
required to compile data when
patients receive services across
different sites, particularly if a
different record format is used.
2. Current use of paper for most
records, which means that trained
staff must manually abstract
information.

PATIENT SURVEYS
Survey instruments capture self-
reported information from patients
about their health care
experiences. Aspects covered
include reports on the care,
service, or treatment received and
perceptions of the outcomes of
care. Surveys are typically
administered to a sample of
patients by mail, by telephone, or
via the internet.

ADVANTAGES OF PATIENT
SURVEYS
1. Captures types of
information for which
patients are the best source.
2. Well-established methods
for survey design and
administration.
3. Easy for customers to
understand and relate to
survey results.

CHALLENGES OF PATIENT
SURVEYS
1. Cost of survey administration.
2. Possibilities of misleading results if
questions are worded poorly, survey
administration procedures are not
standardized, the population sampled
is not representative of the population
as a whole (sampling bias), or the
population is not represented in the
responses (response bias).

STANDARDIZED CLINICAL
DATA
Certain kinds of facilities, such as
nursing homes and home health
agencies, are required to report
detailed information about the status of
each patient at set time intervals. The
Minimum Data Set (MDS), the required
information for nursing homes, and the
Outcome and Assessment Information
Set (OASIS), the data required by
Medical for certified home health
agencies, store the data used in quality
measures for these provider types.

ADVANTAGES OF
STANDARDIZE CLINICAL DATA
 Uses existing data sets
 Characterizes facility
performance in multiple
domains of care.
STOPED HERE -- NEXT
WEEK

CHALLENGES STANDARDIZED
CLINICAL DATA
 May not address all
topics of interest.

CLASS ACTIVITY
DISCUSSION
Is there need for
Standardization of Data sharing
or Dissemination?

THE NEED FOR
STANDARDIZATION
The use of quality measures to
support consumer choice requires a
high degree of data validity and
reliability. To make sure that
comparisons among providers and
health plans are fair and that the
results represent actual performance,
it is critical to collect data in a careful,
consistent way using standardized
definitions and procedures.

WHAT IS DATA EXTRACTION?
Data extraction is the
process of obtaining data
from a database or SaaS
platform so that it can be
replicated to a destination;
such as a data warehouse –
design to support Online
Analytical Processing (OLAP)

TYPE OF DATA EXTRACTION
Data can be extracted in
three primary ways
1. Update notification
2. Incremental extraction
3. Full extraction

1. Update notification: This
is the easiest way to
extract data from a
source system, by setting
that system to issue a
notification when a record
has been changed.

2. Incremental extraction: Some
data sources are unable to
provide notification that an
update has occurred, but
they are able to identify
which records have been
modified and provide an
extract of those records.

DATA EXTRACTION PROCESS
3. Full extraction: The first time
you replicate any source you
have to do a full extraction, and
some data sources have no way
to identify data that has been
change, so reloading a whole
table may be the only way to
get data from that source.

AND USE
HIM 425
NIGERIA.
SECOND CONTACT

BRAIN TEASER
An elevator is on the ground floor. There are four
people in the elevator including me. When the lift
reaches the first floor, one person gets out and three
people get in. The lift goes up to the second floor, 2
people get out, 6 people get in. It then goes up to the
next floor up, no-one gets out but 12 people get in.
Halfway up to the next floor up the elevator cable
snaps, it crashes to the floor. Everyone else dies in
the elevator except me.
How did i survive?

I got off on the first floor.

DATA
SHARING/DISSEMINATION
AND USE
By the end of this lecture we will be able
to:
1. Explain the importance of data sharing to care givers
and other data users
2. Explain data dissemination within a department, between
departments, institutions to institutions, Local
Government, State and Federal levels.
3. State the importance of feedback in data sharing and
dissemination.

IMPORTANCE OF DATA SHARING TO
CARE GIVERS AND OTHER DATA USERS
Sharing data with caregivers can
help them to;
1. Optimize treatments,
2. Initiate preventative measures,
3. Prevent patients from having to repeat their
stories at each point in the care continuum.

DATA DISSEMINATION
1. With in Department of a particular
institution..

DATA DISSEMINATION
2. Between departmental
Institutions and Institution.
Examples are;
a. Local Govt. to State
b. State to Federal level.

IMPORTANCE OF FEEDBACK IN DATA
SHARING AND DISSEMINATION
1. Supporting research
2. Enhancing the credibility of official statistics
3. Improving the reliability and relevance of data
4. Reducing duplication in data collection
5. Increasing return on investment
6. reducing the cost of data dissemination
7. Complying with a contractual or legal obligation
8. Promoting development of new tools for using data.

AND USE
HIM 425
NIGERIA.
THIRD CONTACT

DATA
SHARING/DISSEMINATIO
N AND USE
to:
1. Explain data storage and retrievals
2. Explain storage in data sharing using CD,DVD, Flash and
Zip drives etc
3. Explain data ownership and copyright
4, Explain process of data disposal.

BRAIN TEASER
The number
8,549,176,320
is a unique number.
What is so special
about it?

This is the only number
that includes all the
digits arranged in
alphabetical order.

WHAT IS DATA STORAGE
AND RETRIEVALS
Data Storage and Retrieval is a
systematical process of collecting
and cataloging data so that they
can be located and displayed on
request.

FORMAT OF STORING AND
SHARING OF DATA

WHAT IS DATA OWNERSHIP
Data ownership is the act of having
legal rights and complete control over
a single piece or set of data elements.
It defines and provides information
about the rightful owner of data assets
and the acquisition, use and
distribution policy implemented by the
data owner.

WHAT IS DATA COPYRIGHT
Copyright Data means any
report, document, data,
design, computer software or
any other material (whether
written or machine readable)
which is developed under
the contract.

DATA DISPOSAL
Data disposal is the process of destroying data
stored on tapes, hard disks and other forms of
electronic media so that it is completely unreadable
and cannot be accessed or used for unauthorized
purposes.
But to confirm that data is truly gone, and to comply
with most data protection standards, you need
more. This is where data sanitization and data
erasure (a form of data sanitization) come in.

AND USE
HIM 425
NIGERIA.

DATA
AND USE
to:
1. Explain the meaning of data management/sharing
plan.
2. Explain the meaning of the following data sharing terms;
1. Covered Entity
2. Data Archive
3. Data Enclave
4. Find/Research Data
3. NOTE: We will be evaluating all that we’ve learn so far.

BRAIN TEASER
A man dies of old age
on his 25 birthday.
How is this possible?

He was born on February
29th.

DATA
MANAGEMENT/SHARING
PLAN
A data management/sharing plan (DMP)
is a written document that describes the
data you expect to acquire or generate
during the course of a data collection,
how you will manage, describe,
analyze, and store those data, and what
mechanisms you will use at the end of
your project to share and preserve your
data.

MEANING OF THE FOLLOWING
DATA SHARING TERMS
1. 1. COVERED ENTITY : Covered entities
are defined in the Healthcare Insurance
Portability and Accountability Act (HIPAA)
rules as;
2. (1) Health plans,
3. (2) Health care clearing houses and
4. (3) Health care providers who
electronically transmit any health
information in connection with
transactions for which the Nigerian
Ministry of Health has adopted standards.
5.
6.

DATA SHARING TERMS
2. DATA ARCHIVE : Data archiving
is the practice of identifying data
that is no longer active and
moving it out of production
systems into long-term storage
systems.
1.
2.Archival data is stored so that at
any time it can be brought back
into service.

DATA SHARING TERMS
1.
2.3. DATA ENCLAVE : A data enclave is
a secure network through which
confidential data, such as
identifiable information from census
data, can be stored and
disseminated.
3.In a virtual data enclave a
researcher can access the data from
their own computer but cannot
download or remove it from the
remote server.

DATA SHARING TERMS
1.
2.4. FIND/RESEARCH DATA :
Research data is any information
that has been collected,
observed, generated or created to
validate original research
findings. Although usually digital,
research data also includes non-
digital formats such as laboratory
notebooks and diaries.

PROCESS OF DATA SHARING
PLAN
There is no standard on how
you plan your data sharing.
But its of imperative that the
following key activities MUST
be involved.
1.1. Presentation
2.2. Annual Lecture
3.3. Newsletter
4.4. Updates of data shared
5.

FIRST TEST
1.
2.QUESTION
3.
4. As Health Record Officer,
5. Give reasons why you think its
important that data should be shared.
6.
7.

AND USE
HIM 425
KADUNA SCHOOL OF HEALTH
INFORMATION MANAGEMENT (KSHIM),
KADUNA STATE,
NIGERIA.

DATA
AND USE
By the end of this lecture we will be able to:
1. Explain the Content of Data Sharing Agreement (DSA).
2. Explain the Factors that Works Against Data Sharing Agreement
(DSA).
3. Importance Of Agreement Policy and Regulation in Data Sharing
Administration.

BRAIN TEASER
What English word
retains the same
pronunciation, even
after you take away four
of its five letters?

QUEUE
When we remove ‘ueue’ from
the word ‘Queue’, it still
retains the sound of the letter
‘Q’, which is same as the
word ‘Queue’

ELEMENTS OF DATA
SHARING AGREEMENT
One of the basic requirements of
effective data sharing is assurance
that the data and reported results
are credible and accurate, and that
the rights, integrity and
confidentiality of research
participants are protected.
1.
2.Good data management ensures
that high quality and credible data
sets are produced.

Collaboration approach towards
the explanation of the factors that
works against Data Sharing
Agreement.
FACTORS THAT WORKS AGAINST DATA
SHARING AGREEMENT (DSA).

FACTORS THAT WORKS AGAINST DATA
SHARING AGREEMENT (DSA).
CLASS ACTIVITIES
WE WILL BE WORKING IN FOUR (4) GROUPS
EACH GROUP WILL TAKE TWO (2) FACTORS AND WE
WILL DISCUSS.

FACTORS INFLUENCING DATA
SHARING
Values that underpin data sharing were identified from arguments in the
interview data. No connections were coded where arguments were
absent but where connections could theoretically exist. Competing
analysis was coded as a separate category due to its connections with
various values. Socio-cultural factors were split into role-identification
and enjoyment in collaborative work. Values which push researchers
towards collaboration are framed with red boxes.

ELEMENTS OF DATA
SHARING AGREEMENT
Core values and principles underlying factors
influencing data sharing.
.

AND USE
1. Explain the scope and purpose of data dissemination.
2. Explain the importance of data dissemination within and outside
an organization
3. State the differences between data dissemination and data
communication.
4. Explain the principles of data dissemination.

BRAIN TEASER
You are driving a bus. At the first
stop, two women get on. At the
second stop, three men get on, and
one woman gets off. At the third
stop, three kids and their mom get
on, and a man gets off. The bus is
grey, and it is raining outside.
What color is the bus driver’s hair?

The color of your hair is
the same as the
drivers’.
This is simply because
you are the driver.

SCOPE OF DATA DISSEMINATION
The Scope of dissemination of data is the
extend at which release of data obtained
from a statistical activity to its final
destination (end users) through various
media.
For each data release there is also a need
to effectively communicate the data to
data users and a requirement to make
known the availability of the release.

1. Data dissemination can serve as a call for action –
for example, a public service announcement can
be created outlining what behaviors are
recommended to prevent high blood pressure.
2. It can be used to promote long-term behavior
change, such as encouraging people to quit
smoking to reduce cancer incidence rates and
risk.
3. Data dissemination serves to share new
information or insights about preventative
behaviors or treatment options for
noncommunicable diseases (NCDs).
PURPOSE OF DATA DISSEMINATION

4. It can be used to educate the community about recent
findings such as mortality rates for cardiovascular
disease or to share accomplishments such as
decreasing prevalence of stroke.
5. Data dissemination can also document the magnitude of
health problems and justify program activities.
6. Public health officials may reference past data
dissemination reports to review the type of
intervention, relevant findings, and important
lessons learned from the intervention in preparation for
an upcoming intervention or public health program.
PURPOSE OF DATA DISSEMINATION

DATA DISSEMINATION &
DATA COMMUNICATION
1. Dissemination means sharing data with
potential users. They could be peers in the
research field, industry, other commercial
players and policymakers.
2.
3. On the other hand, Communication means
taking strategic and targeted measures for
promoting the action itself and its
results/success (processed data in most
cases) to a multitude of audiences,
including the media and the public, and
possibly engaging in a two-way exchange.

DIFFERENCES BETWEEN DATA
DISSEMINATION AND DATA
COMMUNICATION
1. Dissemination is one-way, and involves
sending (data) information through two
basic formats (i.e. Hard-copy format or the
soft-copy format). This includes;
publications, social media, presentations, a
project website, a journal, a text literature,
and so on.
2.
3. Communication is two-way, and involves
channels such as workshops, round-table
sessions, events formal education settings,
etc. Also, communication needs a medium
of communication to be effective.

IMPORTANCE OF DATA DISSEMINATION
WITHIN AND OUTSIDE AN ORGANIZATION
Determining tie strength's role in
information dissemination can allow an
organization to utilize this relationship
factor to increase information flow,
resulting in strengthened employee
collaboration and increased collective
knowledge.

PRINCIPLE OF DATA
DISSEMINATION

CLASS ACTIVITY
1. From your understanding, mention or
highlight the principles data
dissemination. Using the previous slide
as a guide.

PRINCIPLE OF DATA
DISSEMINATION
1. 1. Special Data Dissemination
Standard.
2. 2. Timeliness in disseminating
statistical data.
3. 3. Quality of statistics.
4. 4. Schedule for releasing regular
statistics and equal access to
statistics.
5. 5. Means of data dissemination.

AND USE
1. Define the concept of data use.
2. Define data use objectives and state its importance.
3. Explain using data to take action on reprogram or initiate
programs.
4. Explain under-utilization of data/barriers to data use
(factors responsible).
5. Explain methods of improving data use in health project and
programs.

BRAIN TEASER
Two boxers are in a match
scheduled for 12 rounds. (Pure
boxing only. There are no kicking or
take-downs). One of the boxers gets
knocked out after only six rounds,
yet no man throws a punch.
How is this possible?

Both the boxers were
FEMALE.

The use of big data in healthcare allows
for strategic planning thanks to better
insights into people's motivations.
Care managers can analyze check-up
results among people in different
demographic groups and identify what
factors discourage or encorage people
from taking up treatment.
THE CONCEPT OF DATA
USE

Data collection in healthcare allows health
systems to create holistic views of
patients, personalize treatments, advance
treatment methods, improve
communication between doctors and
patients, and enhance health outcomes.
Let’s take a closer look at some case studies.
DATA USE OBJECTIVES AND
IMPORTANCE

A personal Electronic Health Record (EHR) is a system that
collects information about the patient’s health from a
number of sources. These Electronic Health Record (EHR)
includes test results, clinical observations, diagnoses,
current health problems, medications taken by the patient,
the procedures he/she underwent, etc.
This type of medical report is able to send notifications to
patients about the need to undergo a new test or to ensure
compliance with drug prescriptions. This is a vivid example
of predictive analytic in healthcare.
By using a scope of data from digital medical records,
doctors can establish a link between fundamentally
different symptoms, give an accurate diagnosis and
provide adequate treatment..
PREDICTIVE CAPABILITIES OF
E.H.R.

Another example of the importance of data quality in healthcare
is the development of telemedicine — the provision of remote
clinical services. This term includes both primary diagnostics
and consultations, as well as comprehensive monitoring of the
patient’s health status, and even surgical assistance.
Telemedicine has been on the medical market for decades, but
only today, with the advent of smartphones, wireless portable
devices and video conferencing, has it released its full potential.
By eliminating the need for the physical presence of the patient
in the clinic, telemedicine reduces the financial costs of medical
services and prevents the deterioration of the patient’s health. A
qualified consultation may take place at any time and in any
place that is convenient for both the patient and the doctor.
TELEMEDICINE

The following are factors highlighted to be
responsible for the under-utilization of data
or barriers to data use;
1.
2. 1. Technical barriers
3. 2. Motivational barriers
4. 3. Economic barriers
5. 4. Political barriers
6. 5. Legal barriers
7. 6. Ethical barriers
8.
9. NOTE: Under the highlighted factors above, you can
further enumerate sub-factors which will trow more light
on the main factors.
UNDER-UTILIZATION OF
DATA/BARRIERS TO DATA USE

1. Data not collected. As long as severe limitations
persist in public health data collection, data sharing will
not be considered a priority.
1.
2.
3. 2. Data not preserved or cannot be found. Public health
data are often collected for short-term purposes such as
outbreak detection.
4.
5.
6. 3. Language barrier. Routinely collected public health
data are often recorded in local languages, limiting the
possibility to integrate and use such data together with
other data sets, particularly in an international context.
7.
8.
TECHNICAL BARRIERS

4. Restrictive data format. Despite major advances in
computational resources in public health, a large volume
of public health data such as disease surveillance data
and administrative data continue to be collected and
preserved in hardcopy paper format or in electronic
format that may be antiquated or incompatible with
modern software systems.
1.
2.
3. 5. Technical solutions not available. Technical software
solutions to collect, harmonize (transformation and
recoding to enhance inter-operability), integrate
(combining harmonized datasets), and share complex
and heterogeneous data have been developed in the
private or research sector, but have not become widely
available to public health agencies.
4.
TECHNICAL BARRIERS

1.
MOTIVATIONAL BARRIERS
6. No incentives. Data sharing requires time and resources that are
chronically lacking in public health settings.
7. Opportunity cost. Public health officers who have invested time and
effort in data collection could anticipate that scientific credit or
other opportunities may be lost if data recipients with greater
capacity for analysis could gain the majority of credit.
8. Possible criticism. Data providers could be discredited by errors
found during secondary use of their data and disease control efforts
may be criticized if data would reveal continued disease occurrence
9. Disagreement on data use. Data providers may disagree with the
intended secondary use of their data or may consider their data
inappropriate for a certain use

1.
ECONOMIC BARRIERS
10. Possible economic damage. Data sharing in public health is
challenged by the economic damage that this may cause to data
providers. Public sharing of disease outbreak data, for example,
can result in economic damage due to reduced tourism and trade.
11. Lack of resources. The process of data sharing requires human and
technical resources for data preparation, annotation,
communication with recipients, computer equipment, internet
connectivity, etc.

1.
POLITICAL BARRIERS
12. Lack of trust. Trust between a data provider and user greatly
enables data sharing. In the absence of trust, providers
could anticipate potential misinterpretation, misuse or
intentional abuse of the data.
13. Restrictive policies. Agencies may have developed official policy
guidelines that restrict data sharing, resulting from various
possible underlying factors such as a general sense of distrust,
negative prior experiences, or other factors.
14. Lack of guidelines. Frequently, official guidelines on data
sharing simply do not exist, are unclear or inconsistent.

LEGAL BARRIERS
15. Ownership and copyright. Agencies that collect public health data are
often responsible for the protection of individual and community
privacy and may feel that a guardianship or ownership role is
bestowed on them by the public.
16. Protection of privacy. Public health agencies have the mandate and
authority to collect private data from the population governed by the
Health Insurance Portability and Accountability Act (HIPAA) in the US
or similar legislation in other countries.

ETHICAL BARRIERS
17. Lack of proportionality. The issue of proportionality, the careful
deliberation in assessing the risks and benefits that derive from the
amount and type of data requested compared to the potential impact
of its secondary use, has been identified as a guiding ethical principle
for public health data sharing.
18. Lack of reciprocity. Data sharing practices have not always been fair,
and data producers have often felt exploited in transactions where
they receive little credit or benefit from their work, while data users
that can rapidly analyze data and publish results benefit from
academic credit and career advancement as has happened in the past.

METHODS OF IMPROVING DATA
USE IN HEALTH PROJECT AND
PROGRAMS
The spectrum of data applications in the field of
medicine should systematically expand because
data analysis has every chance to change people’s
lives for the better. Information technologies make it
possible both to identify diseases of an individual
and to predict the state of health of entire social
groups.
Therefore, by implementing Big Data into healthcare
is the key to developing preventive measures and
saving lives.
As they say, prevention is even better than a cure.

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