The document outlines key concepts in health informatics, focusing on electronic health records (EHR), clinical decision support systems, and healthcare data analytics. It discusses the importance of data analytics, challenges faced in data management, and the benefits of using analytics to improve healthcare outcomes. Various analytics types and methodologies, as well as applications in clinical settings, are also highlighted.

1. Health
Informatics
BCA-2020: Semester-V

2. Module 3:
Chapter 3
ELECTRONIC
HEALTH RECORDS
CLINICAL DECISION
SUPPORT SYSTEMS
HEALTHCARE DATA
ANALYTICS

3. Module
Content
 Electronic Health Records: purpose of electronic health records,
popular electronic health record system, advantages of electronic
records, challenges of electronic health records, the key players
involved.
 Clinical Decision Support Systems: Making Decisions, the impact
health information technology on the delivery of care in a rapidly
changing healthcare marketplace.
 Healthcare Data Analytics: How is data analytics used in
healthcare? example of data analytics in healthcare, types of
analytics used in healthcare.

4. LearningObjectives
 Discuss the difference between descriptive,
predictive and prescriptive analytics
 Outline the characteristics of “Big Data”
 List several limitations of healthcare data
analytics

5. Introduction
 One of the promises of the growing critical mass of clinical data
accumulating in electronic health record (EHR) systems is
secondary use (or re-use) of the data for other purposes, such as
quality improvement and clinical research.
 The growth of such data has increased dramatically in recent years
due to incentives for EHR adoption in the US funded by the Health
InformationTechnology for Economic and Clinical Health
(HITECH) Act.
 In the meantime, there has also seen substantial growth in other
kinds of health-related data, most notably through efforts to
sequence genomes and other biological structures and functions.
 The analysis of this data is usually called analytics (or data
analytics).

6. Terminology
ofAnalytics
 Defining analytics as “the extensive use of data, statistical and
quantitative analysis, explanatory and predictive models, and fact-
based management to drive decisions and actions.”
 IBM defines analytics as “the systematic use of data and related
business insights developed through applied analytical
disciplines (e.g. statistical, contextual, quantitative, predictive,
cognitive, other [including emerging] models) to drive fact-based
decision making for planning, management, measurement and
learning.Analytics may be descriptive, predictive or prescriptive.”
 Three levels of analytics, each with increasing functionality and
value:
 Descriptive – standard types of reporting that describe current
situations and problems
 Predictive – simulation and modeling techniques that identify trends
and portend outcomes of actions taken
 Prescriptive – optimizing clinical, financial, and other outcomes

7. Analytics
 There are a number of terms related to data analytics.A core
methodology in data analytics is machine learning, which is the
area of computer science that aims to build systems and
algorithms that learn from data.
 One of the major techniques of machine learning is data mining,
which is defined as the processing and modeling of large amounts
of data to discover previously unknown patterns or relationships.
 A subarea of data mining is text mining, which applies data mining
techniques to mostly unstructured textual data.
 Another close but more recent term in the vernacular is big data,
which describes large and ever-increasing volumes of data that
adhere to the following attributes:
 Volume – ever-increasing amounts
 Velocity – quickly generated
 Variety – many different types
 Veracity – from trustable sources

8. TheAnalytics
Pipeline
 Process of big data
analytics resembles a
pipeline, and have
developed an approach
that specifies four major
steps in this pipeline, to
which one can place data
sources and actions on it
pertinent to healthcare
and biomedicine

9. TheAnalytics
Pipeline
 The pipeline begins with input data sources, which in healthcare
and biomedicine may include clinical records, financial records,
genomics and related data, and other types, even those from
outside the healthcare setting (e.g., census data).
 The next step is feature extraction, where various computational
techniques are used to organize and extract elements of the data,
such as linking records across sources, using natural language
processing (NLP) to extract and normalize concepts, and matching
of other patterns.
 This is followed by statistical processing, where machine
learning and related statistical inference techniques are used to
make conclusions from the data.The final step is the output of
predictions, often with probabilistic measures of confidence in the
results.

10. Challenges to
Data
Analytics
 1.The amount of data being collected
 With today’s data-driven organizations and the introduction of big data, risk
managers and other employees are often overwhelmed with the amount of
data that is collected. An organization may receive information on every
incident and interaction that takes place on a daily basis, leaving analysts with
thousands of interlocking data sets.
 There is a need for a data system that automatically collects and organizes
information. Manually performing this process is far too time-consuming and
unnecessary in today’s environment.An automated system will allow
employees to use the time spent processing data to act on it instead.
 2. Collecting meaningful and real-time data
 With so much data available, it’s difficult to dig down and access the insights
that are needed most.When employees are overwhelmed, they may not fully
analyze data or only focus on the measures that are easiest to collect instead
of those that truly add value. In addition, if an employee has to manually sift
through data, it can be impossible to gain real-time insights on what is
currently happening.Outdated data can have significant negative impacts on
decision-making.
 A data system that collects, organizes and automatically alerts users of trends
will help solve this issue. Employees can input their goals and easily create a
report that provides the answers to their most important questions.With real-
time reports and alerts, decision-makers can be confident they are basing any
choices on complete and accurate information.

11. Challenges to
Data
Analytics
 3.Visual representation of data
 To be understood and impactful, data often needs to be visually
presented in graphs or charts.While these tools are incredibly useful,
it’s difficult to build them manually.Taking the time to pull
information from multiple areas and put it into a reporting tool is
frustrating and time-consuming.
 Strong data systems enable report building at the click of a button.
Employees and decision-makers will have access to the real-time
information they need in an appealing and educational format.
 4. Data from multiple sources
 The next issue is trying to analyze data across multiple, disjointed
sources. Different pieces of data are often housed in different
systems. Employees may not always realize this, leading to
incomplete or inaccurate analysis. Manually combining data is time-
consuming and can limit insights to what is easily viewed.
 With a comprehensive and centralized system, employees will have
access to all types of information in one location. Not only does this
free up time spent accessing multiple sources, it allows cross-
comparisons and ensures data is complete.

12. Challenges to
Data
Analytics
 5. Inaccessible data
 Moving data into one centralized system has little impact if it is not easily
accessible to the people that need it. Decision-makers and risk managers
need access to all of an organization’s data for insights on what is happening
at any given moment, even if they are working off-site.Accessing information
should be the easiest part of data analytics.
 An effective database will eliminate any accessibility issues. Authorized
employees will be able to securely view or edit data from anywhere,
illustrating organizational changes and enabling high-speed decision making.
 6. Poor quality data
 Nothing is more harmful to data analytics than inaccurate data.Without good
input, output will be unreliable. A key cause of inaccurate data is manual
errors made during data entry.This can lead to significant negative
consequences if the analysis is used to influence decisions. Another issue is
asymmetrical data: when information in one system does not reflect the
changes made in another system, leaving it outdated.
 A centralized system eliminates these issues. Data can be input automatically
with mandatory or drop-down fields, leaving little room for human error.
System integrations ensure that a change in one area is instantly reflected
across the board.

13. Challenges to
Data
Analytics
 7. Pressure from the top
 As risk management becomes more popular in organizations, CFOs and other
executives demand more results from risk managers.They expect higher
returns and a large number of reports on all kinds of data.
 With a comprehensive analysis system, risk managers can go above and
beyond expectations and easily deliver any desired analysis.They’ll also have
more time to act on insights and further the value of the department to the
organization.
 8. Lack of support
 Data analytics can’t be effective without organizational support, both from
the top and lower-level employees. Risk managers will be powerless in many
pursuits if executives don’t give them the ability to act. Other employees play
a key role as well: if they do not submit data for analysis or their systems are
inaccessible to the risk manager, it will be hard to create any actionable
information.
 Emphasize the value of risk management and analysis to all aspects of the
organization to get past this challenge. Once other members of the team
understand the benefits, they’re more likely to cooperate. Implementing
change can be difficult, but using a centralized data analysis system allows
risk managers to easily communicate results and effectively achieve buy-in
from multiple stakeholders.

14. Challenges to
Data
Analytics
 9. Confusion or anxiety
 Users may feel confused or anxious about switching from traditional
data analysis methods, even if they understand the benefits of
automation. Nobody likes change, especially when they are
comfortable and familiar with the way things are done.
 To overcome this HR problem, it’s important to illustrate how changes
to analytics will actually streamline the role and make it more
meaningful and fulfilling.With comprehensive data analytics,
employees can eliminate redundant tasks like data collection and
report building and spend time acting on insights instead.
 10. Budget
 Another challenge risk managers regularly face is budget. Risk is often
a small department, so it can be difficult to get approval for significant
purchases such as an analytics system.
 Risk managers can secure budget for data analytics by measuring the
return on investment of a system and making a strong business case
for the benefits it will achieve. For more information on gaining
support for a risk management software system, check out our blog
post here.

15. Challenges to
Data
Analytics
 11. Shortage of skills
 Some organizations struggle with analysis due to a lack of talent.This
is especially true in those without formal risk departments. Employees
may not have the knowledge or capability to run in-depth data
analysis.
 This challenge is mitigated in two ways: by addressing analytical
competency in the hiring process and having an analysis system that
is easy to use.The first solution ensures skills are on hand, while the
second will simplify the analysis process for everyone. Everyone can
utilize this type of system, regardless of skill level.
 12. Scaling data analysis
 Finally, analytics can be hard to scale as an organization and the
amount of data it collects grows. Collecting information and creating
reports becomes increasingly complex. A system that can grow with
the organization is crucial to manage this issue.
 While overcoming these challenges may take some time, the benefits
of data analysis are well worth the effort. Improve your organization
today and consider investing in a data analytics system.

16. Research and
Application of
Analytics
 A number of critical clinical situations have been amenable to detection by
analytics applied to EHR and other clinical data:
 Predicting 30-day risk of readmission and death among HIV-infected
inpatients
 Identification of children with asthma
 Risk-adjusting hospital mortality rates
 Detecting postoperative complications
 Measuring processes of care
 Determining five-year life expectancy
 Detecting potential delays in cancer diagnosis
 Identifying patients with cirrhosis at high risk for readmission
 Predicting out of intensive care unit cardiopulmonary arrest or death
 Additional efforts have focused on helping to identify patients for
participation in research protocols or improve diagnosis of disease:
 Identifying patients who might be eligible for participation in clinical studies
 Determining eligibility for clinical trials
 Identifying patients with diabetes and the earliest date of diagnosis
 Predicting diagnosis in new patients

17. ThankYou!