12Epic EMR ImplementationComment by Author 2 Need a

1
2
Epic EMR Implementation
Comment by Author 2: Need a running head. Ex:
RUNING HEAD: Implementation of EMR
Implementation of Electronic Medical Records (EMR)
Comment by Author 2: Your topic is very broad. You
should have a unique identification of basically what you are
trying to investigate with your research. Basically, you need to
try to funnel it. For instance, The impact of the EMR on ......
Comment by Author 2: Also, the title doesn't tell the story
of your research. Basically, the reader should be attracted to
your topic just by reading the title. That is why is very broad
and doesn't present an attractive meaning. Comment by
Author 2: Example: The Implementation of EMR: Tjhe Role of
Data in ... Comment by Author 2: Or, Barriers to Implementing
the EMR in ....
HCIN 699-51 – B-2021/Summer
Applied Project in Healthcare Informatic
Dr. Chaza Abdul and Dr. Glenn Mitchell
Prepared by:
Name: Bolade Yusuf
Student ID: 273092

Harrisburg University
08/18/21
Table of Contents
INTRODUCTION 3
1.1 Background to research problem 3
1.1.1 Electronic Medical Records (EMR) 3
1.1.2 Patient’s Data 4
1.2 Problem Statement 4
1.3 Objectives 5
1.4 Research Questions 5
1.5 Significance of the Research 5
LITERATURE REVIEW 6
2.1 Introduction 6
2.2 Features of an Effective EMR 6
2.3 Barriers to adoption of EMR 8
2.4 Addressing EMR adoption barriers 9
2.5 Related Work 11
RESEARCH METHODOLOGY 12
3.0 Introduction 12
3.1 Research Philosophy 12
3.2 Research design 12
3.3 Study Population Sample 13
3.4 Sample Size and Sampling Procedure 13
3.5 Data Collection 14
DATA ANALYSIS AND FINDINGS 15
4.1 Data Analysis 15
4.2 Findings 15
4.3 Benefits of epic EMR 16
Conclusion 17
References 18
Appendix 1: Survey Questionnaire 20

Appendix 2:Survey Questions Response Analysis 21
INTRODUCTION1.1 Background to research problem
Health care is critical in any society. Managing patient’s data
goes a long way in ensuring good treatment measures are taken.
Health care information therefore must be collected correctly
and stored in a manner which abides by the principled of
confidentiality, integrity and accessibility (Kaushal et al.,
2009). Data regarding a patient should be kept confidential as
much as possible and only retrieved when needed. A good
health records management system should be able to
confidentially store patient’s data. Each patient should have an
account within the system where their data is stored. Access to
this data should be given on privileges basis and only to
individuals who will use it for treatment of the patient. The
patient’s data in a good health information management system
should be of high integrity. Data should be collected from the
source (the patient) and recorded during the collection process.
Having an intermediary stage where data is recorded in in a
secondary avenue before being transferred to the primary
system could lead to errors thus compromising its integrity. A
good health information management system should also ensure
ease retrieval of data wherever needed. A doctor or medical
practitioner shouldn’t should not find it hard to get data on a
patient when in the process of treatment. Comment by
Author 2: What is this? Be specific what you mean with that.
Comment by Author 2: I rather you use the correct term for
this. Rather use the HIPPA, etc. The terms you learned with this
degree. Make it relative to the subject of EMR. Comment by
Author 2: Make sure you use quotations if you copied the
original author prior to citing. Just in case you copied anything
and pasted it. Comment by Author 2: What does this mean?

please identify and don't assume the reader knows what you
mean. Comment by Author 2: Now, that is where you
include a paragraph or two on what your overall research is
investigating and researching.
Then, I would include a paragraph or two to explain what
previous research has proved to date about the same issue you
are researching. Then, should have a paragraph to provide a
good background about the data and why is it important.
Rethink to add more value of information so your reader learn
this knowledge. 1.1.1 Electronic Medical Records (EMR)
Medical records refer to the documentation of a patient’s
medical/health history and care over a period of time within a
particular health care provider’s geographical coverage (Jha (a)
et al., 2009). Being a requirement for all health care providers
to build and maintain patient’s health data, these providers open
an electronic file for each new client and input all data
regarding the client whenever visiting the provider. Such data
will include recording observations and administration of drugs
and therapies, orders for the administration of drugs and
therapies, test results, x-rays and other health reports. These
records should be complete and as accurate as possible to
ensure and medical practitioner within the provider can have a
360-degree view about the patient.
Electronic Medical Records (EMR) also referred to as
eElectronic h Health r Records (EHR) or simply health charts
ensure digital collection, storage, and retrieval of patients
patients’ medical records. The EMR gives a real-time
information concerning a patient’s interaction with a specific
health care provider. With the holistic approach offered by
EMR, practitioners can prescribe offer and provide the best
medical care attention to a patient as perby simply retrieving
real-time data the data at their disposal needed to making timely
medical decisions. Comment by Author 2: Don't use
terminology that leaves the reader wonder what you mean.
Rephrase.

1.1.2 Patient’s Data
This is medical information held about an individual patient.
Access to a particular patient’s health history greatly
determines the type of medical attention such a patient
getsneeded. With reliable, timely and conclusive access to this
data will help in understanding the patient’s illness, underlying
medical conditions, already undertaken medical tests, any
ongoing medication or impending medical procedure to the
client (Parente & McCullough, 2009). Comment by Author 2:
Sentence structure is incorrect or weak.
Don't start with "This is". You need to be specific in every way
possible. If you are referring to data, then say Data is ..., etc.
Comment by Author 2: You have redundancy all over the
intro. When you talk about data, I would prefer to utilize well a
good literature review to talk about what did data offer the
healthcare field and also, the previously discussed challenges or
opportunities.
You have to be consistent. Meaning, you have to stay focused
on your topic. If you are talking about barriers, then stick to
this all around, for instance, make sue that you bring this up as
you talk about anything. Comment by Author 2: very broad
term. Why don't use something like healthcare information?
That is more appropriate to your study as well. Comment by
Author 2: Good. These are the things you need to discuss here.
That is how someone can tell you did a good literature review.
Make sure to use te literature and cite. This is a confirmation
that your research is reliable. 1.2 Problem Statement
The primary Research found that various barriers to EMR
implementation exist in acute care and physician practice
settings including the are implementation costs, the uncertainty
about the return on investment (ROI), the concerns about
maintenance costs, and the lack of physicians’ acceptance of
use (Barbara & Ken 2010). While an EMR might offer an
inclusive approach in addressing medical information

challenges, its implementation greatly determines its level of
success. The sponsors of an EMR implementation must be aware
of these barriers and come up with clear guidelines on how to
overcome them to ensure full realization of the EMR benefits.
In this paper, the i will seek to give a guide on how to
effectively implement an EMR in a health care provider. To
understand this, the various challenges to the implementation
will be analyzed from which elaborate ways will be formulated
to address the barriers. The findings from the research can be
used by small to large health care providers when rolling out
epic EMR.1.3 Objectives
i. Identify the various barriers to implementation of an
Electronic Medical Records (EMR).
ii. Formulate conclusive solutions to address the identified
barriers to EMR implementation.
iii. Give a guide on effective EMR implementation across health
care facilities.
1.4 Research Questions
The study intends to address the following questions;
i. Does EMR improve service delivery in health care providers?
ii. What are the key components of epic EMR?
iii. What type of patient’s information is captured in EMR?
iv. What challenges does health care facilities face in
implementing EMR?1.5 Significance of the Research
The information from this research will be crucial to health care
providers as they will be able to have a clear understanding of
epic EMR. As the paper will point out the various barriers to
EMR implementation and offer possible solutions, health care
providers will have a guide kit on what to do whenever
implementing an EMR. Regulatory bodies will understand the
kind of patient’s information is collected and help in enacting
various regulations deemed necessary to protect this
information.
LITERATURE REVIEW2.1 Introduction
In this second chapter, relevant literature information related

and consistent with the objectives of the study was reviewed.
Important issues and practical problems were brought out and
critically examined so as to determine the current situation.
This section was vital as it determined the information that
links this study with past studies and what future studies would
still need to be explored so as to improve knowledge.
2.2 Features of an Effective EMR
For an EMR platform achieve its overall objectives, the
following key features have to be incorporated in its design.
i. Patient portal (My Chart). This forms the initial point of
contact between service provider and the patients. Any new
patient should be registered by capturing personal details,
contact information and even address data. Having geographical
location of patients is key I planning for scheduling. For
existing patient, by keying in a search criteria such as phone
number or email, one should be able to retrieve all data about
the specific patient. This greatly gives the medical practitioner
a 360-degree view about the patient thus allowing for conscious
decision making in addition to greatly saving time (Mostashari
et al., 2009).
ii. Patient Scheduling (Cadence). Ability to register patients,
schedule them and even choose a purpose for visit is necessary
for an EMR platform. Members should be able to key in
patient’s data in real-time, enable appointment booking and
even reason for appointment. With embedded notification
option, the EMR should be able to send reminders to patients on
their upcoming appointments.
iii. Patient history recordings. A good EMR should keep all
patient’s information and make it easily accessible. This
information should be synchronous in that one can have a view
of the patient in terms of allergies, previous procedures, treated
ailments, any lab tests and even payment information.
Externally available patient clinical records should also form
part of the patient history recordings.
iv. E-Prescribing. This allows for electronic printing and
transmission of patient’s prescription from the doctor’s room to

pharmacy, or even transmission on laboratory test results to the
doctor’s room. The E-Prescribing feature gives automatic and
instant notifications on drugs and any allergies as stored in the
system database regarding the specific patient (Barbara & Ken,
2010).
v. Medical Billing Dashboards (Professional / Hospital Billing).
Billing forms part of medical care process. A system which will
be able to correctly and accurately accumulate and project all
charges across the various treatment processes such as
consultancy fee, lab fees and pharmacy fees is effective. A chart
comparison of the various processes charges makes it more
desirable.
vi. Order Entry (CPOE). This allows medical practitioner to
enter, save and transmit a patient’s order compromising of
prescription, medical tests and any other service offered. These
orders are transferred electronically making it fast and very
effective. Also reduces the error of mix-up in patient orders.
vii. Lab integration (Willow). Lab tests forms part of medical
attention. Doctors will need to have access to lab tests results to
give the right prescription. An EMR should therefore have a
directly link to the laboratory whereas results are updated, the
doctor can view them without having to go through paperwork
filled by laboratory team.
viii. Documents management (HMS). Any system should have a
systematic way to manage various documents. Am EMR is no an
exception. Documents should be easily managed and shared
through the EMR without having to physically move the
documents from on place to another. The platform should offer
charting features for quick clarification whether needed
amongst the medical team.
ix. Centralized Communication (Command Center). Customer
experience in key in care delivery. A good EMR should have an
easy patient-doctor interaction. This should be management at a
central point to ensure no unattended queries and also to
enhance accountability.2.3 Barriers to adoption of EMR
Despite the many adorable features of an EMR, adoption has

been slow than expected. This rate is even worse for small and
medium health care providers. Some of the challenges
associated with the slow adoption of EMR within health care
providers are discussed below (Sameer & Krista, 2010).
i. High capital costs and insufficient returns on investment.
Good Electronic Medical Records systems are not cheap to
acquire. Other overhead costs such as training, support costs
and integration technicalities drives the cost even higher. Many
small heath care providers have only a limited number of clients
thus unable to foot for the initial costs on EMR. Even when they
are able to acquire an EMR platform, the returns might never
realize the initial investments within the estimated financial
period thus making it a less viable investment.
ii. Underestimation of the change management required. More
than often organizations fail to plan enough for deployment of
an EMR platform as a result of overlooking the change
management required. Though it’s just a system, an effective
EMR touches every process in an health care provider.
Whenever change is underestimated then some functionalities of
the EMR might be overlooked thus fail to realize the full epic
EMR potential.
iii. Lack of alignment between clinical process and workflow to
the EMR system. When effectively implement, an electronic
Medical Records system should take over all processes in a
facility dictating the workflow. If not properly aligned to
existing processes and workflows, people might feel that the
EMR isn’t effective thus a barrier to adoption.
iv. Concern that systems will become obsolete. EMR costs are
high thus a fear in investing. With technology changing very
fast, some facilities might feel in no time the technology will be
irrelevant thus loosing the system as a whole.
v. Lack of skilled resources for implementation and support.
EMR will need trained staff from deployment to its support.
Many facilities lack enough staff with technical knowledge on
supporting systems thus fail to adopt. They might consider
hiring new staff to support the EMR platform as an extra

expense and decide against adoption altogether.
vi. Concern regarding negative unintended consequences of
technology. Organizations are always in fear of negative
consequences brought about by new technology and systems.
Health care providers are always concerned on whether staff
will become reluctant and less careful as they believe an
effective EMR makes their work easier thus compromising the
work standards.
2.4 Addressing EMR adoption barriers
The paper has outlined the various challenges associated with
adoption of epic EMR. Through this section, a series of options
and measures will be outlined to address the noted barriers.
i. Preparing for change. Change is never always welcomed. In
most cases, people would prefer the norm way of doing things.
EMR adoption will definitely have a great change in how
processes flow within the healthcare facility. Fear of job losses
due to digitization is a real concern amongst many staff. The
management and drivers of the EMR adoption should engage all
stakeholders well and in advance to seek for acceptance and
support in the implementation. EMR is purely about
streamlining processes and improving on efficiency thus
shouldn’t be shunned away.
ii. Investing in skilled resources to implement, support and train
other users. Implementation is only successful if its deployment
is done in correct way, with enough support and training.
Having few new staff to support the EMR depending on the size
of the facility is far outweighed by the benefits an effectively
implement epic EMR. Such resources should train the medical
practitioners on different modules in the EMR and general use.
iii. Planning for alignment and integration should be properly
done to ensure all processes and workflows are captured in the
EMR. The implementation team need to understand all the
current processes and workflows which them will be matched
with the EMR processes and workflows. Any merging of
processes whenever necessary should be documented so as not
to reach dead ends (Sameer & Krista, 2010).

iv. Though initial costs to acquire epic EMR might be high,
when property implemented the revenue realized would easily
justify the investment. It is therefore necessary for facilities to
shift focus from the initial costs and rather focus on effective
implementation which will bring high returns to cover for the
initial costs which are one off costs.
2.5 Related Work
While Cedars-Sinai has a good reputation in medical
innovation, their EMR implementation was a failure which is
often used as Cautionary to anyone intending to acquire an EMR
platform. While the hospital invested $34 million on EMR
system in 2002, the system was later scrapped as a result of
ineffectiveness and poor implementation (Sameer & Krista,
2010). The system was meant to improve health care by
providing end to end EMR services. Much of the failure was
associated with the introduction of many decisions support
mechanisms way after the actual deployment. Pre
implementation planning was poorly done thus such important
support functionalities had been left out. Much of the drugs and
prescription module had been left out. Insufficient training and
lack of system testing also attributed to the failed
implementation at Cedars-Sinai facility. More critically, a
phased approach wasn’t used as always recommended for such
systems which hugely turned out to be a costly mistake.
Veterans Administration (VA) implemented a national EMR
system called Vista in 1999 which was a success. The success
was majorly attributed to its comprehensive roll out plan for the
system. Careful planning and collaboration with IT personnel,
subject matter experts and end users was critical to the success
as it led to creation of a workable system. The system’s 24/7
technical support and timely feedback sessions were highlights
of the success story. Buy-in was achieved at all levels
throughout the organization prior to implementation (Sameer &
Krista, 2010).

RESEARCH METHODOLOGY
3.0 Introduction
This chapter explains the approach i used to gain information on
the research problem and includes the research design, study
population and sample size, sampling design and procedure,
data collection methods, measurement of variables. Procedures
used of data collection, data processing, analysis and
presentation and anticipated problems to the study.
3.1 Research Philosophy
A research philosophy is a belief about the way in which data
about a phenomenon should be gathered, analyzed and used
(Saunders et al., 2019). In this study ontology research
philosophy will be used. Being a hypothetical-deductive
investigation, subjectivism approach will be used to determine
how effectively can Electronic Medical Records system (EMR)
can be adopted by healthcare providers. The study will also
formulate various ways which can be employed to promote EMR
adoption across the health sector.
3.2 Research design
The research design is the overall strategy that is used to
integrate the different components of a study in a coherent and
logical way, thereby, ensuring effective address of the research
problem; it constitutes the blueprint for the collection,
measurement, and analysis of data (Creswell, 2012). This
study’s is to hypothetically investigate how EMR platform can
be effectively adopted by healthcare providers. The study will
first analysis the various barriers to adoption and therefore
formulate various ways in which such barriers can be addressed.
Questionnaires survey will be used to collect primary data.
Literature analysis with be the key source of secondary data
though the research. Journals and other publications will be
studied to supplement the literature analysis.

3.3 Study Population Sample
The study population will comprise of healthcare providers
within the United States (US). The questionnaires will be
distributed to staff in Trinity Health Hospital and St Peters
Hospital Albany New York. Both hospitals have implemented
epic EMR thus the paper will analyze firsthand knowledge on
epic EMR training and experience. Literature will be reviewed
for other healthcare providers within the US for so as to get a
broader view on epic EMR implementation.
3.4 Sample Size and Sampling Procedure
Sample size is a research term used for defining the number of
individuals/entities included in a research study to represent a
population. This subgroup is carefully selected so as to be
representative of the whole population with the relevant
characteristics. Sampling is a procedure, process or technique of
choosing a sub-group from a population to participate in the
study (Smith, 2013).
i. The sampling plan describes the sampling unit, sampling
frame, sampling procedures and the sample size for the study.
The sampling frame describes the list of all population units
from which the sample was selected (Cooper & Schindler,
2012). Factors considered in determining the sample size
included; Confidence level: the measure of how certain you are
that your sample accurately reflects the population, within its
margin of error. Common standards used in research are 90%,
95%, and 99% (Cooper & Schindler, 2012).
ii. Margin of error: the percentage that describes how closely
the answer your sample gave is to the “true value” is in your
population. The smaller the margin of error is, the closer you
are to having the exact answer at a given confidence level
(Cooper & Schindler, 2012).
The Trinity Health Hospital and St Peters Hospital Albany staff
will form the sample size for primary data. Both hospitals have
a total of one hundred and fifty seven (157) staff. The paper
aims at having all of them fill the survey questionnaire. By use

of literature analysis and publications as a source of secondary
data, a sample size may not be clearly determined. However,
enough of secondary data will be reviewed to enable
formulation of a rich opinion.
3.5 Data Collection
Questionnaires will be used to collect primary data. Secondary
data will be collected by use of literature analysis and reviews
of relevant journals and publications touching both on EMR and
systems adoption within hospitals and healthcare facilities.
When necessary, randomized interviews will be conducted but
the I intend to rely on questionnaires, literature and publications
to deduce an opinion.
Each questionnaire will have twelve questions covering on the
participant’s role within the healthcare facility, opinion on epic
and training conducted in addition to view on trainer’s
knowledge of the epic platform. It is necessary to establish
whether the platform trainers actually pass enough knowledge to
users which greatly determines the level of adoption. A ten
level Likert scale with weights ranging from 1-10 will be used
by respondents to evaluate the level of agreement or
disagreement (strongly agree -10 and strongly disagree -1).
Percentages will be used to find the level of agreement (sum of
respondents for strongly agree and agree), disagreement (sum of
respondents for strongly disagree and disagree) and not sure.
DATA ANALYSIS AND FINDINGS4.1 Data Analysis
This is systematic application of statistical and logical
techniques to describe the data scope, modularize the data
structure, condense the data representation, illustrate via
images, tables, and graphs, and evaluate statistical inclinations,
probability data, and derive meaningful conclusions. The
research sought to identify the challenges faced by healthcare
facilities in EMR adoption and specifically epic. From the
identified challenges, the research then deduced various ways in

which these barriers can be overcome. Data was collected
through questionnaires. Secondary data was collected by
reviewing literature, journals and related publications. In
analyzing the data, the I tried to find answers to the research
questions formulated at the beginning of the research.
4.2 Findings
Upon data analysis, it was clear Electronic Medical Records
(EMR) platforms are such an important system within
healthcare facilities. From storing patients data, relaying lab
tests results and showing patient’s history, EMR greatly
improve on overall efficiency within a healthcare facility.
Initial capital investment for EMR platform, lack of alignment
with existing clinical process and workflow and lack of skilled
resources to implement and support the EMR platform greatly
hindered the adoption and use of EMR mostly in small and
medium healthcare facilities. With many afraid they may not
realize the full return on investment when purchasing an EMR,
such facilities resulted to manual processes.
Lack of skilled resources to effectively implement and support
EMR was noted to be a challenge in effectively adopting epic
EMR. Not many of the small and medium healthcare facilities
have a well-established information technology (IT) department.
Majority they rely on consultants for the basic technical issues.
EMR would require dedicated IT resource to implement and
give any needed support. As integration will mean moving
almost all facility processes to the system, such resource should
always on standby thus relying on a consultant is never a viable
option. An extra cost of hiring and maintaining an IT resource
therefore has to be incurred.
Aligning the epic EMR with the existing clinical process and
workflow was noted to be a major challenge. In most cases, the
healthcare staff didn’t understand how integrating processes to
the EMR will be like. They therefore resulted to working with
the existing workflows thus sidelining the EMR. This easily
rendered the epic EMR absolute as deployment has been done
but it isn’t being used.

4.3 Benefits of epic EMR
With proper implementation and support, EMR is a game
changer in any healthcare facility operations. Some of the wins
realized from acquisition and implementation of an EMR are as
discussed below.
i. Less paper/storage. An EMR will greatly reduce the paper and
physical storage needed for medical records greatly as data will
be captured electronically and storage in digital format which
doesn’t need physical space. Up to $1.3 billion could be saved
yearly by moving from paper use in maintaining medical
records (Girosi et al., 2005).
ii. Reduced redundancy an operational efficiency. EMR greatly
reduces redundancy in record-keeping as a record need to be
stored once but assessed from different places. Operations are
well streamlined thus improving on efficiency from EMR's
capabilities in storage, processing and information retrieval in
computerized methods which are far faster than paper based
(Vreeman et al., 2006).
iii. Great data accuracy. EMR system ensures great accuracy in
billing, prescription and service authorization. An error can as
well be easily corrected from search function as compared to
manual search.
iv. Improved patient control and transparency. EMR system
facilities communication between facility departments. Clinical
personnel have a 360-degree view of patient thus more time
devoted to planning and appropriate care (Vreeman et al.,
2006).
v. Better reporting capabilities. Through an EMR system,
clinical personnel can better analyze and review patient
outcomes. With multiple outputs formats, reports can be
customized for better understanding of the patient, payers and
other parties who might need to use such information (Vreeman
et al., 2006).
Conclusion
Data is a big asset to any organization. However, many entities

lack clear systems which can store data and interrelate to give it
meaning. Hospitals and other healthcare facilities have large
pools of data concerning patients. By sorting and grouping this
data, various processes and practices will be greatly shorted
while at the same time improving on accuracy. This leads to
cost cuts and increased revenues as a result of improve in
efficiency and effectiveness. Epic EMR is such a system which
can make healthcare facilities realize these benefits. Once it’s
implementation and deployment has been properly done, these
facilities stand a big chance in realizing its full potential and
the benefits thereof.
References
Barbara, C. & Ken, C. (2010). Evaluating the Effectiveness of
Electronic Medical Records in a Long Term Care Facility Using
Process Analysis. Journal of Health Engineering.
Cooper, D. R., & Schindler, P. S. (2012). Business Research
Methods (12th ed.). USA: McGraw - Hill.
Creswell, J.W. (2012). Educational research: Planning,
conducting, and evaluating quantitative and qualitative
research. Upper Saddle River, NJ: Prentice Hall.
Girosi F, Meili R & Scoville R. (2005). Extrapolating evidence
of health information technology savings and costs. RAND
Corporation.
Jha, A.K., DesRoches, C.M., Campbell, E.G., Donelan, K., Rao,
S.R., Ferris, T.G., Shields, A., Rosenbaum, S., & Blumenthal, D
(2009). Use of electronic health records in U.S. hospitals. The
New England Journal of Medicine.
Jha A.K., DesRoches, C.M., Shields, A., Miralles, P.D., Zheng,
J., Rosenbaum, S. & Campbell, E.G (2009). Evidence of an
emerging digital divide among hospitals that care for the poor.
Health Affairs.
Kaushal, R., Bates, D., Jenter, C., Mills, S., Volk, L., Burdick,
E., et al. (2009). Imminent adopters of electronic health records
in ambulatory care. Informatics in Primary Care.
Mostashari, F., Tripathi, M., & Kendall, M (2009). A tale of
two large community electronic health record extension

projects. Health Affairs.
Parente, S., & McCullough, J (2009). Health information
technology and patient safety: Evidence from panel data. Health
Affairs.
Sameer, K. & Krista, A. (2010). Overcoming barriers to
electronic medical record (EMR) implementation in the US
healthcare system: A comparative study. Health Informatics
Journal. SAGE.
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research
methods for business students (5th ed.). England: Pearson.
Smith, Scott (8 April 2013). Determining Sample Size: How to
Ensure You Get the Correct Sample Size. Qualtrics.
Vreeman D, Taggard S, Rhine M. & Worrell T (2006). Evidence
for electronic health record systems in physical therapy.
Physical Therapy Journal.
Appendix 1: Survey Questionnaire
Epic Implementation Survey Questions:
1. What do you like about Epic?
2. What is your job role MD, RN, Medical Billing?
3. What department do you work?
4. How could we make it easier for you to adjust to Epic?
5. One a scale of 1-10, 1 being poorly satisfied, 10 being
extremely satisfied, how satisfied are you with your training?
6. How was your trainer's knowledge of Epic: Poor, Fair,
Good, Very Good, or Excellent?
7. On a scale of 1-10, 1 being poorly satisfied, 10 being
extremely satisfied, how satisfied were you with your trainer’s
teaching skills?
communication skills?
people skills?
10. Was your trainer kind and understanding when it comes to
teaching you something you didn’t comprehend?

11. How much time did your trainer spend teaching you the
new system?
12. Overall, how much do you feel you have learned from your
trainer?
Appendix 2: Survey Questions Response Analysis.
After the questionnaire was distributed, 157 participants both
staff from Trinity Health Hospital and St Peters Hospital
Albany took part. The response was analyzed as follows.
1. Why do you like epic?
Responses included because of the systems; effectiveness
(24%), easy to use (27%), fast (9%), no paperwork (28%),
secure (7%) and others (5%).
2. What is your job role?
Two (2) participants where in MD positions, 34
physicians/doctors, 57 nurses, 18 from accounts and billing, 14
from pharmacy, 8 receptions, 14 from the laboratory and 10
other hospital units.
4. How can we make it easier for you to adjust in epic?
Respondents gave the following recommendations; 52% needed
more training, 23% more technical support, 15% needed manual
printouts while 10% gave other recommendations.
5. Training satisfaction levels.
More than half of respondents (85) rated satisfaction levels as 5
and above up to 10 while 72 respondents were not satisfied.
6. How was the trainer knowledge on epic?
7. Trainer’s teaching skills.
Majority of respondents through the trainer needed to improve
on teaching skills with only 27% of respondents saying were
satisfied to extremely satisfied.
8. Trainer’s communication skills.
Response was fairly distributed with satisfied to extremely

satisfied having 51% while 49% gave a scale of 5 and below.
9. Trainer’s people skills
63% of respondents rated the trainer’s people skills between 6
to 10, satisfied to very satisfied. 37% of respondents felt the
trainer needed to improve on people skills.
10. Teaching something not comprehended.
The respondents were evenly distributed in their responses. 35%
said the trainer as understanding, 33% were neutral while 32%
felt the trainer wasn’t understanding when teaching something
they didn’t comprehend.
11. Time teaching new system
The training took 3 days which was the response from all
participants.
12. How much learning from trainer?
While majority of respondents (57%) agreed the learnt much of
the system from trainer, 43% learned little or nothing new.
13. Did the Tip sheet help during Go-Live?
While majority of respondents (70%) agreed the Tip sheet help,
30% learned little or nothing new.
Department Distribution
Physicians Nursing Billing Finance Customer Service
Pharmacy 34 57 12 6 8 14
in
HCIN 500:Healthcare Informatics
PROJECT 2: EMR(EPIC) Implementation Plan

Epic Implementation
EPIC EHR Overview
EPIC is an Electronic Health record System that helps
physicians, doctors , hospitals and other healthcare providers to
add, update, store and view patients medical records.
Epic works with community hospitals, academic facilities,
children’s organizations, safety net providers, and multi -
hospital systems.
It helps in improving patient experience, quality of healthcare
and achieve financial health
Epic can be implemented within a single hospitals or access
multiple hospitals.
For smooth implementation of EPIC EMR, operational risks
must be reviewed and managed within the IT program.
Steps in Epic Implementation
Establishment of effective Implementation team
Implementation team is a very critical resource which ensures
long term EHR implementation success.
The team includes:
Project manager
Nurse representative
Physician representative
Super users i.e. early adopters for training programs
Application developer
2. Establishment and communication of EPIC EHR goals and
priorities.
The objective of the epic implementation should be well
communicated.

These includes:
Both immediate
Long term goals.
3. Establishment of EPIC implementation strategies
Implementation strategies includes:
The phases of implementation
Creation of implementation timeline
budget stating, and
scope definition.
4. Epic implementation plan document
Implementation plan document should have detailed
implementation phases which should be followed for the
successful implementation.
Project Schedule and Time line
Engagement
Go Live Weekend
Post- Implementation
Deployment
Assessment

Preparation and planning
Risk Management
Risk Analysis
Risks response
Back up and downtimes
Privacy and identity management
Develop best practices and legal boundaries
SWOT Analysis
Strength
Weakness
Opportunities
Threats
Communication Management
Communication Matrix
Awareness Champaign
Feedback channels
Constant information of changes
Create communication materials for patients
Communicate change to community and patients.
Epic Implementation Resources
Human Resources
Trainers
Super Users
Project Champions
Hardware and Software Resources
Computers and Tablets

IT requirements
Training manuals
EPIC Services
1. EPIC Training
Implementation will be successful if proper training is done to
the users of the EHR system.
Epic implementation team offers customized peer to peer
training and resource for go live and beyond. This ensures that
the users become confident enough in using the system.
2. Technical Support
Epic offers 24 our support, regular checkups and monitoring to
ensure that the EHR system achieves long term success and
improvement.
3. Ongoing Services
Epic does not stop at the implementation stage. After the system
goes live, the implementation team keeps a close look out to
ensure that the clients satisfied when using the system and can
benefit maximumly from it.
4. Continuous improvement
Epic staff keeps on providing assistance and advice on
performance improvement, monitoring, value from data and
regulatory support to their clients.
Data Audit and Migration
In a healthcare setup, there exists wide varieties of data.
Auditing data before data transfer in an Epic EHR system is
very important.

The data records should be up to data and accurate to avoid
transferring errors into the system.
Data Audit also ensures that the data being migrated is
compliant to set data regulations hence reducing the likelihood
of errors occurring.
Key stages in Data migration:
Data conversions i.e. paper work to Electronic records
Data cleansing and verification
Legacy data mapping
Testing and verification of new data inputs.
Data Audit and Migration
Defining Go live activities
Plan on the go live activities is very important for both the Epic
team and the client.
The roadmap can include:
Patient communication module
Modification of appointments and scheduling
Staff scheduling
Network speed and reliability checks
System reports
Data backup processes
Method to Evaluate the Implementation
Perform return on invest (ROI) calculations
to assess profitability
Record patient throughput
to assess efficiency

Survey patient satisfaction
to assess quality of care
Survey physician satisfaction
to assess user adoption and training
Analyze data error rates
to assess data input and quality
Pitfalls & Roadblocks
Lack of Champions
Organization resistance to change
Inadequate resources
Incompetent trainers/support staff
Poor Testing
Poor Adaptability to new methods
Increases physicians workload.
Multiple ways to perform one task. Excessive “Extra clicking”
adds hours of extra work
Delays and errors because physicians wait until end of day to
finish documentation.
Too many Updates
Cost of set up and maintenance
References
Epic Implementation Services | Healthcare IT | The HCI Group.
(2019). Retrieved from
https://www.thehcigroup.com/vendors/epic-consulting-
services/epic-implementation
Beeson, K. (2017). EHR Implementation Plan: Your 8-Step
Checklist. Retrieved from https://www.ehrinpractice.com/ehr-
implementation-plan.html
Green, J. (2018). A template for your EHR project
implementation timeline. Retrieved from

https://www.ehrinpractice.com/a-template-for-ehr-project-
timeline-627.html
Stasik S. (2019). How Electronic Medical Records can improve
patient safety retrieved from
https://www.travelnursing.com/news/nurse-news/how-
electronic-medical-records-can-improve-patient-safety/
Thank you
Bolade Yusuf
TELEHEALTH
PROJECT 1
Mehak Sharma, Shweta Patel, Na Zeng, Bolade Yusuf
Telehealth
Issues
Traditional Face To Face healthcare models have many
limitations: inadequate mobility; regional distance; operating
hours; parking limitation

Why telehealth?
Can provide education and self-management support
Data Mining methods can support clinical decision making
Benefit doctor-patient communication, patient-patient
communication
Research authors’ goals
Comparing decision trees, data mining technology and
clustering using in Telehealth
Introduction
Exchanging medical information electronically from one site to
another. (Tuckson et al., 2017, p. 1591).
Used between clinician to clinician, clinician to patients as well
as patient to mobile health technology.
Cost-effectively provide customized and preventive treatment.
Telehealth is defined as exchanging medical information
electronically
from one site to another with the purpose of improving patients’
health
Telehealth is used between clinician to clinician, cli nician to
patients as well as
patient to mobile health technology.
The increasing global health spending has enabled healthcare
organizations to adopt emerging health technology for chronic
disease management and cost-effectively provide customized

and preventive treatment.
Introduction
Provide preventive medicine and customized healthcare through
value-based treatment models.
Although Telehealth and technology aspects have existed for
decades.
Telehealth enables patients to be tracked remotely and their
condition development controlled through constant evaluation
Although global health expenditures are expected to grow to
$18.28 trillion by 2040, the future of Healthcare organizations'
is poised to utilize developments in Telehealth technology and
big data analytics to provide preventive medicine and
customized healthcare through value-based treatment models.
Although Telehealth and technology aspects have existed for
decades, the Covid-19 pandemic has taken Telehealth to the
mainstream in the face of a worldwide crisis that is demolishing
health facilities.
Telehealth enables patients to be tracked remotely and their
condition development controlled through constant evaluation;
whereas Big Data Analytics integrates data obtained from
Telehealth modality covering both objective data (e.g. vital
signs, ambient environment) and subjective detail (e.g.
symptoms and patient behavior).

How telehealth and data analytics are making a difference in
healthcare?
Telehealth has great potential to expand the capacity of
healthcare
For example:
Apple is working on a wearable medical-sensor-laden device
“iWatch'' to monitor blood through the skin.
Google announced the development of eye contact lenses that
could analyze glucose levels through tears.
Telehealth has great potential to expand the capacity of
healthcare to reduce risks, improve physicians-patients and
patients-patients communication, and reveal unseen patterns or
sensory features in a ubiquitous, personalized and continuous
manner.
Data Review
Chronic conditions are the primary cause of ill health, affecting
> 68 percent of all deaths around the globe. Many factors can
lead to appointment non-attendance.
Additional obstacles to healthcare that can obstruct access to
standard FTF services.
Telehealth technologies can be used to provide education, self-
management support and have several advantages over
traditional FTF models of care.
Telehealth approaches may help chronic condition patients to
deliver comprehensive treatments and manage a shift in habits.
In clinical decision-support structures, data mining methods are
rapidly being utilized to help doctors in decision making by

analyzing factors, effects and characteristics of patients.
The factors such as Patient-centered barriers, including
inadequate mobility and regional distance, operating hours and
missing appointments, can lead to appointment nonattendance
followed by increased rate of deaths around the world.
Additional obstacles to healthcare that can obstruct access to
standard FTF services including administrative negligence,
inadequate access to clinic facilities, restricted parking and
undesirable clinic operating hours.
Through telehealth services one could facilitate and sustain
lifestyle changes by managing shift in eating habits and are
adjustable in time and location, with the ability to deliver
comprehensive treatments that may not be possible for
conventional treatment models.
Analytical Techniques
Text Mining: is an artificial intelligence (AI) technology, uses
natural language processing to transform the
unstructured text in documents and databases into normalized,
structured data suitable for analysis.
Regression analysis: is the process of identifying and analyzing
the relationship among variables. It can help to understand the
characteristic value of the dependent variable changes, if any
one of the independent variables is varied. It is generally used
for prediction and forecasting.

Analytical Techniques
Decision tree: commonly used in operations research,
specifically in decision analysis, to help identify a strategy most
likely to reach a goal.
Clustering: identifies clusters of similarities and then forms
groups of objects that are more similar in terms of certain
aspects than other groups. Unlike classification, the groups (or
clusters) are not predefined and can take different forms
depending on the data analyzed.
Analytical TechniquesSTUDY INVOLVEDANALYTICAL
TECHNIQUE USEDDETAILSTAGSHow to identify
recipients of
telehealth by
deducing the most
important attributes
from a dataset of
current patients.
1. Data Analysis: Generate a target variable/attribute from
patient data 2. Supervised Learning: Decision tree model
algorithm: 3. Prediction: Process incoming data.1. Setting data
points parameters (age, patient ICD codes, hospital size, patient
location from hospital, hospital stays, telehealth monitoring
capabilities) 2. C4.5 is a statistical classifier tool (aka J48)
Classify data to Supervised, Decision Tree, C4.5, Classification.
allow it to process and flow through decision tree.
Supervised, Decision Tree, C4.5, Classification.

TECHNIQUE USEDDETAILSTAGSAnalyze Twitter
tweets (location,
volume, content)
association with
telehealth with
Covid-19.
1. Text Mining: Natural Language Processing - Breakdown and
Analyze tweets 2. Unsupervised Learning: Descriptive Analysis:
Generalized Linear Regression + K means Clustering Analysis
(+ Elbow Method) 3. Geospatial Analysis: Visual geographical
distribution of tweets correlation with cases.1. Tokenization and
Stem-Rooting to tag and reduce noise and categorize words 2a.
Generalized linear regression to study association between
tweets and number of confirmed cases (P < 0.05) 2b. K means
clustering to classify tweets into topics.NLP, Unsupervised, K-
means Clustering, Generalized Linear Regression, Geospatial,
Association.
TECHNIQUE USEDDETAILSTAGSImpact of telehealth
on a diabetic cohort
over 12 months
Multilevel Models to assess impact with data from the self-
reported questionnaires over a period.Adjustments-covariate

adjustment to control baseline variablesSupervised, Multilevel
Model, Sidak, Covariate, repeated measures design, cluster
randomized controlled trial, Classification.Assessing whether
telehealth had
impact on
glycosylated
hemoglobin among
type 2 diabetes.
1.Mixed Effects logistic regression. 2.Sensitivity
Analysis.Repeated measures, cluster randomized controlled
trial, mixed Effects Logistic Regression.
TECHNIQUES USEDDETAILSTAGSAssess novel
clustering method
based on graph
models.
Clustering System based on Graphs compared it Mean Shift, K-
means, ward hierarchical clustering, db. scan, birch clustering
systems.Supervised learning, clustering, classification,
unsupervised learning, kernel trick.Assess the
implementation of
tele-PCMHI to new
sites
1. Generalized Linear Mixed Models - fixed for innovation and
time and random effects 2. Mixed Logistic Model/or standard
logistic regression model.Standard logistic regression if
intraclass correlation is insignificant; and mixed logistic model
if it is significant.Cross sectional design; multilevel model,
intraclass correlation.

TECHNIQUES USEDDETAILSTAGSAssess telehealth
impact on
implementing
dietary
interventions via
secondary studies
1.Data analysis: Random effects meta-analysis (DerSimonian
and Laird Method) + Fixed effects regression model 2. I-square
to assess heterogeneity, variability between the studies 3.
Sensitivity analysis 4. Egger's plot assess potential publication
bias of studies used.Meta-Analysis, Fixed effects regression, I-
square, Sensitivity Analysis, Egger's
Outcomes
Explored the models to identify the appropriate telehealth
service candidates.
The decision tree model was selected to solve the problem of
telehealth patient classification for the
following reasons:
For the perspective of sensitivity, two models performed
equally well.
For the perspective of accuracy, specificity, and precision.
Compared the differences between the telehealth services and
usual care for different populations.

Explored the models to identify the appropriate telehealth
service candidates.
After comparing the decision tree model provided by heuristic
decision tree telehealth classification approach (HDTTCA) and
the logistic regression, the authors selected the decision tree
model to solve the problem of telehealth patient classification
for the following reasons:
For the perspective of sensitivity, two models performed
equally well.
For the perspective of accuracy, specificity, and precision, the
decision tree model worked better than logistic regression.
Compared the differences between the telehealth services and
usual care for different populations.
Different studies focused on the different populations.
Most studies indicated that there’s no significant difference.
One study showed the telehealth could modestly improve
glycemic control.
Investigated the tweets contents to identify the contributions of
telehealth during COV-19 pandemic.
Study investigated the rapid shift in telehealth adoption amidst
the recent coronavirus Covid-19 pandemics.
Outcomes

Different studies focused on the different populations such as
patients with type 2 diabetes.
Most studies indicated that there’s no significant difference
between the telehealth services and usual care when comparing
the life quality.
There’s one study showed the telehealth could modestly
improve glycemic control among patients with type 2 diabetes,
although it seems unlikely to produce significant patient
benefit.
Investigated the tweets contents to identify the contributes of
telehealth during COV-19 pandemic.
Study investigated the rapid shift in telehealth adoption amidst
the recent coronavirus Covid-19 pandemics. The result showed
the need for widespread implementation of digital health and
the importance of supporting policy changes to unleash the
power of this technology.
Comparison among different Analytical Methods
There’s only one article selected using natural language
processing (NLP) due to the unstructured text data. This
analytical technique would not be considered for our team’s
topic.
Regression, decision tree and clustering were the most used
analytical techniques in the studies. It is generally used for
prediction and forecasting.
Decision tree is commonly used in operations research,
specifically in decision analysis, to help identify a strategy most
likely to reach a goal.
Clustering identifies clusters of similarities and then forms
groups of objects that are more similar in terms of certain
aspects than other groups.
Our topic is a little wide and different so when working on the
topic at the very beginni ng, we could select to use clustering to
have a quick look at the data and then use classification

techniques to do a further exploration. Similar to one article we
found we also need some data analytics to compare different
methods to find out the better technique.
Only one article used natural language processing (NLP), which
is text mining to explore their tweets data. It’s not a typical
analytical technique for telehealth. It was selected due to the
unstructured text data. This analytical techniq ue would not be
considered for our team’s topic.
Regression, decision tree and clustering were the most used
analytical techniques in the studies. Regression analysis is the
process of identifying and analyzing the relationship among
variables and to understand the characteristic value of the
dependent variable changes, if any one of the independent
variables is varied. It is generally used for prediction and
forecasting.Decision tree is commonly used in operations
research, specifically in decision analysis, to help identify a
strategy to reach a goal. Clustering identifies similarities and
then forms groups of objects that are more similar in terms of
certain aspects than other groups. Unlike classification, the
groups (or clusters) are not predefined and can take different
forms depending on the data analyzed.
Telehealth is a wide topic so initially we selected to use
clustering to have fast access to the data followed by
classification techniques and data analytics to compare various
methods with the intense of finding better technique.
Summary
Telehealth steadily increases as it has become a viable modality
to patient care, especially with Covid-19.

Using evidenced based self-management techniques targeting
self-care and QoL delivered via telehealth, shall facilitate
intervention delivery.
Telehealth technologies to manage chronic disease and deliver
cost-effective personalized and preventive care.
Data mining techniques are increasingly used in clinical
decision making for more accurate and effective decisions.
Summary
The classification Model is the most commonly practical.
Decision trees is a good approach in identifying the potential
receivers of telehealth services.
Systematic review and narrative analysis explore telehealth and
patient satisfaction association from effectiveness and
efficiency view.
Lack of consistency in the telehealth literature in the study
methodologies and data analysis techniques used.
Longer-term studies could examine impacts of telehealth on
complications of diabetes and acute MI.
References
1. Massaad E, Cherfan P (April 26, 2020) Social Media Data

Analytics on Telehealth During the COVID-19 Pandemic.
Cureus 12(4): e7838. doi:10.7759/cureus.7838
2. Chern, C., Chen, Y. & Hsiao, B. Decision tree–based
classifier in providing telehealth service. BMC Med Inform
Decis Mak 19, 104 (2019). https://doi.org/10.1186/s12911-019-
0825-9
3. Hirani, S. P., Rixon, L., Cartwright, M., Beynon, M.,
Newman, S. P., & WSD Evaluation Team (2017). The Effect of
Telehealth on Quality of Life and Psychological Outcomes Over
a 12-Month Period in a Diabetes Cohort Within the Whole
Systems Demonstrator Cluster Randomized Trial. JMIR
diabetes, 2(2), e18. https://doi.org/10.2196/diabetes.7128
4. Russell, T. G., Martin-Khan, M., Khan, A., & Wade, V.
(2017). Method-comparison studies in telehealth: Study design
and analysis considerations. Journal of telemedicine and
telecare, 23(9), 797–802.
https://doi.org/10.1177/1357633X17727772
References
5. Caffery, L. J., Martin-Khan, M., & Wade, V. (2017). Mixed
methods for telehealth research. Journal of telemedicine and
telecare, 23(9), 764–769.
https://doi.org/10.1177/1357633X16665684
6. Steventon, A., Bardsley, M., Doll, H. et al. Effect of
telehealth on glycaemic control: analysis of patients with type 2
diabetes in the Whole Systems Demonstrator cluster randomised
trial. BMC Health Serv Res 14, 334 (2014).
https://doi.org/10.1186/1472-6963-14-334
7. Kruse, C. S., Krowski, N., Rodriguez, B., Tran, L., Vela, J.,
& Brooks, M. (2017). Telehealth and patient satisfaction: a

systematic review and narrative analysis. BMJ open, 7(8),
e016242. https://doi.org/10.1136/bmjopen-2017-016242
8. Jelinek, Herbert & Cornforth, David & Kelarev, Andrei.
(2016). Advanced Clustering Method for Neurological
Assessment Using Graph Models. International Journal of
Computer & Software Engineering. 1. 10.15344/2456-
4451/2016/109.
References
9. Owen, R.R., Woodward, E.N., Drummond, K.L. et al. Using
implementation facilitation to implement primary care mental
health integration via clinical video telehealth in rural clinics:
protocol for a hybrid type 2 cluster randomized stepped-wedge
design. Implementation Sci 14, 33 (2019).
https://doi.org/10.1186/s13012-019-0875-5
10. Jaimon T Kelly, Dianne P Reidlinger, Tammy C Hoffmann,
Katrina L Campbell, Telehealth methods to deliver dietary
interventions in adults with chronic disease: a systematic review
and meta-analysis, The American Journal of Clinical Nutrition,
Volume 104, Issue 6, December 2016, Pages 1693–1702,
https://doi.org/10.3945/ajcn.116.136333
11. AbuKhousa E (2017) Analytics and Telehealth Emerging
Technologies: The Path Forward for Smart Primary Care
Environment. J Healthc Commun. 2:67. doi: 10.4172/2472-
1654.100108
12. Akhondzadeh Noughabi, Elham & Ameri, Kimia &
Alizadeh, Somayeh. (2017). Application of Data Mining
Techniques in Medical Decision Making: A Literature Review
and Classification. 10.4018/978-1-5225-2515-8.ch01
13. Tuckson, R. V., Edmunds, M., & Hodgkins, M. L. (2017).

Telehealth. New England Journal of Medicine, 377(16), 1585–
1592. https://doi.org/10.1056/nejmsr1503323
Epic Implementation Survey
Questions:
1. What do you like about Epic?
2. What is your job role MD, RN, Medical Billing?
4. How could we make it easier for you to adjust to Epic?
5. One a scale of 1-10, 1 being poorly satisfied, 10 being
extremely satisfied, how satisfied are you with your training?
6. How was your trainer's knowledge of Epic: Poor, Fair, Good,
Very Good, or Excellent?
teaching skills?
communication skills?
people skills?
10. Was your trainer kind and understanding when it comes to
teaching you something you didn’t comprehend?
11. How much time did your trainer spend teaching you the new
system?
12. Overall, how much do you feel you have learned from your
trainer?

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