Charge: 
You are to assume that the CEO of a hospital has asked you to prepare a presentation to the Board of Directors.
One “white paper” will be required. The written portion of the paper will be no longer than 5 to 8 pages and must incorporate at least 5 (or more) reference. The format must conform to standard APA reference format. The topic for the paper must be related to some aspects of HIT (health information technology) and its impact on the effective and efficient delivery of healthcare services. 
Requirements:
Minimum of 5 references, 12 Font; one-inch margins, 1.5 spacing and the body of the paper must meet the page length standards. APA citation formatting is required. 
Format:
Pretend your boss has asked you to research a health technology and recommend to senior staff what they should You can assume the organization is any healthcare facility: hospital, long-term-care, a telehealth company, or a person.
Describe the health information What is it? What is it supposed to do to help healthcare organizations or individuals?
Research examples that have been successful in the application of the technology
Research examples that were “challenging” or “disruptive” or inappropriate or failures or?
Explain why you would recommend your organization/individual to adopt this
If your topic is an international topic, explore what the US could learn or adopt from this other country.
Will be scored on:

Very clear statement of purpose. Very clear statement of action to be taken. Particularly clear and appropriate sequence of ideas for topic. The flow of ideas is clear and the reader is guided through the memo with no confusion. Wording and tone are very well tailored to organizational climate and recipients). Standard written English consistently under control.
1
A Diabetes Care Dashboard: Addressing Diabetes in Long Term Care Facili es
Sawyer Lindsey, Angelica Graza, Nicole Kelsey, and Brian Cabrera
California State University, Northridge
HSCI 515 Seminar in Healthcare Informa on and Management
May 19, 2022
TABLE OF CONTENTS
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Introduc on
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Product/Service/Methodology
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Key Findings
Key Findings #1
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Key Findings #2
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Key Findings #3
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Visual Data
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Conclusion & Key Takeaways
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References
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INTRODUCTION
Diabetes is a chronic health condi on that directly a ects how an individual’s body processes
food into energy (Centers for Disease Control, 2022). This condi on is classi ed according to the
following levels of dis nc on: Prediabetes, Type 1 diabetes, and Type 2 diabetes. The majority of
individuals’ bodies naturally produce the hormone insulin, which helps convert sugars from the food we
eat into energy that the body can use or store for later, however, when this process malfunc ons,
various chronic condi ons may arise and a ect an individuals lifestyle and behaviors as they progress
into later life stages.
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Further research into the levels of diabetes exposes various treatment and management stages
for the various types of the disease, beginning with prediabetes. Prediabetes is a condi on in which
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blood sugar levels are signi cantly higher than normal and an individual’s ability to regulate blood sugar
levels is beginning to decline (Pra et al., 2022). Approximately ninety six million individuals in the
United States have prediabetes with eight in ten of those a ected unaware they are under the
classi ca on en rely. In addi on, an individual diagnosed with Type 2 diabetes is improperly u lizing the
insulin produced by the body’s pancreas to manage glucose levels (Romeo et al., 2012). Lastly, Type 1
diabetes is iden ed when an individual’s pancreas is no longer producing signi cant amounts of insulin
altogether and must be treated regularly and supported by life saving insulin replacement therapies.
Type 1 diabetes is especially challenging for individuals as it is o en diagnosed early on in a person’s life
and will have life altering implica ons.
Although diabetes is composed of a variety of di erent stages of disease, it is primarily
diagnosed through measurable blood exams. To start, the AC1 test is a blood test that measures average
blood sugar levels over the course of two or three months, and AC1 results indicated above a seven
percent threshold are considered as having diabetes (Oroojeni et al., 2015). The fas ng blood sugar test,
along with the glucose tolerance test, are also blood sugar measurement tests taken a er a fas ng
period or shortly a er inges ng a liquid containing glucose. These tests enable healthcare prac oners
to understand blood sugar levels along with an individual’s response to glucose intake (Osman et al..,
2016).
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As with all chronic diseases, monitoring and treatment of an illness varies depending on age,
gender, and healthcare provider services. Elderly care in par cular is a target popula on that must be
assessed with increasingly more scru ny, as there is a progressive rise in complica ons due to risk factors
associated with advanced age (Selvin et al., 2006). Due to the elderly popula on necessita ng a high
level of care, u lizing advanced clinical decision support systems, including user friendly informa on
systems that support both provider and pa ent understanding, are crucial for advancing the health of
elderly care pa ents.
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PRODUCT/SERVICE/METHODOLOGY
From laboratory blood tests to lifestyle recommenda ons, there are so many di erent
components that go into both preven ng and managing diabetes. So, how does one long term care
facility e ec vely handle this disease? A er researching peer reviewed ar cles, speci cally on clinical
decision support systems for diabetes care, we found that implemen ng dashboards to be par cularly
helpful. These dashboards provide a summary of key performance indicators in which its users are able
to quickly view at once (Daglia et al., 2018). This creates many bene ts. First, it reduces click fa gue for
users and this directly a ects a healthcare provider’s performance for the be er. Secondly, it saves me,
allowing the user to spend more of it on speaking with the pa ent and not looking at a screen. This, of
course, enhances the pa ent experience. Last, but certainly not least, all variables can improve a
pa ent’s health outcomes.
To best prevent and manage diabetes in long term care facili es, we recommend the
implementa on of our diabetes care dashboard. This dashboard, as illustrated in our “Visual Data”
sec on, will be divided into six sec ons with the rst one being the “Pa ent Demographics”. In this
sec on, users will get an overview of who the pa ent is, complete with their name, age, race, and
preferred language.
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Next, we have the “Laboratory Studies Overview”. In this sec on, a user will be able to see the
results of key laboratory tests and rou ne checks for diabetes, such as hemoglobin A1C, lipid panel,
blood pressure, and microalbumin. In the e ort to help healthcare providers understand these levels in a
more mely fashion, these laboratory tests and rou ne checks will be color coded. The results that are
deemed to be in “Good Control” will be green. The results deemed to be in “Subop mal Control” will be
yellow. The results deemed to be in “Poor Control” will be red. The idea here is to help the user take
ac on against any laboratory result falling beneath “Good Control” in the e ort to be as proac ve as
possible or, at the very least, on top of early detec on and treatment.
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The two sec ons that follow will be “Alerts” and “Health Maintenance Reminders”. In the
“Alerts” sec on, healthcare providers will be able to keep up with when these next laboratory tests and
rou ne checks need to be reordered and completed by pa ents. The “Health Maintenance Reminders”
sec on will show what pa ents need to do next, such as examina ons pertaining to the eyes, feet, teeth,
and injec on sites, as well as the meframe in which they need to be done. In both sec ons, all past due
items will be in red for the purpose of promp ng immediate ac on.
Serving as the nal two sec ons, we have the “Pa ent Progress Overview” and “Pa ent Lifestyle
Interven ons” sec ons. In the former sec on, healthcare providers will be able to view a pa ent’s health
over me as it pertains to laboratory results via line graphs. This will help healthcare providers make
evidence-based lifestyle recommenda ons, such as dietary changes and measures to improve the
management of medica on, for pa ents based on their results. This informa on will be found in the
la er sec on.
Unlike other dashboards, our diabetes care dashboard caters to the switch that is occurring in
healthcare and that is value-based medicine and emphasis on preventa ve care. Through the inclusion
of key sec ons, such as “Pa ent Lifestyle Interven ons”, pa ents are given an opportunity to be more
proac ve with their health. This will empower them to become be er partners with their healthcare
providers when it comes to making care decisions. Ul mately, this dashboard will lead to be er health
outcomes and reduced health care costs.
KEY FINDINGS
Key Findings #1
Technology within healthcare is constantly evolving, as EHR and PHI become more interoperable.
In 2017, almost 90% of providers’ o ces adopted an EHR system and nearly 80% adopted a cer ed EHR
(ONC, 2021). This has caused a rise in computerized programs, such as clinical decision support systems
(CDSS). These models analyze data within EHRs to provide prompts and reminders to assist providers in
u lizing evidence-based clinical guidelines during the point of care (The Centers for Disease Control and
Preven on, 2018). There will also be a nancial growth in u lizing CDSS. The global CDSS market size in
2021 was at $4.48 billion. Expected expansion for the compound annual growth rate from 2022 to 2030
is 8.6% (Grandview Research, 2022)
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Research studies have shown how bene cial CDSS have been for our health care system.
Examples of certain bene cial outcomes are reduc on in order turnaround mes, decreased cost of
paper forms, reduc on in duplicate lab tes ng, improved quality of coding, reduc on in drug use and/or
cost, etc. (Tundjungsari et al., 2017). CDSS tools hold great poten al for our na on’s health systems. It
can overall provide access to the best current evidence in usable form and at strategic points within care
as well as the decision making processes (Tcheng et al., 2017).
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Key Findings #2
The par cular CDSS model we focused on were dashboards. Pa ent dashboards have helped
provide be er communica on between pa ent and provider, dis nguish and di eren ate lab work,
provide educa on, and overall monitoring a pa ent’s health. They help with disease management,
especially with common chronic diseases such as diabetes. A study from the Journal of Medical Internet
Research noted that illustra ng the variability of data types related to diabetes (HbA1c, glucose levels)
on a day-to-day basis, can help pa ents priori ze their diabetes, di eren ate the data, and keep it
controlled. For instance, a low glucose variability is more important for diabetes pa ents than having an
in-range HbA1c for preven ng complica ons (Giordanengo et al., 2019).
There has been addi onal research related to other CDSS models and diabetes management.
The Vermont Diabetes Informa on System conducted a study of using CDSS to track laboratory tes ng,
which involved labs monitoring glucose control, kidney func on, proteinuria, and cholesterol to overall
help control and prevent poten al dilemmas. The results led to a decrease in hospitaliza ons by 11%
and ER visits by 25% (Murphy, 2014). This also paved the way for more mobile health devices. Finger
s ck results can help track glucose levels, diet, etc. Addi onally, PHRs and other pa ent monitoring
applica ons can be designed to collect informa on from health devices and other wearables, to create
ac onable insights for providers. One in par cular pioneered by the Stanford School of Medicine uses a
wearable glucose monitor that transmits data to an Apple device, known as HealthKit (Taheri Moghadam
et al., 2021). The pa ent’s glucose readings can even be notated into a pa ent’s portal and routed to
their provider.
Key Findings #3
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Research into how to improve pa ent and provider use rates whilst simultaneously improving
pa ent health have found that mul ple interven on strategies including the widespread use of
Electronic Health Records (EHR), work ow integra on, and proper u liza on of (CDSS) prompts,
encourage high use rates of CDSS models (Connor et al., 2016). Adequate diabetes care demands
a en on to eleven basic domains of diabetes care: blood pressure, lipids, smoking, glucose, aspirin and
weight; screening for eye, foot, renal and vascular complica ons; and immuniza ons. These domains
vary in treatment needs, however, the addi on of con nuously changing guidelines and treatment care
op ons make it a necessity for CDSS systems to be systema cally updated and individualized depending
on pa ent service needs.
Research conducted on alterna ve clinical decision support systems for diabetes have found
success in proper representa ons of data knowledge. These ontologies contain the informa on of
pa ents including symptoms, blood tests, treatment records and diagnoses (Chen et al., 2017). These
ndings support the decision to construct CDSS models that may accurately diagnose and recommend
an diabe c medica ons. Addi onal research primarily focused on providing evidence-based clinical
decisions has found that proper integra on of CDSS interfaces increases the exibility of a CDSS system
along with enabling the ability to treat pa ents who su er from mul ple pathologies at once (Galopin et
al., 2015).
VISUAL DATA
CONCLUSION
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As discussed throughout the paper, diabetes is a complicated chronic condi on with many
comorbidi es and factors that a ect the control of this condi on. A CDSS model allows physicians and
empowers pa ents to take control of their health and improve the management of their diabetes. This
model succinctly delivers all the per nent informa on of a diabetes pa ent to a long term care delivery
provider. These categories are carefully cul vated in order to have the greatest impact on disease
management as well as giving pa ent ac onable informa on. A CDSS model can be manipulated and
updated over me to best serve the pa ent and their health. From our CDSS model, there are three
major takeaways that have a greater impact than a tradi onal CDSS diabetes model: improvement of
popula on health, user friendly for pa ents and providers, and focus on both medicinal and lifestyle
interven ons.
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Key Takeaways
The rst major takeaway of the CDSS diabetes care model is improvement of popula on health.
Given that the target of this model is long term care delivery providers, there is a need for improvement
of the large popula on of diabetes pa ents, not just a single pa ent. Much of the healthcare industry is
shi ing toward value-based care which is focused on quality and care improvement, not just volume of
care delivered. This means priori zing the pa ent’s en re diabetes care journey. Having a standardized
dashboard for diabetes pa ents in a long term care delivery system will allow for immediate recogni on
among providers and show exactly where interven on is needed. This will allow them to look at each
pa ent individually, but with the familiarity of the system. A CDSS dashboard speci c for diabetes will
streamline the process and help improve popula on health as a whole.
Another takeaway of the Diabetes Care Dashboard, a CDSS model, is the interoperability and
user friendly focus for the providers and pa ents. In order for providers and pa ents to have a desire to
use this model, it needs to be easy to use. If the dashboard is confusing, overwhelming, and unhelpful, it
will be a product that “gathers dust” like many others. Instead, this CDSS model is engaging and allows
for improvement of clinical and quality decisions. The di erent categories show the diabetes disease in
di erent lights and allows the pa ent to be up to date on all care needs. It also highlights di erent areas
so the en re diabetes care spectrum is covered. Having a simple, comprehensive, color coded dashboard
allows the providers and pa ents to understand more. Lastly, the dashboard is available and geared
toward pa ent needs as well. Instead of just being provider-facing, this dashboard takes into account
another major stakeholder, the pa ent. This diabetes care dashboard focuses on the availability of use
for both providers and pa ents.
The nal key takeaway of the CDSS diabetes model is the integra on of both medicinal and
lifestyle interven ons. O en mes in modern medicine in the United States, we are focused on reac ve
medicine and using medica on to x problems. There has been a large shi to pa ents wan ng a more
holis c experience and incorpora ng alterna ve and lifestyle treatment op ons. Diabetes care can be
greatly in uenced by diet and exercise habits. This diabetes care dashboard integrates both of these care
methodologies in order to cater to the pa ent care experience. The medicinal por on is targeted at the
providers while the lifestyle interven ons are targeted at the pa ents. This gets the pa ents involved in
their care journey which has been proven to improve the pa ent’s health.
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Overall, the Diabetes Care Dashboard provides insight on diabetes care delivery to both
providers and pa ents allowing for greater diabetes care delivery. With the long term care pa ent
popula on being the largest risk and value-based care being the movement of the future in the United
States, addressing the value and not the volume of care through this CDSS model will be very bene cial.
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