Major findings from the Community Health Needs AssessmentCurr

Major findings from the Community Health Needs Assessment
Current population - 473,408
Median household Income - $66,494
8.7% of the population live below the federal poverty level
29.4% of the households have incomes under $50,000
74.9% of the population is Non-Hispanic and 21.7% is Hispanic
(U.S. Census Bureau, 2019)
The population of Seminole County, Florida is 473,408 (U.S.
Census Bureau, 2019). The average household income is
$66,000 (U.S. Census Bureau, 2019). Over 8% of the population
live below poverty level (U.S. Census Bureau, 2019).
1
Unemployment 5%
Poverty Rate 11%

People without health insurance 10%
109 Overdose death in 2020 (31% Increase)
17,571 Students In poverty
538 Violent crime offenses
(Seminole County, 2021)
.
Social determinants of health affecting Seminole County
residents include
5% of the population in Seminole County, Florida are
unemployed (Seminole County, 2021). Poverty rate is 11%
(Seminole County, 2021). 10% have no health insurance
(Seminole County, 2021).
2
Lack of affordable and adequate housing and homelessness
Lack of access to affordable food
Lack of good paying jobs, jobs with advancement options, job

training
Lack of transportation
Adverse childhood experiences (ACEs)
Increased need of behavioral and mental health services.
.
Social determinants of health affecting Seminole County
residents include
The social determinants of health affecting Seminole Count y
residents are lack of affordable and adequate housing, lack of
access to affordable food, lack of good paying jobs or job
advancement, lack of transportation, and increased need of
behavioral and mental health services (Seminole County, 2021).
3
IDENTIFIED PRIORITIES:
Health Equity

Behavioral Health (Including Mental Illness & Substance
Abuse)
Healthy Weight, Nutrition & Physical Activity
(FloridaHealth, 2021)
.
Community Health Improvement
Plan (CHIP)
The community health improvement plan know as the CHIP has
identified priorities. These priorities include health equity,
behavioral health and healthy weight, nutrition and physical
activity (FloridaHealth, 2021).
4
Health Equity:
Access to Health Care (Mental Health & Dental Care)
Affordable Care
Poverty/Low Wages

Lack of Insurance and Transportation B
.
Health Equity
Health equity has integrated focus on key elements which
include health literacy, access to healthcare, affordable care,
poverty and lack of insurance. Focus on these elements present
the need to maintain a strong emphasis on care as well as
commitment to improved health within the population
(FloridaHealth, 2021).
5
Behavioral Health
(Including Mental Illness & Substance Abuse)
Access to Healthcare (Mental Health)
Mental Health/Behavioral Health {Suicide, AGEs)

.
IDENTIFIED PRIORITIES
The priorities within the social context involve care and
commitment to improved level of development. The major
issues that are addressed in this case involve behavioural health,
access to healthcare. These elements present the basis of a
strong emphasis on care which address change development
strategy in a given healthcare setting (FloridayHealth, 2021).
6
Health Literacy
Affordable Care
Access to Health (Mental & Dental Care)
Lack of Insurance {Underinsure & Uninsured)
Mental Health/Behavioral Health (Suicide, ACEs)

.
Community Health needs
Assessment
Community health needs involve diverse processes which must
be fully integrated within care environment to improve the level
and quality of care. The needs within a given setting present a
broad context where it is possible to build change and improve
the quality of care. Health literacy, affordable care, mental
health and access to health present a stronger context for
improved care management context (Seminole County, 2021).
7
Hypertension/High Blood Pressure
High Cholesterol
Obesity and overweight

Access to Healthy Affordable Food
Dental hygiene/dental care
Diabetes
.
Community Health needs
Assessment
Health challenges within the community are diverse and thus it
is imperative to address the common health needs which include
high blood pressure, high cholesterol level, obesity, dental
hygiene as well as diabetes (Seminole County, 2021).
8
13.5 % of adults in Seminole county have diabetes
337 deaths per 100,000 in 2018

One of the leading causes of premature deaths in Seminole
county.
In 2017, roughly 87 men per 100,000 died from diabetes.
For women, the rate was 55 deaths per 100,000
.
Diabetes
African Americans and Native Americans have the highest rate
of diabetes related mortality (U.S. Census Bureau, 2019).
Asians/Pacific Islanders have the lowest (U.S. Census Bureau,
2019). Death related to diabetes is higher in the elderly (U.S.
Census Bureau, 2019).
9
Type 1 diabetes is an autoimmune disease

5-10 percent of cases
Must take insulin
Type 2 diabetes is adult onset
90-95% of cases
Can be prevented
Healthy lifestyle changes
(Seminole County Diabetes, 2021)
Diabetes
Diabetes occurs in two ways which include Type 1 and Type 2.
Type 1 diabetes occurs in 5-10 % of the cases. These patients
must duly take insulin. Type 2 diabetes is the most common and
occurs in around 90 to 95% of the cases. This can be prevented
through a healthy lifestyle (Seminole County Diabetes, 2021).
10
Weight

Inactivity
Family history
Race or ethnicity
Age
High blood pressure
Risk Factors
There are different elements that are associated with
development of diabetes. Older age is associated with increased
risk of diabetes with type 2 more common in adults above 50
years. The fattier tissue you have, the more resistant your cells
become to insulin. The less active you are, the greater your risk.
High blood pressure, family history have also been found to
significantly influence the development of diabetes (Seminole
County, 2021).

11
Diabetes
This data is based on CDCs multiple cause of death data (U.S.
Census Bureau, 2019). Diabetes in noted in the death record but
may not be the underlying cause of death (U.S. Census Bureau,
2019).
12
Diabetes

Nationally men are more likely to have diabetes as a cause of
death than women (U.S. Census Bureau, 2019). 87 men per
100,000 died from diabetes and 55 women per 100,000 (U.S.
Census Bureau, 2019). Women death rate with diabetes continue
to decrease (U.S. Census Bureau, 2019). The decrease could be
related to differences in behavioral risk factors, access to
medical care and biological differences (U.S. Census Bureau,
2019).
13
Teens – Lack of housing and affordable nutritional food
Children – Adverse Childhood Experiences (ACEs) and parental
stress on a child
Intravenous drug users – Endocarditis (infection inside the heart
as a result of IV drug use), hepatitis C (due to needle sharing)
and sexually transmitted diseases
African-Americans have the highest rates of infant mortality per
1,000 births, colorectal cancer and asthma incidences, compared
to Whites and Hispanics
Whites have the highest rates of breast and lung cancer

compared to Blacks and Hispanics.
.
Health inequities identified in Seminole County:
Health inequities are inevitable in any given setting since they
present a broader basis within which it is possible to improve
the quality of care. Lack of housing and affordable nutritional
food present a major challenge in delivery of quality care
(Seminole County, 2021).
14
Intravenous drug users – Endocarditis (infection inside the heart
as a result of IV drug use), hepatitis C (due to needle sharing)
and sexually transmitted diseases
African-Americans have the highest rates of infant mortality per

1,000 births, colorectal cancer and asthma incidences, compared
to Whites and Hispanics
Whites have the highest rates of breast and lung cancer
compared to Blacks and Hispanics.
.
Key Performance indicators
Commitment to improved quality of care present a strong basis
within which it is possible to maintain a higher focus on quality
healthcare. It is imperative to help create a strong platform to
improve the quality of care. Building change involves
integration of different approaches which influence the quality
of healthcare services (Seminole County, 2021).
15

Increase patient satisfaction by 15% in a year
Decrease obesity rate in the community by 5% in a year
Increase annual primary care visits by 10% within a year
Increase free exercise classes in the community
Decrease emergency diabetes cases by 5%
(Gumber & Gumber, 2017).
Key performance measures
Performance assessment is essential factor that help understand
the specific elements that need to be integrated within
healthcare quality to improve efficiency and commitment to the
need of individuals within the community. Patient satisfaction,
internal process quality and financial performance index are
crucial elements that present a strong basis to improve the
quality of care. Diabetic patients require enhanced care which is
crucial in attaining improved level of outcome (Gumber &

Gumber, 2017).
16
Personnel
Technology systems
Expertise and knowledge
Financial resources
Teamwork
Current resources available
Achieving the set goals requires higher commitment to existing
strategies that can help aid improve service delivery. Utilizing
available resources forms the basis of change and attaining
higher level of engagement. The healthcare setting must
effectively focus on using the available resources to attain
needed resources. Personnel, technology, expertise and
knowledge and financial resources are available resources that

can be utilized to advance the set goals (Seminole County,
2021).
17
Multi-centered approach – Ensure cross sectional engagement
within healthcare setting with priority to the target group
Integrated community care approach – utilize the needs of the
community in defining change strategy.
Introduce mobile clinics in the community.
(Abdoli et al., 2019)
Strategies to improve diabetes in the community
Improving the level of care engagement within the community
require a stronger understanding on change processes which
build significant changes that promote change and create a more
enhanced basis for improved change and level of engagement.

Therefore it would be imperative to ensure the approaches
embraced are collaborative and specific in nature based on the
underlying problem (Abdoli et al., 2019).
18
Abdoli, S., Jones, D. H., Vora, A., & Stuckey, H. (2019).
Improving diabetes care: should we reconceptualize diabetes
burnout? The Diabetes Educator, 45(2). 214-224
FloridaHealth.gov (2021). Programs and Services.
https://floridahealth.gov
Gumber, A., & Gumber, L., (2017). Improving prevention,
monitoring and management of diabetes among ethnic
minorities: contextualizing the six G’s approach. BMC
research notes. 10(1), 1-5
Popeck, L. (2017). Diabetes Rate Rising in Central Florida:
How to Reduce Your Risk. Orlando Health.
https://www.orlandohealth.com/content-hub/diabetes-rate-
rising-in-central-florida-how-to-reduce- your-risk
Seminole County Diabetes Death Statistics. (2021). LiveStories.
https://www.livestories.com/statistics/florida/seminole-county-
diabetes-deaths- mortality
Seminolecountyhealth.gov (2021). Community Health
Improvement Plan. http://seminole.floridahealth.gov/programs-
and-services/community- health-planning-and-
statistics/accrediation-performance/_documents/seminole-chna-
02-24-2020.pdf
U.S. Census Bureau (2019). Quick Facts of Seminole County,
Florida.
https://www.census.gov/quickfacts/fact/table/seminolecountyflo
rida

.
References
Dashboard DIABETES INDICATOR DATANATIONAL DATA
INDICATORSCOUNTY DATA
INDICATORSRaceAgePrevalencePhysical Inactivity
RiskGenderEducationObesity Risk
Percentage Total Population with Diabetes
Total 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
2010 2011 2012 2013 2014 2015 2016 2017 2018 6 6.4
6.5 6.6 7 7.3 7.6 7.5 7.9 8.6
8.6999999999999993 8.4 8.4 8.6999999999999993
8.4 8.6999999999999993 8.5 8.5 9.1

Population with Obesity
Sum of Total_OBS 2000 2001 2002 2003 2004 2005 2006 2007
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 0
0 0 0 0 20 21.3 23.6 23.9 23.8 25 24.5
24.9 24.1 24.3 25.3 26.7 26.3 26.1 Sum of
Male_OBD_Pop. 2000 2001 2002 2003 2004 2005 2006 2007
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 0
0 0 0 0 21 22.9 25.6 26.2 25.9 26.9 26.8
27.1 26.1 25.8 26.8 28.7 27.7 26.5 Sum of
Female_OBS_Pop 2000 2001 2002 2003 2004 2005 2006 2007
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 0
0 0 0 0 19 19.7 21.8 21.8 21.8 23 22.3
22.8 22.3 23 24 24.8 25 25.7
Race-Ethnicity
Sum of Hispanic 2000 2001 2002 2003 2004 2005 2006 2007
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
8.8000000000000007 9.1999999999999993
9.1999999999999993 8.5 10.1 9.6 10.3 10.9 10.8
12.2 13 11.9 12 12.3 11.8 12 11.7 12.5 12.4 Sum of
Non-Hispanic White 2000 2001 2002 2003 2004 2005 2006
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
2018 5.2 5.6 5.8 5.9 6 6.6 6.7 6.3 7 7.6

7.5 7.2 7.2 7.6 7.1 7.4 7.4 7.3 7.8 Sum of Non-
Hispanic Black 2000 2001 2002 2003 2004 2005 2006 2007 2008
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 10.1
10.3 9.9 10 11.1 11.3 11.8 12.3 11.1 13 12.6 12.5
12.8 12.3 13 12.8 12.7 11 12.4 Sum of Non-Hispanic
Asian 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
2010 2011 2012 2013 2014 2015 2016 2017 2018 4.5 5
6.2 6.3 7.5 6.3 8.1999999999999993
8.6999999999999993 7.9 8.1 9 8.5
8.6999999999999993 8 7.5 8.5 7.4 8.6 10
Gender
Sum of Male_N 2000 2001 2002 2003 2004 2005 2006 2007
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
6.4 6.9 7.2 7.1 7.5 7.7 7.8 7.9 8.1 9.4 9.6 9
8.6999999999999993 9.1999999999999993 8.9
9.1999999999999993 9 9.5 9.8000000000000007
Sum of Female_N 2000 2001 2002 2003 2004 2005 2006
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
2018 5.6 6.1 6 6.1 6.5 7 7.4 7.3 7.8 7.9
7.9 7.9 8.1999999999999993 8.1999999999999993
7.9 8.3000000000000007 8.1999999999999993
7.6 8.6
Age

Sum of 18-44 2000 2001 2002 2003 2004 2005 2006 2007 2008
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1.9 2
1.9 1.9 2 2.4 2.7 2.2000000000000002
2.2999999999999998 2.9 2.7 2.4 2.4 2.7 2.4
2.2000000000000002 2.8 2.7 3.3 Sum of 45-64
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2011 2012 2013 2014 2015 2016 2017 2018
8.3000000000000007 9.3000000000000007
9.3000000000000007 9.1 9.9 10.5 10.5 10.6 11.9
12.5 12.1 12 12.5 12.3 12 12.8 12.1 12.7 12.4 Sum of
65-74 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
2010 2011 2012 2013 2014 2015 2016 2017 2018 15.8 16.7 17
17.600000000000001 18.5 18.600000000000001
18.2 20 19.8 19.899999999999999 21.4 22.2 20.5 21
21.5 22.1 22 19.100000000000001 21.4 Sum of 75+
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2011 2012 2013 2014 2015 2016 2017 20 18 13.2 13.6
14.8 15.5 16 15.3 17.899999999999999 17.3
16.899999999999999 18.899999999999999 21.3
18.7 19.399999999999999 20.9 19.2 21.2
18.600000000000001 19 21.8
Education
Sum of < High School 2000 9.1999999999999993

Sum of High School 2000 5.9 Sum of > High
School 2000 4.8
Prevalence (%)
Sum of Age-adjusted Rate (per 100)
Females Males Overall 13.7 10.4 12 Sum of Crude
Rate(per 100)
Females Males Overall 13.1 9.9 11.5
Prevalence By Age (%)
Sum of Estimated Cases ('000s)
18-64 65+ Total 1825.6 1043.7 2869.4 Sum of
Estimated Cases Attributable to Diabetes ('000s)
18-64 65+ Total 985.3 422.5 1444.7
Total Affected Popultion in the County
Total 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
2010 2011 2012 2013 2014 2015 2016 2017 2018 0 0 0
0 0 7.5 7.3 7.8 7.7 8.3000000000000007

10.1 10.1 10.3 9.4 9.1999999999999993 9 9.1
8.5 9.1
Population Showing Phyiscal Inactivity
Sum of Total_PI_Pop 2000 2001 2002 2003 2004 2005 2006
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
2018 0 0 0 0 0 19.2 19.600000000000001
21 20.5 20.3 20.2 20.7 20.399999999999999
20.399999999999999 19.899999999999999 19.7
19.899999999999999 19.899999999999999 21.3
Sum of Male_P.I_Pop 2000 2001 2002 2003 2004 2005
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
2017 2018 0 0 0 0 0 16.5 17.2 18.8 18.2
18.2 18.100000000000001 18.600000000000001
18.3 18.5 18.600000000000001 18.399999999999999
18.7 18.8 19.7 Sum of Female_P.I_Pop 2000 2001 2002
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
2014 2015 2016 2017 2018 0 0 0 0 0 21.7
21.9 23.1 22.6 22.4 22 22.4 22.3 22.1 21.1 21 20.9
20.8 22.8
Data BoardCountyRow LabelsSum of Total Pop.
Affected_CRow LabelsSum of Total_OBSSum of
Male_OBD_Pop.Sum of Female_OBS_PopRow LabelsSum of
Total_PI_PopSum of Male_P.I_PopSum of Female_P.I_PopRow
LabelsSum of Male_CSum of
Female_C20000200000020000002000002001020010002001000
20010020020200200020020002002002003020030002003000200
300200402004000200400020040020057.52005202119200519.2

16.521.7200587.220067.3200621.322.919.7200619.617.221.920
0686.720077.8200723.625.621.820072118.823.120078.77.12008
7.7200823.926.221.8200820.518.222.620088.76.920098.320092
3.825.921.8200920.318.222.420099.27.6201010.120102526.923
201020.218.122201011.19.3201110.1201124.526.822.3201120.7
18.622.4201111.29.3201210.3201224.927.122.8201220.418.322
.3201211.49.520139.4201324.126.122.3201320.418.522.120131
0.38.620149.2201424.325.823201419.918.621.1201410.18.4201
59201525.326.824201519.718.42120159.78.420169.1201626.72
8.724.8201619.918.720.920169.78.620178.5201726.327.725201
719.918.820.820178.88.320189.1201826.126.525.7201821.319.
722.820189.68.8Grand Total123.4Grand
Total339.8364317Grand Total283256.6307.1Grand
Total134.5114.7Row LabelsSum of Total_NRow LabelsSum of
< High SchoolSum of High School Sum of > High SchoolRow
LabelsSum of Hispanic Sum of Non-Hispanic White Sum of
Non-Hispanic BlackSum of Non-Hispanic
Asian2000620009.25.94.820008.85.210.14.520016.4Grand
Total9.25.94.820019.25.610.3520026.520029.25.89.96.220036.6
20038.55.9106.320047200410.1611.17.520057.320059.66.611.3
6.320067.6200610.36.711.88.220077.5200710.96.312.38.72008
7.9200810.8711.17.920098.6200912.27.6138.120108.72010137.
512.6920118.4201111.97.212.58.520128.42012127.212.88.7201
38.7201312.37.612.3820148.4201411.87.1137.520158.72015127
.412.88.520168.5201611.77.412.77.420178.5201712.57.3118.62
0189.1201812.47.812.410Grand Total148.8Grand
Total209.2129.2223144.9Row LabelsSum of Male_NSum of
Female_NRow LabelsSum of 18-44Sum of 45-64Sum of 65-
74Sum of 75+
20006.45.620001.98.315.813.220016.96.1200129.316.713.6200
27.2620021.99.31714.820037.16.120031.99.117.615.520047.56.
5200429.918.51620057.7720052.410.518.615.320067.87.420062
.710.518.217.920077.97.320072.210.62017.320088.17.820082.3
11.919.816.920099.47.920092.912.519.918.920109.67.920102.7
12.121.421.3201197.920112.41222.218.720128.78.220122.412.
520.519.420139.28.220132.712.32120.920148.97.920142.41221

.519.220159.28.320152.212.822.121.2201698.220162.812.1221
8.620179.57.620172.712.719.11920189.88.620183.312.421.421.
8Grand Total158.9140.5Grand Total45.8212.8373.3339.5Row
LabelsSum of Estimated Cases ('000s)Sum of Estimated Cases
Attributable to Diabetes ('000s)18-
641825.6985.365+1043.7422.5Total2869.41444.7Grand
Total5738.72852.5Row LabelsSum of Age-adjusted Rate (per
100)Sum of Crude Rate(per
100)Females13.713.1Males10.49.9Overall1211.5Grand
Total36.134.5
DataCOUNTYNATIONAL DATATOTAL POPULATION
AFFECTEDRISK
FACTORSGENDEREDUCATIONRACE_ETHNICITYGENDER
AGEYearTotal Pop.
Affected_CTotal_OBSMale_OBD_Pop.Female_OBS_PopTotal_
PI_PopMale_P.I_PopFemale_P.I_PopMale_CFemale_CTotal_N
< High SchoolHigh School > High SchoolHispanic Non-
Hispanic White Non-Hispanic BlackNon-Hispanic
AsianMale_NFemale_N18-4445-6465-7475+
200000000000069.25.94.88.85.210.14.56.45.61.98.315.813.220
010000000006.49.874.99.25.610.356.96.129.316.713.62002000
0000006.59.86.95.39.25.89.96.27.261.99.31714.8200300000000
06.69.26.15.98.55.9106.37.16.11.99.117.615.520040000000007
9.87.15.910.1611.17.57.56.529.918.51620057.520211919.216.5
21.787.27.310.37.46.39.66.611.36.37.772.410.518.615.320067.3
21.322.919.719.617.221.986.77.69.98.26.510.36.711.88.27.87.4
2.710.518.217.920077.823.625.621.82118.823.18.77.17.511.57.
96.110.96.312.38.77.97.32.210.62017.320087.723.926.221.820.
518.222.68.76.97.911.996.210.8711.17.98.17.82.311.919.816.92
0098.323.825.921.820.318.222.49.27.68.612.99.47.112.27.6138.
19.47.92.912.519.918.9201010.12526.92320.218.12211.19.38.7
12.597.5137.512.699.67.92.712.121.421.3201110.124.526.822.3
20.718.622.411.29.38.412.98.96.911.97.212.58.597.92.41222.21
8.7201210.324.927.122.820.418.322.311.49.58.412.39.57127.21
2.88.78.78.22.412.520.519.420139.424.126.122.320.418.522.11
0.38.68.712.29.47.512.37.612.389.28.22.712.32120.920149.224.

325.82319.918.621.110.18.48.412.99.56.711.87.1137.58.97.92.4
1221.519.22015925.326.82419.718.4219.78.48.712.79.87.4127.
412.88.59.28.32.212.822.121.220169.126.728.724.819.918.720.
99.78.68.512.99.47.211.77.412.77.498.22.812.12218.620178.52
6.327.72519.918.820.88.88.38.513.29.27.212.57.3118.69.57.62.
712.719.11920189.126.126.525.721.319.722.89.68.89.113.410.2
7.812.47.812.4109.88.63.312.421.421.8SexAge-adjusted Rate
(per 100)Crude Rate(per
100)Overall1211.5Males10.49.9Females13.713.1Self-reported
Severe Vision Impairment or Blindness95% Confidence
IntervalAge GroupEstimated Cases ('000s)Crude Rate(per
100)Lower LimitUpper LimitEstimated Cases Attributable to
Diabetes ('000s)18-
641,825.6012.311.513.2985.365+1,043.7010.49.611.2422.5Tota
l2,869.4011.51112.11,444.70
4
Health information technology has become a required skill for
all kinds, dimensions, and specializations of healthcare
providers. Healthcare systems have invested heavily in the
methods and processes necessary to ensure adequate
management of human beings. As a risk-based contract, the
health system works on compensation arrangements to provide
the enhanced economic rewards for providing health plans and
the ability to track clients across the continuum of care. The
following are the numerous trends of population health that the
health care system is trying to develop based on examples and
highly formed reasoning. Appropriate population health
management (PHM) necessitates methods that access each
citizen or patient in any way phases of life in one of the most
acceptable manners for people. They include Assessment of
Treatment Processes and Access, where performance measures
are critical for determining a care management initiative
(Cramm & Nieboer, 2016). These aspects assist health
institutions that fail to grasp which measurements to use,
analyze information to satisfy people, and transform raw

performance figures into implementable enhancements.
On the other hand, health care systems are interested in finding
additional vocational and professional resources to assist them
in sorting via their quantification obligations and presenting
useful information to healthcare professionals at the delivery of
access.In addition, care coordination from across the spectrum
is another pattern to develop since convoluted patients
frequently necessitate care from multiple insurance carriers
across the medical care continuum (Daumit et al., 2019).
Experts, nurses, general practitioners, mental health workers,
and post-acute infrastructure should be able to converse to
ensure that people obtain all of the care they require.
It is noteworthy that notions addressing health and the care
system are personal issues; however, securing the public's
health entails more than the individual health statuses. It
mandates a population approach to healthcare. For exampl e, in
the US, the country’s health status does not reflect the
substantial national investment into the sector. Nevertheless,
according to Siegel et al. (2021), for countries to experience
improved health status, initiatives should be implemented to
address issues beyond the clinical intervention for high-risk
groups. However, it should be noted that though its initiatives
are noble and critical for population health, it is hindered by its
inability to prevent people from becoming ill in the first place
(Siegel et al., 2021). Moreover, it fails to address disparities
such as lack of access and poor quality in preventive and
curative measures.
Regardless, diabetes is a condition that is viewed as an
epidemic in the US; the chronic disease has two variants
attributed to different factors. Based on data, type-2 diabetes is
the most common as its causing factors are highly preventable.
For example, in Seminole County, 90-95% of type-2 diabetes
accounts for all diabetes cases (Seminole County, 2021). The
disease is attributed to lifestyle choices such as inactivity and
poor eating habits that contribute to obesity (Perlman et al.,
2017).

Though lifestyle choices play a critical role, age, race and
family history also contribute. With such information, it
augments the need to implement systems that provide data and
information for population health management. Taking
advantage of technology is the most effective approach for
collecting big data that can be used to develop complex and
effective population health management ecosystems (Perlman et
al., 2017). Among the most effective significant data sources is
the EHR (electronic health record) system employed by
healthcare facilities.
Being able to access data on patients' ethnicity, age,
socio-economic status, and geographical location can be
employed in developing initiatives that can reduce the number
of diabetes cases (Gamache et al., 2018). Approximately half of
all healthcare facilities have implemented the EHR system. The
technological changes would support the government’s annual
$240 billion contributions in improving the quality of diabetes
care through technology (Gamache et al., 2018).
Exploiting the information collected on patient successes,
failures, and unintended costs can help practitioners understand
and develop strategies and tools to leverage positive patient
outcomes. By also taking advantage of the high efficacy of the
system, the number of mistakes is expected to be limited,
thereby assuring patients of positive outcomes (Gold et al.,
2017).
With the cases of type-2 diabetes expected to rise due to
poor lifestyle habits, it is essential to rely on information
collected from the population to develop a healthy ecosystem
(Gold et al., 2017). It would entail obtaining data on risk factors
such as age, ethnicity, sex and race, among others. Additionally,
the availability and access to healthcare facilities will allow
practitioners to understand further the problem's scope (Gold et
al., 2017). The overall objective of electronic health records in
population health management is to collect data and create
preventive measures. It would be achieved by exploiting big
data; medical practitioners would be expected to learn how to

exploit the available data effectively. It is critical as their role
entails promoting efficacy and safety in population care.
Teams can improve and excel by keeping track of their progress
alongside a red time and evaluating their results to colleagues in
and around their institution's, reducing mistakes and increasing
patient safety. Through trips, health systems could perhaps take
the time to talk about the issues with the patient in the diverse
populations so that they can create individualized management
systems that best meet their needs.On the other hand, Members
of staff relocating and hiring evolving a synchronized
healthcare provider, and the tools to improve it may necessitate
recruiting new employees or the reallocation of financial
methods to management recruits for some services (Khan &
Yairi, 2018). For the diverse population employing more health
practitioners may assist in offering continuous services to
people. Finally, patients are being stratified by risk, where
managed care includes risk stratification. Once an ascribed
number of people has been identified, the health care system
must recognize their most vulnerable patients and aim for
effective responses as needed.
Detailed performance stratification necessitates knowledge with
data analytics as well as access to medical IT tools capable of
proactively identifying trends and pinpointing opportunities to
improve. Attaching key metrics to healthcare patients
determined by the number and intricacy of their chronic
illnesses, economic and social obstacles, and neurobiological
risk factors can assist the health system in preventing crises and
engaging clients in healthy activities before symptoms worsen
(Silva et al., 2018). When technology is used properly, it can
improve teamwork, build teamwork. Fast development in any
area necessitates the measurement of outcomes, which is a well-
known management principle.
References
Cramm, J. M., & Nieboer, A. P. (2016). Is “disease
management” the answer to our problems? No! Population

health management and (disease) prevention require
“management of overall well-being”. BMC health services
research, 16(1), 1-6.
Daumit, G. L., Stone, E. M., Kennedy-Hendricks, A., Choksy,
S., Marsteller, J. A., & McGinty, E. E. (2019). Care
coordination and population health management strategies and
challenges in a behavioral health home model. Medical
care, 57(1), 79.
Gamache, R., Kharrazi, H., & Weiner, J. (2018). Public and
Population Health Informatics: The Bridging of Big Data to
Benefit Communities. Yearbook Of Medical
Informatics, 27(01), 199-206. https://doi.org/10.1055/s-0038-
1667081
Gold, R., Cottrell, E., Bunce, A., Middendorf, M., Hollombe,
C., & Cowburn, S. et al. (2017). Developing Electronic Health
Record (EHR) Strategies Related to Health Center Patients'
Social Determinants of Health. The Journal Of The American
Board Of Family Medicine, 30(4), 428-447.
https://doi.org/10.3122/jabfm.2017.04.170046
Perlman, S., McVeigh, K., Thorpe, L., Jacobson, L., Greene, C.,
& Gwynn, R. (2017). Innovations in Population Health
Surveillance: Using Electronic Health Records for Chronic
Disease Surveillance. American Journal Of Public
Health, 107(6), 853-857.
https://doi.org/10.2105/ajph.2017.303813
Seminole County Diabetes Death Statistics. (2021). LiveStories.
https://www.livestories.com/statistics/florida/seminole-county-
diabetes-deaths-mortality
Siegel, S., Brooks, M., & Curriero, F. (2021). Operationalizing
the Population Health Framework: Clinical Characteristics,
Social Context, and the Built Environment. Population Health
Management, 24(4), 454-462.
https://doi.org/10.1089/pop.2020.0170
1

7
Diabetes has become a common disease among many adults,
both men and women, in Seminole County, Florida, which has
significantly led to most individuals experiencing adverse
effects on their health and well-being. Diabetes is a health
condition that occurs when the blood glucose of an individual,
commonly known as blood sugar, is too high. Blood glucose is
usually the primary source of energy in human beings, and it is
obtained from the food that we eat. A hormone that is made by
the pancreas, insulin, assists glucose acquired from food to get
into the cells of human beings in order to be used for energy.
The main types of diabetes that the population in Seminole
county is suffering from are type 1 and type 2 diabetes. Type 1
diabetes is described as an autoimmune illness and the immune
system attacks and damages cells in the pancreas in which
insulin is made. Both men and women who have type 1
diabetes, their body does not produce insulin, and in order to
stay alive, they must take insulin each day.
Type 2 diabetes normally emerges when an individual's body
becomes resistant to insulin which leads to sugar building up in
the blood. Individuals who have type 2 diabetes, their bodies do
not generate or utilize insulin properly, and this is the most
common type of diabetes among many people in Seminole
county. The common symptoms of diabetes include sores that do
not heal, extreme fatigue, blurry vision, frequent urination, loss
of weight, increased thirst and increased hunger. Besides the
overall symptoms of diabetes, men with this disease might have
a diminished sex drive, erectile dysfunction and poor muscle
strength, while ladies may also experience symptoms like dry
and itchy skin, yeast infections and urinary tract infections.
Big data is very important to every undertaking of a health care
organization, and it is defined as the abundant health
information which is obtained from several sources. Big data is
distinguished from electronic medical and human health data

used for decision making by certain features, including being
obtained from numerous sources, it is extremely variable in
structure and nature, it moves at a high velocity and spans the
enormous digital universe, and it is available in extraordinarily
high volume. Some of the major sources of big data include
medical devices, wearable devices, pharmaceutical research,
payer records, genomic sequencing, medical imaging and
electronic health records.
An electronic health record is a digital form of the paper chart
of a patient. Electronic health records are usually are centered
on the records of patients; they are real-time, can be acquired
instantly and safely by authorized users. These records are
computerized and contain certain information such as the
patient's billing information, hospital discharge instructions, lab
test results, immunization status, allergies, medicines, health
history, ethnicity, gender and age (Cowie et al., 2017). Health
care providers can share these digital health records if they are
in the same health care facility, clinic or health care system.
For instance, in case a doctor orders a laboratory test, the other
health care providers will also be able to see the results.
Whenever a clinician puts a patient on a new medicine, the
other clinicians will also be able to see the type of medicine
that was prescribed to the patient; hence there will be less
chance of a clinician prescribing medicine that could lead to
problems in case it is used with another medicine.
The use of electronic health records will significantly help in
enhancing how well health care providers talk to each other and
coordinate the treatment of patients well and improve the care
of patients. Some of the benefits of electronic health records
include security, education and fewer mistakes. There are
always chances that papers records can get lost or be misfiled;
hence electronic health plays a key role because there is less
chance of those things happening, and the majority of the
records are protected by passwords. Patients are also able to
see their medical files, which lets them participate in their own
health care. Patients are able to see their test results, keep track

of things like glucose, check errors and review medical
instructions given.
Wearable devices are also a source of big data in health care
which has been advanced by wearable technology. Wearable
technology in health care comprises various electronic devices
that customers can wear and are meant to gather the data of
users’ individual health and exercise. The improvement of
wearable technology has enabled many patients to take control
of their own health, which has greatly impacted the medical
industry, comprising technology companies, providers and
insurers, to create more wearable devices. Some of the common
wearable devices that can be used include wearable biosensors,
blood pressure monitors, ECG monitors, smart health watches
and wearable fitness trackers.
Wearable biosensors hold the possibility to revolutionize
remote healthcare ad telemedicine because these devices are
portable and can be acquired in different forms like implants,
bandages, clothing and gloves. These devices develop two-way
feedback between the user and their health care providers,
which enhances continuous and noninvasive diagnosis of
disease and monitoring of health from physical motion and
fluids of the body. Wearable blood pressure monitors usually
measure blood pressure and the daily activities of the users,
including the steps that they take to burn calories.
The various sources and types of big data have several
advantages, such as generating real-time alerting, enabling
improved health care with fitness devices, delivering greater
insights into the cohorts of patients and ensuring there is the
reduction of general health care costs. Other benefits of big data
include easing the diagnostics of patients with electronic health
records, predicting patients at higher risk quickly and
efficiently and improving the health care of patients (Dash et
al., 2019). Clinical insights are obtained from the knowledge
that is derived from big data analytics, which improves patient
care in the health care system because health care providers are
able to prescribe effective treatment and make clinical decisions

that are more accurate.
Data elements typically describe the logical unit of data, and
these elements can greatly help the health care providers to
make immediate gains in the well-being of patients as they
develop the best practices for future initiatives. Some of the
specific data elements that can be used by health care providers
include the name of the patient, their age, ethnicity, gender,
city, country and state (Bruland et al., 2016). Other data
elements that can be required include billing information,
hospital discharge instructions, laboratory test results,
immunization status, allergies, medicines and health history.
Every data element is usually defined by the type of information
it represents. For instance, when distinguishing numeric data
from text data, the data element will describe code values that
should be observed with a certain type of data, minimum and
maximum length and the data type of numeric, alphanumeric,
time or date. Some of the common features that are included in
the descriptions of data elements comprise the ID of the
element, the name, the type of data, input and output formats
and validation criteria to ensure that correct information is
captured by the system.
Learning to use more readily accessible information such as
ADT alerts, ICD- 10 codes and demographics is very important
because it will promote incorporating more complex and
different big data into the population health management
ecosystem. Demographics serve as crucial information, and in
order for health care providers to make sure that there is
equitable treatment of outcomes, they should look at a wide
range of the patients' personal information. This personal
information can include the ethnicity, state, race, gender or age
of the patient. Demographic information can significantly assist
in informing treatment plans since physicians normally create
standardized processes for collecting data and ensure that all
patients have the potential to reach optimal treatment outcomes
(Dinov, 2016). The ethnicity and race data can be used to
improve the quality of care for all patients since it helps in

developing more patient-centered devices, evaluating whether
the practice is delivering culturally competent care,
distinguishing which populations do not achieve optimum
interventions and identifying and dealing with differences in
care for certain populations.
Learning to use ICD-10 codes is also important because these
codes have various advantages in the health care system. Some
of the benefits of using ICD-10 codes consist of tracking public
concerns and evaluating risks of adverse public health events,
inhibiting and detecting health care fraud and abuse, refining
clinical, financial and administrative performance and
monitoring utilization of resources (Khokhar, et al., 2016).
Other advantages of using ICD-10 codes include setting health
policy, enhances carrying out of research, designing payment
systems, processing claims of reimbursement and measuring the
quality, safety and efficacy of care.
In conclusion, every individual should strive to ensure that they
prevent and control diabetes disease by taking appropriate
measures like avoiding foods with a lot of calories and taking
part in exercises regularly. Every health care organization
should also ensure that they implement appropriate big data
sources like electronic health records, which offer various
advantages like the security of information and eliminate errors
that might be experienced in paper records. Health care
providers should also learn to use more readily available da ta in
order to promote the safety and efficacy of care to all patients.
References
Bruland, P., McGilchrist, M., Zapletal, E., Acosta, D., Proeve,
J., Askin, S. & Dugas, M. (2016). Common data elements for
secondary use of electronic health record data for clinical trial
execution and serious adverse event reporting. BMC medical
research methodology,16(1), 1-10.
https://doi.org/10.1186/s12874-016-0259-3
Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford,

I., Fritz, F., & Zalewski, A. (2017). Electronic health records to
facilitate clinical research. Clinical Research in Cardiology,
106(1), 1-9. https://doi.org/10.1007/s00392-016-1025-6
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019).
Big data in healthcare: management, analysis and future
prospects. Journal of Big Data, 6(1), 1-25.
https://doi.org/10.1186/s40537-019-0217-0
Dinov, I. D. (2016). Volume and value of big healthcare data.
Journal of medical statistics and informatics, 4.
https://journalofbigdata.springeropen.com/articles/10.1186/s405
37-019-0217-0
Khokhar, B., Jette, N., Metcalfe, A., Cunningham, C. T., Quan,
H., Kaplan, G. G., ... & Rabi, D. (2016). A systematic review of
validated case definitions for diabetes in ICD-9-coded and ICD-
10-coded data in adult populations. BMJ Open, 6(8), e009952.
http://dx.doi.org/10.1136/bmjopen-2015-009952
2
LITERATURE REVIEW
2
Data sets are essential for accurate analysis (Dekker, 2021).
Having data to measure against other data enhances one’s
ability to understand the information. Data sets are an important
component when conducting research (Dekker, 2021). Scientific
research is based on the gathering and analysis of measurement
data. Scientific datasets are intermediate results in scientific
research projects (Dekker, 2021). Computers have brought
substantial improvements
to technology that led to the production of data (Zia & Khan,
2017). The healthcare industry contains very large and sensitive
data and can benefit society (Zia & Khan, 2017).
Improving the quality of care is a priority and main goal for
healthcare organizations. Data comes from patients as well as
patients’ health record. The information assists healthcare
workers on developing prevention measures for healthcare
concerns and enhance quality of care.

The United States has an epidemic of type 2 diabetes (Stevens,
2020). Diabetes is a chronic disease that occurs either when the
pancreas does not produce enough insulin or when the body
cannot effectively use the insulin it produces (Chan, 2016).
Diabetes is a health care concern (Chan, 2016). Diabetic
Mellitus is a set of diseases in which the body is unable to
control the quantity of sugar in the blood (Zia & Khan, 2017). It
is a group of metabolic diseases which results in high blood
sugar level, may be as the body does not produce sufficient
insulin, or may because cells do not react to the produced
insulin (Zia & Khan, 2017). The number of cases and the
prevalence of diabetes have been increasing over the past few
decades (Chan, 2016). An estimate of over 50% of the adult
population will have diabetes by 2022 (Stevens, 2020). More
than 90% of people with prediabetes are unaware and 25% of
people with diabetes are unaware (Stevens, 2020).
Over the past decade, diabetes prevalence has risen faster in
low- and middle-income countries (Chan, 2016). Diabetes
caused 1.5 million deaths in 2012 (Chan, 2016). Increases the
risks of cardiovascular disease (Chan, 2016). Many people with
diabetes are affected by type 2 diabetes and occur in children
now (Chan, 2016).
Diabetes causes a substantial economic loss to people with
diabetes and their families (Chan, 2016). It affects the health
systems and national economies through direct medical costs
and loss of work and wages (Chan, 2016). Hospitals and
outpatient care is the major expense factor, however; the rise in
cost of insulins is a concern (Chan, 2016).
Even though type 1 diabetes can’t be prevented at this time,
type 2 diabetes can with appropriate interventions. This is by
implementing and adapting a healthy lifestyle at school, home
and in the workplace (Chan, 2016). Exercising regularly, eating
healthily, avoiding smoking, and controlling blood pressure and
lipids (Chan, 2016). Education is key to success as in many
areas. A whole-of-government and whole-of society approach,
in which all sectors systematically consider the health impact of

policies in trade, agriculture, finance, transport, education and
urban planning (Chan, 2016).
The prevalence of diabetes in the United States is increasing
rapidly (Grundy, Howard, Smith, Eckel, Redberg, & Bonow,
2020). Individuals with diabetes are at high risk for
cardiovascular disorders that affect the heart, brain, and
peripheral vessels (Grundy, Howard, Smith, Eckel, Redberg, &
Bonow, 2020). The increasing of obesity and sedentary
lifestyles, major underlying risk factors for type 2 diabetes in
both developed and developing countries, predicts that diabetes
will continue to be a growing clinical and public health problem
(Grundy, Howard, Smith, Eckel, Redberg, & Bonow, 2020).
Collecting data from electronic health records, claims, and
patient-reported outcomes is essential in diabetes (Centers for
Disease Control and Prevention, 2018). The dataset contains
patient vitals, family history, and laboratory values (Centers for
Disease Control and Prevention, 2018). Obtaining information
from physician office visits is another data set utilized for
diabetes. It contains objective data on selected risk factors (e.g.,
age, sex, race, ethnicity), laboratory results, ambulatory health
care services, pharmaceuticals, diagnoses, and a list of diseases
drawn from the physician office electronic health record,
including diabetes (Centers for Disease Control and Prevention,
2018). Risk factors to diabetes include family history, obesity,
ethnicity, and abnormal lipids. Utilizing this information for
data set may mitigate diabetes or enhance prevention measure.
Data sets including socioeconomic determinants such as
education level, occupation, income, ethnicity, sex, and age
within a specific area is essential. Data reflecting the number of
individuals diagnosed with type 2 diabetes between the age of
18 and 33. Data reflecting the education level of the individuals
with diabetes between the same age group. Also, the education
level of the parents of these individuals. The education level
will help identify if diabetes may be associated with making
healthy lifestyle choices. Data sets of ethnicity and sex will
help determine if either groups are effected more than the other.

References
Centers for Disease Control and Prevention. (2018). Novel
Approaches to State-level Diabetes
and Prediabetes
Surveillance.https://www.cdc.gov/diabetes/research/modeling/in
dex.html
Chan, M. (2016). Global Reports on Diabetes. World Health
Organization.
https://apps.who.int/iris/bitstream/handle/10665/204871/978924
1565257_eng.pdf;Sequence=1
Grundy, S., Howard, B., Smith, S., Eckel, R., Redberg, R., &
Bonow, R. (2020). Diabetes and
Cardiovascular Disease Executive Summary Conference
Proceeding for Healthcare Professionals.105:8 (2231-2239).
https://doi.org/10.1161/01.CIR.0000013952.86046.DDCirculatio
n
Stevens, S. (2020). The United States of Diabetes: Challenges
and opportunities in the decade
ahead. UnitedHealth
Group.https://www.unitedhealthgroup.com/content/dam/UHG/P
DF/UNH-Working-Paper-5.pdf

Zia, U. & Khan, N. (2017). Predicting Diabetes in Medical
Datasets Using Machine Learning
Techniques. International Journal of Scientific & Engineering
Research. 8:5. https://www.ijser.org/researchpaper/Predicting-
Diabetes-in-Medical-Datasets-Using-Machine-Learning-
Techniques.pdf

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