Competency

Develop a population health plan to address a health concern in the current healthcare industry.

Scenario

The key to an effective and sustainable population health management program is to know your chronic disease patients and coach them. The success of population health and chronic disease management efforts hinges on a few key elements: identifying those at risk, having access to the right data about them, creating actionable insights about patients, and coaching them daily toward healthier choices.

Instructions

As your health system is drafting a strategic framework for the PHM program, you are tasked with creating a PowerPoint presentation with detailed speaker notes in each content discussion slide. Explain the relationship between disease management and population health needed in the following areas:

• Describe the prevalent chronic diseases for the population your health system is serving.
• Describe the risks associated with the proliferation of these chronic diseases.
• Assess how the population will access information and resources to prevent and manage chronic diseases.
• Construct a chronic disease communication plan that helps patients with chronic diseases to pursue healthier choices and to use population health resources.

Use your findings from prior summative assessments in modules 01, 02, 03, 04, and 05 to create the PowerPoint. Use five quality references to support your assessment and findings.

Resources

This

link

has information for creating a PowerPoint presentation. 

Community Needs Assessment

Marion County

Marion County Florida
Located in Central Florida with a population of 343, 778.

Marion county is in central Florida.
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Social Determinants
Factors included in this category, generational poverty, widespread homelessness, persistent issue of overweight and obesity, lack of affordable housing, shortage of healthcare and dental care providers, water fluoridation is lacking in most communities, struggling and failing schools, and built environment impedes access to recreation areas and safe places for physical activity.

Addressing social determinants of health is important for improving health and reducing health disparities.
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Marion County Most Utilized Hospitals

Hospital Name Number of Discharges
Florida Hospital Ocala 15,739
Ocala Regional Medical Center 8,940
West Marion Community 6,532

Medical Resources Available
Clinical and nutrition services
Wellness programs
Environmental health
Infectious Disease services

Clinical and nutrition services include – Supplements for women and children, immunizations throughout various locations within the county, dental services, family planning, and centers which treat sexually transmitted diseases.
Wellness programs which include – disease prevention and management such as diabetes. Weight programs, children healthy promotional programs, and health education.
Environmental health which includes – Environmental Health programs are essential to public health. They work to achieve a safe and healthy environment for the community. Environmental Health staff monitor conditions that could present a threat to health and safety of the public.
Infectious Disease services which involves, The Florida Department of Health in Marion County is responsible for the surveillance of reportable communicable diseases, including enteric diseases, vaccine-preventable diseases, invasive bacterial diseases, arthropod-borne diseases, and others. Infectious disease control programs are designed to protect the residents and visitors of Marion County
5

Community Needs Assessment
Marion County community needs include, access to primary prevention and healthcare, oral health, mental and behavioral health, education and training.

Primary prevention efforts are focused on preventing illness and injury before it happens. Prevention includes environmental and policy change as well as education, behavior revision and lasting investments in systems that encourage healthy living.
Oral health influences physical, emotional, and social well-being. Poor oral health causes pain and disability. With pain and disability hinders work and school which causes issues with attendance and performance. Oral issues will in turn costs residents, taxpayers and healthcare systems millions of dollars to treat.
Mental and physical health are equally important factors for overall health and quality of life. Mental and behavior health includes emotional, psychological and social well-being and impacts how stress is handled, interpersonal relationships, and healthy decision-making.
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Priority Concerns
Access to Primary Prevention
Oral Health
Mental and Behavioral Health

Access to Primary Prevention and Health Care Services with focus on, Community wellness and Access to primary care.
Oral Health including, Access to oral health services and Expansion of prevention initiatives
Mental and Behavioral Health including, Access to care for mental health conditions and substance abuse treatment services
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Steps to improvement
Promote Community Wellness
Improve Access to Primary Care
Improve Access to Oral Health Services
Improve Oral Health Through Expansion of Prevention Initiatives

Community Wellness
Decrease tobacco access, increase educational opportunities in middle schools, and increase treatment in high schools. Reduce the number of middle school and high school students who used cigarettes, cigars, smokeless tobacco, or electronic vapor products.

Increase access to healthy affordable foods within food deserts, increase education on healthy eating options and increase participation in physical activity, reduce the Marion County middle and high school obesity rate.
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Access to Primary Care
Increase the number of individuals using free clinics, reduce the uninsured rates, and increase the number of services provided. Decrease the number of adults who could not see a doctor in the past year due to cost.

Increase Medicaid providers who will see high risk pregnancies and provide resource guide to pregnant women, and decrease the number of women receiving late or no prenatal care.
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Access to Oral Health Care
Increase mobile and portable clinic dental availability in high risk neighborhoods, increase evening and weekend dental appointment availability, and strengthen referral system. Reduce the oral health emergency department visit rate. Increase the percentage of adults who have visited a dentist or dental clinic.

Prevention Initiatives
Education to water system operators on funding available to add fluoridation to water systems and increase the percentage of Marion County population receiving optimally fluoridated water.

Performance Indicators
Performance indicators used include:
Number of individuals that have established a primary care physician.
Amount of those individuals who have decreased risk factors associated with chronic illnesses from the result of seeking primary prevention.  
Increased percentage of adults who have visited or received services for oral care.
Decreased percentage of oral health emergency department visit rate.
Percentage of individuals within the county who have fluoridated water

Performance indicators are used as a form of measurement to demonstrate if goals are being achieved.
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References
FloridaHealth.gov (2021). Programs and Services. Https://floridahealth.gov
Institute of Medicine (U.S.). (2012). Primary Care and Public Health : Exploring Integration to Improve Population Health. National Academies Press.
Marioncountyhealth.gov (2020). COMMUNITY HEALTH IMPROVEMENT PLAN. 2020_2024_community_health_improvement_plan_chip (floridahealth.gov)
Rollins, L., Gordon, T. K., Proeller, A., Ross, T., Phillips, A., Ward, C., Hopkins, M., Burney, R., Bojonowski, W., Hoffman, L., Daniels, Y., Mobley, M., Mubasher, M., & Akintobi, T. H. (2020). Community-Based Strategies for Health Priority Setting and Action Planning. American Journal of Health Studies, 35(2), 102–114.
Wellglorida.ord (2020). County Health Profile: Marion County Data. Marion County Data |
WellFlorida.

Running head: Assessing Data Sets for Population Health Management 1

Assessing Data Sets for Population Health Management

8

Assessing Data Sets for Population Health Management

Kimberly Huff

Rasmussen College

Author Note

Deliverable 2 Submitted January 17, 2021

Data Sets

Data sets are an important component when conducting many different forms of research. Collecting data allows one to examine the information from different elements. After data is collected it could be manipulated when needed using a computer. In previous times patient information was recorded using the pen and paper method. Using computer methods today allow for ease of access.

Increasing the quality of care is at the forefront of many healthcare organizations. With that said, medical data serves as an essential component where the quality of care is concerned. The majority of data comes from the patient. Patients can supply data that lead healthcare workers to develop patient-specific ways to prevent diseases, personalize treatments, and enhance overall health outcomes. Healthcare data is also essential in the management and administration of the hospital or facility and makes it easier to track a patient’s medical trek. Data is also crucial in reducing or eliminating medical errors.

Population health management focuses on the improvement of the healthcare delivery system within a specified area. To determine appropriate measures to take the data must be current and accurate. Different information to include in the data sets that can be utilized are socioeconomic determinants and electronic health records.

Socioeconomic determinants will provide valuable information such as the level of education, occupation, and income of the people residing in a selected area. Once evaluated this type of information can aid in tracking prominent illness within the area. Using electronic medical records will aid in logging different illnesses/diseases and their outcomes. These data sets will be critical in allowing the health system PHM program in the selected area. The data sets will provide relevant information which will in turn be beneficial when organizing the PHM program.

Gathering personal information such as age, sex, and ethnicity, occupation, and income will be important as once analyzed it will allow one to determine if those factors influence disease and illness. Using the HER system will allow all participants to view the information as needed and offer the chance to gather additional information when needed. The data gathered should be checked for accuracy. Having accurate data will ensure the success of the program.

Gathering data from different sources will provide a broad range overview. Many areas are large such as Marion County so accurate data would be essential when determining ways to decrease illness and disease. Data within a PHM program is typically informed data so all data must be true and accurate in its entirety to be analyzed correctly.

References

Dahl, S., & Brennhofer, S. (2018). PHM-Focused Healthcare Delivery. In S. Dahl, & S. Brennhofer, Training to Deliver Integrated Care (pp. 65-78). Cham: Springer.

Institute of Medicine (U.S.). (2012). Primary Care and Public Health : Exploring Integration to Improve Population Health. National Academies Press.

Marioncountyhealth.gov (2020). COMMUNITY HEALTH IMPROVEMENT PLAN.

2020_2024_community_health_improvement_plan_chip (floridahealth.gov)

\

Mennis, J., & Stahler, G. (2016). Racial and ethnic disparities in outpatient substance use disorder treatment episode completion for different substances. Journal of substance abuse treatment, 63, 25-33.

Rollins, L., Gordon, T. K., Proeller, A., Ross, T., Phillips, A., Ward, C., Hopkins, M., Burney, R., Bojonowski, W., Hoffman, L., Daniels, Y., Mobley, M., Mubasher, M., & Akintobi, T. H. (2020). Community-Based Strategies for Health Priority Setting and Action Planning. American Journal of Health Studies, 35(2), 102–114.

Running head: BIG DATA IN DIABETES MANAGEMNT 1

BIG DATA IN DIABETES MANAGEMNT 8

Big Data in Diabetes Management

Kimberly Huff

Rasmussen College

Author Note

Deliverable 3 Submitted January 21, 2021

Big Data

Many organizations use big data for the sole purpose of data analytics. However, before firms can get valuable information about big data, they may need knowledge of various significant data sources. As a PHM program leader, I would focus on diabetes management and find out the significant data sources used to analyze patients who have diabetes. Examples of substantial data sources include the media, cloud, the web, IoT, and databases (Russom, 2011). The different sources of big data are aimed at providing data for purposes of customer analytics, industrial analytics, business process analytics, and analytics for fraud detection. In our case, healthcare information can be found in many sources throughout the web.

The best big data that may use in diabetes management would be artificial intelligence and IoT. Digital health takes into account advanced medical technologies and digital communication (Al-Turjman, 2019). Machine learning enables us to take into account the identification, prediction of patterns, and inductive reasoning. Today, diabetes management is facing a whole lot of challenges, including the decreased number of diabetologists and an increase in the number of patients (Eswari, Sampath, & Lavanya, 2015). With the use of artificial intelligence, diabetologists can take full responsibility for their patients (Ross, Anderson, Kodate, Thompson, Cox, & Malik, 2014). Robust data analysis will ensure that gaps in care are identified, and the necessary measures are taken to mitigate risks.

Less digital ways of acquiring patient information in the past included ADT alerts, demographics, and ICD-10 codes (Nyenwe, Ashby, Tidwell, Nouer, & Kitabchi, 2011). Although there are effective ways, they may not provide varied data on patients with diabetes. They also do not help in proper analysis, and therefore integrating artificial intelligence and IoT may help in obtaining and analyzing big data for diabetes patients.

When building the PHM it is beneficial to use data from various sources to improve positive patient outcomes. In order to develop a comprehensive portrait of a patient’s clinical, financial, and social risks, healthcare providers must collect key data from across the care field before they are able to leverage risk scoring frameworks and target interventions to individuals. By combining diagnosis and procedure codes, organizations can better understand which treatment pathways are most effective for certain conditions. This process offers a more patient centered approach to care.

Utilization of ADT alerts such as patient demographics, vitals, laboratory results, progress notes, and allergy alerts offers patient safety promotion and ensures that individuals are being given treatments that will improve their conditions. Once the information is in a format that can be easily accessible it is easier to analyze when needed. Using these resources for the PHM offers improved quality of care while promoting best practices and patient safety.

Patient demographics can be used to group patients into categories such as age groups which can then be used to target specific interventions that are appropriate for the largest number of people in these specific groups. A patient’s vital signs can be used in a variety of ways to predict risks, understand the development of chronic diseases, and prevent acute events. Lab results offer similar opportunities to flag risks and chart the effectiveness of ongoing interventions, such as monitoring a diabetic patient’s blood glucose levels over time and offers a way to represent the data. Progress notes are an important source of patient data but can be very time consuming when in a narrative form. This process can be very time consuming. Allergies can have major implications for a patient’s quality of life and interaction with the health care system. Ensuring allergies are up to date can improve patient safety and decrease incident of unwanted interactions.

Using these resources help improve the quality and outcomes of the PHM. The goal is to improve patient outcomes. Using ICD-10 codes and ADT processes within the PHM helps to Identify patients at high risk of developing chronic diseases such as diabetes. They also help to maintain protocols in place for management of many other chronic illnesses. Utilizing these programs within the PHM is an essential part of succeeding in the value-based care environment.

References

Al-Turjman, F. (Ed.). (2019). Artificial Intelligence in IoT. Springer.

Eswari, T., Sampath, P., & Lavanya, S. (2015). Predictive methodology for diabetic data analysis in big data. Procedia Computer Science, 50, 203-208.

Nyenwe, E. A., Ashby, S., Tidwell, J., Nouer, S. S., & Kitabchi, A. E. (2011). Improving diabetes care via telemedicine: Lessons from the Addressing Diabetes in Tennessee (ADT) project. Diabetes Care, 34(3), e34-e34.

Ross, A. J., Anderson, J. E., Kodate, N., Thompson, K., Cox, A., & Malik, R. (2014). Inpatient diabetes care: complexity, resilience and quality of care. Cognition, technology & work, 16(1), 91-102.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

Running head: POPULATION HEALTH MANAGEMENT DASHBOARD

POPULATION HEALTH MANAGEMENT DASHBOARD 2

Population Health Management Dashboard

Kimberly Huff

Rasmussen College

Deliverable 4 Submitted January 24th, 2021

Population Health Management Dashboard

Health Needs of the Population

Diabetes patients should receive an accurate diagnosis to determine the type of disease that they could be ailing from. It follows that the diagnosis will be made based on the fasting plasma glucose, random plasma glucose as well as the oral glucose tolerance tests. These tests should be repeated to ensure that the patient gets an accurate diagnosis. Following an accurate diagnosis, a management plan is established for patients to address various areas including physical activity, meal planning and nutrition, SMBG (self-monitoring blood glucose), the treatment of hyperglycemia and hypoglycemia and revaluation of the treatment goals after which medications are adjusted accordingly.

Data sources

Data Sets

It has been found that administrative data that is used for health insurance claims, survey data, laboratory reporting systems, clinical data from EHR (electronic health records). The various data types have their pros and cons. These data sets are important in the evaluation of the condition, its impacts on the population and the role of the health care system in managing the condition.

Pros

Cons

Clinical and administrative data

Large sample size
Clinical data collects comprehensive information in laboratory tests, vital signs, medications, procedures and diagnoses.
Administrative data entails information on procedure codes, diagnosis, prescriptions and insurance enrollment.

Individuals that have not been served by the health system are left out
Geographical clustering
Does not have critical information on income and race
Gaps exist when the test have not been ordered
Non-random samples exist

Survey data

Entails important information such as income and race
Represent specific populations.
Some of the data that is used can represent the state level
Some of the tests that are conducted can cover undiagnosed diabetes

Possibility of bias
It does not entail the tests for undiagnosed cases of diabetes
The data is not adequate to be applied at the national and state levels

1

Running Head:

Population Health Management Patterns

5

Population Health Management Patterns

Population Health Management Patterns

Kimberly Huff

Rasmussen College

Deliverable 5 Submitted February 22nd, 2021.

Population Health Management Patterns

Population health management is an essential part of the health care sector, and every health system needs to effectively create it in different forms due to the presence of diverse populations. It is the sum of patient data in several health information technology resources, and it involves the analysis of information into one, actionable record for patients, and the regular actions that are taken by health care providers to improve the clinical results of patients and the financial outcomes of health care professionals (Devereaux, & Zilz, 2018). Our health system is creating several patterns of population health management that will serve the diverse population effectively. These patterns include expanding chronic disease management, investing in in-home intervention, managing care transitions, and optimizing network management. These patterns will heavily rely on data that is comprehensive, timely, and relevant in order to enable our organization to make better decisions.

In optimizing the management of the network, the first thing we will do is to determine the network we will use to refer patients to specialists because a higher percentage of our health care spending is used to pay the cost of services that are offered by physicians. The rate of physician referrals is increasing each day, and the management of the network has the likelihood of yielding greater savings into our health care organization. In contracts that will be based on value, the physicians will refer patients to specialists who provide cost-effective and high-quality care in their best interests. They will be able to analyze the claims and clinical data of their patients, which will help them to determine specialists who provide the best care at the best value (May, Byonanebye, & Meurer, 2017). Sharing of data on the performance of physicians will be made part of the organization’s culture by showing them what they do instead of telling them what to do. We will set up a physician culture that will allow us to share analytics transparently. Optimization of network management through data transparency will have a positive effect on the individual patients and the entire population.

Another strategy will be paying much attention to the health providers’ efforts in managing care transitions. Health professionals will be incentivized to make sure that those patients who go home or shift to another skilled nursing amenity acquire the support that they require both outside and inside the facility. Proactive and post discharge outreach will assist to make the transition of patients from one health care to another more easy which will prevent readmissions in our organization (Quinn, et al., 2018). We will introduce programs with complex care management consisting of various workers such as behavioral health specialists, pharmacists, social workers and care managers. The addition of a community based model to supplement our workers’ telephonic care management programs will help to utilize the costs that are driven by some subsets of our population (Puro, & Falca-Dodson, 2016). Launching an integrated transitions care management program will increase our health system’s telephonic engagement with patients discharged from our health facility. Analytics will provide more strength in care management programs by aiding to forecast which patients need additional support.

Investing in the in-home intervention will consist of various models and targets of different populations, which is very critical from a population, cost, and quality perspective. In order to find the right patients to target with our in-home interventions, we will start by applying analytics to data. Other methods of intervention that we will use apart from an acute care environment comprise in-home monitoring, wellness education, and telephonic case management (Moraros, Lemstra, & Nwankwo, 2016). These interventions will assist the health care providers to improve post-acute care by allowing them to recommend and ensure proper follow up appointments, identifying critical topics for patients to discuss with primary care physicians, triggering alerts for possible urgent issues, sharing outcomes with treating physicians, and carrying out thorough in-home assessments.

References

Devereaux, D. S., & Zilz, D. A. (2018). Population health management: A community imperative. American Journal Of Health-System Pharmacy, 75(2), 46-48.

May, T., Byonanebye, J., & Meurer, J. (2017). The Ethics of Population Health Management: Collapsing the Traditional Boundary Between Patient Care and Public Health. Population Health Management, 20(3), 167-169.

Moraros, J., Lemstra, M., & Nwankwo, C. (2016). Lean interventions in healthcare: do they actually work? A systematic literature review. International Journal for Quality in Health Care, 28(2), 150-165.

Puro, J., & Falca-Dodson, M. (2016). Population Health: How Two Community-Based Collaborations Are Changing the Face of Healthcare in New Jersey and Beyond. MD Advisor: A Journal For New Jersey Medical Community, 9(1), 4-7.

Quinn Ahonen, E., Kaori, F., Cunningham, T., & Flynn, M. (2018). Work as an Inclusive Part of Population Health Inequities Research and Prevention. American Journal Of Public Health, 108(3), 306.