Running head: DATA SOURCES 1
Deliverable Three: Locating Data Sources
Rasmussen University Online
LaToya T. Benson
August 17, 2022
Managing homelessness is a crucial undertaking in society due to its significant role in addressing its various health concerns. In this case, data provides the information required to recognize individuals subject to homelessness and affiliated health concerns while ensuring those leaving homelessness do not return. Locating ideal data sources and sets regarding homelessness is crucial to facilitating effective public health management. The paper discusses the significance of finding data sources to prevent and respond to infectious illnesses within the homeless population in Illinois.
Identifying Data Sources and Sets
Several data sources and types exist for the homeless population in Illinois. For instance, the Committee on Healthcare for Homeless People (CHHP) provides information regarding the degree to which homeless individuals receive health care services from hospitals, clinics, and emergency rooms, among other facilities. It offers information regarding the various obstacles homeless persons face in receiving care (IDHS, 2022). CHHP also identifies the unique needs of homeless individuals, creating outstanding services for them.
The U.S. Department of Housing and Urban Development (HUD) is a crucial data source. It offers information concerning the diverse issues homeless people face, especially when it comes to homeownership, devising approaches that would create homeownership opportunities to end the matter of homelessness in society. The HUD also offers information regarding affordable and safe housing options, minimizing severe homelessness, and battling housing discrimination (Fusaro et al, 2018). These efforts aid in fostering adequate housing for the homeless and addressing the health issues attributed to homelessness.
The Illinois Homeless Prevention Program provides information concerning utility assistance, mortgage/rental assistance services, and acknowledged services aimed at eradicating homeless to people and families in danger of homelessness, foreclosure, and eviction. The program targets stabilizing persons and families, aiding in eliminating the health concerns attributed to homelessness (IDHS, 2022). In this vein, the different data sources provide information that would be crucial to devising approaches to prevent and respond to infectious illnesses among homeless populations in Illinois.
Patient-Wellbeing and Best Practice Data Elements
Focusing on specific data elements among homeless individuals is crucial to enabling providers to realize gains in patient wellbeing and establish future best practices. Since enumerating homeless individuals is a challenging process that consumes immense resources, it becomes essential to rely on specific data elements that would offer flexibility in accommodating distinct homeless people’s situations. In this sense, the primary data elements to consider would comprise Point-in-Time (PIT), Housing Inventory Count (HIC), and Homeless Management Information Systems (HMIS). PIT features information concerning the homeless population in a given area in line with data concerning particular subpopulations, comprising veterans, homeless individuals, and unaccompanied youth (Morton et al., 2018). The information gathered enables providers to understand people’s housing conditions and how these impact their health.
HIC is also a data element that is helpful to providers, enabling them to boost patient wellbeing and exercise best practices. HIC data comprises the yearly catalog of units, beds, and programs to serve a homeless population in a given area. The data availed contains homes with kids, those without kids, and those with an adult and a kid. It also details these individuals’ health concerns due to their housing status. HMIS provides information regarding individuals who can easily access homeless services. HMIS data aids in tracking homeless people while seeking community services, making it possible to create detailed data sets concerning homeless individuals. In this sense, the distinct data elements, comprising PIT, HIC, and HMIS, are essential in offering relevant information to providers regarding people’s homeless conditions together with how these make them susceptible to various health conditions, such as infectious illnesses (HUD User, 202). The data plays a critical role in fostering the development of strategies that aid in coping with such situations effectively, ensuring overall improved population health.
Using Readily Available Data
Learning to utilize more readily available data, such as demographics, ICD-10 codes, and ADT alerts would play a critical role in integrating increasingly sophisticated and diverse data into managing population health. Demographic information is crucial, mainly due to the numerous concerns regarding homelessness in society, notably in Illinois. Demographic information is essential to addressing the homelessness issue; particularly, it documents various traits comprising the needs of homeless individuals and the number of persons receiving the services, aiding in proposing ideal strategies to address the matter. It is not possible to manage what is not possible to measure. Since gaining demographic information about homeless individuals poses significant challenges and consumes immense resources, relying on demographic information would aid in establishing complex and flexible systems that accommodate distinct local situations (Atlanta Mission, 2022). These should match with the aggregate local while availing a holistic picture of the entire homeless population.
Shelters do not serve as the issue of concern. Social demographic forces, including family tragedy, addiction, domestic violence, job loss, and mental health, play a critical role in understanding homelessness. Often, persons experiencing homelessness start seeking housing to enable them to secure stable lives, although support systems lack to assist them. In this vein, other forces, comprising relapse, loneliness, and lack of support, can lead persons to end up back homeless (AmeriHealth, 2022). In this sense, understanding population demographics regarding housing would aid in incorporating increasingly sophisticated and diverse data in managing population health.
Moreover, ICD-10 codes can contribute to integrating more complex and diverse data to manage population health. ICD-10 codes emphasize challenges related to housing and economic standing. These address the complex necessities of patients encountering homelessness. Understanding ICD-10 codes in a complex ecosystem of managing population health is essential in aiding in serving the homeless individuals better while using the healthcare system’s components. In this sense, it becomes vital to emphasize ideal coding to enhance data gathering processes to inform responsive health and clinical initiatives for the population (AmeriHealth, 2022). ICD-10 emphasizes forces that affect health while they are possible to use in distinct healthcare environments to address matters related to homelessness and affiliated healthcare issues.
Furthermore, learning about Admission, discharge, and transfer (ADT) alerts is essential to incorporating increasingly sophisticated and varied data into facilitating the management of population health. ADT notifications broadly serve as the foundation for enhancing care coordination among patients via health information exchange. ADT alerts are typically sent to patients in healthcare institutions. The notifications sent normally target updating physical and other teams responsible for managing patient care regarding patient status. These play an essential role in facilitating the post-transition shifts, requiring follow-up and enhancing communication among providers (IDHS, 2022). These typically support patients having suffered from multiple or chronic illnesses while exposed to homelessness in society.
Additionally, ADT alerts are crucial to recognizing patients who serve as high or frequent healthcare service users. These create room for directing patients to non-clinical or clinical interventions, minimizing overutilization by hindering unnecessary emergency department visits and hospital readmission. In this sense, the data obtained from IDT alerts is crucial to integrating the increasingly sophisticated and diverse data on managing population health, especially the homeless individuals in society (HUD User, 202). The data would be crucial to fostering prevention and response to infections and illnesses among homeless individuals.
AmeriHealth. (2022, August 15). ICD-10-CM Code Z59.0. Retrieved from Ameri Health Caritas: https://www.amerihealthcaritasdc.com/pdf/provider/comm/2017/icm-10-cm-code-z59.pdf
Atlanta Mission. (2022, August 15). Social factors and ending homelessness around the world. Retrieved from Atlanta Mission: https://atlantamission.org/social-factors-ending-homelessness-around-world/
Fusaro, V. A., Levy, H. G., & Shaefer, H. L. (2018). Racial and ethnic disparities in the lifetime prevalence of homelessness in the United States. Demography, 55(6), 2119-2128. https://doi.org/10.1007/s13524-018-0717-0
HUD User. (202, August 15). Using data to understand and end homelessness. Retrieved from Office of Policy Development and Research: https://www.huduser.gov/portal/periodicals/em/summer12/highlight2.html
IDHS. (2022, August 15). Homeless prevention. Retrieved from Illinois Department of Human Services: https://www.dhs.state.il.us/page.aspx?item=30360
Morton, M. H., Dworsky, A., Matjasko, J. L., Curry, S. R., Schlueter, D., Chávez, R., & Farrell, A. (2018). Prevalence and correlates of youth homelessness in the United States. Journal of Adolescent Health, 62(1), 14-21. https://doi.org/10.1016/j.jadohealth.2017.10.006