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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 example, 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