125 words each with two references each

One: It is difficult to forecast health care delivery in the United States due to many sectors involved which influence and shape the system. They are referred to as stakeholders which consists of those such as providers, patients, and insurance companies. Every stakeholder has a consistent influence on the structure of healthcare. According to Knickman & Elbel, “One constant in social interactions is that when a stakeholder affected by a changing environment, the stakeholder reacts and tries to improve its position (2019).”Therefore, it is difficult to predict in which direction that it can possibly go. There are also other factors to consider such as natural disasters. According to Reidpath & Soyiri, “Health forecasting is a valuable resource for enhancing and promoting health services provision; but it also has a number of drawbacks, which are related either to the data source, methodology or technology (2012).”

References

Knickman, J. R., & Kovner, A. R. (2019). Jonas and Kovner’s health care delivery in the

United States (12th ed.). New York, NY: Springer Publishing.

Soyiri, I., & Reidpath, D. (2012). An overview of health forecasting. Environmental Health Prev Med, 18(1), 1-9. doi:10.1007/s12199-012-0294-6

Two: Why is it difficult to forecast health care delivery in the United States?

Health forecasting is an original part of predicting, and a valued implement for forecasting upcoming health proceedings or circumstances such as stresses for health facilities and healthcare requirements (Wharam & Weiner 2012). Furthermore, forecasting generally implies predicting an individual’s costs or healthcare utilization for interventional purposes such as proactive disease management, patient education, or surveillance to promote population health.

It is not easy to forecast health care delivery in the U.S since there are some major catastrophes, e.g., epidemics, terrorism, currently COVID 19 pandemic, etc. that can impact health care delivery, at least in a local area, cannot be predicted with any expectation of accuracy. Other events, such as increases in health care costs, are easier to predict by extrapolating current trends and adjusting estimates based on the continued strength of forces driving the trend ( Module 8). For example, health care costs will increase as more Americans are insured under health reform; however, such a complex arena as health care costs is subject to many drivers, and the accuracy of the overall cost prediction will depend on how well those various drivers are understood (Knickman & Kovner, 2015). Even at the individual patient level, there is more and more attention to the issue of predicting the future. The concept of predictive analytics is the attempt to look at a patient’s past utilization of services, past behavioral choices, and existing health needs to forecast the patient’s use of health care in the future (Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014). This field is getting increasingly sophisticated in using big data to make forecasts about a broad range of health issues such as whether a specific patient is at high risk of falling while in a hospital or whether a specific person is at high risk of becoming addicted to opioids if they are prescribed (Knickman & Elbel, 2019). This new type of forecast information shapes the delivery of both medical care and prevention-oriented interventions. It is said that this type of prediction will advance, though likely not as fast as many currently assume (pg. 11149).

Additionally, some predictive model vendors willingly acknowledge that their forecasting tools can be used to avoid high-risk patients or to identify those that will remain healthy. Moreover, because forecasts are used to include patients in interventions, they can also be viewed as excluding the persons not identified (Wharam & Weiner 2012). For example, using forecasting or other structured case-finding methods, some health plans explicitly exclude patients with mental health diagnoses, addictions, and language barriers from disease management, because these factors might predict a minimal impact of interventions. More so, no forecasts are a hundred percent accurate; its accuracy depends on the method of forecasting and the availability of data (Technecon healthcare, 2019). While applying these forecasts to healthcare organizations, close attention has to be paid to the sample size, the method adopted, challenges faced, and the limitations of the study and its conclusions. There are indicators used to monitor health like mortality and morbidity rates, disease prevalence or incidence rates, etc. (Marco, Kamal & Cox, 2019). Basing on the health system tracker report, the U.S health care is positively progressing over time.

References

Knickman, R. J & Elbel. B. ( 2019). Jonas and Kovner’s Health Care Delivery in the United States (12th ed.). New York, NY: Springer Publishing Company, Kindle Edition. ISBN: 9780826172723

Knickman, J. R & Kovner, A. R. (April 8, 2015). Jonas and Kovner’s Health Care Delivery in the United States, 11th Edition Publisher: Springer Publishing Company; ISBN 978-0-8261-2628-3. Test Bank: ISBN 978-0-8261-7159-7

Marco, R. Kamal, R & Cox, C. (April 17, 2019). How has the quality of the U.S. healthcare system changed over time? Retrieved from https://www.healthsystemtracker.org/chart-collection/how-has-the-quality-of-the-u-s-healthcare-system-changed-over-time/#item-start

Technecon healthcare. (2019). Importance of Forecasting in the Healthcare Industry. Retrieved from https://www.techneconhealthcare.com/blog/importance-of-forecasts.html

Wharam, J. F & Weiner, P. J, 2012). The Promise and Peril of Healthcare Forecasting. The American Journal of Managed Care, 2012;18(3):e82-e85). Retrieved from https://www.ajmc.com/journals/issue/2012/2012-3-vol18-n3/the-promise-and-peril-of-healthcare-forecasting