Introduction
The systems of healthcare globally experience changes that are intensive as well as developments and reforms over the years. Moreover, different governmental and non-governmental institutions and states have played a huge role in the global healthcare system reformation (Siegel, 2013). Prediction of future market behaviors enables hospitals to facilitate better strategies for health care intervention and preventive medication. This is through health care providers being pre-informed on the appropriate actions needed to be taken to reduce risk as well as damage management. However, future market prediction requires the reliability of data, analytical tool, and information which need to be appropriate for the prediction of certain health conditions thus having no specific or single health forecasting techniques (Weigend, 2018). Hospitals are significant in addressing various underlying issues that affect health in a community as well as hospital care access.
Hospitals have provided more job opportunities and massively contributed to the economic stabilization of the United States of America and individual communities as well. Hospitals also provide coverage on health insurance and subsidized spending on local and community health services. Also, hospitals have aided states in their finances and created safe healthcare systems for individuals that are uninsured at reduced costs (Weigend, 2018). The development and implementation of programs on social needs enable patients in accessing relevant medical documentation letters on protection from attorneys as a result preventing shut off of utility. Partnerships between hospitals and community organizations enhance the provision of food banks that enable patients to resolve issues of food insecurity as well as accessibility to healthy meals and healthy cooking education. Integration of value-based healthcare meets social needs that are related to health.
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Methods of Predicting Future Market Behaviors
Development of environmental management that is certified in hemodialysis units in outpatient and hospitals facilitates determination of the implementation degree of environmental management systems as well as referral centers for healthcare environmental management (Siegel, 2013). Management of healthcare technology in clinical engineering facilitates the use of medical devices in most processes of healthcare delivery and reduce instances of complex issues that may arise in the delivery of medication. Disaster management imparts emergency nursing and medical personnel with attitudes, knowledge, and practice that facilitates effective hospital management and effective system affiliation.
Discriminant analysis model of forecasting on outcomes of first-trimester pregnancy facilitates the development of successful in vitro fertilization as well as generic and branded pharmaceuticals (Siegel, 2013). It forecasts on unit numbers of generic and branded forms of dispensed pharmaceuticals and their importance due to huge market value and reduced newly branded drugs which as a result reduce imposed cost on national healthcare systems. Forecasting on the daily attendance and practical classification of patients and their flow in the emergency department facilitates the better provision of quality services to patients (Weigend, 2018). This is because patients’ main point of entry in modern hospitals has become the emergency department thus increasingly concerning hospital managers.
Conclusion
Therefore forecasting on the flow of patients enhances better decision-making for optimization of humans, equipment, and allocation of resources. Nevertheless, healthcare data has been an agenda for institutions where the issuance of quality data has become increasingly apparent. Scattered data of patients in clinical and enterprise systems brings a difficult task in the monitoring of different processes which are used in data product that is timely, accurate and reliable. Moreover, personnel in health organizations that are in the top management work towards developing a framework of data governance through IT systems that are advanced that produce good results.
References
Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die (p. 148). Hoboken: Wiley.
Weigend, A. S. (2018). Time series prediction: forecasting the future and understanding the past. Routledge.
