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Class, just to clarify substantive posts. The minimum word count should be no less than about 75 words so about 4 sentences at least. Substantive posts must be more than an agreement of another classmate’s or professor’s post, but you can add information that you’ve learned through your studies with scholarly sources.

Post 1:

Epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data : 1. interfaces, 2. data formatting, and 3. reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling and informatics work would be significantly streamlined, which can in turn yield public health health decision making capabilities. Epidemiological data have a variety of uses. From a public health perspective, they can be used to gain an understanding of population-level disease progression. This understanding can in turn be used to aid in decision-making and allocation of resources. Outbreaks like Ebola and Zika have demonstrated the value of accessible epidemiological data for emergency preparedness and the need for better data sharing. These data may influence vaccine distribution and hospitals can anticipate surge capacity during an outbreak, allowing them to obtain extra temporary help if necessary. From a modeler’s perspectives. high quality reference data (also commonly referred to as ground truth data) are needed to enable prediction and forecasting. These data can also be used to train and validate machine learning and statistical models.

The internet has become the predominant way to publish, share and collect epidemiological data. While data standards exist for observational studies and clinical research, for example, no such standards exist for the publication of the kind of public health-related epidemiological data described above. Despite the strong need to share and consume data, there are many legal, technical, political, and cultural challenges in implementing a standardized epidemiological data framework. As a result, the methods by which data are presented to the public often differ significantly among data-sharing institutions (for example., public health departments, ministries of health , data collection or aggregation services). Moreover, these problems are not unique to epidemiological data, the issues described in this paper are common across many different disciplines. First, epidemiological data on the internet are presented to the user through a variety of interfaces. These interfaces vary widely not only in their appearance but also in their functionality. Some data are openly available through clear modern web interfaces, complete with well-documented programmer-friendly application programming interfaces (APIs), while others are displayed as statistic web pages that require error-prone and brittle web scraping. Still others are offered as machine-readable documents (for example., comma-separate values (CSV), Microsoft, Excel, Extensive Markup Language(XML), Adobe PDF)

Reference

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Chrentien JP, Rivers CM, Johansson MA, Make data sharing routine to prepare for public health emergencies.

US Department of Health and Human Services US Department of Homeland Security. Guidance on Allocating and Targeting Pandemic Influenza vaccine (2018). 

https://asprtracie.hhs.gov/technical-resource/2846/guidance-on-allocating-and-targeting-pandemic-influenza-vaccine

Moran K R, FairchildG, Generous N, Hickman KS, Osthus, D Priedhorsky R. et al, Epidemic forecasting is Messie than weather forecasting: the role of human behavior and internet data streams in epidemic forecast.

Post 2: Epidemiology is considered as one the most essential disciplines of public health. Its goal is to contribute to the fulfilment of the definition of public health as “a science and art to promote health and prevent disease by organized effort of society”. However, to improve the health status of the population, the knowledge produced by epidemiology needs to be used and translated into interventions. Epidemiology has a long tradition and has created a wealth of accumulated experience to assess micro-environments and specific agents that may impact health. However, epidemiology has been infrequently applied to assessment of public health issues at the policy or strategic level. In addition, epidemiology itself does not equip to deal with dialogue between stakeholders within its scientific discipline. The health of populations depends on many different factors. Epidemiology is a discipline that has a crucial role in describing health status, identifying risk factors, and analyzing relationships between health and different hazardous agents (Gulis, G., & Fujino, Y. (2015).

 The classical epidemiological triangle of host-agent-environment describes how individuals become ill. Disease occurs when an outside agent (vector) capable of causing disease or injury meets a host that is vulnerable to the agent. This happens in an environment that allows the agent and host to interact. Epidemiology not only measures the relationships between hosts and agents in certain environments but also analyses the health status of the population living in that environment. Epidemiology helps determine by statistic the number of people being affected by spread of the infection, and it also provides the mechanism how the infection is spread and how to prevent it from progressing. Through epidemiology, it is easier to detect more vulnerable populations and target them for treatment as needed. Most important of all, epidemiology helps population with necessary mechanism such as keeping distance to reduce spread, necessity of hand washing, and nutritious products as defense to counteract the infections (Bhopal, R. S. (2016).

References

Bhopal, R. S. (2016). 
Concepts of epidemiology : an integrated introduction to the ideas, theories, principles, and methods of epidemiology (Third edition.). Oxford University Press.

Gulis, G., & Fujino, Y. (2015). Epidemiology, population health, and health impact assessment. 
Journal of epidemiology, 
25(3), 179–180. https://doi.org/10.2188/jea.JE20140212

Post 3:

Epidemiology plays a significant role in describing the health status of a population, identifying risk factors, and analyzing the relationship between health and different hazardous agents that may exist. These details may be used in the planning and execution of initiatives aimed at improving health. As such, epidemiological data is derived from epidemiology, and it is paramount in implementing and targeting evidence-based control measures used to protect public health and safety. Further, (Fairchild et al., 2018) highlight that epidemiological data greatly benefits emergency preparedness and responses. It also offers better insights into understanding disease progression through populations and building statistical and mechanist disease models that enable forecasting. Within the clinical environment, epidemiological data goes a long way in influencing changes in health care practices, especially during pandemic times. This is especially considering that it provides a better understanding and insights to healthcare researchers on what needs to be investigated, addressed, and prevented. An example of how epidemiological data can influence healthcare includes.

During pandemics and especially during the recent coronavirus pandemic, epidemiological data were used to monitor and anticipate infections (Xu et al., 2020). Xu et al. further highlight that epidemiological data provide openly available, accurate, and robust data during the recent covid 19 pandemics. The data was essential in tackling unexpected health-related events, and it went a long way in providing meaningful inferences on how to prevent and stop them from spreading. On the other hand, epidemiological data can influence health by providing a better understanding of population-level disease progression. (Fairchild et al., 2018) highlights that this understanding goes a long way in aiding decision-making and resource allocation. For instance, epidemiological data can be used to influence vaccine distribution, prepare healthcare systems to anticipate a surge in capacity during an outbreak, and at the same time allow healthcare organizations to get temporary help when required.

References:

Fairchild, G., Tasseff, B., Khalsa, H., Generous, N., Daughton, A. R., Velappan, N., Priedhorsky, R., & Deshpande, A. (2018). Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards. Frontiers in Public Health, 6. 

https://doi.org/10.3389/fpubh.2018.00336

Gulis, G., & Fujino, Y. (2015). Epidemiology, Population Health, and Health Impact Assessment. Journal of Epidemiology, 25(3), 179-180. https://doi.org/10.2188/jea.je20140212

Xu, B., Gutierrez, B., Mekaru, S., Sewalk, K., Goodwin, L., Loskill, A., Cohn, E. L., Hswen, Y., Hill, S. C., Cobo, M. M., Zarebski, A. E., Li, S., Wu, C.-H., Hulland, E., Morgan, J. D., Wang, L., O’Brien, K., Scarpino, Samuel V., Brownstein, J. S., & Pybus, O. G. (2020). Epidemiological data from the COVID-19 outbreak, real-time case information. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0448-0

Post 4:

My organization uses technology with the addition of the “Care Everywhere” function in the EPIC electronic medical record system to direct patient care. The “Care Everywhere” feature in EPIC allows access to patient data from other healthcare organizations where the patient has received care. This function enables providers to obtain radiology media records and data from other visits, facilitating continuity in care. For instance, a patient arrives at our ER with a Migraine. If that same patient visited another ER in a different health care system the day before and had a CT scan, the provider would have access to that information. Suppose the provider tries to enter orders for another CT scan. In this case, EPIC will display a message that a CT scan was obtained one day ago at a different facility and will prompt the provider to access those results. “The EMR has many opportunities to prevent errors by prompting clinicians to look at symptoms and results while grouping them in meaningful ways. Currently, Epic uses this ability to identify the symptoms of sepsis and prompts clinicians to implement the sepsis protocol” (Davis & Khansa, 2016). My organization also uses an electronic “Sticky note” in the patient’s medical record to flag particular patient-specific information. It includes details such as arm restrictions for blood pressure or mobility information such as requiring a room with a lift. These details promote patient safety. Using the electronic medical record correctly and to its fullest potential facilitates better patient care and safety.

Reference

Davis, Z., & Khansa, L. (2016). Evaluating the epic electronic medical record system: A dichotomy in perspectives and solution recommendations. 
HEALTH POLICY AND TECHNOLOGY, 
5(1), 65–73. https://doi-org.lopes.idm.oclc.org/10.1016/j.hlpt.2015.10.004

Class, just to clarify substantive posts. The minimum word count should be no less than about 75 words so about 4 sentences at least. Substantive posts must be more than an agreement of another classmate’s or professor’s post, but you can add information that you’ve learned through your studies with scholarly sources.

Post 5:

Health information is defined as an electronic system used in healthcare setting to assist in collecting, storing, analyzing and sharing clinical data. Nair and amp; Dreyfus (2018), have argued that use of technology in healthcare information has improved the effectiveness and quality of patient care delivery system. In addition, apart from improving diagnosis accuracy it has also reduced medical faults. The above have led to several organization develop their own healthcare software tool that eases communication between workers as well as making patient care more effective and productive. Depending on the needs of clients and treatment offered different have develop different software to suit their needs. Moreover, through the software organizations have increased information sharing, better follow up activities, and reduced paperwork.

All patients’ information are done electronically within my organization. The information include; TARS, MARS, care plans, assessments, lab reports, demographic, orders and advance directives. Nurse complete direct patient care on the floor of medication administration, daily documentation assessment, placing orders, consulting physicians as well as reviewing and updating care plans (Sittig, & Singh, 2015). Patient care is completed by reviewing IDT. Increased falls, catheter wounds, weight changes, uncontrolled blood sugars and opioid’s are the main causes in healthcare. Therefore, immediate interventions are placed and updated when necessary.

Reference

Nair, A., & Dreyfus, D. (2018). Technology alignment in the presence of regulatory changes:

The case of meaningful use of information technology in healthcare. International

journal of medical informatics, 110, 42-51.

Sittig, D. F., & Singh, H. (2015). A new socio-technical model for studying health information technology in complex adaptive healthcare systems. In Cognitive informatics for biomedicine (pp. 59-80). Austria, Springer Cham.

Post 6:

SCATTERPLOT

Class, if you were examining a scatterplot where one variable increases as another increases, this is an example of:

a.     A cause and effect relationship.

b.    A positive correlation.

c.     No correlation.

d.     A predictive relationship.

e.     A negative correlation. 

Please respond for a substantive credit.