DNP 805 Topic 4 DQ 2 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
DNP 805 Topic 4 DQ 2 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
DNP 805 Topic 4 DQ 2 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
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DNP 805 Topic 4 DQ 2 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
Topic 4 DQ 2
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Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require. How could the EHR database facilitate this type of integration between clinical and administration systems
REPLY TO DISCUSSION
Electronic Health Record (EHR) are used in our healthcare organization and widely used to research as well. The validity of the results is dependent upon the assumptions of the healthcare system. EHR based data have challenges and some threats to validity and includes target population, availability and interpretability of clinical and non-clinical data. EHR includes socioeconomic status, race, and ethnicity that can be compared. Availability of data for fundamental markers of health are important for identifying inequities. The data has the ability to capture individuals clinical trials , data sets and measures the outcome that has potential risk factors. The EHR can be robust, informative and important to the understanding of health and disease in the population.
The Veterans Health Administration is a one-of-a-kind healthcare organization that can illuminate how to implement a community health strategy to increase vaccine acceptance. I work at the VA, and I can tell you that we serve a distinct community population. The COVID-19 pandemic, combined with vaccine reluctance, has posed a public health risk. The use of EHR-based tools in a population health approach to vaccine uptake can have a significant impact on healthcare system immunization rates. In the years preceding the COVID-19 pandemic, vaccine hesitancy, defined as “the unwillingness or refusal to vaccinate despite the availability of vaccines,” was listed as a “top 10” global health issue. Large-scale vaccine skepticism focuses on authoritative voices, involving health care workers, scientists, and techniques. The scale and scope of the Veterans Health Administration, the characteristics of EHR primary focuses in health population, a track record of high quality preventive care, and the development of an evidence-based vaccine hesitancy strategy The ultimate goal is to boost vaccine uptake in clinical and operational settings. Identifying educational opportunities for clinicians and veterans is one step toward increasing vaccine acceptability. The development of vaccination acceptance tools, as well as the implementation of a population health approach, will be easily accessible.
References:
Centers for Disease Control and Prevention. COVID Data tracker. Available at: https://covid.cdc.gov/covid-datatracker/#vaccinations_vacc-total-admin-rate-total. Accessed September 1, 2021.
Ni K, Chu H, Zeng L, et al. Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study. BMJ Open. 2019;9(7):e029314. doi: 10.1136/bmjopen-2019-029314. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Verheij RA, Curcin V, Delaney BC, et al. Possible sources of bias in primary care electronic health record data use and reuse. J Med Internet Res. 2018;20(5):e185. doi: 10.2196/jmir.9134. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
World Health Organization. Ten threats to global health in 2019. Available at: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019. Accessed September 1, 2021.
REPLY
Great topic Beverly—The Covid pandemic came on quickly like a rush of a whirlwind or tornado and caused so much havoc and left in its wake a lot of devastation and then dissipated or slowly dissipating. In the wake of the Pandemic, the administration and the clinical
DNP 805 Topic 4 DQ 2 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
systems had to work together to follow the center for disease rules and regulations concerning this pandemic by creating policies for their decisions and the informaticists created the electronic health records (EHR) that would be used to obtain some form of minimal history and physical before the vaccine could be given and also used to record the type of vaccine that was given so that CDC can monitor the effectiveness of the medication to determine the need for further treatment or not. Like you pointed out Beverly, the EHR was used to keep track of those who had not taken the vaccine since there was no record of them taking it. Which led to increased process of re-education. So, with the EHR, it shows the need for educating people to the need for vaccinations. In my hospital facility, there were those who also refused to take it until it was mandated and then some took it and others presented exemptions.
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Reference:
Centers for Disease Control and Prevention (CDC). (2021, April 2). Monitoring COVID-19 Vaccine Effectiveness How and Why CDC Tracks How Well the Vaccines Are Working. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/effectiveness/how-they-work.html
REPLY
Thank you for providing an insightful post. The COVID-19 pandemic has tremendously impacted all healthcare systems. According to Balut et al. (2021), sufficient uptake of the vaccination is imperative to slow the spread of COVID-19, especially among the most vulnerable such as homelessness. Of the 83,523 Veterans who experience homelessness, about 45.8% were vaccinated based on the database collected by the U.S. Department of Veteran Affairs (VA). In addition, there is a strong correlation between COVID-19 vaccinations and Veterans who utilize VA healthcare and service (Balut et al., 2021). With this, I agree with you that a vaccine acceptance tool should be readily available, ly to those who are more vulnerable such as Veterans experiencing homelessness.
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Reference
Balut, M. D., Chu, K., Gin, J. L., Dobalian, A., & Der-Martirosian, C. (2021). Predictors of COVID-19 vaccination among veterans experiencing homelessness. VACCINES, 9(11). https://doi-org.lopes.idm.oclc.org/10.3390/vaccines9111268
REPLY
The electronic medical record One of the most important data sources for data analysis is the electronic medical record (EMR). It can now be used to drive public health decision-making, identify risk factors for infectious diseases and treat them, and provide continuity of care among various medical institutions while improving healthcare quality and pushing forward medical and scientific research (Wang, 2019). Data integration is the process of collecting a cluster of raw data from various sources and combining them into one source, which is then stored and distributed to various applications as new data from the storage location. As a result, data mining would yield a wealth of information needed to provide useful insights for research, allowing the EMR to be compatible with various hospitals. It is the process of combining two different companies’ systems into a single centralized data set. As a result, the integration and interoperability of healthcare data from various sources of information and communication technology (ICT) in a region or country is critical for hospital care and treatments (Sreemathy, Naveen Durai, Lakshmi Priya, Deebika, Suganthi, & Aisshwarya, 2021). (Wang, 2019).
Integration is frequently misunderstood as simply entering data into a system, but it goes far beyond that. Because the two systems are not built the same and may have different levels and vendor policies, there is a need to include the social factors as well as the broader context in the integration process (Bjrnstad, & Ellingsen, 2019).
Chronic heart failure (CHF) patients are the patient population for whom I would like to integrate their data information. It is a chronic debilitating disease with a high mortality rate and a severe symptom burden that lasts for a long time. Shortness of breath (SOB), Dyspnea, pain, fatigue, decreased physical activity, anxiety, and depression are physical symptoms of CHF (Siouta, Heylen, Aertgeerts, Clement, Janssens, Van Cleemput, & Menten, 2021). The patient demographic, which includes age, gender, allergies, weight, admitting symptoms, prior diagnosis, history, and physical with any chronic symptoms such as dyspnea, lower extremity edema, any use of oxygen, medications, laboratories, diagnostics, procedures, treatment care plans, and any tolerable physical activity, would be the integration data from this population. For there to be integration between clinical and administrative systems, the integration process must adhere to the facilities’ and regulators’ ethical and legal standards. Integrative systems such as enterprise resource planning systems, enterprise application integration, component ware, and middleware are in place to allow all clinical and administrative systems to integrate. System standardization is also required for integration and other purposes. The most recent is the open EHR standard 17, as well as an international initiative to structure and standardize clinical knowledge through global consensus (Bjrnstad, & Ellingsen, 2019).
IT systems in hospitals support cooperative work. Schmidt and Simone28 argue that cooperative work interleaves distributed tasks; articulation work manages the consequences of the distributed nature of the work. Hence, information technology (IT) systems in hospitals need coordination and articulation work to function (Bjørnstad, & Ellingsen, 2019).
Improving the processes for patients and providers with the policy approaches must be evaluated to make sure that they remove unnecessary steps and complications for patients, while decreasing administrative burdens for providers. Standards and approaches must reflect how information flows through the health care system, the technical systems that are needed, and the crucial role of health information professionals play in translating across clinical and administrative domains. Also, the sharing of health information across payers and providers requires consideration of privacy policies, to ensure that only the minimum necessary information is shared, and they are not used beyond the specific transaction limited (American Health Information Management Association (AHIMA), 2020)
The electronic health record EHR or the electronic medical record (EMR) is one of the most important sources for data analysis. It can be used today to drive decision-making in public health, identify risk factors for infectious diseases and treat them, and provide the continuity of care among various medical institutions while improving the quality of healthcare and continue to push forward medical and scientific research (Wang, 2019). Data Integration is the process of collecting a cluster of raw data from different sources and combining them into one source and it is stored and distributed to various applications from the storage place as new data. So, data mining would yield great knowledge of information needed to provide useful insights for research that would enable the compatibility of the EMR with different hospitals. It is the process of merging the systems from two different companies into one centralized data set. So, the integration and interoperability of healthcare data from different sources of information and communication technology (ICT) in a region or a country is of the utmost necessity for care and treatments in hospitals (Sreemathy, Naveen Durai, Lakshmi Priya, Deebika, Suganthi, & Aisshwarya, 2021), (Wang, 2019).
Integration is often times looked upon as easy and just inputting data into a system but it is beyond that. The systems that have targeted only the technical aspects has led to many failures because the two systems are not built the same and may have different levels, and vendor policies, so there is a need to include the social factors as well and the broader context in the integration process (Bjørnstad, & Ellingsen, 2019).
The patient population that I would like to integrate their data information would be the chronic heart failure (CHF) patients. It is a chronic debilitating disease with a very high mortality rate and severe symptom burden for a long duration. The physical symptoms of CHF are shortness of breath (SOB), Dyspnea, pain, fatigue, decreased physical activity, anxiety and depression because of the declining quality of life (QoL), (Siouta, Heylen, Aertgeerts, Clement, Janssens, Van Cleemput, & Menten, 2021). The integration data from this population would be the patient demographic which includes the age, gender, allergies, weight, admitting symptoms, prior diagnosis, history and physical with any chronic symptoms such as dyspnea, lower extremity edema, any use of oxygen, medications, laboratories, diagnostics, procedures, treatment care plans, and any tolerable physical activity. For there to be an integration between the clinical and the administrative systems, the integration process has to comply with the ethical and legal standards of the facilities and the regulators. For all the clinical and administrative systems to integrate, there are integrative systems in place like the enterprise resource planning systems, enterprise application integration, component ware, and middleware. Also, the standardization of systems is also necessary with integration and many more. The most recent being the open EHR standard 17 and international initiative to structure and standardize clinical knowledge by global consensus (Bjørnstad, & Ellingsen, 2019).
IT systems in hospitals support cooperative work. Schmidt and Simone28 argue that cooperative work interleaves distributed tasks; articulation work manages the consequences of the distributed nature of the work. Hence, information technology (IT) systems in hospitals need coordination and articulation work to function (Bjørnstad, & Ellingsen, 2019).
Improving the processes for patients and providers with the policy approaches must be evaluated to make sure that they remove unnecessary steps and complications for patients, while decreasing administrative burdens for providers. Standards and approaches must reflect how information flows through the health care system, the technical systems that are needed, and the crucial role of health information professionals play in translating across clinical and administrative domains. Also, the sharing of health information across payers and providers requires consideration of privacy policies, to ensure that only the minimum necessary information is shared, and they are not used beyond the specific transaction limited (American Health Information Management Association (AHIMA), 2020)
References:
American Health Information Management Association (AHIMA). (2020, February). AHIMA Policy Statement on Integrating Clinical and Administrative Health Data. AHIMA Home. https://ahima.org/media/cufldn1p/icad-policy-statement-final.pdf
Bjørnstad, C., & Ellingsen, G. (2019). Data work: A condition for integrations in health care. Health Informatics Journal, 25(3), 526-535. https://doi.org/10.1177/1460458219833114
Siouta, N., Heylen, A., Aertgeerts, B., Clement, P., Janssens, W., Van Cleemput, J., & Menten, J. (2021). Quality of life and quality of care in patients with advanced chronic heart failure (CHF) and advanced chronic obstructive pulmonary disease (COPD): Implication for palliative care from a prospective observational study. Progress in Palliative Care, 29(1), 11-19.
https://doi.org/10.1080/09699260.2020.1831248
Sreemathy, J., Naveen Durai, K., Lakshmi Priya, E., Deebika, R., Suganthi, K., & Aisshwarya, P. (2021). Data integration and ETL: A theoretical perspective. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). https://doi.org/10.1109/icaccs51430.2021.9441997
Wang, Z. (2019). Data integration of electronic medical record under administrative decentralization of medical insurance and healthcare in China: A case study. Israel Journal of Health Policy Research, 8(1). https://doi.org/10.1186/s13584-019-0293-9
The data for trauma care is a requirement for the designation of a trauma center. It is actually required for a year prior to having your first visit for designation. Most of this data is raw data that should be able to be pulled directly from predefined fields within the Electronic Medical Record (EHR). This allow for not only streamline entry, but it also takes out the human factor of manual entry errors. This data can be looked at more globally for tracking and trending data. This data collection can truly help say patients lives. Thru the collection of data and comparing it to patient outcomes to determine gold standards in practice. An example of this is the discovery of the trauma triad of death and the importance of increasing trauma room temperature to prevent hypothermia that will lead to coagulopathy and metabolic acidosis. This continual collection of data allows for ongoing process improvement centered on improving patient outcomes. This is the foundation of the performance improvement plan. There are many different ways that you can take various interventions and compare them by the patient outcome to identify interventions to improve outcomes. This is not only beneficial for hospital interventions, but it is vital for prehospital (Hossenizadeh et al., 2022). This allows the hospitals to close to loop on patients with traumatic injuries to let them know if they went to the right facility for the patient, but also feedback based on the findings. For example, a stable patient with a pelvic fracture and sternal fracture was taken to a facility that was not a trauma facility. This allows for follow up for them that there was a better facility to take them too.
References
Hosseinzadeh, A., Karimpour, A., Kluger, R., & Orthober, R. (2022). Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records. Journal of Safety Research. https://doi-org.lopes.idm.oclc.org/10.1016/j.jsr.2022.01.003
According to Centers for Disease Control and Prevention (CDC), kidney diseases are the leading cause of death in the United States, with about 37milion adults in the US estimated to have CKD and most of this population are not yet diagnosed. In every 24 hours, a whopping 360 people begin dialysis treatment for kidney failure. The leading causes of kidney failure in the US are diabetes and high blood pressure accounting for 3 out of 4 new cases. The financial burden on the government is astronomical with CKD costing more than $87 billion and ESRD, and an additional 37.3 billion. (CDC, 2022).
Data integration is important in the care of patents for better outcomes. Not all patient data is collected in a single clinic, single hospital, or a single office. EHRs focus on computerizing existing workflows to ensure that patient data is collected in a way that allows for integration, sharing, research, and generation of evidence (Raden, 2017). If data integration is excluded, a collaboration of healthcare providers will be difficult and health outcomes will be compromised.
Centers for Disease Control and Prevention (CDC). (2022). Chronic kidney disease: common-serious-costly.
https://www.cdc.gov/kidneydisease/index.html
Raden, (J. 2017) A patient-first approach to EHRs and data integration for value …https://www.hcinnovationgroup.com › article › a-patien…
