DNP 805 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
DNP 805 Discuss the type of integration data from your defined patient population in Topic 4 DQ 1 would require
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 unique healthcare organization that provides good insight into the implementation of a population health approach to vaccine acceptance. I work at the VA and I can say that we cater to a special population in the community. The COVID-19 pandemic and vaccine hesitancy, and has been a threat in public health. Population health approach to vaccine acceptance using EHR-based tools can greatly impact vaccination rates in the healthcare system. Vaccine hesitancy—“the reluctance or refusal to vaccinate despite the availability of vaccines”—was identified as a “top 10” threat to global health in the years leading up to the COVID-19 pandemic. Vaccine hesitancy on a large scale focuses in voices of authority, engaging health care workers, scientists and strategies are addressed. The size and scope of the Veterans Health Administration, the characteristics of EHR primary focuses in health population, record of high quality preventive care and implementation of an evidenced-based framework to address vaccine hesitancy. The goal is to improve clinical and operational vaccine uptake. Steps that improve vaccine acceptance includes the identification of education for clinicians and veterans. Development of vaccine acceptance tools and application of population health approach will readily available.
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.
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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).
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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)
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.
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.
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
Healthcare data integration services entail integrating technology, concepts, and teams when creating the infrastructure capable of big housing data and using it in a meaningful way while addressing data accessibility, ownership, and privacy (Austin et al., 2020). This framework is essential because it provides a way to use existing data to create a comprehensive health record that closely examines a multitude of sourcing informational summaries, enabling proper attention to be paid to the needs of clinicians, as well as providing opportunities for patient lifetime development (Austin et al., 2020). Coinciding with healthcare data integration, clinicians can now benefit from seamlessly searching among a wide array of healthcare systems to grasp a detailed understanding of an individual patient is HER (Austin et al., 2020). Integrating different data types within both exchange of health information as well as EHR systems can assist healthcare organizations in getting more out of their EHR systems. In contrast, firm health data governance policies can improve EHR data integrity (Austin et al., 2020). The strengths of data integration in healthcare combine real-time and historical data analysis to predict trends, improve care, and drive long-term growth (Austin et al., 2020). Most systems currently grant providers demographic informational accessibility, results from lab examinations, and lists of medicinal and allergic aspects, accompanied by various other patient EHR information (Austin et al., 2020). Despite this, social determinants of health data are still largely absent from clinical data (Austin et al., 2020).
EHRs can be comprehensive systems that manage clinical and administrative data; for example, an EHR may collect medical histories, diagnostic data, laboratory data, and physician notes, consults, and assist with billing, inter-practice referrals, appointments, scheduling, and prescription refills (Colquhoun et al., 2020). Clinical data derives from reputable sources such as laboratory records, reports from radiology, and HER (Colquhoun et al., 2020). Administrative information entailing billing employed information data (e.g., hospital as well as professional billing documentation) alongside the involvement of oversight pertaining towards health systems (i.e., system documentation from either transfer, admission, or event registration, accompanied by even discharge) (Colquhoun et al., 2020). Sourcing from clinical-based information may provide additional comprehension compared to administrative information but does provide the downside of additional processing when ensuring secondary usage (Colquhoun et al., 2020). For example, a CHF diagnosis with origins deriving from clinical data possibly needs to acknowledge laboratory values from serum blood sugar, consider medication treatments, and text search unstructured clinical notes (Colquhoun et al., 2020; Park et al., 2009). Data from administration provide a powerful framework but can also provide scope restrictions. For example, a CHF diagnosis may be readily identified in administrative data as a single structured data element (e.g., International Classification of Diseases, Tenth Revision [ICD-10], code I50. 20). Registries can potentially blend these by including both clinical and administrative data to leverage the strength of each at the cost of additional data validation or adjudication (Colquhoun et al., 2020; Park et al., 2009).
Austin, R. C., Schoonhoven, L., Richardson, A., Kalra, P. R., & May, C. R. (2020). How do SYMPtoms and management tasks in chronic heart failure imPACT a person’s life (SYMPACT)? Protocol for a mixed‐methods study. ESC heart failure, 7(6), 4472-4477.
Colquhoun, D. A., Shanks, A. M., Kapeles, S. R., Shah, N., Saager, L., Vaughn, M. T., … & Mathis, M. R. (2020). Considerations for integration of perioperative electronic health records across institutions for research and quality improvement: the approach taken by the Multicenter Perioperative Outcomes Group. Anesthesia and analgesia, 130(5), 1133.
Park, H. Y., Kim, K., & Park, E. J. (2009). Study on the Feasibility of CHF Data Integration.