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 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 impor
Order this paper