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Healthcare Improvements through Technology

The healthcare industry over time has created large data, determined by actual record keeping, directing requirements, and patient health care. Even though most information is kept in hard copy, the present development is concerning prompt digitization of the large amounts of   data. Big data are large and complex sets of electronic healthcare information that cannot be managed manually or with common information management tools and techniques. This paper explains big data analysis in health care, discourses the benefits, and the possible challenges.

Possible Benefit of Utilizing Big Data

Potential benefits of using big data include disease detection at earlier stage when they are curable more efficiently, establishing a viable healthcare structure by handling specific patient health and encouraging effective use of resources. In clinical operations, comparative study can be applied to determine clinically applicable and cost-effective techniques to analyze and manage patients. The public health can analyze disease patterns and follow-up disease outbreaks and spread to improve public health surveillance. The large amounts of information can be turned into actionable data that can be utilized to recognize requirements, deliver services, forecast and avoid crises.

In several nations, big data has turn out to be an essential record where data produced could be utilized for treating and managing diseases. The field of nursing keeps improving with proper data application and research, which has resulted to the effective assessment of data that allows rapid improvements in the patient health care and outcomes. Big data contains information that needs in-depth examination. Rallapalli, Gondkar, and Kumar (2016) demonstrated that a Chief Nursing Executive examines data manually. It is a labor-intensive procedure, comparatively, since the technology structures have traditionally been established in silos. The synthesis of big data allows medical practitioners to evaluate significant quantities of information in a structured manner. Through research and development, the nurses can use predictive modeling to reduce attrition and cultivate a more directed research and development line in medicines and policies. Medical trials and patient records can be examined to ascertain consequent suggestions and determine adverse results before the products are distributed in the market.

Examining big data permits medical professionals to evaluate the influence of their patient involvement and establish appropriate adjustments for improved patient outcomes. Pastorino et al. (2019) showed that analytic skills in health care can be utilized to ascertain forms of health care and determine links from substantial healthcare registers, consequently allowing a wider understanding for evidence-based medical preparation. Integrating big data into nursing exercise gives medical practitioners the opportunity to improve patient health care participations and outcomes.

The psychiatric facility in the medical institution I work for currently use big data analysis in supervising and evaluating patient threats and control patterns. At the medical institution, they used the data collected from the electronic health record, and thereafter populate program databases on either monthly or yearly basis that could be used for more examination of health care. We could do a comparison of the data from our medical facility to that of the local affiliates on every level. Access to this data gave us the opportunity for patient care adjustment and future involvement forecasting concerning the effective use of big data.

Integrated documentation consist of every patient risk valuations in the database, and this entails circumstances of injury and suicidal thinking. For example, we can supervise specific patient health throughout their stay in the hospital, and we can entirely examine safety steps in the database. We then link these stages amid every residential performances and the inpatient divisions at the associate psychiatric clinic to assess strategies and apply any essential changes.

Challenges of Utilizing Big Data

The main challenge of utilizing big data in a medical organization would be the likelihood of human mistakes (Pastorino et al., 2019). The skills that we use for medical information certification and examination is formulated to operate well with the data we install. It needs guarantee that proper steps are applied to install the data correctly to obviate future mistakes in analyzing data. The existing process for recording any risk in the formerly stated information assessment rests exclusively on the nursing department. We are answerable for recording patient risk valuations and adding this information in the examination of the data. In case a nurse records a risk assessment in the shift note record, this will not automatically be filled into the monthly reviews. We are liable for assessing every graph and then transfer the data to the next examination chain. In doing so, it is usually easy for a nurse to miss the documentation process when shifting that information to a database. Therefore, all the documentations must be clear and accurate (Thew, 2016). Even though using big data can yield useful outcomes for nursing exercise, we should be sure that we are accurately utilizing the existing technology.

Strategy of Mitigating this Problem

Numerous measures can be introduced to lessen the extent of the risk of human mistake with the use of big data. We have structured many features into the data research approach in our medical facility to guarantee the information we are examining is correct. The source of human error starts from shift note record, and it goes on to the whole review procedure. To solve this problem, we have to install an auto-populated catalogue that automatically transfers the data directly from every authenticated shift note. Even though the solution is viable in the future, it needs confirmation to avoid potential errors.

When using big data, one needs to first confirm that the information is being recorded correctly and consistently from the start since the nurses record the risk data manually in the designated places. The assigned nurses must consistently be trained on proper documentation of risk data, and the method of transferring data into the patient database, on either monthly, quarterly or yearly basis. Evidence-based risk assessments can be integrated into the preferred records to validate that the risk conditions are conforming to the hospital regular measures. Zhu, Han, Su, Zhang, and Duan (2019) demonstrated that when single comprehensive data is developed, medical structures are likely to facilitate proficient information; nevertheless, upholding compliance with privacy guidelines. Throughout the improvement plans, the medical institution must continue to emphasize the importance of appropriate nursing recording and the importance of the information. Nurses should be provided with a conducive environment to record the shift notes and examine the data. This will allow effective analysis and usage of these data examination approaches in our preparation.

Big Data has a significant prospective of improving health care position, for example, in discovering new drugs, patients healthcare, effective treatment, development in health outcomes, and patients welfare management. Big data analysis can transform the method healthcare practitioners utilize complex technologies to gain understanding from their medical and other data sources and make well-versed results.