One of the factors is reliability and the other being validity (Thiru, Hassey & Sullivan, 2003). Reliability, which is also the predecessor to validity, is defined as the measure of stability. It is also appraised through comparing and subjecting the various prevalent rates (Thiru, Hassey & Sullivan, 2003).

According to Cagle, Rokoske, Durham, Spence & Hanson (2012), one aim of conducting the study was to estimate the levels of use of the electronic data in hospitals, to determine whether the quality measurement practices
Problems brought about by Research in Nursing a. Relevance of the Research Problem According to Cagle, Rokoske, Durham, Spence & Hanson (2012), one aim of…

Problems brought about by Research in Nursing
a. Relevance of the Research Problem
According to Cagle, Rokoske, Durham, Spence & Hanson (2012), one aim of conducting the study was to estimate the levels of use of the electronic data in hospitals, to determine whether the quality measurement practices used had discrepancies especially by comparing the electronic data and manual data, and to also identify the various organizational characteristics, which are associated with the use of electronic data instead of the manual data collection. One importance of using the electronic data is the ability to monitor a wide range of quality-related data (Cagle, Rokoske, Durham, Spence & Hanson, 2012). This information provides much more useful information that has minimal discrepancies as compared to the manual data collection process.
The use of superior components such as the use of advanced planning care, better experience during the nursing care of patients especially those who are dying, the type and quality health care services are more likely to be included and documented in the electronic data by users. The use of electronic data is important in the monitoring of quality (Cohen, Elhadad & Elhadad, 2013). Most hospitals have migrated from the previous methods of using manual data entry. This method had a lot of discrepancies because during that period, the health care industry was being faced with increased and radical regulatory scrutiny. Most hospitals had to comply with the daily activities using electronic data (Cohen, Elhadad & Elhadad, 2013). Due to the improvements made in the health care services, most hospital providers become focused on documentation of clinical data in order to ensure that the data elements to be recorded are tracked down properly and that eligible and comprehensive data are recorded clearly (Cohen, Elhadad & Elhadad, 2013).
b. Levels of Evidence
Regarding the use electronic documentation, and how it is useful in improving the quality of treatment for Hemodialysis patients, there has been little evidence supporting these practices. In terms of measuring quality, two factors still remain dominant. One of the factors is reliability and the other being validity (Thiru, Hassey & Sullivan, 2003). Reliability, which is also the predecessor to validity, is defined as the measure of stability. It is also appraised through comparing and subjecting the various prevalent rates (Thiru, Hassey & Sullivan, 2003). In the past, most studies that were conducted used old statistical methods. An example of such methods includes MSGP4 (Thiru, Hassey & Sullivan, 2003).previous studies also incorporated variations, which include making better decision making based on the reliability of the live data. Such old methods of collecting statistics cannot be able to measure validity of the electronic patient record and therefore, presenting discrepancies.
The electronic patient record provides adequate and sufficient information unlike the manual patient record. The electronic patient record is sensitive to discrepancies and it provides a positive and predicative value (Thiru, Hassey & Sullivan, 2003). The manual data collection methods include questionnaires, surveys and reference standards (Cusack, Hripcsak, Bloomrosen, Rosenbloom, Weaver, Wright, Vawdrey, Walker & Mamykina 2012). Patients form part of the reference standards but the problem with such data is the perception pertaining to morbidity or even the concordance with treatment. As a result, the health care is left with the task of answering the question relating to the real health condition of a patient. In answering the question, three objectives have to be put in place. One objective is whether the answer lies within the subjective dimension. The other ways is to determine whether the answer exists in the diagnostic or objective aspects (Thiru, Hassey & Sullivan, 2003). Surveys and the use of questionnaires on the other hand, can be used in the provision of very uncertain answers.
c. Clarity of Included Studies, specifically the designs
In recent times, manual data collection of patient records have been replaced with new and better methods including the electronic health record data (Cohen, Elhadad & Elhadad, 2013). With the increased ease of use of the electronic health record information, certain opportunities present themselves as well as the free-text notes by patients especially when it comes to addressing the issue of phenotype extraction (Cohen, Elhadad & Elhadad, 2013). One such method is the text mining method. This method in particular is crucial for disease modeling by means of mapping the named entity beforehand to terminologies. After mapping the named entities, text mining method helps to cluster the related terms semantically (Cohen, Elhadad & Elhadad, 2013). The other related study is the electronic health record, EHR, corpora, which exhibits certain linguistic and statistical traits when compared to the bio-medical corpora literature domain.
A good number clinicians prefer to copy and paste the information obtained from previous notes, which had been retrieved and being used in present encounters with patients (Cohen, Elhadad & Elhadad, 2013). Major discrepancies are likely to be observed when such methods are used and are found within the patient records. After much analysis of an EHR corpus system used on a large scale, and then quantify the redundancy in terms of semantic and concept of word reputation, one observes that the levels of redundancy come to about thirty percent and the distribution of the semantics and words not being uniform. Paying a careful and well thought attention towards the structure of corpus analysis in advance helps in ensuring better text mining techniques. An example is evident when the results obtained when the EHR corpus is preprocessed with fingerprints and in the end, the results become better (Cohen, Elhadad & Elhadad, 2013).
d. Describe Overall Findings


 

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