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
tyle=”text-align: justify;”>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
Quite a number of hospital agencies are faced with the challenge of shifting their focus on the constantly changing regulatory regulations and at the same time, maintaining the high standards of quality health care. One way of increasing efficiency is through the use of electronic medical records, which provide a platform for recording data that is consistent (Melnyk, & Fineout-Overholt, 2011). The electronic data is important than the manual data collection method because it helps the medical practitioner to use eligible and evidence based protocols within the work place therefore, delivering adequate and better patient services. According to Walrod (2012), the compliance with the regulatory regulations has made the electronic health documentation become the tool of preference by most hospitals nationwide.
Thiru, Hassey & Sullivan (2003), proved that in the past, research was conducted to investigate the importance of electronic health data and their usefulness. In order to obtain quality data, focusing the research attention towards patient identification and the diagnostic data became crucial. Surveys served the purpose in quite a number of the research carried out. The use of survey became ideal in situations that incorporated external forces in setting the direction of changes that would take effect (Thiru, Hassey & Sullivan, 2003). The other factor, which has been at the forefront in favoring the variety of practices that embrace setting the speed for scientific advancements include the appraisal of the quality of data obtained (Thiru, Hassey & Sullivan, 2003). In so doing, the future of research related to nursing would become fruitful. Even though the information in the electronic health record may be positive, at times such information tends to be an over estimate of the intended data (Thiru, Hassey & Sullivan, 2003).
e. Conclusions with implications for your current practice and future research.
The future of research in nursing is dependent on current practices within the health care department. Most of the current electronic health data responds to an increasing complex and authoritative medico-legal system, which incorporates stringent regulatory measures as well (Cusack, Hripcsak, Bloomrosen et al, 2012). Such processes, more often, result to unnecessary data capture and in the end; the process of recording such data becomes bulky. As per the health policy meeting held by the American Medical Informatics Association in 2011, certain key issues were addressed in order to ensure the future of EHR is secure. One way was through supporting the care of patients by improving the outcomes of individuals and the larger population as well (Cusack, Hripcsak, Bloomrosen et al, 2012). Advancements in technology would continue to grow over time especially with relation to EHR. More sophisticated data would be incorporated within the health care in terms of recording and analysis of patient records. Even with technological advancements, the core issuers addressed in the paper ought to be considered and implemented sufficiently so as to have a more holistic electronic health data record that promoters the well being of an individual as well as proper treatment of diseases.
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