Adverse Trend and Data Management.
Instructions: An elderly patient has been admitted to the medical/surgical unit from the local nursing care facility. The new lab results in her electronic health record (EHR) indicate that she has methicillin-resistant Staphylococcus aureus (MRSA) in her urine. She is placed on contact precautions per the hospital guidelines.
The health care provider arrives to examine her new patient and inquires about the need for contact precautions. The nurse explains the lab results reveal the patient has MRSA in her urine. The health care provider states that she reviewed the patient’s lab results in the emergency room and the urine results were normal. The unit coordinator reveals to the staff nurse and nurse manager that this is the third incident this month of lab results being uploaded to the wrong patient’s EHR when admitted from the emergency department.
Write a 1,050- to 1,400-word paper based on the case study in which you:
Analyze the adverse trend, including how it affects patient care.
Evaluate the data that needs to be collected, reviewed, and applied in the decision-making process to address the adverse trend.
Evaluate the information system methods that will be used to collect the data needed in the decision-making process, including the source(s) of the data.
Evaluate the information technology and tools needed in each step of the decision-making process.
Evaluate any regulatory, legal, ethical, political, sociocultural, and financial issues related to the data and information technologies that will be used in the decision-making process.
Analyze at least two strategies used to advocate and access social justice in health care design and delivery.
Include at least three peer-reviewed references (you may not use your course textbooks or the Electronic Reserve Reading article) to support your process improvement and change project.
Adverse Trend and Data Management
In the health care environment, the accessibility of data is a highly important component. As such, the management of data is the process through which data is controlled to safeguard its integrity, while ensuring maximum utility and accuracy. The introduction of Electronic Health Records (EHRs) transformed nursing informatics, the accessibility of patient data, and the decision making process. The EHRs systems have been effective in helping health care professionals provide patient with better care, amounting to decreased healthcare costs. Nevertheless, there has been an occurrence of severe unintended consequences as a result of adopting such systems, especially in patient data management/. As a result, the credibility of the EHRs data has been jeopardized, resulting in errors that threaten the safety of the patient and compromised health care quality. This paper reviews the impact of wrong patient data on health care and the relationship between data management and the process of decision making.
The Adverse Trend and Its Effect On Patient Care
The development of the EHR system was to facilitate proper information sharing among health care providers and hence improving both the quality of care and the safety of the patient. Nevertheless, evidence shows a link between the usage of this system and various safety issues, a problem referred to as e-iatrogenesis (Harrington, Kennedy, & Johnson, 2011). As such, the emergence of EHRs related data errors, especially data that has been wrongly entered, as seen in the presented case, has comprised the integrity of health information. Health care information systems are designed to minimize medical errors, especially those that arise from manual handling of patient data in the hospital. However, as much as the EHRs has proved to be effective in promoting safety and reducing such errors, there are cases in which these systems have led to wrong relaying of information to the health care providers and hence contributed to other consequential events such as poor prognosis, poor medication, and delayed treatment. This is a major problem as it exposes the patients to adverse health consequences, ranging from aggravated disease status, to death (Harrington, Kennedy, & Johnson, 2011). In the presented case, as a result of wrong patient data entry into the EHR system, the patient was diagnosed with MRSA, an aspect that not only led to the introduction of contact precautions, but would lead to the treatment of an absent condition.
Data to be Collected
The data that is to be collected in this case is the data that is entered into the EHR system from the laboratory, and the data provided at the emergency unit from the laboratory. Such data should include the data on the specific test carried out on the patient, the samples used, and the exact readings recorded for each test under each component of reference. By collecting the data that has been provided at the emergency department during the admission of the patient, and that which has been provided via the EHR system, one would be able to compare the two sets of data on different patients in order to determine trends in consistency of the data, and the accuracy of the information shared. To develop a better understanding of why data on some patients is wrongly reported, it is important to have a better understanding of the discrepancies between the accurate data and the reported data, to identify the cause of such discrepancies, which could either be human or computer related, and to effectively address such causes in order to improve on the quality of data and to affirm its integrity.
Data Collection Methods
Hebda and Czar (2013) suggests that reports that provide for data that has been qualitatively analyzed make it easier for such information to be easily fine-tuned and entered into different areas of the database. The computer will be used to analyze the collected data as their computational abilities readily make them responsive to qualitative data’s statistical analysis, making the provide results highly accurate as compared to any statistics that may be calculated manually. Data uploaded to the EHR system from the laboratory can be imported to the Excel spreadsheet using the Amazing Charts Importer. The data provided at the emergency department should also be imported to the excel spreadsheet for analysis. Considering the nature of the data sets, one can compare their means for bias using different t-test variants (Marusteri & Bacarea, 2010). It is important for nurse leaders to develop and understanding of the depth of the reports that are available to them, and to learn how they can develop custom reports that would be directed at the important quality areas. Qualitative reports can be generated using the EHR systems, which can then be analyzed using the excel spreadsheet.
Information Technology and Tools Used
The first step in this case involves direct importation of data from the EHR system and exportation of the same data to the Microsoft Excel spreadsheet as the latter has visual aids that would allow the data to be statistically displayed. The information referred to in this case includes information on the various laboratory results of specific patients as uploaded to the EHRs. These include the specific readings of the applied measures in each case, such as the vital signs. The next step involves exporting the data of the same patients as recorded at the emergency department on the computer, to the same Microsoft Excel spreadsheet. The third step would involve comparing the data using Microsoft Excel tools such as the t-test or graphical presentations, in order to note any discrepancies between the data set from the emergency department and those from the EHRs. Just like the data itself the display of data is highly important in the analysis process (Rees, Leahy-Gross, & Mack, 2011). The last step would involve using the information on the level of discrepancies between patient data shared from the laboratory at the emergency unit, to the data uploaded from the laboratory to the EHR system.
Regulatory, Ethical, Legal, Political, Sociocultural, And Financial Issues
Since most of the data compared in their case involves information from lab test of the patients, issues of privacy are of great importance. The Health Insurance Portability and Accountability Act (HIPAA) applies to the management of patient information using the EHR system. The Act requires healthcare organizations to safeguard the privacy of patient information at all times and to limit access of such information to authorized parties only (Schweitzer, 2012). In this case, upon the importation of the patient records to the Excel spreadsheet, it would be important to replace the actual names of the patients with codes or other abbreviations that could be used to distinguish them and to identify them with their data sets.
Strategies Used to Advocate and Access Social Justice in Health Care Design and Delivery
One of the approaches through which advocacy for social justice can be upheld is through proper communication of the issue. Effective advocacy is highly dependent on proper passing across of the message. As such, much attention should be given to the message that is to be communicated. The message ought to clearly communicate the proposal being made, the significance of such change, and how the change would impact the health care system (Braveman, et al., 2011). It is important to consider all the targeted stakeholders when establishing the message in order to ensure that it reflects their knowledge and interests. It is important that a given message is enforced through repetition and secondary audiences. Another strategy that can be used to advocate for social justice involves building partnerships among the various stakeholders within the health care environment (Braveman, et al., 2011). This involves mobilizing the support of the various health care providers and staff members in the health care environment affected by the given issue. In this case, it is important to establish measures that would facilitate collective ownership, trust, and proper collaboration among he involved stakeholders. Case in point, in the presented case, such coalitions would involve the different health care professionals within the health care environment, the IT department, and the administration.
It is clear that as much as EHR systems are effective
in promoting patient safety through allowing for proper record keeping and
timely sharing of patient data, wrong sharing of patient information may lead
to highly devastating results. Lack of consistency and accuracy in the shared
data and the actual data may lead to various problems such as poor diagnosis of
the patient and hence poor treatment, or delayed treatment of the patient,
factors that contribute to poor health care quality and reduced safety of the
patient. As such, it is important to ensure that proper measures are put in
place to effectively address both human error and technological factor that may
interfere with the accuracy of the information shared through these systems.
Braveman, P. A., Kumanyika, S., Fielding, J., LaVeist, T., Borrell, L. N., Manderscheid, R., & Troutman, A. (2011). Health disparities and health equity: the issue is justice. American Journal of Public Health, 101(S1), S149-S155.
Harrington, L., Kennedy, D., & Johnson, C. (2011). Safety issues related to the electronic medical record (EMR): synthesis of the literature from the last decade, 2000-2009. Journal of Healthcare Management, 56(1), 31.
Hebda, T., & Czar, P. (2013). Handbook of informatics for nurses & healthcare professionals (5th ed.). London: Pearson Education.
Marusteri, & Bacarea, V. (2010). Comparing groups for statistical differences: how to choose the right statistical test? Biochemia Medica, 20(1), 15-32.
Rees, S., Leahy-Gross, K., & Mack, V. (2011). Moving data to nursing quality excellence. Journal of Nursing Care Quality, 26(3), 260-264.
Schweitzer, E. J. (2012). Reconciliation of the cloud computing model with US federal electronic health record regulations. Journal of the American Medical Informatics Association, 19(2), 161-165.