Application of Course Knowledge in Advanced Practice Nursing

Questions
Application?of?Course?Knowledge: Answer all questions/criteria with explanations and detail.
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a.  Describe one source of big data that you are likely to use in your future advanced practice nursing role.  
b.  Identify the types of information that can be obtained from this source.  
c.  Examine three ways data from this source can be used to impact client care. 
d.  Discuss the role of the advanced practice nurse in data stewardship. 

Answer
1. Source of Big Data in Advanced Practice Nursing
The last source is the data collected from wearable devices. Wearable devices are electronic tools that can be worn on the body. Often, these devices have sensors attached to them and can be connected to the internet to transfer data. The big data source that comes out from wearable devices is very broad and varies from device to device, but it includes all information about a person’s health, from lifestyle to vital signs and even location. The purpose of this data collection is to make the user self-aware about their own health, and the data can be shared with healthcare providers to constantly keep track of the patient’s condition. The use of these devices is increasing mainly due to the evolution of smartphones and the simplicity to make the devices compact and user-friendly. APN can use this data to constantly monitor the patient’s condition from home, and in the long term, can assess if by using the device, the patient’s health outcome increases.
Another source is Clinical Decision Support Systems (CDSS), which is a computer program designed to help clinical decision making. It accomplishes this by taking data from the patient, combining it with available knowledge, and providing possible courses of action. CDSS is designed to help clinical decisions in which arriving at a single well-accepted answer is difficult. It usually aids in patient assessment, forming a diagnosis, and selecting therapy. These systems are usually based on a knowledge base that can be created from various sources, including medical journals, expert opinions, etc., and it also uses an inference engine method to provide the user with a solution. CDSS has shown high potential in improving healthcare quality and reducing costs. It can also be used in managing chronic diseases and reducing adverse events that usually occur in the medication process. APN can use the big data from CDSS to correlate the clinical decisions made and the patient’s outcome to show if CDSS really improves patient care and to improve the CDSS itself.
There are three sources of data which are the EHR, CDSS, and wearable devices that serve as a new method of APN to collect various data in formulating a clinical decision. Big data in Electronic Health Records (EHR) refers to the vast data on patients that includes demographic information, medical history, medication, etc. that can be managed and reviewed systematically. It also provides a tool for clinical quality and performance measures to improve healthcare. APN can use EHR data to measure and report healthcare quality and outcomes, to analyze patient safety, to compare the effectiveness of different treatments, etc. and it can also help in developing a clinical practice guideline that will lead to evidence-based practice to improve patient outcomes. In the long term, the guideline will be assessed and refined in a continuous cycle. EHR assists in the progression toward improved care, improvement in the health of the population, and lower healthcare costs.
1.1 Electronic Health Records (EHR)
The source of data when relating EHRs to nursing comes from the information that is put into EHRs by the patient or the family of the patient. Data also comes from the patient’s visits to healthcare facilities. EHRs help improve patient care because they can contain the information that was collected in multiple care settings, assisting the coordination of care provided by nurses and other healthcare professionals. For example, if a patient has visited the emergency room multiple times for one issue, all the information from these visits will be contained in one place in the EHR. This will prevent the patient from receiving the same treatment multiple times and increase the probability of diagnosing the problem.
An electronic health record (EHR) is defined as the “systematized collection of a patient’s health information in a digital format.” This includes a variety of types of data, including demographic, medical history, medication and allergies, immunization status, laboratory test results, radiology images, and vital signs. They are real-time, patient-centered records that make information available instantly and securely to authorized users. EHRs have the potential to access the record simultaneously and independently, increasing accuracy of diagnoses. This, in turn, increases patient safety and the overall quality of care. EHRs help with diagnoses and treatments made by healthcare providers. With the patient’s overall information available, providers are able to determine, based on statistical data, what the best diagnosis or treatment plan should be. This has the potential to increase the cost-effectiveness of the treatment, enhancing the healthcare that patients receive. With the large amount of information available in EHRs, they encourage better management of chronic diseases by detecting the warning signs and ensuring patients receive the appropriate treatments.
1.1 Electronic Health Records (EHR)
1.2 Clinical Decision Support Systems (CDSS)
Clinical decision support systems have been in use for more than 30 years (Kawamoto et al., 2005). However, they are only now beginning to take hold in healthcare. CDSS can take the form of “active”, meaning the system solicits the user with inferences and recommendations, or “passive”, meaning the system waits for the user to access it for support (Delpierre et al., 2004). Most are integrated into EHR systems and provide assistance in making clinical decisions by filtering knowledge and patient information to offer the best possible assessment and plan (Kawamoto et al., 2005). Data mining with CDSS makes use of algorithms to search databases and form patterns, generating information which was not previously known (Greene et al., 2014). At present, the most widely used CDSS applications are for preventive care and chronic disease management. However, they are underutilized in medical oncology compared to other fields and have been shown to improve adherence to guidelines and potential outcomes (Tolbert et al., 2013). CDSS align with the nursing process and best practices by providing assessment of the patient, diagnoses, identification of outcomes, planning, and implementation. The WHO has described this as the key to quality care and the gold standard within the information age. This attribute to evidence-based practice should enable greater use of structured data collection techniques and documentation at the point of care, thereby enhancing the quality of big data from said encounters.
1.3 Wearable Devices
Health informatics professionals have been especially successful in developing wearable devices which monitor health status and health behaviors continuously in real time in an efficient and non-invasive manner. Wearable devices have been categorized into two types: those which are worn on the body, which has been further subcategorized according to the body part, and smart accessories (smartphones). They are designed to measure certain health parameters and behaviors valuable to the maintenance of health and management of chronic conditions. Examples of these health parameters and behaviors include heart rate, blood pressure, body temperature, physical activity, eating, and sleep patterns. The data collected from wearable devices has been referred to as quantified self data, defined as self-knowledge through self-tracking with technology. The term was coined by scholars from the Quantified Self community, an international collaboration of users and makers of self-tracking tools who share an interest in self-knowledge through self-tracking. Wearable devices provide multiple forms of big data using both structured and unstructured data, thus offering vast potential to improve patient outcomes through health data analysis, enhanced clinical decision-making, and improved patient engagement.
2. Types of Information Obtained from the Source
2.1 Patient Demographics
2.2 Medical History
2.3 Vital Signs
2.4 Laboratory Results
2.5 Medication Records
3. Impact of Data on Client Care
3.1 Personalized Treatment Plans
3.2 Early Detection of Health Issues
3.3 Improved Clinical Decision Making
3.4 Enhanced Patient Safety
3.5 Efficient Resource Allocation
4. Role of Advanced Practice Nurse in Data Stewardship
4.1 Ensuring Data Privacy and Security
4.2 Data Collection and Analysis
4.3 Collaborating with Interdisciplinary Teams
4.4 Implementing Evidence-Based Practice
4.5 Continuous Quality Improvement