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

Barriers to Effective Care Coordination and Proposed Solutions

Questions
Barriers to Effective Care Coordination:
Identify and explain at least 3 major barriers that can hinder effective care coordination for chronic conditions.
Examples:
Fragmented healthcare systems with limited communication channels between providers.
Lack of patient engagement and understanding of their care plan.
Socioeconomic disparities impacting access to healthcare resources and technology.
Propose solutions to overcome these barriers and create a more coordinated care system.

Answer
1. Fragmented healthcare systems
Effective coordination requires good communication between those involved, so limited communication channels between providers can act as a major barrier to coordination. Communication can be limited in a number of ways, the most simple being the inability to contact a specific individual. This was a common issue witnessed by the author while on a GP attachment. Secretaries often did not take messages from other healthcare providers or would take a message and not pass it on. Email contacts between providers are rarely available, and faxing is now outdated. Phone calls between providers are, of course, a good way of communication. However, without a direct line to the individual, the call is often lost. The use of voicemail is not an effective form of communication.
1.1 Limited Communication Channels
Healthcare systems have areas of specialization divided amongst different providers. This can lead to a patient receiving care from multiple providers within the same health issue, resulting in duplication of tests, uneven care provided, and varied outcomes. Patients with complex needs and chronic diseases often require treatment from multiple providers and specialties. Coordination of care for these patients is often inadequate due to the division of specialization among providers (James, 2003). Effective coordination is an essential component of good healthcare delivery and can be defined as the deliberate organization of patient care activities between two or more participants involved in a patient’s care to ensure that it is safe, efficient, and cost-effective. Coordination can be complex, involving tasks from different individuals across varying facilities and specialties (Gittell et al., 2000).
Introduction to Fragmented Health Care Systems
1.1 Limited communication channels
Providers in hospitals do not receive timely information about the discharge of their patients from the hospital or consultations with specialists. The quality and completeness of clinical information available at the time of a consultation was also identified as a problem, as well as difficulties in obtaining further information from hospital doctors. Changes in patients’ medications were often unclear and undocumented. General practitioners reported that they often had to admit patients to hospital because they could not obtain the medical or paramedical support necessary to sustain them at home or in a residential care facility. In some cases, hospital doctors would not accept patient referrals. These access block problems were perceived by general practitioners to be due in part to public and/or private hospital specialists having waiting lists of their own private patients, and being less inclined to treat public patients. Failure to provide follow-up treatment advice to referring doctors was described as a disincentive to further referrals. In mental health services, the lack of a booking system for patient appointments often complicated the task of arranging specific follow-up treatments. Most of New Zealand’s new health initiatives involve some level of care coordination from primary and community care. Examples include early discharge schemes, health of the older person and disability services programs, needs assessment and long-term care following the closure of hospital beds and the shift of a wider range of medical and surgical treatments from secondary to primary care. At present the potential gains of these initiatives are often not fully realized because they are not underpinned by improved communication and coordination with secondary care services. In some cases primary care doctors have been forwarded discharge and treatment change information for their patients months after the event, and because there is often no guarantee that hospital services will re-accept referred patients, primary care teams may give up on attempts to obtain further services that their patients still require. A lack of clear communication and understanding between the providers of secondary and primary care has also meant that some of the changes to service delivery described in the Introduction have occurred in a way that is ad hoc and unplanned.
1.2 Lack of information sharing
Virtual care coordination (e-health) has become more and more common, and is the use of IT services to plan and manage patient care. Most advances are in web-based, patient-to-provider cases, as they are easy to schedule, document, and revise. These cases have maximum coordination, but think about a patient who needs to see a specialist or get a procedure done. The patient instance again has much coordination with a defined specialist and a procedure time, but these cases are not easily transferable between different sectors of the health care system. He currently still has to fax or email procedure details, with possible drug specifications to his private practice proceduralist, which is basically less than a handoff, so this information could get lost or missed in potential coordination to a follow-up case.
The main issue with destroying the concept of an effective care coordination is that although there are several different forms of care coordination, most cannot be accurately displayed or compared to the traditional method of physician to patient, and the vegetative and emergency cases. Most of this essay is based around the transfer of information from one health care system to the next, and across the broad spectrum of managing the case. Care coordination has shifted to a multidisciplinary team effort over the past decade. The concept of care coordination is taking health care out of the passive mode and the linear patient to provider model, to design patient cases with an emphasis on preventing medical mistakes and anticipating potential setbacks.
1.3 Inadequate coordination between providers
Changes are needed to ensure the right type of coordinated mental health care is provided. For this to happen, mental health specialists must define in common terms what successful coordination will look like. It is too easy to say that coordination is occurring when a patient is seen by various providers in the same agency. Measures of coordination often involve event monitoring and evaluation of treatment effectiveness on the part of the patient and involved providers. Successfully coordinated care will result in a greater effectiveness of simpler treatments in the primary care setting and less need for referral to severe psychiatric medication management. With better measures of successful coordination, it will be possible to reward managed care organizations and provider groups that are coordinating mental health services and more effectively treating mental health patients.
Inadequate coordination between healthcare providers can adversely affect patient care. A healthcare provider may recommend different medications or a treatment course that interferes with treatment priority or diagnosis from another provider. Recommendations for, but no direct referrals to, psychological evaluation or therapy can be interpreted as stigmatizing patients and result in less motivation to follow a treatment course. Patient non-adherence is common in this chaotic healthcare system scenario, as patients often feel confused about proper treatment and may not believe therapy treatment will be effective. Then healthcare providers may misinterpret non-adherence as resistance rather than a problem with access and coordination, resulting in further exacerbating mental health problems. This lack of coordination for mental health treatment is in stark contrast to the care coordination in primary care and general medical settings.
2. Lack of patient engagement and understanding
2.1 Limited health literacy
2.2 Insufficient patient education
2.3 Ineffective communication with patients
2.4 Non-adherence to care plans
3. Socioeconomic disparities impacting access to healthcare resources
3.1 Financial barriers
3.2 Limited availability of healthcare facilities
3.3 Inadequate transportation options
4. Technological challenges in care coordination
4.1 Lack of interoperability between systems
4.2 Inconsistent use of electronic health records
4.3 Limited access to telehealth services
5. Proposed solutions for fragmented healthcare systems
5.1 Implementing care coordination platforms
5.2 Enhancing communication channels between providers
5.3 Establishing care teams and care coordinators
6. Proposed solutions for lack of patient engagement and understanding
6.1 Improving health literacy programs
6.2 Enhancing patient education materials
6.3 Promoting shared decision-making
6.4 Utilizing digital health tools for patient engagement
7. Proposed solutions for socioeconomic disparities
7.1 Expanding access to affordable healthcare services
7.2 Implementing transportation assistance programs
7.3 Addressing social determinants of health
8. Proposed solutions for technological challenges
8.1 Advancing interoperability standards
8.2 Encouraging widespread adoption of electronic health records
8.3 Expanding telehealth infrastructure and reimbursement policies