Plan Comparison Worksheets For Spring 2023 Please Use The

Plan Comparison Worksheetsnhp 220spring 2023please Use The Following T

Plan Comparison Worksheet SNHP 220 Spring 2023 Please use the following three plans and answer the questions below. If you can’t find the answer, explain how you looked and why you can’t find it. Plan 1 Cigna Connect Plan 2 KP Platinum Plan 3 Blue Choice HMO Young Adult Premium $310/month $534/month $208/month Summary of Benefits and Coverage Cigna Connect SBC KP SBC Blue Choice SBC Prescription Drug List Cigna Connect Prescription Drug List KP prescription Drug List Blue Choice Prescription Drug List · Go to “Those who buy directly from Carefirst†and click on “Exchange formulary†Provider Network Cigna Connect Provider Network · Pick Arlington County, VA (zip 22204) · Choose “enter as guest†KP Provider Network · Pick “Kaiser Permanente Added Choice POS†Blue Choice Provider Network · Choose “continue as a guest†· Choose BlueChoice Advantage network Question 1: Which plan covers Rubina Dolvane (located in Arlington, VA)?

And how much will it cost to see her? How much do you have to pay before your plan starts paying (e.g., what is deductible for an individual)? Question 2: Which plans cover Eliquis (any strength), a brand-name drug, used to treat and prevent blood clots and how much does it cost for each plan? How much do you have to pay before your plan starts paying (e.g., what is deductible)? Question 3: For each plan, how much will you pay for an HIV test?

Question 4: Assume you are a young, relatively healthy single adult without children (so your health care utilization will consist of mostly primary care and preventive care) which plan would you choose and why? Question 5: Now, assume you are living with HIV and take the anti-retroviral medication Biktarvy and need to see both a primary care doctor and an infectious disease specialist regularly. Which plan would you choose and why? In the modern era, there are few professions that do not to some extent rely on data. Stockbrokers rely on market data to advise clients on financial matters.

Meteorologists rely on weather data to forecast weather conditions, while realtors rely on data to advise on the purchase and sale of property. In this and other cases, data not only helps solve problems, but adds to the practitioner’s and the discipline’s body of knowledge. Of course, the nursing profession also relies heavily on data. The field of nursing informatics aims to make sure nurses have access to the appropriate data to solve healthcare problems, make decisions in the interest of patients, and add to knowledge. In this Discussion, you will consider a scenario that would benefit from access to data and how such access could facilitate both problem-solving and knowledge formation.

Paper For Above instruction

In modern healthcare systems, the integration and effective use of data are pivotal in enhancing patient outcomes, optimizing resource utilization, and advancing clinical knowledge. This paper presents a hypothetical scenario within a healthcare organization that underscores the critical role of data access in solving real-world problems and fostering knowledge development in nursing informatics. The scenario focuses on a community health clinic experiencing rising rates of hospital readmissions among patients with chronic conditions, notably diabetes and hypertension. Addressing this issue necessitates comprehensive data collection, analysis, and application to improve patient management strategies and health education efforts.

Scenario Description

The community health clinic aims to reduce hospital readmissions by identifying at-risk patients and designing targeted interventions. The challenge lies in accessing, integrating, and analyzing diverse data sources — clinical records, patient self-reports, social determinants of health, and medication adherence data. The goal is to develop predictive models to identify patients at highest risk for readmission, enabling proactive, personalized care plans. The clinic also seeks to understand gaps in patient education and support systems that contribute to poor disease management.

Data Collection and Access

Data in this context includes electronic health records (EHRs), pharmacy refill data, and patient-reported outcomes collected through surveys or mobile health applications. Advanced health informatics tools facilitate secure access to this data, ensuring compliance with privacy regulations such as HIPAA. The EHR system consolidates clinical encounters, lab results, medication lists, and demographic information. Patient self-management data can be gathered via mobile apps that track medication adherence, blood glucose levels, and lifestyle behaviors. Additionally, social determinants data—such as socioeconomic status, housing stability, and social support—are integrated from community health databases.

The data is accessed through a centralized health information exchange (HIE), enabling real-time or near-real-time analysis. Secure logins, role-based access controls, and encryption protocols protect sensitive data during retrieval and analysis. Clinicians, data analysts, and nurse informaticists collaborate within this infrastructure, bringing diverse perspectives to interpret and act on the insights generated.

Knowledge Generation

From this rich dataset, multiple forms of knowledge can be derived. Predictive analytics identify patients at high risk for hospitalization, informing targeted outreach and intervention strategies. The analysis of medication adherence patterns may reveal barriers such as side effects, costs, or forgetfulness, leading to tailored educational and support programs. Insights into social determinants can highlight community-level factors influencing health outcomes, guiding resource allocation and policy development.

The data-driven approach allows for continuous quality improvement, where new data streams refine models and interventions over time. Moreover, the aggregation of de-identified patient data contributes to research on chronic disease management, expanding the evidence base for best practices.

Nursing Leadership and Clinical Reasoning

Nurse leaders leverage clinical reasoning to interpret data insights within the context of individual patient needs and organizational goals. They facilitate interdisciplinary team discussions, ensuring data-driven strategies align with patient preferences and social contexts. By applying evidence-based guidelines derived from analyzed data, nurse leaders can advocate for policies that bolster patient engagement, adherence, and health literacy.

Furthermore, nurse leaders use their judgment to identify potential biases in data collection, recognize limitations in predictive models, and ensure ethical considerations remain paramount. They advocate for the ongoing education of staff in informatics competencies, fostering a culture of continuous learning and data literacy. Ultimately, their ability to synthesize data insights with clinical expertise enhances decision-making, improves patient outcomes, and advances nursing knowledge within the organization.

Conclusion

The deployment of comprehensive data collection, access, and analysis in healthcare is vital for problem-solving and knowledge generation. The hypothetical scenario demonstrates how data-driven approaches enable proactive patient management, inform organizational strategies, and contribute to the evolving body of nursing knowledge. Nurse leaders play a crucial role in orchestrating these efforts through clinical reasoning, ethical oversight, and fostering a culture of data-informed practice. As healthcare continues to evolve, the integration of informatics and data analytics will remain central to delivering high-quality, patient-centered care.

References

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