Please respond to each response with substantial detail that provokes further discussion. Readily offers new interpretations of discussion material. Ideas are expressed clearly, concisely
Discussion question:
1. What data sets would be valuable in gaining knowledge related to the policy you choose? Discuss.
2. How does population based data be used to create community level health policy?
Response: Joanna
1) The policy I chose was sickle cell care expansion. Sickle cell disease (SCD) can lead to several complications where management of the disease becomes complicated. The importance of expanding care to these patients increases the size and capacity of the medical workforce that is trained to treat sickle cell. Data sets that would be valuable in gaining knowledge related to this policy include: Population-based public health surveillance, Medicaid and Health Insurance Program databases, death certificates, hospital discharge and emergency department records, and clinical records or case reports. With this type of data, SCD population prevalence, demographic characteristics, health care access and use, and health outcomes can be identified. Additionally, big data sets can be used to gather data from social media, web visits, call logs, and other sources maximize value .
2) Population-based data assesses community needs that can be used to create community level health policy. This data provides insight to public issues that need more attention where the analysis of this data can be used to assist in population health management in enhancing care, addressing SDOH and improving patient outcomes. According to Milstead (2022), this type of data provides an outline for action. Information like demographics and clinical data are useful in identifying geographic areas of greater need and health issues where there are disparities and inequalities (Stoto et al., 2019). Population data provides important evidence for health policy decision makers but can also help achieve health equity by addressing the need for changing systems and policies that have resulted in health disparities.
Response: Angelica
1. The policy I chose to write about in my advocacy paper was the nurse-to-patient ratio. Specifically, I wrote about how the inclusion of an off-care charge RN on an oncology unit to oversee chemotherapy administration would be beneficial and would help lessen the nurse-to-patient ratio. Logically, patients with a higher acuity would call for more attention from staff. As per Milstead (2022), a data set refers to structured data that can be retrieved via a link or index. Big data refers to very large data sets. As such, “examination of big data enables an organization to identify effective processes, eliminate wasteful processes, improve products and services, enhance the customer experience, and establish a competitive advantage” (Milstead, 2022. p 203). Big data would be an appropriate data set to use because as relayed by Milstead (2022), big data provides a tool to benchmark performance against other organizations, improve patient outcomes, measure innovation and may help with cost-saving opportunities. For the policy I described, comparative effectiveness research would examine the benefits and harms of this method in improving patient care. I think data sets could be pulled from surveying patients about their experience receiving inpatient chemotherapy and from the nurses administering it. We could compare results of HCAPS scores between hospitals that used the extra off-care charge RN to oversee chemotherapy administration and those that did not use the off-care RN. In this way, we would be able to tell if that extra nurse did anything to improve patient experiences, enhance patient outcomes, and lessen the burden for the nurses involved. Furthermore, we would be able to assess cost-effectiveness of the off- care RN by determining how many patients were re-admitted after chemotherapy infusion because of lack of discharge teaching. Sometimes, nurses are so busy with patients that they don’t provide adequate instructions to patients. These patients would return to the hospital because they were sent home with instructions they didn’t understand, further causing the hospital to lose money because if a patient is re-admitted within 30 days of discharge, the hospital does not get paid.
2. Population data contains details such as birth, death, age, sex, annual income, occupation, and language (Fleetwood, 2023). Therefore, if we were to study a specific community and understand what resources they have available to them and what level of education they have, we can better accommodate their needs. For example, if we had a patient population that primarily relied on public transportation as a means to get to and from doctor’s visits, we would have to be mindful when prescribing interventions as they may not be able to access them. Furthermore, in such an area, we might want to make Community Health Centers more accessible to patients and have more resources available in a single location to meet the population’s needs. Such community health programs address disparities in health care by ensuring equal access to health resources for those in lower socioeconomic classes as revealed through population data assessment.
Expert Solution Preview
Introduction:
In this response, we will discuss two different policies chosen by Joanna and Angelica in the context of medical college assignments. The policies selected are sickle cell care expansion and nurse-to-patient ratio, respectively. We will explore the data sets that would be valuable in gaining knowledge related to these policies and how population-based data can be used to create community-level health policy.
1. What data sets would be valuable in gaining knowledge related to the policy you choose? Discuss.
For the policy of sickle cell care expansion, Joanna mentions several data sets that would be valuable in gaining knowledge. These include:
– Population-based public health surveillance: This data set would provide information on the prevalence of sickle cell disease (SCD) within the population, helping identify the size and demographics of the affected population.
– Medicaid and Health Insurance Program databases: These databases would provide insights into healthcare access for individuals with sickle cell disease, including coverage, utilization, and potential barriers.
– Death certificates: These records would help understand the impact of sickle cell disease on mortality and identify any disparities or trends.
– Hospital discharge and emergency department records: These records would provide information on hospitalizations and emergency visits related to sickle cell disease, allowing for the identification of patterns, complications, and resource utilization.
– Clinical records or case reports: These records would provide specific patient-level data, offering insights into individual experiences, treatment outcomes, and potential areas for improvement.
In addition to these traditional data sets, Joanna suggests the use of big data sets that gather information from various sources such as social media, web visits, and call logs. By leveraging these sources, valuable insights can be obtained regarding patient experiences, treatment preferences, and perceptions.
For the policy of nurse-to-patient ratio and the inclusion of an off-care charge RN in oncology units, Angelica suggests the use of big data sets. These data sets are particularly useful in benchmarking performance, measuring outcomes, and identifying cost-saving opportunities. Angelica proposes the use of survey data from patients and nurses to assess the benefits and harms of incorporating an off-care charge RN. This would involve comparing HCAPS scores between hospitals with and without the off-care RN, evaluating patient experiences and outcomes. Cost-effectiveness can also be determined by assessing the rate of readmissions due to inadequate discharge instructions, which may be impacted by the nurse-to-patient ratio.
2. How does population-based data be used to create community-level health policy?
Population-based data plays a crucial role in creating community-level health policy. Firstly, population data provides insights into the demographics, needs, and health issues of a specific community. By analyzing population data, policymakers can identify areas of higher need and health disparities, helping prioritize interventions and allocate resources effectively.
For example, Angelica highlights that population data can reveal details about income, occupation, and language, providing a comprehensive understanding of a community’s resources and educational levels. This information allows for tailored interventions that consider the specific needs and limitations of the population. In her example, the reliance on public transportation prompts the need to make healthcare services more accessible and convenient.
Moreover, population-based data helps address disparities in healthcare by ensuring equal access to health resources for those in lower socioeconomic classes. By identifying the population groups most affected by health inequities, policymakers can develop targeted interventions to bridge the gaps and improve health outcomes.
In summary, population-based data is essential for evidence-based policymaking at the community level. It provides a comprehensive understanding of community needs, health disparities, and resource availability. By utilizing this data, policymakers can design interventions that address specific community concerns and promote health equity.
