Please respond to each response with substantial detail that provokes further discussion. Readily offers new interpretations of discussion material. Ideas are expressed clearly, concisely
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?
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.
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
In this response, we will discuss the valuable data sets that can be utilized to gain knowledge related to the chosen policy and how population-based data can be used to create community-level health policy.
Response to Question 1:
The policy chosen by Joanna is focused on the expansion of sickle cell care. To gain knowledge related to this policy, several data sets can be valuable. Firstly, population-based public health surveillance can provide insights into the prevalence of sickle cell disease (SCD), demographic characteristics of the affected population, and their health outcomes. This data can help in identifying the areas with higher disease burden and the specific healthcare needs of the population.
Medicaid and Health Insurance Program databases can be another valuable data set as they provide information on healthcare access and utilization. By analyzing this data, the availability and utilization of healthcare services by individuals with sickle cell disease can be determined. It can also shed light on any disparities or barriers they face in accessing appropriate care.
Death certificates can provide valuable information on mortality rates and causes among individuals with sickle cell disease. This data can help identify any specific areas or populations that require additional attention in terms of healthcare services and interventions.
Hospital discharge and emergency department records can contribute to understanding the healthcare utilization patterns of individuals with sickle cell disease. This data can help identify the frequency of hospitalizations, emergency department visits, and reasons behind them. It can also provide insights into the different complications related to sickle cell disease.
Lastly, clinical records or case reports can provide detailed information on individual patient cases, their treatment plans, and outcomes. This data can help in understanding the effectiveness of different treatment approaches and identifying any gaps in the care provided.
Furthermore, Joanna mentions the use of big data sets that can gather data from social media, web visits, call logs, and other sources. This type of data can provide additional insights into the experiences, preferences, and challenges faced by individuals with sickle cell disease. It can support the development of patient-centered care approaches and interventions.
Response to Question 2:
Population-based data is crucial in creating community-level health policy. By analyzing population data, policymakers can identify the specific health needs and priorities of the community. For example, demographic data can reveal the age distribution, sex, and socioeconomic characteristics of the population, which can influence their healthcare requirements.
Population data can also provide insights into the prevalence and distribution of different health conditions within the community. This information helps in identifying the burden of diseases and conditions that require targeted interventions. By prioritizing these health issues, policymakers can allocate resources and develop policies to address them effectively.
Additionally, population data can help in identifying health disparities and inequalities within the community. By examining factors such as income levels, occupation, and language, policymakers can identify groups that may face barriers in accessing healthcare services. This information can guide the development of policies that aim to reduce disparities and ensure equal access to healthcare resources.
Moreover, population data can inform the design and implementation of community health programs. By understanding the resources available to the community and their level of education, policymakers can tailor interventions to meet their specific needs. For example, if a community relies heavily on public transportation, policymakers can ensure that healthcare services are easily accessible through strategic placement of healthcare facilities or transportation assistance programs.
In conclusion, population-based data sets are invaluable in gaining knowledge related to a chosen policy and creating community-level health policy. These data sets can provide insights into disease prevalence, healthcare access and utilization, health outcomes, and disparities within the population. By leveraging these data sets, policymakers can develop evidence-based policies that address the specific needs of the community and promote health equity.