Description INITIAL POST Quantitative research is essential in healthcare for many reasons. It met ...
Description INITIAL POST Quantitative research is essential in healthcare for many reasons. It meticulously analyzes healthcare trends, events, and results (Marciki? et al., 2016). Quantitative research tests hypotheses quantify relationships and draws statistically supported findings. Quantitative research supports evidence-based healthcare policy and practice. For instance, health services research questions include "How does telemedicine adoption affect rural patient satisfaction?" The response can be quantified via surveys or randomized control trials. Before and after telemedicine deployment, randomized controlled trials can assess patient satisfaction between a control group receiving traditional therapy and an experimental group receiving telemedicine (S, 2017). Challenges and biases that may impact the study results include selection bias, measurement bias, and confounding variables. Selection bias might cause non-generalizable outcomes if different patient groups participate more often. Only approved patient satisfaction tools might bias measurement and produce consistent results. A study design that ignores demographics may impair telemedicine acceptance and patient satisfaction. In quantitative health services research, the population and sample must be separated (Marciki? et al., 2016). The sample is a portion of the population being studied. Through random, stratified, or convenience sampling, population representation and bias are reduced. Quantitative healthcare demand analysis examines trends and resource consumption. Assessments include patient demographics, disease prevalence, healthcare access, and utilization. Regression, time series, and predictive modeling find healthcare demand drivers and forecast needs. References Marciki?, A., Pejanovi?, R., Sedlak, O., Radovanov, B., & ?iri?, Z. (2016). Quantitative analysis of the demand for healthcare services. Management: Journal for Theory and Practice Management, 21(80), 55–65. https://doi.org/10.7595/management.fon.2016.0019 S, S. (2017, October 14). Difference Between Population and Sample (with Comparison Chart) - Key Differences. Key Differences. https://keydifferences.com/difference-between-population-and-sample.html RESPONSES TO INITIAL POST REQUIRED #1 Respond to Professor: For the example you have presented, can you highlight a specific kind of condition where receiving traditional therapy and telemedicine would "ethically" assess patient satisfaction among rural dwellers? I ask because of the randomized nature of the intervention. #2 Respond to Cristal: Your example regarding the impact of telemedicine adoption on rural patient satisfaction shows how quantitative methods can effectively address complicated healthcare questions by quantifying relationships and drawing statistically supported findings as you mentioned. Navigating challenges and biases essential in quantitative research is important to ensure the validity and reliability of study outcomes. Strategies such as random, stratified, or convenience sampling can help mitigate selection bias and improve population representation. Considering the sampling you provided such as convenience and stratified sampling, what strategies do you believe are most effective in mitigating biases within the sampling and ensuring the validity and reliability of quantitative health services research findings? RESPOND TO STUDENTS POST REQUIRED #3 Respond to Shelby: There are many reasons for quantitative research. In healthcare, managers should how to use effective methods for decision making as well as improving the organization. With medical organizations growing, managers need to follow the trends and data to plan for budgeting (Marcikic, Pejanovic, Sedlak,Radovanov, Ciric,2016). What is the relationship between staff callouts and illness rates? Research methods that could help answer this could be observation, surveys, case studies. The challenges that you can face with this research question would be the PPE that staff are wearing and what department they work in. if a staff member is doing patient care, they are more likely to be exposed to illnesses versus someone that is a receptionist. References Marcikic, A, Pejanovic, R, Sedlak, O,Radovanov, B,Ciric, Z. (2016). Quantitative Analysis of the Demand for Healthcare Services. Management - Journal for theory and practice of management. 21. 55-65. 10.7595/management.fon.2016.0019. #4 Respond to Kyhanna: As we know from the COVID-19 Pandemic, there is a tendency for the healthcare industry to have to deal with unexpected circumstances that may arise. When this happens, the typical baseline of how to deal with circumstances doesn’t always work. Quantitative data is important because it allows you to gather statistical information to predict a future outcome based on the current baseline so that a healthcare organization can be prepared. “It is possible to predict the future variations of the demand and consequently, human, financial and material resources that should be available at certain periods of time. For this purpose, it is necessary to perform various statistical analyses to examine whether it is possible to determine the daily, weekly or monthly patterns.” (Marcikic, 2016, para.2) In my personal experience, quantitative research in my healthcare field has been to prepare for the unexpected by reviewing previous trends and expecting a reoccurrence. Quantitative data allows you to prepare for the demand that may arise, and in my health care organization, quantitative data is usually centered around medical equipment. For example, how many medical kits will we need to send for each patient that we will receive on this mission? This question warrants a numerical range and a descriptive design with information answering the question based on the needs and medical history of each patient. “Descriptive research aims to accurately and systemically describe a population, situation or phenomenon.” (McCombes, 2023, para.1) The challenges that may impact the study results are the condition of the patient, as their health can decline causing them to need more resources. Also, the biases of assumption that you won’t need additional equipment or certain types of equipment based on the similar experience of a previous patient, but as we know, each patient outcomes can be different despite their initial medical condition.
