Prescriptive analytics is data analysis tools and processes that predict and prescribe the best approach to a given scenario. These tools and processes perform multiple analyses based on diverse factors to produce different outcomes that could come about under different scenarios. As such, prescriptive analytics are essential in decision-making in different settings, such as healthcare management, because they provide feedback based on the available data and the desired goals. Therefore, the essay offers an overview of the applicability of prescriptive analytics tools in healthcare.
Implementing Sensitivity Analysis
Sensitivity analysis is an essential tool for evaluating healthcare practice decisions. Ideally, a sensitivity analysis is used to establish the outcomes of a given event or situation, given variations in assumptions, variables, models, values, or methodology (Sharda et al., 2018). According to Mowbray et al. (2022), a crucial reason for including sensitivity analysis in healthcare practice is to assess the influence of different factors on patient health and clinical outcomes. In care facilities, decision-making is premised on the consideration of different variables. These include patient demographic factors, available treatment, and medical care (Parpia et al., 2022). As such, healthcare professionals must establish the appropriate combination of these variables to improve patient care. Therefore, sensitivity analysis is used to decide on the most optimum conditions to enhance patient outcomes and overall health priorities.
Additionally, sensitivity analysis is used to resource allocation and financial decisions and how they affect healthcare operations. More often than not, healthcare facilities have constrained budgets due to resource scarcity (Fernandes Antunes et al., 2022). As such, there is a need to ensure optimum utilization of the limited financial and physical resources. Accordingly, sensitivity analysis is essential for establishing operating costs in different departments, revenue streams from different operations, and areas that require more administrative support. Based on the outcomes of these analyses, healthcare facilities can plan for their resources to ensure they meet the client’s needs and generate enough revenue to keep the facility afloat. In addition, health facilities are affected by external factors beyond their control (Mowbray et al., 2022). As such, sensitivity analysis on upcoming healthcare policy changes or economic conditions enables them to plan and evolve with the prevailing changes. Therefore, sensitivity analysis is crucial for planning in healthcare administration.
Challenges to Analyzing Multiple Goals
In healthcare practice, practitioners often grapple with competing goals during patient care. According to Scott et al. (2021), multiple goals, such as controlling the cost of care, improving patient health, treatment decisions, and advocating for patient health, compete for consideration during patient-practitioner conversations on care modality. Ideally, when goals compete, it is challenging to develop the single most appropriate solution. As such, practitioners and administrators must reflect on these multiple needs vital to the healthcare facility, its core mission, and its vision to ensure the overarching decision reflects their strategic objectives. Therefore, it is more often challenging to reach a consensus given the relevance of each goal to different stakeholders involved in decision-making.
Additionally, multiple goals are based on different criteria, which is difficult when assessing their essence in the care process. Ideally, healthcare outcomes are diverse and utilize varying qualitative and quantitative metrics (Mowbray et al., 2022). For instance, patient health goals could vary from the cost minimization goals, which are often conflicting. Here, considering one goal could imply neglecting another equally essential goal (Fernandes Antunes et al., 2022). As such, conflicting priorities could affect the analysis of multiple goals because of the complicated process of evaluating and assigning weights to each goal. Therefore, little can be achieved when analyzing multiple goals without adequate consensus, communication, and collaboration.
What-if Analysis
What-if analysis is the process where one is allowed to alternate scenarios or values in a particular evaluation to establish how the changing situations affect the outcomes. In healthcare practice, what-if analysis becomes useful when administrators seek to evaluate the possible influence of various conditions or situations on patient outcomes, healthcare operations, or resource allocation (Ghaffarzadegan, 2021). Ideally, a what-if analysis could be used to simulate how different situations can affect the operations of a healthcare facility. For instance, health equipment, staffing levels, patient volume, and treatment programs are essential variables that affect the operations of a facility. As such, an administrator could use what-if analysis to hypothesize how the operation or patient outcomes could be affected by variations in patient volumes and staffing levels. The outcomes of these analyses are pivotal in decision-making since these scenarios could be used to convince stakeholders to improve funding or allocate more resources toward the identified limitations.
Additionally, what-if analysis is used by healthcare administrators to plan and implement strategic changes. Ideally, healthcare is dynamic with diverse issues that require healthcare administrators to establish contingency plans for unforeseen eventualities. Administrators can simulate probable events through what-if analysis and establish how they could impact hospital operations (Ghaffarzadegan, 2021). For instance, a simulation of patient wait time and satisfaction could inform administrators of the appropriate strategy to improve operations and reduce patient dissatisfaction. In particular, the adoption of healthcare technologies such as health records management systems is premised on evaluating scenarios related to costs, care quality, efficiency, ensuing disruptions, and overall benefits before implementation. As such, administrators could rely on the analysis for planning, risk management, and decision support to benefit healthcare facilities.
Goal Seeking Analysis
Goal seeking is a component of what-if analysis where one works backward from a known outcome towards objectives that lead to the known outcome. Goal-seeking is helpful because, at the outset, policymakers have established goals that all healthcare practitioners should strive to achieve. As such, the goal-seeking analysis provides an avenue for targeting specific objectives that lead toward process improvement to achieve the specified goals. According to Lehmann et al. (2019), goal-seeking is based on people’s expectations over a given process. Ideally, the more people expect better outcomes, the more desire to achieve the set goals. Consequently, healthcare administrators use goal-seeking analysis to establish performance benchmarks that should be used to determine the process changes needed to achieve the goals (Lehmann et al. 2019). In particular, setting clinical outcomes or financial performance benchmarks sets the basis for a backward evaluation of what is needed to achieve the set expectations. For instance, an administrator could set the goal of achieving a given nurse-to-patient ratio and then work backward to determine the required workload and staffing levels to achieve the set goals. Therefore, goal-seeking analysis sets the optimal requirements to achieve specific targets and enables administrators to optimize resource allocation for maximum impact on care outcomes.
Decision Tables and Decision Trees
Decision tables and decision trees are tools used to evaluate the conditions of a process and provide comparisons to inform decisions. According to Sharda et al. (2018), decision tables provide a tabular representation of decision rules. The rows represent combinations of different conditions or situations, while the columns outline possible outcomes based on the conditions or values in the rows. As such, decision tables are necessary when required to organize and analyze complex data that includes multiple conditions or actions. As Sharda et al. (2018) show, decision tables have a structured and systematic framework that allows the evaluation of different conditions to establish the expected outcomes. Therefore, decision tables visually represent relationships between different conditions to establish how their outcomes differ in decision choices.
On the other hand, decision trees provide a hierarchical representation of decisions combined with possible outcomes in a tree-like graphical outlook. In decision trees, the nodes represent decision points, while each branch offers possible outcomes with a continued breakdown of the outcome or possible decision (Sharda et al., 2018). As such, decision trees provide a visual, logical flow of decisions from the originating point to the final consequence or outcome of the choices. Therefore, they provide essential and simplified illustrations for complex decision-making processes that require multiple conditions and nested logical flow of conditions.
Importance of Decision Trees
Decision trees are instrumental in healthcare organizations. They are used to support both clinical and administrative decision-making. For instance, decision trees are used in diagnosing health conditions and identifying the appropriate treatment for patients. Ideally, a one-size-fits-all approach is not suitable for healthcare provision. As such, decision trees augment the patient-centered model of care where treatment incorporates unique patient factors such as the condition, medical history, and age. Therefore, decision trees justify a treatment decision to improve patient outcomes. In addition, some healthcare have notable side effects or risks when carried out. As such, decision trees are used to map out all possible outcomes. These are used to inform patients so they are aware of what they are signing up for. Furthermore, decision trees are also used for decision-making on administrative matters related to resource allocation, planning, and management. Analyzing the decision tree structure and available outcomes helps administrators identify concerns in operations. As such, management can establish corrective measures or strategies to mitigate the observed or anticipated operational challenges. Therefore, decision trees provide an informative, data-driven approach to healthcare decision-making that is used to improve care delivery and achieve operational goals in health facilities.
Conclusion
Prescriptive analytics are instrumental in healthcare management and care decision-making. The analytics processes and tools assist healthcare providers and managers make targeted decisions about patient treatment resource allocation, planning for the future, and process optimization to improve health and patient outcomes and reduce costs.
References
Fernandes Antunes, A., Jacobs, B., Jithitikulchai, T., Nagpal, S., Tong, K., & Flessa, S. (2022). Sensitivity analysis and methodological choices on health-related impoverishment estimates in Cambodia, 2009–17. Health Policy and Planning, 37(6), 791-807. https://doi.org/10.1093/heapol/czac028
Ghaffarzadegan, N. (2021). Simulation-based what-if analysis for controlling the spread of Covid-19 in universities. PLoS ONE, 16(2). https://doi.org/10.1371/journal.pone.0246323
Lehmann, A. I., Brauchli, R., & Bauer, G. F. (2019). Goal Pursuit in Organizational Health Interventions: The Role of Team Climate, Outcome Expectancy, and Implementation Intentions. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.00154
Mowbray, F. I., Manlongat, D., & Shukla, M. (2022). Sensitivity Analysis: A Method to Promote Certainty and Transparency in Nursing and Health Research. The Canadian Journal of Nursing Research, 54(4), 371-376. https://doi.org/10.1177/08445621221107108
Parpia, S., Morris, T. P., Phillips, M. R., Wykoff, C. C., Steel, D. H., Thabane, L., Bhandari, M., & Chaudhary, V. (2022). Sensitivity analysis in clinical trials: Three criteria for a valid sensitivity analysis. Eye, 36(11), 2073-2074. https://doi.org/10.1038/s41433-022-02108-0
Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: a managerial perspective. pearson.
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020). Interpretability of machine learning‐based prediction models in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1379.
Scott, A. M., Harrington, N. G., & Spencer, E. A. (2021). Primary care physicians’ strategic pursuit of multiple goals in cost-of-care conversations with patients. Health Communication, 36(8), 927-939.
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