Fluent Essays

Instructions For this assignment, you will create an annotated spreadsheet describing the cost analysis strategies and decision-making approaches of your selected governmental entity.  You will also write a 2 to 3-page paper discussing the following: Evaluate the tools used by the entity to calculate current and project future costs. Consider the mix of mandatory and discretionary spending. Assess the alignment of budget decisions and policy goals. Explain the entity’s approach to capital planning. Discuss prioritization, compromise, and decision-making approaches. Provide specific examples where appropriate. Evaluate the effectiveness of this process; make two or more recommendations to improve the process. Length: 1 spreadsheet with a 2 to 3-page explanatory brief, not including title and reference pages References: Include a minimum of 3 scholarly resources. The completed assignment should address all of the assignment requirements, exhibit evidence of concept knowledge, and demonstrate thoughtful consideration of the content presented in the course. The writing should integrate scholarly resources, reflect academic expectations and current APA standards, and adhere to Northcentral University's Academic Integrity Policy Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48 https://doi.org/10.1186/s12962-018-0128-5 R E S E A R C H Resource allocation in decision support frameworks Charles Phelps1* and Guruprasad Madhavan2 From Priority Setting in Global Health Symposium Boston, MA, USA. 5–6 October 2016 Abstract Background: Cost–benefit and cost-effectiveness analysis place limits on the dimensions of value that the models can incorporate. Cost–benefit analysis requires monetization of all measures of value (including life), a task sometimes deemed either difficult to accomplish or even repugnant. Cost-effectiveness analyses include health care gains in natural units (e.g., quality-adjusted life years or QALYs) rather than purely monetizing them (e.g., in dollars) and offers an efficiency perspective based on the ratio of cost per QALYs or similar health measures. These two methods use different rules for investment. Cost–benefit analysis says to invest whenever benefits exceed costs. Cost-effectiveness analysis says to invest if the intervention has a cost per QALY that meets—or is below—a designated cutoff value. Methods: Multi-criteria frameworks expand decision analyses by considering value tradeoffs from decision makers, and then producing a synthetic measure that summarizes the performance of investment options. This evaluation is done across all chosen dimensions of value, based on the weights provided by the decision makers, but this flexibility comes at a cost. To date, no approach is widely accepted to suggest how much to invest (how to determine a budget constraint) using multi-attribute models. Moreover, there is no agreed-upon method to measure willingness to pay for incremental multi-attribute value improvements. Our paper proposes a way forward. Results: Based on existing dollar estimates of willingness to pay for QALYs, our concept creates a comparable cutoff for multi-criteria value measures. Our proposed method expands the acceptable cost per QALYs in proportion to how much of the total measure is accounted for by the QALY component. Agreed-upon values for cost per QALY are thus extrapolated to account for extra value created by non-QALY attributes of each intervention. Conclusion: Using our proposed methods, the cost per QALY cutoff can serve as a benchmark toward creating a resource allocation cutoff in multi-criteria frameworks. Keywords: Multi-criteria decision analysis (MCDA), Cost effectiveness analysis (CEA), Cost–benefit analysis (CBA), Budget constraints, Quality-adjusted life years (QALYs), Policy development, Investment planning, Portfolio analysis © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Background Methods to evaluate and allocate societal investments have evolved over time. Cost–benefit analysis grew from the work of French engineer-economist Jules Dupuit in 1844 [1], later formalized by economist Alfred Marshall [2]. The US government began the specific requirement of cost–benefit analyses for water navigation projects in 1936, further codified in the 1939 Flood Control Act, which embodied into law an operative investment deci- sion rule: Invest when “the benefits to whomever they accrue [be] in excess of the estimated costs” [3]. This illuminates an important limitation of the cost–benefit analysis: it cannot consider the distribution of those ben- efits and costs, yet issues of distribution and equity are the center of many public policy debates. Open Access Cost Effectiveness and Resource Allocation *Correspondence: [email protected] 1 University of Rochester, Rochester, NY, USA Full list of author information is available at the end of the article http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://crossmark.crossref.org/dialog/?doi=10.1186/s12962-018-0128-5&domain=pdf Page 86 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48 Planners and analysts of health care have been reluc- tant to fully embrace the concept of cost–benefit analy- sis, since it requires an explicit statement by the analyst of the value of a human life or life-year. Over time, cost- effectiveness emerged as an appealing criterion, wherein the analyst can evaluate the incremental costs and health benefits of various medical interventions and then report their ratio. Decision makers then set the cutoff value for approval of investments. The proof that this approach flowed directly from a single person’s lifetime utility maximization pro- gram came only in 1997 [4]. Until that point, the intui- tive appeal of cost-effectiveness was all that supported its legitimacy. The use of cost-effectiveness significantly expanded during the latter part of the twentieth century, most notably within the British National Health Service which—through its National Institute for Health and Care Excellence—evaluates medical interventions using a cutoff value currently set around £30,000 per quality adjusted life-years (QALYs) [5]. The World Health Organ- ization recommends the use of modified cost-effective- ness analysis to evaluate health care interventions: using disability-adjusted life years (DALYs), it recommends a cutoff value of one to three times per-capita gross domes- tic product to guide resource allocation [6]. Once a cutoff value for an acceptable investment has been made, cost-effectiveness analysis and cost–benefit analysis are virtually equivalent, with the difference being the choice for the value measure, be it lives, life-years, QALYs or DALYs [7]. That said, how does one establish the cost-effectiveness cut-off? A general discussion of this issue appears in the most recent cost-effectiveness “handbook” [8]. A complication arises when a health system announces one cost-effectiveness threshold (e.g., $100,000 per QALY) but establishes a budget that is insufficient to fund all technologies passing the estab- lished threshold. This creates an affordability conundrum. This issue has recently even entered the British court sys- tem with a lawsuit over prescription drugs facing a conflict between cost-effectiveness thresholds and affordability [9]. If the budget is too tight to fund all approved technolo- gies, then that implies a more stringent threshold in actual use. In all that follows, we intend to use the constraint that binds more tightly (typically the budget). Embedded in a tight budget the “shadow price” for QALYs—the price that really matters. So if the official cutoff is $100,000 per QALY and the budget only would fund activities with cost- effectiveness ratios of $80,000 per QALY, then we would intend that the $80,000 value be used. Both cost–benefit and cost-effectiveness approaches share a common defect: they are narrow, and cannot include practical critical factors such as the distribu- tion of benefits and costs, broader impact of societal programs, public values and perceptions, and related matters [10]. The importance of these distributional issues have been considered [8, 11–13]. One model meas- ures the distribution of health benefits and costs across various population subgroups [14] but does not provide a mechanism to synthesize this information into a unified value measure. More comprehensive decision support systems such as multi-criteria decision analysis can include these issues such as equity and social distribution directly and trans- parently [15–17]. Multi-criteria approaches have demon- strated their value, especially when decision makers have various—and often competing—priorities [18]. Multi-criteria models differ in an important way from standard economic analyses. Economists typically esti- mate the structure of people’s preferences from observed choices they make using the formal tools of utility maxi- mization and demand theory. These “revealed prefer- ences,” as economists call it, are inferred from actual choices. Multi-criteria decision analysis does something completely different: it elicits the preferences of the deci- sion makers and the trade-offs that they are willing to make. Subsequently, most of these approaches use a sim- plified method for approximating the total value of any portfolio, often using linear approximation. These “attrib- utes” might include considerations relating to finances, health gains, social justice, or patient preferences to avoid certain side effects of treatments some of which could (in theory) be included in formal cost-effectiveness models (with relevant data) but often are omitted for simplicity and practicality. Multi-criteria analyses do come at a cost. In their current form, unlike cost–benefit and cost-effectiveness analyses, multi-criteria models do not explicitly guide resource allocation. No widely accepted rule exists for multi-attribute approaches that match the logical ease and spirit of “invest when the net benefits are positive” (as in cost–benefit analysis) or “approve the project if the cost per unit of health gained falls below some pre- determined cutoff ” (as in 1X to 3X per capita GDP for cost-effectiveness analysis). A recent task force of the International Society for Pharmaceutical and Outcomes Research (ISPOR) concluded that the best practices to support the use of multi-criteria decision analysis to consider budget constraints is “still unclear, and further research should focus on this topic” [19]. This paper seeks to contribute to that discussion. Methods As a starting point for discussion, resource allocation relates to how basic measurements are done. With a fixed budget, if all one desires is a prioritized rank order Page 87 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48 of investments, then ordinal scales (placing choices in the desired order) or interval scales (such as Fahrenheit and Celsius temperatures) suffice. Many forms of multi- criteria decision analysis produce interval scales. In this setting, investments are made until the fixed budget is exhausted. A more refined approach might allocate a fixed invest- ment budget across scalable investments by choosing how large or small each potential project might be to maximize the overall value of the investments. Assign- ing appropriate budget shares to each potential invest- ment option requires that the multi-attribute model provide a ratio scale, not just an interval scale. Finally, if one wishes to have a clear decision about whether or not to invest—analogous to the outcome of cost–ben- efit analysis—then benefits and costs must be measured in the same monetary units (e.g., dollars, euros, yuan or rupees). Since multi-criteria models do not automati- cally convert benefit measures to monetary units, and often only determine ordinal priority ranks, they do not yet provide generalizable resource allocation rules. In some settings, resource allocation does not enter the picture. For example, an individual health care patient choosing among insured treatment alternatives need not consider resource costs, but rather selects among the options provided by the patient’s health plan. But in most real decisions—such as insurance coverage decisions and health technology assessment— budget constraints invariably enter the picture. In our own recent work focused on systems analysis for pri- oritizing new vaccine research and development, the issue of choosing investments within a fixed budget— or determining the level of such a budget—was not a design factor [15, 20, 21]. But in some settings, more guidance about resource allocation is needed. The recent ISPOR task force assessing multi-criteria decision analysis models discussed this challenge, sum- marizing previous work on the topic and offering three alternatives (none of which they deemed wholly satis- factory), and urged further research on the issue [19]. The approaches they identified from that literature sur- vey included the following: Directly include costs One approach to solving this problem directly includes cost as an attribute in the multi-criteria analysis—lower costs being better—with a user-assigned weight within the model. This has the equivalent effect of asking the decision maker (when establishing the weights) to evalu- ate willingness to pay for the benefits. But as the ISPOR task force report notes “stakeholders do not have the knowledge to estimate the benefits that would have to be forgone to fund an alternative. Instead, this would require the forgone alternatives to be identified and evaluated using the same” multi-criteria framework. Score comparable interventions The scoring approach seeks to find existing interventions that might be eliminated to free-up funds for the new investment, hence identifying the “opportunity cost” of the new intervention. The scores for the candidates are generated by the multi-criteria model providing a com- parative view of their performances. But this approach contains circular logic: how does one know in which pro- grams to disinvest until all programs have been evaluated in the same multi-criteria metric? Those interventions that may seem available for elimination in cost-effec- tiveness analysis may look very good in a multi-criteria model, and vice versa. So the selection of a group of comparable interventions is an incomplete and defective approach. Rankings of investment priorities can read- ily shift importantly when other criteria—such as public fear during a disease outbreak—enter the analysis beyond cost-effectiveness [10]. Modified cost–benefit calculations This approach omits “cost” in the multi-criteria model, evaluates each of the various options, and then calcu- lates a cost–benefit ratio, similar to an incremental cost- effectiveness ratio. The only difference is that here, the “benefit” metric has multiple dimensions—unlike the unidimensional QALY, DALY, or similar health benefit measure in cost-effectiveness analysis. Multi-attribute models create an index specific to each decision makers’ preferences, and thus such indexes are not comparable with one another. But in this situation, unless all multi- attribute evaluations use the same ratio scale measure- ment, comparing cost–benefit ratios is impossible. Yet, forcing all measurements into a single multi-attribute framework defeats its very purpose—that is, allowing dif- ferent stakeholders the ability to specify their own prefer- ence functions. In a similar fashion, one recent analysis recommends calculating the ratio of multi-criteria value scores to cost [22]. This approach then sorts the available choices from the most favorable to the least favorable, and then pro- ceeds until the investment budget is exhausted. Unfor- tunately, this approach does not provide advice on the proper investment budget size—it is exogenous in their analysis. Nor does it allow for the possibility that at least some of the possible investments are scalable, which would introduce further investment options beyond those originally considered. This rule is akin to calcu- lating QALYs per cost (the inverse of the usual metric) and investing in the most favorable until the budget Page 88 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48 is exhausted. It provides neither a cutoff rule—as has become common in cost-effectiveness analysis—nor a mechanism for budget setting mechanism; so while it places organizations on the efficient frontier, it does not identify the best part of that frontier. Economists call this technical or “X-efficiency” but it is an incomplete meas- ure of overall efficiency, since it ignores “allocative effi- ciency” [23]. In real world settings, budgets do not ultimately descend from the top: somebody has to work through and determine the desired level of investment. Thus something further is needed to guide such decisions. To make further headway, we need an approach to guid- ing either cutoff values or a mechanism for shaping an optimal budget. The two tasks cannot be done indepen- dently—one implies the other. League tables One could also construct a league table for multi-attrib- ute models of various interventions to provide guidance, just as early cost-effectiveness analysis used league tables to guide resource allocation before people became com- fortable with choosing a cutoff value. Earlier use of league tables for cost-effectiveness contains a strong assump- tion, namely that previous decisions about health care interventions were made with implicit cost-effectiveness tradeoffs in mind. But if those league tables also included (even informally) the value of other attributes, then they could overstate the willingness to pay for a QALY. This approach is logically equivalent to one of the approaches evaluated by the ISPOR task force and it contains the same defect. One does not know ex ante which technolo- gies appropriately belong in the league table and which are simply out of bounds. Willingness to pay and accept As a proxy for resource allocation, one could also sim- ply survey the measures of willingness to pay (WTP) and willingness to accept (WTA), as is common, for example, in environmental policy [24]. However, behav- ioral scientists cast serious doubt on the validity of such approaches, arguing that the responses may reflect atti- tudes, but do not represent true willingness to pay [25]. A prominent concern is that of framing, where responses depend on the way the question was posed. The distinc- tion often hinges on WTP (as in “how much would you be willing to pay to avoid unpleasant situation X”) versus WTA (as in “how much would you need to be paid before you would accept unpleasant situation X”). Answers dif- fer greatly in these issues depending on the framing: whether or not the object is currently owned, and is available or not. A review comparing numerous WTA and WTP studies on the same economic area (e.g., health, environmental) concluded that WTA values regularly exceed WTP val- ues, the gap highest for non-market goods. The authors of this summary concluded that the less the good is like an ordinary market good—that is, it cannot be readily be bought and sold—the higher the ratio [26]. They reported a typical WTA/WTP ratio of 7.2 for analyses carried out in a number of different subject areas (including health, environmental, water resources and others). A more recent review found a WTA/WTP ratio of 5.1 for goods involving health and safety [27]. These results appear to show that WTA studies—such as those involving wage premiums for risky occupations—severely overstate the more desirable measure of willingness to pay. If so, then relying on these WTA measures instead of agreed-upon cost per QALY measures would be inappropriate. Since “health” is perhaps the quintessential non-mar- ket good, one might expect the WTA/WTA ratios for the value of life and life years to possibly be significantly higher than for typical market goods. However, some proponents of the use of labor force studies (wage dif- ferentials for risky occupations) to measure value of life argue that the gap is not nearly so large as this literature suggests, if properly interpreted, thus seeking to restore confidence in the large value of life measures found in the health and safety literature [28]. Results Our approach requires that any multi-criteria decision model contain a component of health benefits for which there is at least some agreement regarding a proper cut- off for cost-effectiveness analysis. Suppose that the health benefits measure (e.g., QALY or DALY) has a weight w, and all other attributes combined have a weight of (1 − w). If the agreed upon cost-effectiveness cutoff is to accept any intervention with cost per QALY (or DALY) ≤ K, then the proper cutoff in the multi-criteria model is K/w. K is the more binding of the announced cost-effectiveness threshold or the implicit and more stringent value from a tight budget. Using QALYs (or DALYs) as a standard of value, we can scale the total willingness to pay for the aggregate ben- efit by using the fraction of the total benefit attributable to QALYs (or DALYs). Our proposal therefore leverages previous agreement about proper cost-per-QALY thresh- old into a new threshold for the newly defined portfolio of benefits. The value of QALY serves as the numeraire. Suppose two decision makers have created their respective multi-attribute models where QALYS account for different percentages of the total value weight. In Table  1, these two decision makers are presumed to agree on the proper cutoff for a cost-effectiveness model Page 89 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48 at $100,000 per QALY. This generalizes to the situation where they have different initial cutoffs, as shown in Table 2. In both cases (Tables 1 and 2) once we know the decision maker’s cutoffs for cost-effectiveness and their weights assigned to health outcomes in the multi-criteria analysis, then we can infer the proper cutoff for decision making using multi-attribute models. Returning to the affordability conundrum raised earlier, the notion that one should be willing to pay more for an item with greater value seems incontestable. However, if a fixed budget health care system suddenly introduces an expanded multi-criteria measure of value (and logically, a greater willingness to pay for that expanded concept of value) then the budget constraint will likely become more binding, and the gap between the stated willingness to pay and the shadow price in the budget can widen. To see how this works, suppose that a health care sys- tem introduced a multi-criteria model with two attrib- utes of value—QALYs gained and the extent to which disease burden of identified disadvantaged populations is equitably reduced. Some interventions that might not meet a population-wide cost-effectiveness criterion might now have higher priority. Tuberculosis prevention or treatment might provide a good example—low priority in a general population but high priority in a disadvan- taged population. Using the new measure of value would increase the desire to fund (in our example) the tuber- culosis-treatment program, hence further stressing the overall budget for the health care system. To bring things into alignment logically one of three things must occur: (a) new resources must be added to the budget (b) some previously funded activities must be defunded, or (c) the threshold for accepting interventions must tighten (or some combination of these options). In using this approach in  situations with a fixed budget (or until a budget can be adjusted to accommodate new items of value), it is important to use the appropriate threshold, which may well be more stringent than the announced threshold, and which will change even further as the extra value of non-QALY items is introduced into the analysis. Discussion One can ask under what circumstances our simple extrapolation procedure remains valid beyond a simple linear utility model. As a starting point, the extrapolation of the value for QALYs to the entire multi-criteria model remains valid whenever the decision maker’s assumed utility function has constant budget shares (proportions of the total budget spent on a particular good). Cobb– Douglas utility models have this feature—the budget shares are constant over all incomes and prices. A more general set of utility functions—those with constant elas- ticity of substitution—assures that the method is globally correct while allowing incomes to change, but holding relative prices of QALYs and the other goods constant. In more generalized utility structures, budget shares vary with changes in income and relative prices of goods. In such cases, the simple proportional extrapo- lation from the value of a QALY to the value of a more complex multi-criteria bundle will require adopting a specific functional form for the utility structure and then calculating the appropriate extrapolation method. Multi-criteria models in general are meant to help structure problems for decision makers and to pro- vide general guidance, not to provide precise measures of value. There is always a tradeoff between accuracy and simplicity, and most practitioners of multi-criteria Table 1 Comparison of multi-criteria cut-off points for 2 hypothetical decision makers with same starting point cost- effectiveness cut-offs Weight on QALYs Weight on other attributes Cost-effectiveness ($/QALYs) cutoff Multi-criteria cutoff Decision maker A 0.5 0.5 $100K $200K Decision maker B 0.666 0.333 $100K $150K Table 2 Comparison of multi-criteria cut-off points for 2 hypothetical decision makers with different starting point cost- effectiveness cut-offs Weight on QALYs Weight on other attributes Cost-effectiveness ($/QALYs) cutoff Multi-criteria cutoff Decision maker A 0.5 0.5 $80K $160K Decision maker B 0.666 0.333 $100K $150K Page 90 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48 decision analysis generally opt for simplicity when possi- ble. Under what circumstances our extrapolation method remains valid can and should be a topic for further research. Since much of the literature on choosing a cutoff for an acceptable cost per QALY has focused on its relationship to income, this suggests that our extrapolation method will be reasonably useful even if ignoring differences in relative prices for QALYs and other goods in the multi- criteria bundle. We also note that even the standard model of cost- effectiveness analysis and the associated “acceptable technology” cutoff rules are not invariant to changes in economic conditions. The current debate about how to incorporate “affordability” into cost-effectiveness analy- sis highlights this issue. If a new technology emerges that has widespread use yet its cost-effectiveness ratio is “acceptable” by current norms, the situation can easily arise where the technology is both acceptable and unaf- fordable (with a fixed budget). If the underlying economic conditions change markedly (income, prices, or techno- logical opportunities), then the original behavioral rules that emerge (e.g., a cost per QALY rule) must be revised. This is true both in a pure QALY-based model and in our more general model that incorporates both QALYs and other goods. Conclusion The basic idea of our approach is straightforward: If one knows the value of part of a package of valuable items, and one knows the proportion of overall value of the package attributable to that particular part of the pack- age, then one can readily deduce the overall value of the package. In the realm of health, the most likely com- ponents to serve this purpose appear to be QALYs or DALYs. The benefit of using existing cutoff measures such as cost per QALYs is simply that a considerable liter- ature exists on determination of those values. We note— referring to the obvious—that the difficulty in reaching agreement about the proper cost per QALY threshold suggests that reaching consensus about a cutoff for multi- attribute decision models may be even more difficult. Declarations Authors’ contributions Both authors contributed to the conceptualization of the ideas herein, both contributed to the writing and editing of the manuscript. Both authors read and approved the final manuscript. Author details 1 University of Rochester, Rochester, NY, USA. 2 National Academies of Science, Engineering and Medicine, Washington, DC, USA. Acknowledgements The views expressed in this article are those of the authors and not necessarily of the National Academies of Sciences, Engineering, and Medicine. This work was supported in part by grant to Charles Phelps from the National Cancer Institute of the National Institutes of Health, U01CA183081. Competing interests The authors declare that they have no competing interests. Availability of data and materials Not applicable. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Publication funding The publication costs for this article were funded by Mark O’Friel, the Brinson Foundation, and the Payne Family Foundation. About this supplement This article has been published as part of Cost Effectiveness and Resource … 2 2 Evaluating Cost Analysis and Capital Planning for a State Budget Fesseha B. Mulugeta PUB-7005 v3: Public Administration Dr. Erin Rae Hoffer Northcentral University September19, 2021 The selected government entity is using the three tools for project cost analysis that includes. 1. Reserve analysis 2. Vendor analysis 3. Group decision-making. The reserve analysis is a technique used to deal with the uncertainty in project costs by allocating funds. It is a technique to mitigate identified and possible risks in the project associated with cost. Business organizations face numerous risks of project costs exceeding the estimated budget; thus, it is important to estimate the project cost using reserve analysis. It is important to keep the reserve budget in order to meet with exceeding parts of the project. The second tool that the organization is using is vendor analysis to estimate the cost of proposed bids. The analysis is used to get an idea of average bid values. One of the most important techniques used by the organization in decision-making is to make project costs analysis and implement the collective decision. By involving all the stakeholder's organizations, they gain more details and recommendations estimated more accurately (Taghadosi & Ajorpaz, 2020). There are numerous laws, acts, and legal regulations that guide the financial policy of HHS. The government uses the accountability act, the economy act, the financial management improvement act, and numerous other laws to guide its financial decisions and the costs of the services. Capital Planning Capital planning and investment control at HHS is an important part of the financial decision-making of the organization to use collective resources to make financial decisions. HHS is getting advice and assistance from CIO or Chief Information Officer to ensure that the organization manages and assists health in a better way and brings productivity and improvement in the country's healthcare system. HHS is structured in units and different business areas, and operating divisions. It is responsible for accomplishing its objectives and goals and delivering quality health care for individuals in business times. HHS relies on a federal model for capital investment and planning in conformance with CPIC policy (HHS, 2016). Decision Making Approaches The government entity prioritizes group decision-making and keeps its stakeholders first in all major decision-making activities. Conclusion: there are numerous decision-making approaches used by the healthcare organization that enable the organization to make effective decisions. 1. Participation of all stakeholders 2. Participative decision-making style 3. Research-based decisions in all departments 4. Effective communication of decisions through the organization among the stakeholders to ensure their participation and management of their opinions (Christine, 2019) Recommendations 1. The decision-making approaches used by the organization are effective in bringing satisfactory outcomes. However, still, I would recommend it to the organization to use advanced techniques and tools to manage productive outcomes. Using advanced techniques and tools means the organization could use the online survey method through collective opinions of all the stakeholders to make decisions and measure the effectiveness of each decision to gain a competitive advantage. 2. The organization must hire audits while making decisions to ensure that their decisions meet competitive market standards. References Christine, S. (2019). Top 10 Tools and Techniques to Estimate Project Cost. https://www.tutorialspoint.com/top-10-tools-and-techniques-to-estimate-project-cost. HHS. (2016). HHS OCIO Policy for Information Technology Capital Planning and Investment Control. https://www.hhs.gov/digitalstrategy/hhs-ocio-cpic-policy-2016.html. Taghadosi,  . M., & Ajorpaz,  . N. (2020). Clinical decision-making approaches by health caregivers of patients with COVID-19. http://www.bmrat.org/index.php/BMRAT/article/view/641. 2