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Nurse Executive Budgeting Assignment

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SLMC Cardiac Unit Budget Forecasting

Forecasting is an important tool used in foretelling future threats, opportunities, shortcomings and budget allocation in many health organizations as well as in other forms of business units. This paper will examine forecasting in the context of the healthcare industry. In health organizations, forecasting involves screening for areas that confer negative costs to the facility and the patients and then developing strategic plans to minimize these costs while optimizing the health care benefits and the quality of medication. Therefore, this study will concentrate on budget forecasting at SLMC Cardiac Unit so as to minimize the costs in the department and optimizing the performance, outputs, benefits and overall quality of care at the unit. The paper will also examine the underlying theories behind budgeting strategies as well as the associated variances.

Underlying Theories and Key Concepts

Healthcare budget forecasting is a complex task that requires in-depth analysis, assessment and clearly articulation of the concepts involved in the budgeting models. According to Slyster, Hernandez, Borkowski, Hearld and Smith (2017), forecasting in healthcare organizations is supported by four main pillars namely corrective action plans, change initiatives, variance analysis and trend examination. The nurse executive plays a critical role in bringing these pillars together to redefine process, restructure existing plans or introduce new strategies that will propagate the institution or the department forward (Slyster, Hernandez, Borkowski, Hearld and Smith, 2017). Variance analysis is a crucial tool that nurse executives use in decision making. Through the use of the variance analysis tool, the leader looks into the discreptancies between what was budgeted for an occurrence and what is actually used upon the onset of the forecasted occurrence. For instance, the cardiac unit can hypothesize that for the coming month, given the cost of treatment will be $15,000. However, for that month, there occurs a lot of cardiac arrests (probably resulting from an unforeseen natural calamity) and the patients exceed expectations so that the hospital ends up using $16,000, then, the variance is ($16000-$15000=$500). The leader can trace these variances over time and be able to trace a pattern. The manager will then use the data to inform the decisions regarding the amount to be allotted for the precautionary budget. Running variance examinations over time can be used in line with trend analysis to inform the process of decision making (Iqbal, Geer & Dar, 2017). Trends helps in analyzing habit and frequent occurrence to foretell the most probable course of things in the near future (Iqbal, Geer & Dar, 2017). Change refers to shifts in worker behavior, roles, attitude, levels of motivation among other aspects that impact internal organizational environment. The corrective action plans are used as remedies for the unforeseen occurrences. For instance, a precautionary budget set aside to fund a likely surge of patient inflow to the cardiac unit (Pentecost, 2016).

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Supporting Evidence

Trend can be defined as any consistent pattern of changing performance or behavior in organizations which can be either predictable or unpredictable. According to Patel et al. (2012), lengthy hospitalizations, low nurse-patient ratios, poor healthcare services and large volumes of uninsured patients are leading causes of healthcare trends. Healthcare researchers such as Heering and Mennella (2017) explain that the trends are vital determinants of the amount of money an organization sets aside for the flexible budget. In turn, this flexible budget is used to offset the internal variances hindering the smooth delivery of services. Heering and Mennella (2017) explain that the variances emanate from the adoption of new technology, deteriorating health care services, adoption of new equipment and policy changes as well as procedural shifts.

In a study by Hughes, Bobay, Jolly and Suby (2015), change is seen to be a major hindrance in the budget forecasting task. Examples of negative changes include increasing nursing work hours as a way of minimizing cost, nurse personnel turnover and overloading staff. These changes hinder the ability to forecast a budget effectively. Therefore, there is need to reduce the effects of change in budget forecasting. Harvey and Lemaire (2015) recommend the use of action plans to minimize and eliminate the negative variances and changes and hence smoothening the process of budget forecasting.

Budget Strategies

There are various strategies to hospital budget. In this paper, three strategies are selected which include the use of data analytics strategy, the conventional budget modeling (fee-for-service) and the input-output approaches to budgeting (Cleverly, 2005 and Worth, 2015). In the conventional budget, the manager examines the fee to be paid by the patient and the cost of the medication materials and human labor needed and then allocate the appropriate budget amount. This practice is largely affected by changes and variances in the external and internal performance environments. On the other hand, the data analytics strategy involves the use of large quantity of data to analyze trends, inform decision making and forecast the likely outcomes that can distort the set budget. Over the past decade, the use of artificial intelligence is supporting the data analytics models in budgeting (Worth, 2015). In the Input-Output (I-O) approach to budgeting, a manager should first subdivide a department into sub-units with defined tasks, roles and responsibilities and finite number of workers (Cleverly, 2005.) Secondly, the budgeting leader defines how to measure the output and the cost associated to the production of a unit output. Thirdly, the executive links these two inputs and outputs and identifies the trade-offs (Cleverly, 2005.) The production process that minimizes costs and optimize benefits is selected. Budget is then allocated to facilitate the production of the units (Cleverly, 2005.) Higher amounts of inputs call for larger budgets and yield more outputs.


In summary, this paper has identified the main concepts used in budget forecasting. More importantly, the paper reveal that variances and changes hinder the process of budget forecasting. Therefore, in the SLMC cardiac unit budget forecasting, the executive nurse should focus on the input-output strategy to budgeting. First, sub-divide the roles and activities in the Cardiac Unit into sub-units. Every unit should then define and measure the inputs required and the resultant output. Secondly, associates the outputs and inputs to their respective costs. Thirdly, allocate the sufficient budget to fund the inputs required to give rise to the desired output. Finally, set a flexible budget to offset unforeseen variances and changes.

1. Cleverly, W. O. (2005). Input-output analysis and the hospital budgeting process. Health services research, 10(1), 36.

2. Harvey, D., & Lemaire, B. (2015). Corrective action plans. Internal Auditor, 72(5), 17-19. Retrieved from http://eds.a.ebscohost.com.proxy.chamberlain.edu.

3. Hughes, R. G., Bobay, K. L., Jolly, N. A., & Suby, C. (2015). Comparison of nurse staffing based on changes in unit-level workload associated with patient churn. Journal of Nursing Management, 23(3), 390-400.

4. Heering, H. C., & Mennella, H. A. (2017). Flexible Budgeting. CINAHL Nursing Guide. Retrieved from http://eds.b.ebscohost.com.proxy.chamberlain.edu.

5. Iqbal, M. J., Geer, M. I., & Dar, P. A. (2017). Evaluation of Medicines Forecasting and Quantification Practices in Various Public Sector Hospitals Using Indicator Based Assessment Tool. Journal of Applied Pharmaceutical Science Vol, 7(12), 072-076.

6. Patel, I., Chang, J., Suh, W. S., Lin, H., Kim, S., & Balkrishnan, R. (2012). Does state budget pressure matter for uncompensated care spending in hospitals? Findings from Texas and

7. California. The International Journal of Health Planning And Management, 27(2), 88-103

8. Pentecost, M. J. (2016). Forecasting Health Care Spending. Journal of the American College of Radiology, 13(12), 1473.

9. Slyter, M., Hernandez, S. R., Borkowski, N., Hearld, L. R., & Smith, D. (2017). The Relationship between Hospital Budget Variances and Operating Margins. In Academy of Management Proceedings (Vol. 2017, No. 1, p. 13915). Briarcliff Manor, NY 10510: Academy of Management.

10. Worth, T. (2015). How to best use analytics in the hospital budgeting process. Health Care Finance News [website]. Retrieved from http://www.healthcarefinancenews.com/news/how-best-use-analytics-hospital-budgeting-process

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