HCF 7010 Module 3 Budgeting and Technology Written Assignment Example

Over Budget for the Right Reasons and the Wrong Ones: A Unit's Labor Variance and a $1.2 Million Revenue Cycle Technology Decision

Student Name

MBA in Healthcare Management, Thomas Edison State University

HCF-7010: Healthcare Finance

Mentor Name

Month Day, Year

Over Budget for the Right Reasons and the Wrong Ones: A Unit's Labor Variance and a $1.2 Million Revenue Cycle Technology Decision

Budgeting connects a hospital's plans to its resources. At Barnegat Bay, the composite 220-bed Ocean County hospital used in this course, the finance committee has two questions this quarter. First, the fourth-floor medical-surgical unit's nursing labor costs are well over budget, and the committee wants to know why. Second, the revenue cycle director has proposed buying an automated eligibility and prior authorization system, and the committee wants a capital budgeting analysis and an assessment of the technology's risks. This assignment answers both, using the budgeting framework in Berger (2014).

Part 1: The Operating Budget and Variance Analysis

The unit's annual budget assumed 9,125 patient days, 8.0 nursing hours per patient day and an average wage of $52 an hour, for a static budget of $3,796,000. The year actually brought 9,600 patient days, 79,680 nursing hours, or 8.3 hours per patient day, and an average wage of $54, for an actual cost of $4,302,720. The overrun of $506,720 looks alarming, but a static comparison mixes changes the manager controls with changes in volume that the manager does not.

Berger (2014) recommends flexing the budget, recalculating the spending that the year's actual patient volume would have justified. At 9,600 patient days, the budgeted hours and wage would have cost $3,993,600. The difference from the static budget, $197,600, is a volume variance: more patients required more care, which is not a management failure and is matched by additional revenue. The remaining $309,120 is the flexible-budget variance, which splits into two parts.

The efficiency variance means the unit used 0.3 more hours per patient day than planned; the rate variance means each hour cost $2 more. Interviews with the nurse manager explain both: two vacancies were filled with overtime and agency nurses, which raised the average rate, and the unit received more post-surgical patients requiring frequent assessments, which raised hours per patient day. The first problem calls for recruitment and scheduling action, such as filling the two vacancies and building a float pool so that overtime is no longer the default; the second suggests that the budget's staffing standard should be updated for the unit's changing case mix. A flexible budget turns a single alarming number into three explainable ones, each with a different owner.

Part 2: A Technology Investment as a Capital Decision

The proposed system checks patients' insurance eligibility and authorization requirements automatically before scheduled services. It costs $1.2 million to purchase and implement and $180,000 a year to maintain. The revenue cycle director estimates that by preventing front-end denials it will preserve about $900,000 a year of revenue now written off and save about $150,000 a year in staff time, for net annual benefits of $870,000. Discounted at the hospital's 6 percent cost of capital over a five-year life, the investment has a net present value of about $2.46 million and pays back in under a year and a half.

Berger (2014) cautions that capital decisions depend on the quality of the benefit estimates. The denial savings are plausible given the hospital's current 11 percent initial denial rate, but they assume that front-end errors cause a large share of denials, which the hospital should test with its own denial data before committing. Even if benefits were half the estimate, at $435,000 a year, the net present value would remain positive, at about $630,000.

Part 3: Financial Implications and Risks of Health IT

Technology does not improve finances automatically. Collum et al. (2016) studied hospitals over several years and found that increasing the level of electronic health record adoption was not associated with changes in operating margin, although hospitals that moved from no EHR to comprehensive, hospital-wide adoption saw improved total margins after two years. Partial adoption brought costs without measurable financial gains. The lesson for Barnegat Bay is to implement the authorization system fully, with workflow changes and training, rather than as an add-on that staff work around.

Technology also concentrates risk. In 2024 a cyberattack on Change Healthcare, a major claims clearinghouse, disrupted billing for providers across the country; the Centers for Medicare & Medicaid Services distributed $3.3 billion in relief payments, $2.2 billion of them to hospitals (Neprash et al., 2025). A hospital that depends on a single vendor for claims or authorization should budget for cybersecurity, maintain contingency processes and hold enough cash to survive weeks of delayed payments, which links back to the liquidity analysis in Module 1.

Conclusion

Flexing the unit's budget shows that $197,600 of the overrun reflects more patients and $309,120 reflects higher hours and wages, each with a cause the hospital can address. The proposed authorization system is a sound investment with a positive net present value even if benefits fall by half, provided it is fully implemented and its vendor risk is managed. Budgeting and technology decisions are strongest when each number is tied to a cause and an owner.

References

Berger, S. (2014). Fundamentals of health care financial management: A practical guide to fiscal issues and activities (4th ed.). Jossey-Bass.

Collum, T. H., Menachemi, N., & Sen, B. (2016). Does electronic health record use improve hospital financial performance? Evidence from panel data. Health Care Management Review, 41(3), 267-274. https://doi.org/10.1097/HMR.0000000000000068

Neprash, H. T., Beebe, T. J., Yost, G., & Carroll, C. (2025). Lessons from CMS relief funding after cyberattack on Change Healthcare. Health Affairs, 44(12), 1466-1472. https://doi.org/10.1377/hlthaff.2025.00990