HSA 6175 Financial Management Of Health Systems Assig 351487
HSA 6175 Financial Management Of Health Systemsassignment 1problem 1ov
The assignment involves two primary problems related to financial management in healthcare settings. The first problem requires calculating Dr. Rossi's revenues for 2016 based on his payor mix and determining whether he should accept an HMO capitation offer. The second problem involves analyzing hospitalization data for the Orthopedic Unit at Collins General Hospital, including calculating the average Per Diem paid by Medicare and the hospital's actual Per Diem, as well as exploring reasons for discrepancies in length of stay.
Paper For Above instruction
Introduction
Financial management in healthcare organizations involves the accurate analysis of revenue streams, payor mixes, and cost structures to ensure sustainability and profitability. The given problems in this assignment highlight crucial aspects such as revenue calculations based on different payor types, contract arrangements, and payment rates, as well as evaluating hospital stay metrics to understand utilization and efficiency. This paper provides a detailed analysis of Dr. Rossi’s revenue for the year 2016, and a comprehensive examination of the orthopedic unit's hospitalization data, to illustrate essential financial management concepts in healthcare.
Problem 1: Dr. Rossi’s Revenue Calculation and Capitation Decision
Part A: Revenue Calculation
The first part of the problem involves calculating Dr. Rossi's total revenue for 2016 using the provided payor mix and visit data. To perform this calculation, we consider each payor group, their respective contract types, rates per visit or per member per month (PMPM), and the number of patients or utilization visits.
The data indicates:
- HMO A (Commercial Fee-for-Service): 1,320 patients, rate of $85 per visit
- HMO B (Medicare Capitation): 1,860 patients, capitation rate of $45 PMPM
- HMO B (Medicaid Capitation): 378 patients, capitation rate of $15 PMPM
- Medicare (Fee-for-Service): 1,632 patients, rate of $65 per visit
- Medicaid (Fee-for-Service): 574 patients, rate of $35 per visit
- Self Pay (Fee-for-Service): 307 patients, rate of $85 per visit
To compute annual revenue:
- Fee-for-Service revenue = number of patients × visits per year × rate per visit
- Capitation revenue = number of patients × 12 months × capitation PMPM rate
Assuming:
- Each patient in fee-for-service groups makes at least one visit per month, total visits per patient annually will be estimated based on typical utilization patterns, or the problem implies using the visit counts as a basis for revenue estimations.
Given the data constraints, the calculation simplifies to:
- For fee-for-service groups, total visits = patients × visits per month (assuming one visit per patient per month unless specified otherwise)
- For capitation groups, revenue = number of patients × 12 months × capitation rate
The detailed calculations:
1. HMO A (Commercial Fee-for-Service):
- Patients: 1,320
- Visits per patient: 1 (assuming one per month)
- Total visits = 1,320
- Revenue = 1,320 × $85 = $112,200
2. HMO B (Medicare Capitation):
- Patients: 1,860
- Capitation rate = $45 PMPM
- Revenue = 1,860 × 12 × $45 = $1,008,600
3. HMO B (Medicaid Capitation):
- Patients: 378
- Revenue = 378 × 12 × $15 = $68,040
4. Medicare Fee-for-Service:
- Patients: 1,632
- Revenue = 1,632 × $65 = $106,080
5. Medicaid Fee-for-Service:
- Patients: 574
- Revenue = 574 × $35 = $20,090
6. Self Pay Fee-for-Service:
- Patients: 307
- Revenue = 307 × $85 = $26,095
Adding these:
Total Revenue = $112,200 + $1,008,600 + $68,040 + $106,080 + $20,090 + $26,095 = $1,341,105
This is an approximate total revenue based on available data and assumed utilization.
Part B: HMO Capitation Offer Evaluation
HMO A offers Dr. Rossi an $11 PMPM capitation rate. To evaluate if accepting this rate is advantageous, compare it with existing revenue streams:
- Current capitation rates for HMO B are $45 and $15 PMPM, significantly higher than the proposed $11.
- At an $11 rate, annual revenue per HMO patient would be: 12 × $11 = $132.
- For 1,860 Medicare patients, that would total 1,860 × $132 = $245,520.
- For 378 Medicaid patients, total = 378 × $132 = $49,896.
- Total potential revenue with the new rate could be substantially lower than existing capitation income, which is over $1 million under current terms.
Furthermore, accepting a lower capitation rate could imply reduced income per patient unless volume significantly increases or quality incentives are included. The decision should factor in cost savings, risk sharing, administrative costs, and the potential for increased patient volume.
Given the significant difference between existing rates and the offered rate, it appears financially disadvantageous for Dr. Rossi to accept the $11 PMPM offer unless there are other strategic benefits, such as improved patient access, reduced administrative burden, or long-term contractual advantages.
Problem 2: Hospital Per Diem and Length of Stay Analysis
Part A: Average Per Diem by Medicare vs. Actual
The data provides:
- Medicare cases: 300
- Average MS-DRG payment: $3,500
- Average MS-DRG length of stay: 2.6 days
- Actual mean length of stay: 2.9 days
The average Per Diem from Medicare is calculated as:
\[
\text{Average Per Diem} = \frac{\text{Total Payment}}{\text{Length of Stay}}
\]
Thus:
\[
\frac{\$3,500}{2.6\ \text{days}} \approx \$1,346.15
\]
The hospital’s actual average Per Diem is:
\[
\frac{\$3,500}{2.9\ \text{days}} \approx \$1,206.90
\]
The hospital’s actual Per Diem is slightly lower than the calculated Medicare reimbursement based on DRG payment and length of stay, which suggests effective cost management on the hospital’s part or potential under-funding.
Part B: Reasons for Higher Actual ALOS
The actual average length of stay (2.9 days) exceeds the MS-DRG average of 2.6 days. Possible reasons include:
1. Patient Complexity and Comorbidities: Patients may have been more complex or had additional comorbidities requiring longer hospitalization for stabilization and recovery.
2. Discharge Planning and Post-Acute Care Access: Delays in arranging post-acute services or home care can extend hospital stays beyond typical durations.
3. Variability in Individual Recovery Times: Variations in individual patient recovery rates often lead to longer stays, especially in cases with unpredictable clinical courses.
4. Institutional Practices: Differences in hospital discharge policies or conservative clinical practices can lead to longer stays.
5. Resource Availability and Staffing: Adequate staffing and resource availability can influence discharge readiness, sometimes resulting in longer stays to ensure patient stability.
6. Coding and Data Collection Variances: Potential discrepancies in coding practices can influence LOS metrics and perceived differences from regional or national averages.
Understanding these factors helps in identifying areas for operational improvement to optimize hospital resource utilization, reduce unnecessary stays, and improve patient flow management.
Conclusion
This analysis illustrates the importance of comprehensive financial analysis in healthcare, where understanding payor mixes, contract terms, and operational metrics is vital for strategic decision-making. Dr. Rossi's revenue calculations demonstrate the impact of different reimbursement models, while hospital LOS analysis underscores the significance of operational efficiency and clinical factors affecting patient care duration. Overall, effective financial management requires integrating revenue analysis with clinical and operational insights to promote sustainability and quality in healthcare delivery.
References
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- Fetter, R. B., & Reisch, M. (2019). Hospital Utilization and Length of Stay. Healthcare Economics Review, 9(3), 150-159.
- Halpern, M. T., & Mack, D. (2017). Capitation and Fee-for-Service Models in Healthcare. Medical Economics, 94(5), 23-29.
- Jacobson, G., & Neuman, T. (2020). Understanding Medicare Payments and DRGs. Health Affairs, 39(4), 623-630.
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