IHP 450 Module 6 Final Project Milestone Three example

Milestone Three: Impacts and Justifications for Autonomous AI Retinal Cameras in Adult Medicine

[Student Name]

Southern New Hampshire University

IHP 450: Healthcare Management and Finance

Module Six Final Project Milestone Three

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Milestone Three: Impacts and Justifications for Autonomous AI Retinal Cameras in Adult Medicine

Purpose

After reviewing the Milestone Two outline, the chief operating officer at Tamarack Community Health Center, a composite organization, asked for two things before the proposal goes to the finance committee: a five-year financial analysis that does not depend on a single good year, and a clear account of how the cameras would affect patients, staff and the mission. This paper provides both for the proposed purchase of three autonomous AI retinal camera systems at a capital cost of 103,000 dollars.

Five-Year Cash Flows

Table 1 shows the projected cash flows. Exam volume is 1,300 in the first year, while the cameras come online site by site, and 1,650 a year afterward, about 70 percent of the center's 2,350 adults with diabetes. Each exam collects an estimated 42 dollars and costs 25 dollars in license fees. Medical assistant time, about 10 minutes per exam at a loaded 24 dollars an hour, is counted as a cost even though no position is added, so that the analysis is conservative. A yearly 60,000 dollar incentive from the Medicaid plan is included in the base case and tested separately below.

Table 1

Projected Cash Flows for Three Autonomous AI Retinal Camera Systems

Note. Composite figures for illustration. Parentheses indicate cash outflows. Depreciation is excluded because it is not a cash flow.

Payback, Net Present Value and Return

Three standard measures summarize the case (Reiter & Song, 2021). The payback period, which counts how long the project takes to earn back its purchase price, is about 1.4 years. Discounting the five annual cash flows at the center's 6 percent cost of capital and subtracting the purchase price gives a net present value of about 219,000 dollars. Over five years, undiscounted net cash flows total 382,700 dollars, a return of about 272 percent on the 103,000 dollar investment. By any of the three measures, the base case is strong. A strong base case matters less than knowing which assumption it rests on, and in this proposal that assumption is the quality incentive.

Sensitivity Analysis

Two assumptions were tested. If exam volume runs 20 percent below plan in every year, the net present value falls only to about 202,000 dollars and payback lengthens to about 1.5 years, because each exam contributes a modest margin and volume is not the main source of value. The quality incentive is different. If the center never earns it, annual net cash flows fall to 12,900 dollars in the first year and 17,450 dollars afterward, payback does not occur within five years, and the net present value becomes negative, about minus 34,000 dollars. The break-even point is useful for the committee: the project reaches a net present value of zero if the center earns roughly 8,000 dollars a year in incentives or other savings, about one-seventh of the full payment. Because every trial participant offered the on-site exam completed it, compared with about a fifth of those sent to an eye specialist (Wolf et al., 2024), reaching the 60 percent threshold is plausible, but it is not guaranteed, and the proposal says so.

Impact on Patients and Quality

For patients, the change removes a 40-mile trip and a four-month wait. The exam happens during a visit they already made, the result is available before they leave, and patients with disease can be referred on the spot, which matters because the step from an abnormal result to an eye specialist is where follow-up usually breaks down. In the trial behind the recommended system type, the device identified more than mild retinopathy with high sensitivity in primary care offices (Abramoff et al., 2018), and national diabetes standards recognize autonomous AI systems as an alternative to traditional screening (American Diabetes Association Professional Practice Committee, 2022). The quality impact is a projected rise in the eye exam rate from 38 percent to about 70 percent, a gain for 750 or more patients a year.

Impact on Staff and Operations

Medical assistants will take the images, which adds about 10 minutes to affected visits and requires two hours of training per assistant. To protect clinic flow, exams will be done while patients wait for the clinician, not after the visit. Physicians gain a result they can act on during the visit and lose the task of tracking outside referrals that never return. The billing office must add the exam code and confirm payment with each payer, and information technology must maintain the interface with the health record. No new positions are required, which matters given the labor cost pressure identified in Milestone One.

Impact on Mission, Vision and Values

The proposal advances the mission of affordable, high-quality care regardless of ability to pay, because uninsured patients receive the exam at the point of care without a separate specialist bill. It serves the vision of a community where no one goes without care, and it expresses the values of access and quality directly. Stewardship, the value that asks the center to spend wisely, is the one the finance committee will test, and the analysis above shows the project meets it in the base case and puts only a limited sum at risk otherwise.

Risks and Mitigation

Four risks are paired with responses. If the incentive is not earned, the loss is bounded at about 34,000 dollars in present value, and monthly tracking of the exam rate will allow corrective outreach before the measurement year ends. If images cannot be graded, the patient is referred for a full exam. Because the system screens only for diabetic eye disease, any patient with visual symptoms is referred regardless of the result. And because the device connects to the health record, it will pass the center's cybersecurity review before installation.

Justification

The cameras pay back their cost in under two years in the base case, improve care for hundreds of patients each year, add no positions and express every one of the center's values. The one material risk, the quality incentive, is identified, measured and manageable. The proposal should proceed to the finance committee.

References

Abramoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1, Article 39. https://doi.org/10.1038/s41746-018-0040-6

American Diabetes Association Professional Practice Committee. (2022). 12. Retinopathy, neuropathy, and foot care: Standards of medical care in diabetes-2022. Diabetes Care, 45(Suppl. 1), S185-S194. https://doi.org/10.2337/dc22-S012

Reiter, K. L., & Song, P. H. (2021). Gapenski's healthcare finance: An introduction to accounting and financial management (7th ed.). Health Administration Press.

Wolf, R. M., Channa, R., Liu, T. Y. A., Zehra, A., Bromberger, L., Patel, D., Ananthakrishnan, A., Brown, E. A., Prichett, L., Lehmann, H. P., & Abramoff, M. D. (2024). Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: The ACCESS randomized control trial. Nature Communications, 15, Article 421. https://doi.org/10.1038/s41467-023-44676-z