HI499 Unit 7 Implementation and Cost Plan Example

Sixteen Weeks, $16,800 Up Front and $5,443.83 a Year: A Unit 7 Implementation and Cost Plan That Rests Its Case on Overlays Avoided

[Student Name]

Purdue University Global

HI499: Bachelor's Capstone in Health Information Management

Unit 7 Implementation and Cost Plan

[Instructor Name]

[Date]

Callowfield Health, its vendors and every cost figure are composite; the sources cited are real.

Recommendation

Callowfield Health should change the self-scheduling tool so that uncertain matches go to a staff review queue instead of creating a new record, add an address standardization service before matching, and add two questions and an optional last-four field to the booking screen. These are the changes that survived the Unit 6 constraint review. Together they are expected to bring the tool's duplicate rate from 4.09 percent to the target of 1.0 percent.

Sequence

Order matters more than cost. The address service must be live before the queue opens, or reviewers will spend their time on pairs that standardization would have resolved, and the queue must be staffed and trained before the setting changes, or uncertain bookings will pile up with nobody to decide them. The plan runs in four phases, each with a gate that must be met before the next begins.

1. Phase 1, weeks 1 to 4, configuration and testing. The application analyst builds the new routing for uncertain matches and the two booking questions in the test environment, and a data integrity specialist tests them with 200 historical bookings. Gate: every test booking with an uncertain score lands in the queue, and none creates a record.

2. Phase 2, weeks 5 to 8, address standardization. The patient access director signs the vendor's business associate agreement, the analyst connects the service, and the privacy officer confirms that original entries are retained. Gate: signed agreement on file and 98 percent of test addresses returned in standard form.

3. Phase 3, weeks 9 to 12, queue staffing and training. The health information management director assigns queue review to the two data integrity specialists, with the call center lead as backup, and the specialists complete training on the second-person check. Gate: written queue procedure approved and both reviewers trained.

4. Phase 4, weeks 13 to 16, go-live and start of a 90-day watch. The new routing goes live on a Tuesday, the data integrity lead checks the queue twice daily for the first two weeks, and the project lead reports measures weekly. Gate for closing the watch: three consecutive closed months at or below target.

The address service comes before the queue because every pair it separates is a pair a person never has to review.

Cost Assumptions

Every figure below comes from a stated assumption so it can be checked or changed.

1. Loaded labor rate for specialists and billing staff: $32.00 an hour.

2. Time to research, merge and notify for one duplicate pair: 45 minutes, or $24.00.

3. Time to review one uncertain match in the queue: 6 minutes, or $3.20.

4. Uncertain matches from the tool: about 236 a month, or 2,832 a year, from the engine's logs.

5. Claims held each month because of tool duplicates: 23; each held claim takes about 34 minutes to rework, or $18.00.

6. Duplicates at target: 1.0 percent of about 2,152 tool records a month, or about 21.5, compared with 88 now.

Costs

One-time:

1. Vendor configuration change to the tool's matching routing and booking screen: $14,500.00.

2. Training for queue reviewers, call center staff and clinic schedulers: $2,300.00.

3. Total one-time: $16,800.00.

Recurring, each year:

1. Address standardization service: $8,400.00.

2. Queue review time: 2,832 reviews at $3.20, or $9,062.40.

3. Total recurring: $17,462.40.

Savings

Savings are counted at the target rate, not at zero, since some duplicates will continue.

1. Merge labor: 66.5 fewer pairs a month, or 798 a year, at $24.00, which comes to $19,152.00.

2. Claim rework: held claims fall in proportion to duplicates, from 23 to about 5.6 a month, or about 208.6 fewer claims a year, at $18.00, which comes to $3,754.23.

3. Total recurring savings: $22,906.23.

4. Net recurring saving: $22,906.23 minus $17,462.40, or $5,443.83 a year.

5. Payback of the one-time cost: about 3.1 years.

The Case the Numbers Leave Out

On labor alone, this is a modest investment. The stronger case is what the figures do not price. Two of the system's three overlays last year involved a record created by the tool, and each took the privacy officer and data integrity lead about two weeks to investigate and unwind, along with notifications and claim corrections. Overlays carry the risk of one patient's allergy, medication or result appearing in another's record. A duplicate also starts a patient's first visit on an empty record, which the root cause analysis showed can hide an allergy for days. None of these harms has a reliable dollar value, and the plan does not invent one. Health IT projects that succeed are usually those that tie the technical change to a clear clinical or operational purpose and plan for the people who will run it (Kaplan & Harris-Salamone, 2009); this plan's purpose is safer records, and its staffing is built in.

Staffing Effect

The queue adds about 23.6 hours of review a month, but merge work falls by about 49.9 hours a month, so the two data integrity specialists gain time overall. The plan does not add positions. It moves specialist time from cleaning up duplicates after the fact to preventing them at the point of booking.

Measures

The plan uses one outcome measure, two balancing measures and one process measure, each read monthly.

1. Outcome: the tool's duplicate rate, defined exactly as in the proposal, with the 60-day counting window. Target 1.0 percent.

2. Balancing: the share of online bookings abandoned before completion, since a slower path could drive patients away. Alert if it rises more than 2 points from its baseline of 11 percent.

3. Balancing: queue age, the share of uncertain bookings decided within one business day. Target 95 percent.

4. Process: the share of tool records whose address was standardized before matching. Target 98 percent.

Monthly readings will be plotted on a run chart, and a sustained shift, not a single month, will count as evidence of change (Langley et al., 2009). Published work on standardization gives reason to expect improvement in matching accuracy, though not in a booking tool specifically (Grannis et al., 2019), so the first three months will be watched closely.

Risks

The main risk is that patients abandon bookings when they wait for review. To prevent that, the booking is confirmed to the patient at once, and only the record decision waits for the queue. A second risk is vendor delay in Phase 2; if the agreement is not signed by week 8, Phase 3 begins anyway and go-live moves back by the length of the delay. A third is that reviewers attach a booking to the wrong record; the second-person check for mismatched surnames or birth dates addresses it.

References

Grannis, S. J., Xu, H., Vest, J. R., Kasthurirathne, S., Bo, N., Moscovitch, B., Torkzadeh, R., & Rising, J. (2019). Evaluating the effect of data standardization and validation on patient matching accuracy. Journal of the American Medical Informatics Association, 26(5), 447-456. https://doi.org/10.1093/jamia/ocy191

Kaplan, B., & Harris-Salamone, K. D. (2009). Health IT success and failure: Recommendations from literature and an AMIA workshop. Journal of the American Medical Informatics Association, 16(3), 291-299. https://doi.org/10.1197/jamia.M2997

Langley, G. J., Moen, R. D., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The improvement guide: A practical approach to enhancing organizational performance (2nd ed.). Jossey-Bass.