HI499 Unit 3 Departmental Data Review Example

2,544 Confirmed Pairs, Divided by the Records Each Source Created: A Unit 3 Departmental Data Review of Duplicate Patient Records by Registration Channel

[Student Name]

Purdue University Global

HI499: Bachelor's Capstone in Health Information Management

Unit 3 Departmental Data Review

[Instructor Name]

[Date]

Callowfield Health, its merge log and every figure are composite; the sources cited are real.

Data Sources

Three extracts were used, each covering the twelve closed months from last October through this September. The merge log, kept by the data integrity specialists, records every confirmed duplicate pair with the date each record was created, the registration source of the newer record, how the pair was found and the date of the merge. It records confirmed pairs only, so suspected duplicates still waiting in the work queue are absent, and duplicates never found are absent too. The registration volume report, run by the patient access analyst, counts new records created each month by source. The claims hold report, run by the revenue cycle analyst, lists claims held at billing for authorization or eligibility problems, with the record number involved. All three were provided with patient identifiers removed, under the sponsor's approval.

Rates by Source

Counts alone would mislead, because sources differ greatly in volume. The figures below are monthly averages across the twelve months.

1. Online self-scheduling tool: 2,152 new records, 88 confirmed duplicates, rate 4.09 percent.

2. Emergency department registration: 4,310 new records, 79 duplicates, rate 1.83 percent.

3. Clinic front desks: 4,240 new records, 28 duplicates, rate 0.66 percent.

4. Central call center: 1,142 new records, 17 duplicates, rate 1.49 percent.

5. All sources: 11,844 new records, 212 duplicates, rate 1.79 percent.

Emergency registration produces nearly as many duplicates as the tool, 79 against 88, but from twice the volume, and its patients often arrive unable to give full details. The tool's patients, by contrast, sit at home with time to type, which makes its rate the more surprising. Its share of new records is 18.2 percent and its share of duplicates is 41.5 percent. The self-scheduling tool is not the largest source of new records, but it is by far the most likely to create a record for someone who already has one.

Trend Across the Year

If the tool's rate had been falling, the problem might have been a launch issue that was settling on its own. It was not. Month by month, the rate ranged from 3.8 percent to 4.4 percent, with no downward slope: the first six months averaged 4.11 percent and the last six 4.07 percent. Volume through the tool rose by about 9 percent across the year as more clinics opened booking slots online, so the number of duplicates grew even though the rate held. A flat rate on rising volume means the monthly workload for the data integrity desk is increasing.

How Duplicates Were Found

1. Found by staff at the patient's next visit: 61 percent.

2. Found by the nightly possible-duplicate report: 23 percent.

3. Found in coding or billing: 16 percent.

4. Median time from creation of the newer record to confirmation: 19 days.

Most duplicates are discovered by accident, when a front desk worker or nurse notices a missing history, not by any system designed to catch them. The 19-day median matters because care often happens in that window. A patient who books online for a first visit at a new clinic may see a physician, have labs drawn and receive results, all on a record that does not show the allergies and medications on the original. Research on identifier matching has linked exactly this split of information to patient safety risk (McCoy et al., 2013).

Consequences

1. Claims held each month for authorization or eligibility problems traced to a duplicate created by the tool: 23 on average, each needing manual rework before release.

2. Merge work: 88 pairs a month from the tool, at about 45 minutes each for research, merge and notification, roughly 66 hours of specialist time.

3. Overlays: three incidents in the twelve months across the whole system, two of them involving a record created by the tool. In each, information from two different people ended up in one record after an incorrect merge.

The overlays deserve their own paragraph. Three a year is a small number, but an overlay is the error with consequences out of proportion to its size: one person's results, diagnoses or medications appear in another person's record, which can affect treatment and is a privacy incident to investigate. Each one took the privacy officer and the data integrity lead about two weeks to unwind. Fewer duplicates mean fewer merges, and fewer merges mean fewer chances to merge the wrong pair.

What the Data Cannot Show

The review cannot show why the tool creates duplicates; that is the work of Unit 5. It cannot count duplicates that were never found, so all rates are floors. It also cannot separate patients who made a typing error from patients whose details genuinely changed, such as a new address or surname, since the merge log records the outcome and not the reason. Finally, twelve months is enough to describe a stable rate but not to judge seasonal patterns with confidence. Health statistics texts advise reporting rates with their numerator, denominator and period stated every time (Horton, 2017), and every figure above follows that rule.

Conclusion for the Proposal

The data support the proposal's focus. The tool's rate is more than twice the system rate, it has not improved on its own, its duplicates are found late and mostly by chance, and they drive claim holds, specialist time and a share of the system's overlays. The proposal's baseline of 4.09 percent and its 60-day counting window both hold up against the twelve months reviewed. Studies of index data report that mismatches cluster in a few fields that patients or staff enter by hand (Just et al., 2016), which points the root cause analysis toward what the tool asks patients to type and what it does with the answer.

References

Horton, L. A. (2017). Calculating and reporting healthcare statistics (5th ed.). AHIMA Press.

Just, B. H., Marc, D., Munns, M., & Sandefer, R. (2016). Why patient matching is a challenge: Research on master patient index (MPI) data discrepancies in key identifying fields. Perspectives in Health Information Management, 13(Spring), 1e.

McCoy, A. B., Wright, A., Kahn, M. G., Shapiro, J. S., Bernstam, E. V., & Sittig, D. F. (2013). Matching identifiers in electronic health records: Implications for duplicate records and patient safety. BMJ Quality & Safety, 22(3), 219-224. https://doi.org/10.1136/bmjqs-2012-001419