HI150 Unit 6 Scanning and Indexing Review Example

One Hundred Twenty Pages Pulled From One Day's Batch: A Unit 6 Scanning and Indexing Review Checking Each Image Against Its Paper Source at a Composite Clinic and Tracing Every Error to the Step Where It Began

[Student Name]

Purdue University Global

HI150: Automation of Health Information 1

Unit 6 Scanning and Indexing Review

[Instructor Name]

[Date]

The clinic, its batch and its counts are composite; the review method, the identification principle and the sources cited are real.

Method

Population: all documents scanned on Tuesday, October 8, totaling 1,860 pages in 642 documents. Sample: 120 documents chosen by taking every fifth document in scan order and adding any document type not otherwise represented. Each sampled image was compared with the paper original, which the clinic keeps for 30 days after scanning. Five checks: image quality, page count against the source, patient match on two identifiers, encounter match and document type.

Findings

1. Image quality: 4 documents with a skewed or cut-off page edge, all legible. Rate 3.3 percent.

2. Page count: 2 documents missing a back page, both two-sided consent forms scanned on a one-sided setting. Rate 1.7 percent.

3. Patient match: 1 laboratory requisition indexed to the wrong patient, a different patient with the same last name seen the same morning. Rate 0.8 percent.

4. Encounter match: 2 consent forms under the right patient and the wrong visit, filed to the patient's previous encounter. Rate 1.7 percent.

5. Document type: 5 documents labeled "correspondence" that were outside records from another provider. Rate 4.2 percent.

6. Documents passing all five checks: 106 of 120, 88.3 percent.

One page in 120 belonged to someone else, and nothing in the system would have shown it until a clinician opened the wrong chart.

Severity

Wrong-patient indexing ranks first: it exposes one patient's information in another's record and hides it from the right one. Missing pages rank second, since a consent missing its signature page may be legally incomplete. Wrong-encounter indexing ranks third: the document is in the right person's record but may be missed when the visit is reviewed. Document type errors rank fourth, as they make records harder to find. Image quality problems rank last, since every page was legible.

Where Each Error Began

The wrong-patient requisition began at indexing, when the technician matched on last name and date of service rather than two identifiers. Identification practice in health care rests on using at least two identifiers for every match (Just et al., 2016). The missing back pages began at scanning, with the scanner left on a one-sided setting after a batch of single pages. The wrong-encounter consents began at indexing, when the default encounter was the patient's most recent visit rather than the visit on the form. The document type errors began at the front desk, where outside records were placed in the correspondence tray.

Fixes

1. Require two identifiers, name plus date of birth, before any index entry is saved.

2. Set the scanner to two-sided as the default.

3. Remove the default encounter; require the technician to select it from the date on the document.

4. Add an outside records tray at the front desk.

5. Repeat the review monthly with 60 documents and report the rates.

Correcting What Was Found

The misindexed requisition was moved to the correct patient, and the incident was reported to the privacy officer, who must decide whether the misplacement amounts to a breach under the federal definition (45 C.F.R. § 164.402, 2024) and document the review either way, as the privacy rule requires for complaints and incidents (45 C.F.R. § 164.530, 2024). Records found in the sample are corrected, but a 0.8 percent rate across 642 documents suggests about five other wrong-patient documents from that day alone, so the clinic re-checked every laboratory requisition from October 8.

References

45 C.F.R. § 164.402 (2024). https://www.ecfr.gov/current/title-45/section-164.402

45 C.F.R. § 164.530 (2024). https://www.ecfr.gov/current/title-45/section-164.530

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.