| Course | HS420 Health Informatics |
|---|---|
| Unit | Unit 6 |
| Paper type | Data quality review |
| Length | About 620 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Health Science |
| Updated | October 2026 |
Free sample paper for HS420 Unit 6
Complete but Not Correct: A Unit 6 Data Quality Review of Parent Mobile Numbers Used for Vaccine Reminder Texts
[Student Name]
Purdue University Global
HS420: Health Informatics
Unit 6 Data Quality Review
[Instructor Name]
[Date]
Hollis Park Pediatrics, its records and every count are composite; the data quality frameworks and the reminder review cited are real.
Purpose
The review examines one field, the parent's mobile number, because three decisions depend on it: reminder texts for vaccines that are due or overdue, after-hours callbacks from the on-call nurse, and the registry's own reminder program, which receives the number through the new connection. Reminder and recall messages are an effective way to raise vaccination rates when they reach families (Jacobson Vann et al., 2018), so a wrong number costs vaccinations, not just postage.
Dimensions Tested
The review tests four dimensions separately, since a field can do well on one and badly on another. The dimensions follow widely used frameworks for data quality in general and for health records in particular (Wang & Strong, 1996; Weiskopf & Weng, 2013).
Completeness
[9,140] active patients; [8,866] with a mobile number on file, 97.0 percent. On completeness alone, the field looks excellent. But [61] records carry the clinic's own main number, entered by staff when a parent gave none, which counts as complete and is useless for texting.
Accuracy
Accuracy was checked against delivery reports from the texting service for one quarter. Of [2,180] reminder texts sent, [412], 18.9 percent, were reported undeliverable or went to a number that replied that the child was unknown. A sample of [60] of those records, checked by phone with the family at their next visit, found [41] numbers that had changed and [11] that had been mistyped at registration.
Ninety-seven percent of records have a number, but almost one in five texts did not reach the family, so completeness was measuring the clinic's typing, not its ability to reach parents.
Timeliness
The number is entered at a child's first registration and is supposed to be confirmed at each visit. Audit trails show that the field was updated in only [14] percent of visits last year. For children who come only for annual checkups, a number can be a year or more out of date before anyone notices.
Consistency
The practice management system and the health record each hold a copy of the number, and the interface sends changes only one way, from practice management to the record. In [7] percent of records the two copies differ, because nurses sometimes update the number in the record during a phone call, and that change never reaches practice management or the texting service.
Causes
Three causes explain most of the problem. The registration screen accepts any ten digits and does not check format. The visit check-in asks staff to confirm the address and insurance but not the phone number. And the two-copy design lets the systems drift apart.
Recommendations
1. Add a check-in prompt asking parents to confirm the mobile number on a tablet, with the current number partly masked.
2. Flag the clinic's own number and other placeholder values as missing rather than complete.
3. Make practice management the single source of the number and remove editing from the health record's copy.
4. Feed undeliverable reports from the texting service into a weekly worklist for front desk staff to call families.
5. Report accuracy, measured as the share of texts delivered, alongside completeness every quarter.
Limits of the Review
Delivery reports confirm that a message reached a phone, not that a parent read it. The sample of [60] records checked by phone is small. And the review covers one field; similar problems may affect addresses and email.
References
Jacobson Vann, J. C., Jacobson, R. M., Coyne-Beasley, T., Asafu-Adjei, J. K., & Szilagyi, P. G. (2018). Patient reminder and recall interventions to improve immunization rates. Cochrane Database of Systematic Reviews, 2018(1), CD003941. https://doi.org/10.1002/14651858.CD003941.pub3
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099
Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681
Reading the HS 420 Unit 6 instructions
The sixth unit usually asks you to review the quality of one data element or dataset, using named dimensions such as completeness, accuracy, timeliness and consistency, and to recommend fixes. Reviews typically span three or four pages. Credit goes to a clear statement of why the data matter, each dimension tested with a method and a result, causes traced to specific screens or processes, and recommendations tied to those causes. Many sections reward showing that one dimension can look good while another fails. Use real or composite counts and say how each was obtained. Cite a recognized data quality framework and, where relevant, evidence on why the data matter.
Inside the HS420 Unit 6 example
A purpose section names the field and the three decisions that depend on it, citing a Cochrane review on reminder and recall. A short section introduces the four dimensions, citing two frameworks. Completeness is reported first, along with the placeholder numbers that inflate it. Accuracy uses a quarter's delivery reports and a phone check of sixty records; the highlighted sentence separates completeness from accuracy. Timeliness draws on audit trails, and consistency compares the two copies of the number. A causes section traces the failures to the registration screen, the check-in routine and the two-copy design. Five numbered recommendations and a limits section close the review.
Where the points sit in the HS420 Unit 6 rubric
Data quality reviews are typically scored on the use of named dimensions, the rigor of each test, the tracing of causes, the quality of recommendations and presentation. Rigor of testing generally earns the most, because the unit's lesson is that each dimension needs its own method and evidence. Dimension credit rewards defining each one before testing it. Cause tracing earns points when failures are linked to specific design or workflow choices. Recommendations are rewarded when each answers a named cause. Presentation covers clear counts, labeled sections and citations to a data quality framework. A review that shows a strong figure hiding a weak one demonstrates the insight graders hope for. Reporting the sample size behind each accuracy figure helps a grader judge how far the finding can be trusted.
HS420 Unit 6 help from the desk
Many reviews stop at completeness, reporting how often a field is filled and calling it quality. Another frequent problem is testing accuracy without any outside source to compare against. Some students describe problems in general terms, such as poor data entry, without finding the screen or step responsible. Others recommend more staff training for every problem, even when the cause is a design flaw. Leaving out limits overstates what a small sample shows. If you are reviewing another field, perhaps race and ethnicity or allergy lists, name the decisions that depend on it, and the dimensions and tests can be chosen to fit. A review without a purpose statement leaves the reader unsure why the field matters.
Get an HS420 Unit 6 example written to your instructions
Pick the field or report Unit 6 revolves around, mention any dataset your section provides, and include the rubric. The review measures each quality dimension separately, compares the field with an independent source and places root cause after the evidence. First custom sample free, delivered in 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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HS420 Unit 6 questions, answered
Where can I find data for a data quality review?
Some sections supply a dataset or a scenario with figures. Where they do not, a small sample you are permitted to use, such as de-identified counts from a workplace report, or published figures on a common field can work, clearly labeled. The example's numbers are composite and bracketed. What matters most is comparing the field against something independent of it.
Which data quality dimensions should the review use?
Accuracy, completeness, timeliness and consistency cover most fields, and AHIMA's data quality model lists several more, such as definition and granularity. Four measured well usually earn more than ten named in passing. The example chose dimensions whose failure would change a decision, which is a defensible way to limit the list.
Is a default value always a data quality problem?
No. Defaults are sensible where one answer is nearly always right and a wrong entry causes little harm. The trouble starts when a default fills a field that drives decisions, and when nothing prompts staff to change it. The example keeps defaults for two low-stakes fields on the same screen and removes only the language one.
Where can I find a free HS420 Unit 6 sample paper?
This page shows the complete Unit 6 data quality review, testing parent mobile numbers against delivery reports for vaccine reminder texts.
Can a field be complete but inaccurate?
Yes. A field can be filled in every record and still be wrong, as when a placeholder or an outdated number is entered, which is why completeness alone is a weak test.