| Course | HLTH 4403 Healthcare Information Management |
|---|---|
| Module | Module 3 |
| Paper type | Online health risk assessment analysis |
| Length | 1,180 words, about 4 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | B.S. in Healthcare Administration |
| Updated | September 2026 |
Free sample paper for HLTH 4403 Module 3
Six Thousand Scores and Few Blood Tests: Analyzing an Online Prediabetes Risk Test at a Community Health Center Network
Student Name
American College of Education
HLTH4403: Healthcare Information Management
Module 3 Assignment
Instructor Name
October 19, 2026
The Tool
In early 2025, the composite community health center network discussed in earlier modules added an online prediabetes risk test to its website and to tablets in its waiting rooms, in English and Spanish. The tool is the seven-question risk test that the federal CDC promotes nationally. It asks about age, sex, a history of gestational diabetes, family history of diabetes, high blood pressure, physical activity and weight relative to height. Each answer adds points, and a total of 5 or more is reported as high risk, with advice to talk with a clinician about a blood test.
The network adopted the tool because about a third of adults nationally have prediabetes and most do not know it, and because it runs a lifestyle change program for people with prediabetes. In its first 12 months, the tool was completed about 6,200 times. This paper analyzes whether the tool is valid, usable, connected to the record, safe with patients' information and, most importantly, whether it leads anyone to care.
What the Score Means
The scoring comes from a diabetes risk score developed by Bang et al. (2009) using national survey data. They found that age, sex, family history, hypertension, obesity and physical activity were associated with undiagnosed diabetes, and that a cut-point of 5 or more points identified 79% of people with undiagnosed diabetes in the development data, with a specificity of 67% and a positive predictive value of 10%. The score performed at least as well as the other instruments they compared.
Those figures describe a screening tool, not a diagnosis. The score is built to be sensitive, catching most people at risk while flagging many who turn out to be fine, and at a positive predictive value of 10% most people scoring high for undiagnosed diabetes will not have it. That is appropriate for a tool meant to send people for a blood test, but only if the blood test follows. A high score that leads nowhere has all of the tool's false alarms and none of its benefit.
The tool's purpose also fits current screening guidance. Current federal screening guidance points the same way: the Task Force advises testing for prediabetes and type 2 diabetes in people aged 35 through 70 who carry excess weight, and sending anyone found to have prediabetes to programs shown to work (US Preventive Services Task Force, 2021). A risk test can help find people who have not been screened, but it does not replace screening those whom the guideline already covers.
Usability and Reach
The questions are short and the arithmetic is automatic, but three usability problems appeared when five patients were observed using the waiting-room tablets. The weight question asks for pounds and inches, and two patients did not know their height. The Spanish version translates the questions but not the result screen's explanation, which appears in English. And the tablets are placed near the check-in desk, where patients hurry to finish before being called. Of the 6,200 completions, 71% came through the website rather than the tablets, and website users skewed younger and English-preferring, which repeats the pattern the network saw with its portal.
Where the Data Go
The tool's results do not enter the electronic health record. Website completions are not linked to any patient identity, and tablet results appear on screen and vanish. A patient who scores high and tells the medical assistant may be offered a test; a patient who does not mention it will not be. In the language of the first module, the risk score is not even unstructured data in the record; it is outside the record entirely.
The network should decide whether the score belongs in the record. For tablet completions during a visit, it should: the answers could feed a structured screening field, prompting the clinician when a patient without a recent A1c scores high. For anonymous website use, keeping results out of the record is reasonable, but the result screen should then offer an easy next step, such as booking a lab visit online.
Privacy
Because the tool asks about health conditions, how its data are handled matters even when results are not stored. A review of the web page found that it loaded a third-party analytics script that recorded page visits and button clicks, which could reveal to an outside company that a visitor had completed a diabetes risk test. The network's privacy officer had not reviewed the page because it was built by the marketing department. The script has since been removed from the tool's pages pending review. The broader lesson is that consumer-facing health tools need the same privacy review as clinical systems, whoever builds them, and that the review should include what the page sends to others as well as what it stores.
Does It Lead to Care?
Follow-through is the tool's weakest point. Of tablet users who scored 5 or more and had no A1c in the prior year, only 19% had an A1c drawn within 90 days. Of patients later found to have prediabetes, 7% enrolled in the network's lifestyle program. The benefit of finding prediabetes depends on what happens next: among adults with elevated glucose in the landmark federal prevention trial, the group coached on diet and activity went on to develop diabetes 58% less often than the placebo group, and the metformin group 31% less often (Diabetes Prevention Program Research Group, 2002). A risk score that does not lead to a blood test, and a blood test that does not lead to the program, prevent nothing.
The network should therefore measure the tool by three linked rates: the percentage of high scorers without a recent A1c who are tested within 90 days, the percentage of those with prediabetes who are referred to the lifestyle program, and the percentage who enroll. It should also connect high tablet scores to the diabetes registry's outreach work so that care coordinators follow up.
Recommendations
The analysis supports five changes. First, move the waiting-room tablets from the check-in desk to exam rooms, where the medical assistant can help with height and weight from the vital signs just taken, and where the patient is not rushed. Second, translate the result screen and its advice into Spanish, and have the network's diabetes educators review both versions for reading level. Third, send tablet results into a structured screening field in the record, so that a high score in a patient without a recent A1c triggers a prompt to the clinician during the same visit. Fourth, add a button to the website result screen that books a lab visit directly, since anonymous users cannot otherwise be followed. Fifth, place every consumer-facing tool under the privacy officer's review before it goes live, including the scripts a page loads. Together these changes turn the test from an awareness exercise into the first step of a care process, and the three follow-through rates will show within two quarters whether they have worked.
References
Bang, H., Edwards, A. M., Bomback, A. S., Ballantyne, C. M., Brillon, D., Callahan, M. A., Teutsch, S. M., Mushlin, A. I., & Kern, L. M. (2009). Development and validation of a patient self-assessment score for diabetes risk. Annals of Internal Medicine, 151(11), 775-783. https://doi.org/10.7326/0003-4819-151-11-200912010-00005
Diabetes Prevention Program Research Group. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine, 346(6), 393-403. https://doi.org/10.1056/NEJMoa012512
US Preventive Services Task Force, Davidson, K. W., Barry, M. J., Mangione, C. M., Cabana, M., Caughey, A. B., Davis, E. M., Donahue, K. E., Doubeni, C. A., Krist, A. H., Kubik, M., Li, L., Ogedegbe, G., Owens, D. K., Pbert, L., Silverstein, M., Stevermer, J., Tseng, C.-W., & Wong, J. B. (2021). Screening for prediabetes and type 2 diabetes: US Preventive Services Task Force recommendation statement. JAMA, 326(8), 736-743. https://doi.org/10.1001/jama.2021.12531
What the HLTH 4403 Module 3 instructions ask for
In HLTH 4403 Module 3, the prompt usually asks you to analyze a health risk assessment or wellness tool that people use online or on a device, such as a risk calculator, a symptom checker or a workplace wellness questionnaire. Expect to describe what the tool asks and how it scores, judge whether its results are valid and useful, consider who can use it easily and who cannot, and examine how its data are stored, shared and protected. Many versions also ask how the tool connects to care, whether results reach clinicians or records, and what the organization should change. Support claims about accuracy with published research on the tool or its scoring. Check Canvas for whether you should complete the tool yourself and describe the experience.
How this HLTH 4403 Module 3 example is built
This analysis opens by describing the tool's seven questions and scoring, then sets criteria that go beyond use counts. It reports the published accuracy of the score and explains what sensitivity and predictive value mean for patients, then places the tool within current screening guidance. Usability is judged from observing patients, and reach is broken down by channel and language. A data flow section traces results from the screen to nowhere and distinguishes in-visit from anonymous use. A privacy section reports a concrete finding about third-party tracking on the web page. The final section measures the tool by follow-through, uses trial evidence to show why that matters, and proposes three linked rates.
Reading the HLTH 4403 Module 3 rubric
Graders of risk tool analyses typically look for accurate description, evidence on validity, attention to users and data, and practical recommendations. The validity criterion rewards citing research on the tool's accuracy and explaining what the figures mean, not just quoting them. A usability and access criterion looks for who the tool serves well and poorly. Data management and privacy often form a separate criterion, rewarding papers that trace where information goes and who can see it. Higher scores go to analyses that connect the tool to outcomes or next steps rather than stopping at use counts. Scholarly sources, organization and APA 7 formatting complete most versions of the rubric.
Common HLTH 4403 Module 3 mistakes, and how to avoid them
Risk tool papers lose marks when they describe the questions and stop, as though the tool's existence were the result. Another common problem is quoting accuracy statistics without explaining that a screening tool is designed to produce false positives. Students also forget the data: where answers go after the result screen is often the most important finding. Look at the web page itself, including what it loads from other companies. Measure the tool by what users do next. Keep privacy claims accurate and avoid stating legal conclusions you cannot support. If your tool is a symptom checker, a heart risk calculator or a wellness app, tell us which one and share the prompt, and we can write a Module 3 analysis of it.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official American College of Education document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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HLTH 4403 Module 3 questions, answered
What does HLTH4403 Module 3 usually ask for?
In HLTH4403, the third module usually centers on an online health risk assessment or wellness tool, asking what it measures, how accurate it is, who uses it, how its data are handled and whether it leads to care. Your classroom's instructions decide the tool.
How accurate is the prediabetes risk test?
The score it is based on identified about 79% of people with undiagnosed diabetes at 5 or more points in its development data, with many false positives, which suits a tool meant to send people for a blood test.
Are online health risk tools covered by privacy rules?
It depends on who runs them and how. Tools run by health care providers need privacy review, including what the web page shares with third parties, not only what it stores.
Where can I find a free HLTH 4403 Module 3 sample paper?
You can read it here. This page holds the full Module 3 analysis of an online prediabetes risk test at a health center network, covering accuracy, usability, data flow, privacy and follow-through, with margin notes.
How should an online risk tool be evaluated?
Judge it by what happens after the score: how many high scorers get tested, how many with a diagnosis are referred, and how many enroll in care, not only by how many people complete it.