A launch-day setting that sends uncertain matches straight to new records, not patients' typing, emerges as the root cause according to this HI499 Unit 5 analysis. Searches like "hi 499 unit 5 assignment example", "hi499 unit 5 sample" and "hi499 unit 5 example" land here.
What a finished HI499 Unit 5 root cause analysis looks like
Three figures and about five pages. A cause-and-effect diagram groups candidate causes under people, process, technology, data and policy, with each branch marked supported, partly supported or unsupported by the evidence gathered. A Pareto chart of the [120] sampled pairs ranks what differed between the two records: name variants [46], new phone or email [29], a changed address [21], a mistyped birth date [14], other [10]. A process map of the tool's matching step shows where the decision is made, with the uncertain band's route to record creation drawn in red. The text walks one why-chain from a missing allergy at a clinic visit back to the configuration decision, five links in all. A short final section explains why teaching patients to type carefully is not the answer.
How a HI499 Unit 5 example is structured
The analysis separates where the problem surfaces from where it is produced, and says so in its first paragraph. Surfacing is covered briefly, since the data review already showed that most duplicates are found at a later visit. Production is the subject. The diagram comes first to show the full field of candidate causes, then evidence narrows it: the Pareto chart shows what varies between paired records, and the uncertain-band figure shows that the variation was usually caught by the engine and overridden by the tool's setting. The why-chain makes the path concrete for one composite case. A section on rejected causes follows, including patient carelessness and registration staff, each rejected with a reason. The analysis ends by naming the root cause in one sentence, a configuration decision, and two contributing causes: unstandardized addresses and a search that checks exact surnames first.
Surfaced in the clinic, produced online
The first paragraph separates the two locations and commits the analysis to the second, since improving detection alone would leave the tool producing pairs at the same rate.
Every branch marked
Each candidate cause on the diagram carries a label, supported, partly supported or unsupported, so the diagram records what the evidence showed rather than a brainstorm.
The engine found them
[97] of [120] pairs scored in the uncertain band: the existing record was identified and then passed over. That single figure moves the cause from patient behavior to the tool's settings.
Five links from an allergy
One composite why-chain runs from an allergy missing at a clinic visit, through the second record and the uncertain score, back to a launch decision to keep booking frictionless.
Causes considered and set aside
Patient carelessness and front desk staff are both examined and rejected, each with the evidence against it, so the root cause is chosen over alternatives instead of simply asserted.
Where marks go in HI499 Unit 5
The heaviest loss goes to analyses that stop where the problem is noticed. Finding that duplicates surface at clinic visits and recommending better checking at clinic desks treats the symptom and leaves the source running. Causes asserted without evidence lose marks next; a diagram full of plausible branches and no indication of which were tested reads as brainstorming. Blaming patients for typing errors costs points twice, once for ignoring that the engine usually caught the variant, and again for proposing a remedy the organization cannot control. Pareto charts drawn without a stated sample size or selection method are marked down. So are why-chains that jump from symptom to conclusion in two links. Analyses that never name rejected causes leave the chosen one unsupported.
Get a HI499 Unit 5 example written to your instructions
Cause-and-effect diagram, why-chain, or both? Say which tool your section expects, and attach the Unit 5 instructions, the rubric and the data earlier units produced. Diagrams arrive editable, and any cause your data cannot test is labeled untested rather than argued. Turnaround is 24-48h, with no fee for a first sample.
HI499 Unit 5 questions, answered
What is the difference between a root cause and a contributing cause?
A root cause is the one whose removal would stop most of the problem; contributing causes make it worse or more likely. In the example, the uncertain band's default route is the root cause, because most pairs passed through it. Unstandardized addresses and an exact-surname search are contributing causes: fixing them helps, but the tool would still create records from uncertain matches.
How large should the sample of duplicate pairs be?
Large enough that the categories stabilize, which for a single tool is often a hundred or so. The example reviews [120] because that was about six weeks of the tool's confirmed pairs, and the proportions changed little after the first eighty. The sample's size, period and selection method are all stated, which matters more to markers than the number itself.
Should the analysis recommend a fix?
Usually it should point toward one without designing it, since later units handle constraints and cost. The example ends with the root cause and two contributing causes, then notes in one sentence the direction a fix would take. Detailed recommendations here tend to get rewritten once the regulatory review shows what the organization is and is not permitted to do.