Take My SNHU HIM 550 Class

HIM 550 takes the HIM professional's oldest concern, an accurate record, and extends it to the data that now drives payment, quality ratings, research and population health. A diagnosis coded wrongly once now flows into a risk score, a quality dashboard and a research data set.

The opening HIM 550 modules usually define data quality and its dimensions, often through AHIMA's data quality management model: accuracy, accessibility, comprehensiveness, consistency, currency, definition, granularity, precision, relevancy and timeliness. Students learn how poor quality starts at capture, in registration screens, templates, copy-and-paste notes and interfaces, and how it spreads.

The data management modules cover the data life cycle from capture to archiving, data dictionaries and metadata, standard data sets such as the United States Core Data for Interoperability, data warehouses and repositories, and secondary use for research, registries and analytics, including de-identification.

The meaningful use modules look at how certified EHR data is used for programs that grew out of the original meaningful use incentives, now called Promoting Interoperability, and for electronic clinical quality measures, where data quality decides the score. The patient-centered modules cover portals, personal health records, patient-generated health data and patients' role in correcting their records.

Graded work typically includes discussions, papers, data dictionary or data quality audit exercises and a data quality improvement project.

For HIM 550, the data quality manager starts by asking what kind of data you work with, whether coded data, registry abstracts, quality measures or registration records, since the projects land best on a data problem you recognize. Then the syllabus and every rubric are read with that problem in mind.

Each HIM 550 week has its post ready ahead of time. In a week on the quality dimensions, the post might show how a blood pressure stored as free text fails the consistency and precision dimensions and breaks a hypertension measure. In a week on secondary use, it might explain how a de-identified data set can still be re-identified when zip code, birth date and sex travel together. HIM 550 replies to classmates add a dimension they missed, a measure affected or a fix at the point of capture.

Data dictionary exercises define each element with its name, definition, format, allowed values, source and owner. Data quality audits state the sample, the error types by dimension, the error rate and the root cause.

The HIM 550 improvement project is built in stages, and each HIM 550 grade shapes the next stage.

HIM 550 is written by data quality and HIM analytics professionals with the RHIA, many also holding the CHDA, and graduate degrees in HIM, informatics or health data analytics. They have run data integrity programs, managed quality measure reporting, abstracted for registries and built data dictionaries for health systems.

They have traced quality failures from a dashboard back to a screen at registration, which is how HIM 550 papers explain root causes.

A second data professional checks each HIM 550 paper and exercise for correct dimensions, sound method and rubric fit.

All data sets used in HIM 550 work are invented or supplied by your course.

The first difficulty in HIM 550 is precision about quality. Students often call any problem an accuracy problem, while the rubric expects the right dimension: a missing value is a comprehensiveness problem, an outdated address a currency problem and two departments counting visits differently a definition problem.

The second is root cause. HIM 550 papers that stop at the error, without tracing it to the template, workflow, interface or training gap that created it, lose points.

The third is the technical vocabulary of data management: metadata, data models, warehouses, extract-transform-load, data profiling and de-identification standards. Students from coding or release-of-information backgrounds meet much of it for the first time.

The fourth is linking data quality to consequences. Rubrics reward papers that show how a quality gap changes a quality measure, a payment, a research finding or patient safety. The improvement project then asks for measurable targets and a plan to sustain them, which is harder than describing the problem.

The weight in HIM 550 falls on the data dictionary and audit exercises and on the improvement project. HIM 550 discussions are lighter, though each expects a dimension or standard named correctly.

Picking the data problem for the HIM 550 project before the first module means every later exercise can feed it. If HIM 550 is under way, your earlier posts and exercises are read before the open module is written.

The HIM 550 figure covers every remaining post, paper, exercise and project stage with a delivery day for each.

If you handle data at work, a problem like one you have seen can be the HIM 550 project, described with invented data only.