| Course | C813 Healthcare Statistics and Research |
|---|---|
| Task | Task 2 |
| Paper type | Post-implementation statistics presentation |
| Length | About 1,200 words, 2 pages |
| Format | APA 7 |
| School | Western Governors University (WGU) |
| Program | BS Health Information Management |
| Updated | September 2026 |
Free sample paper for C813 Task 2
Six Months Before, Six Months After: HIM Statistics From an EHR Go-Live at a Composite Community Hospital
Student Name
Leavitt School of Health, Western Governors University
C813: Healthcare Statistics and Research, Task 2
Course Instructor
Month Day, Year
Slide 1: What We Measured and Why
Marlow Valley Hospital moved from paper charts to an EHR in January
HIM compared six months before with six months after
Two areas: release of information and coding
Speaker notes: Good afternoon. In January, Marlow Valley Hospital, a composite 160-bed community hospital, replaced its paper charts with an electronic health record. Leadership asked the HIM department whether the change improved our own work. We compared six months before go-live, July through December, with six months after, February through July. We left out January itself, the go-live month, because its numbers reflect the disruption of the switch rather than either way of working. We looked at two areas that HIM owns: release of information and coding.
Slide 2: The Measures
Release of information: requests per month, median turnaround, share answered within 30 days
Coding: charts coded per coder hour, coding accuracy
Revenue cycle: discharged not final billed (DNFB) days
Speaker notes: We chose six measures. For release of information, we tracked volume, the median number of days from receiving a request to sending the records, and the percentage of patient requests answered within 30 days, the deadline that 45 C.F.R. § 164.524 sets for a patient's own request (Office for Civil Rights, 2016). For coding, we tracked productivity as charts coded per coder hour, separately for inpatient and outpatient work, and accuracy from our quarterly audits. We also tracked discharged not final billed days, which measures how long completed stays wait before a claim can be sent. We used the median for turnaround because turnaround times are skewed: a few complicated requests take far longer than the rest, and the median describes the typical request better than the mean (Centers for Disease Control and Prevention [CDC], 2012).
Slide 3: Release of Information Results
Requests per month: 820 before, 870 after (+6.1%)
Median turnaround: 12 days before, 6 days after (-50%)
Answered within 30 days: 91% before, 99% after
Speaker notes: Release of information improved clearly. Request volume rose slightly, from an average of 820 to 870 a month. The percent change is 870 minus 820, which is 50, divided by 820, or about 6.1%. Despite that increase, median turnaround fell from 12 days to 6. That is a change of minus 6 days divided by 12, or a 50% reduction. The share of patient requests answered within 30 days rose from 91% to 99%. In the paper era, staff had to pull charts from storage and photocopy them; now most records are compiled electronically and released through the patient portal or secure email.
Slide 4: Release of Information Chart
Bar chart: median turnaround by month, July to July
Dashed line marks go-live
Every month after go-live below the earlier median
Speaker notes: This chart shows the median turnaround for each month, with a dashed line at go-live. We used a bar chart because each bar is a separate monthly summary. Every month after go-live is below the pre-implementation median of 12 days, and the six post-implementation months range from 5 to 8 days. A run of six or more points all falling below the earlier median is what run chart rules count as a real shift rather than chance variation (Perla et al., 2011).
Slide 5: Coding Productivity Results
Inpatient charts per coder hour: 2.1 before, 1.7 after (-19.0%)
Outpatient charts per coder hour: 9.5 before, 11.0 after (+15.8%)
Inpatient productivity recovering: 1.5 in February, 1.9 in July
Speaker notes: Coding results were mixed. Outpatient productivity rose from 9.5 to 11.0 charts per coder hour, an increase of 1.5 divided by 9.5, or 15.8%, mostly because coders no longer wait for paper encounter forms. Inpatient productivity fell from 2.1 to 1.7 charts per hour, a change of minus 0.4 divided by 2.1, or 19.0%. The monthly figures tell a more hopeful story: inpatient productivity was 1.5 in February and had climbed to 1.9 by July. Coders report two causes: learning to navigate the new record, and longer inpatient notes, many with copied text that has to be read carefully.
Slide 6: Coding Accuracy and DNFB
Coding accuracy: 94% before, 95% after
DNFB days: 5.8 before, 7.4 in February to April, 4.9 in May to July
Early rise, then improvement below baseline
Speaker notes: Accuracy held steady, moving from 94% to 95% on our quarterly audits, which is within the variation we usually see, so we do not claim an improvement. Discharged not final billed days, averaged by quarter, rose from 5.8 before go-live to 7.4 in the first three months after, then fell to 4.9 in the next three. We show those two quarters separately rather than as one six-month average, because averaging them would hide both the early problem and the later improvement.
Slide 7: Coding Chart
Line chart: inpatient charts per coder hour by month
Line chart: DNFB days by month
Both show a dip after go-live and a recovery
Speaker notes: These line charts show the same two measures month by month. A line chart suits measures that we expect to change gradually over time. Both show the same shape: a dip after go-live, then steady recovery. For DNFB days, the recovery has already passed the old baseline. For inpatient productivity, it has not yet, but the trend suggests it may within a few months.
Slide 8: Interpreting the Results Carefully
A before-and-after comparison shows change, not proof of cause
Other factors: volume, staffing, seasonality, measurement changes
Paper logs and EHR timestamps are not identical measures
Speaker notes: Before drawing conclusions, we have to be honest about limits. A before-and-after comparison shows that something changed, but it cannot prove the EHR caused it. Other things changed too. Request volume rose. One inpatient coder retired in March and was replaced by a new graduate, which affects productivity. The before and after periods cover different seasons. And the way we measure changed: before go-live, turnaround came from handwritten logs, and afterward from system timestamps, which are more precise and may record some steps differently. We think the release of information improvement is too large and consistent to be explained by these factors, but the smaller coding changes should be read with caution.
Slide 9: Recommendations
Release of information: keep portal release as the default; set a 10-day internal target
Coding: targeted EHR navigation training; copy-and-paste feedback to providers
DNFB: continue weekly review until stable for three months
Measurement: keep monthly run charts for all six measures
Speaker notes: Four recommendations follow from these results. For release of information, the portal should remain the default route for patient requests, and we propose an internal target of 10 days, well within the legal limit. For coding, we recommend a short training course on navigating inpatient documentation in the new record, and we ask the clinical documentation team to share feedback with providers about copied text in progress notes. For DNFB, the weekly review meeting that brought days down should continue until the figure is stable for three months. And we recommend keeping monthly run charts for all six measures, so that the next set of changes can be judged on data rather than impressions. That concludes the findings; questions are welcome.
References
Centers for Disease Control and Prevention. (2012). Principles of epidemiology in public health practice: An introduction to applied epidemiology and biostatistics (3rd ed.). U.S. Department of Health and Human Services. https://archive.cdc.gov/www_cdc_gov/csels/dsepd/ss1978/index.html
Office for Civil Rights, U.S. Department of Health and Human Services. (2016). Individuals' right under HIPAA to access their health information 45 CFR § 164.524. https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/access/index.html
Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895
What the C813 Task 2 instructions ask
The second C813 task asks you to analyze and present statistics about a health information change. Most versions ask you to define measures, report results before and after, display them in charts, interpret them carefully and make recommendations. The audience is usually leadership or HIM staff. Evaluators look for measures defined with formulas, percentage changes calculated correctly, charts that suit the data and interpretation that acknowledges the limits of before-and-after designs. Recommendations should follow from the results. Claiming that the new system caused every change, without considering other explanations, is a common reason presentations fall short. Your version may set how many measures to report. The audience is usually HIM leadership.
How this C813 Task 2 example is built
The deck opens with what was measured and why. A measures slide defines each indicator. Results slides report before and after values with percentage change, followed by charts that show the monthly pattern. The interpretation slide explains that other factors, such as staffing or seasonal volume, could contribute and that run charts help separate trends from noise. Recommendations are specific to each area. Speaker notes explain calculations and cite improvement science sources on run charts and federal guidance on access. The deck could be presented to an HIM leadership meeting as written. The notes explain each calculation step. Each chart pairs with a results slide. The final slide lists recommendations by area.
Where the C813 Task 2 rubric puts the marks
C813 Task 2 aspects are rated competent, approaching competence or not evident. A measures aspect checks that indicators are defined clearly. A results aspect rewards accurate calculations of change. A display aspect looks for charts suited to the data. An interpretation aspect asks for careful conclusions that acknowledge limits. A recommendations aspect wants actions that follow from the results. Readable slides and full notes are scored as well. Evaluators check arithmetic and notice when interpretation avoids overclaiming cause, and sources on run charts should be cited where they guide analysis. Evaluators notice when every figure on the slides matches the notes. Evaluators check percentage changes against the before values.
C813 Task 2 help: what sends it back
Statistics presentations come back most often when change is attributed to the new system without qualification. Say what else could explain the change. Second, percentage change is calculated wrongly. Divide by the starting value. Third, charts are chosen for looks. Use line charts for trends over time and bars for comparisons. Fourth, slides are crowded. Put explanations in the notes. Finally, recommendations should name owners and timing, so leaders know what to do next. Label every chart with units and time periods. State the number of months in each comparison period. End with one recommendation per area, each with an owner. Round figures consistently across slides. State the data source for each measure.
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C813 Task 2 questions, answered
Should the C813 presentation say the EHR caused the change?
Only with caution. A before-and-after comparison shows change but cannot prove cause. The sample explains other possible factors and uses run charts to read trends. State other factors that could explain the change.
How is percentage change calculated for C813?
Subtract the before value from the after value, divide by the before value and multiply by 100. The sample shows this for each measure in its notes.
Is the C813 hospital in the sample real?
No. Marlow Valley Hospital and its statistics are hypothetical. The improvement science sources on run charts and federal access guidance cited are real. Its go-live dates are illustrative.
What charts suit C813 before-and-after data?
Line or run charts for monthly trends and bar charts for simple comparisons. The sample pairs each results slide with a chart that shows the pattern over time.
Where can I find a free C813 Task 2 sample paper?
The complete presentation and notes are published above with comments. Share your C813 task and data, and the first tailored deck from the desk is free. Include your before and after figures.