HIM 220 Module 5 Healthcare Statistics Short Paper Example

Counting Beds, Days and Returns: June Inpatient Statistics for Kettle River Health

[Student Name]

Southern New Hampshire University

HIM 220: Healthcare Data Management

Module Five Short Paper

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Counting Beds, Days and Returns: June Inpatient Statistics for Kettle River Health

Each month Kettle River Health's board receives a page of inpatient statistics for its two hospitals. The numbers look simple, but each depends on precise counting rules. This paper defines the core statistics, calculates them for June at the 180-bed flagship hospital and the 40-bed rural hospital with formulas shown, and explains how to interpret them responsibly.

Counting Days and Patients

The daily inpatient census counts patients present at the census-taking hour, usually midnight, plus any patient admitted and discharged the same day. Adding the daily censuses for a period gives inpatient service days, the basic unit of hospital workload. Discharge days, also called length of stay days, are counted differently: for each patient discharged during the period, the number of days from admission to discharge, counting the admission day but not the discharge day. Newborns are counted separately from adults and children in both measures.

Average Daily Census

To get average daily census, add up the period's inpatient service days and spread them across the calendar days in that period. In June, the flagship recorded 4,698 inpatient service days over 30 days, for an average daily census of 156.6 patients. The rural hospital recorded 780 service days, an average daily census of 26.0.

Bed Occupancy

The bed occupancy rate equals inpatient service days divided by bed count days, which are the number of licensed or staffed beds multiplied by the days in the period, then multiplied by 100. The flagship's bed count days were 180 times 30, or 5,400, so occupancy was 4,698 divided by 5,400, or 87.0%. The rural hospital's bed count days were 1,200, giving 65.0%. Occupancy near 90% signals strain and delays for emergency admissions, while occupancy near 65% suggests unused capacity.

Average Length of Stay

Average length of stay equals total discharge days divided by the number of discharges. The flagship discharged 1,012 patients in June with 4,859 discharge days, an average of 4.80 days. The rural hospital discharged 212 patients with 830 discharge days, an average of 3.92 days. The difference partly reflects case mix, since the flagship treats sicker patients and performs major surgery, and partly reflects transfers of complex patients from the rural hospital to the flagship.

Table 1. June Inpatient Statistics

Note. Adults and children only; newborns counted separately. Data from the warehouse, June.

Death Rates

For the gross death rate, count every inpatient death, divide by all discharges with the deaths counted among them, and express the result as a percentage. The flagship recorded 24 deaths among 1,012 discharges, a gross death rate of 2.37%. The net death rate excludes deaths within 48 hours of admission, on the reasoning that the hospital had little time to affect those outcomes: deaths minus early deaths, divided by discharges minus early deaths. Seven of the 24 deaths occurred within 48 hours, so the net rate was 17 divided by 1,005, or 1.69%.

Readmission Rate

Using the board headline definition from the data dictionary, Measure C, the readmission rate equals index discharges followed by an unplanned inpatient or observation return to either hospital within 30 days, divided by eligible index discharges. For the quarter ending in June, the system rate was 12.9%. Zuckerman et al. (2016) showed that trends in readmissions can be influenced by how hospitals use observation status, which is why Measure C counts both inpatient and observation returns.

Table 2. Death and Readmission Rates

Note. Calculated by the author from warehouse data.

Averages Hide Outliers

Length of stay is skewed: most patients stay a few days, while a few stay weeks. At the flagship, four patients awaiting nursing home placement stayed more than 40 days each and raised the June average noticeably. Reporting the median length of stay, 3.6 days at the flagship, alongside the mean gives a truer picture of the typical patient. Outlier stays deserve their own review, since they often reflect discharge barriers rather than clinical care.

Statistics Depend on Data Quality

Every statistic inherits the quality of its data. Weiskopf and Weng (2013) described plausibility as a key dimension, and Module Two found 212 encounters last year with discharge times before admission times. Before calculating June's figures, the author ran a plausibility check and found four such records, which were corrected with the nursing informatics team. Without that step, negative stays would have lowered the average length of stay and distorted the readmission windows.

Comparisons Need Context

Raw rates can mislead when hospitals serve different patients. Joynt and Jha (2012) argued that readmission rates reflect patients' social circumstances and community resources as well as hospital care, and that only a portion of readmissions are preventable. Comparing the rural hospital's rate with the flagship's, or Kettle River's with a national average, without accounting for age, illness severity and social risk, can lead to wrong conclusions.

Presenting the Numbers

For the board, each statistic will appear with its definition, the prior twelve months as a trend line and a note when a change reflects a known cause, such as a unit closure or a coding policy change. Rates with small denominators, such as the rural hospital's monthly death rate, will be reported quarterly to avoid overreacting to random variation.

Conclusion

Census, occupancy, length of stay, death rates and readmission rates are straightforward to calculate once their counting rules are clear. Interpreting them responsibly requires attention to outliers, data quality, risk and definitions. With formulas and definitions shown, Kettle River's board can trust not just the numbers but what they mean.

References

Joynt, K. E., & Jha, A. K. (2012). Thirty-day readmissions: Truth and consequences. New England Journal of Medicine, 366(15), 1366-1369. https://doi.org/10.1056/NEJMp1201598

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

Zuckerman, R. B., Sheingold, S. H., Orav, E. J., Ruhter, J., & Epstein, A. M. (2016). Readmissions, observation, and the Hospital Readmissions Reduction Program. New England Journal of Medicine, 374(16), 1543-1551. https://doi.org/10.1056/NEJMsa1513024