The Patient Classification section of the Facilities Detail page contains four tables with corresponding pie charts that reveal significant patient demographics for the facility listed on the page.
Data Calculation Highlights:
- The Reporting period for the data in these tables is the standard 1 year period. (Please see Reporting Periods for more details.)
- The counts do not necessarily reflect unduplicated patient counts. For example, a patient who moved from one ZIP code to another within the year who also had two separate claims would be counted once in each ZIP code to reflect the whole of the facility's scope of practice.
Diagnostic Category Breakout
This table provides a breakdown of the 20 Diagnostic Categories by percentage of patient for each category listed. There is an entry titled, “Aggregated INS MDC’s” which represents the combined percentage for all diagnostic categories that are too small to be significant on their own. If you see a diagnostic category that is presented in bold print, those are categories that your leadership team has specified as categories requiring special attention.
For more information about how we determine diagnostic groupings in the Trella Health Solutions, see Trella Health Diagnostic Groupings.
County and ZIP Code Breakout Tables
- The counts that yield the percentages in these tables are based on the county or ZIP code of the patient residence at the time the claim was filed, not the location where treatment was provided.
- Any and all counties or ZIP codes where the percentage would represent a count of less than 11 patients will not be identified in the table but will be aggregated into the row titled, “Other.” For example, if the agency had 100 patients in the reporting period, any county or ZIP code with 10% of the total or less would represent less than eleven patients in that location and therefore, that location will not be identified, but would be combined with all other locations also not specified.
- County or ZIP code data might reveal a higher patient population of urban patients, or rural patients, which could suggest a need for higher levels of a specific type of care.
- County or ZIP code data could identify a location where no Hospice services exist, which could explain lower PAC utilization by the listed physician.
Age Breakout Table
- This table gives a simple breakout of the patients for the listed Hospice by age.