The Patient Diagnostic Mix by Unique Patients table provides a breakdown of three metrics against the 20 Major Diagnostic Categories used in the Trella Health Solutions. For each metric we have included comparative county and state averages. Use this table to uncover strengths and weaknesses of your home health agency and competitors. For more information on the MDCs see CMS Major Diagnostic Categories.
The use of the word "unique" in this table should be construed as synonymous with the word "distinct" as used throughout the remainder of the Solution.
Each row contains a single MDC with comparative metrics for the following three sections:
- Percentage of Patients - This is the percent of patients with diagnoses that fit into the listed MDC. This section provides county and state averages for comparison. If a home health agency has a higher average than the county and state, this suggests an area of specialization or focus by the home health agency. An area of specialization tied to excellent performance in the other sections suggests a strong story for marketing.
- Average Length of Stay (Days) Mean and Median - The average length of stay sections contain two different averages related to the time period from patient admission to discharge for the home health patients treated by this agency during the reporting period. Based on these Lengths of Stay we have calculated two Averages: ALOS is the mean average where the total patient days are divided by the total patients. MLOS is the median average where we identify the length of stay that is the center of the data set, that is, there are as many numbers in the overall set below the center number as there are above the center number. For each of these averages, we have included state and county averages for comparison.
Understanding the Data
There are two categories that contain data that can't be included in specific MDC rows.
- Aggregated INS MDC's - In any case where the total number of patients in a specific MDC drops below 11 patients, we can't show that number for privacy reasons. We roll all MDCs that represent counts <11 into this one category and present that count with this header. To examine this header in steps, this row includes all MDC's that have insufficient counts aggregated into a single metric.