How to Read the NYU-ED algorithm Output file

All three versions of the ED algorithm programming – the Access, SAS, and SPSS versions – will produce a microdata (record-level) file, with one record for each encounter record in your ED database.)

The output microdata file will simply have a new set of variables in addition to your original data set variables. The names of the new variables are:
 • ne = “Non-emergent”
• epct = “Emergent/Primary Care Treatable”
 • edcnpa = “Emergent ED Care Needed Preventable/Avoidable”
 • edcnnpa = “Emergent ED Care Needed Not Preventable/Avoidable”
 • injury = “Injury principal diagnoses”
 • psych = “Mental health principal diagnoses”
• alcohol = “Alcohol-related health principal diagnoses”
 • drug = “Drug-related health principal diagnoses (excluding alcohol)”
 • unclassified = “Not classified - not in one of the above categories”
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New York University Emergency Department visit severity algorithm

For each ED encounter, the numbers in the new fields represent the relative percentage of cases for that diagnosis falling into the various classification categories.

For example, in the case of urinary tract infections (ICD-9-CM code 599.0), each case is assigned 66% “non-emergent”, 17% “emergent/primary care treatable”, and 17% “emergent - ED care needed - preventable/avoidable”. The sum of the values in the new data fields will always total 1, and the injury, psych, alcohol, drug, and unclassified fields are always binary (equal to 1 or 0).

HOW YOU CAN APPLY ED Algorithm

There are couple of different analytical platform that you can use to apply ED/ER Algorithm. The most commons are Microsoft Access 2000 Version and SAS or SPSS Version.

USE Microsoft Access 2000 Version:
The Microsoft Access version of the ED algorithm contained a total of 2 files - “NYU ED Algorithm X.X.MDB" and NYUED.HLP.
Users need to place the two files in the same directory of the computer, open the file using Microsoft Access.  All further instructions can then be accessed by pressing F1 on your keyboard.  Context-specific help can be accessed after selected each menu choice, EXCEPT for the "Import a Dataset" menu choice.  To access help for that function, user need to use F1 button when viewing the main menu, and then select "Import a Dataset" from Help Table of Contents.

This version of the ED algorithm requires that your ED dataset be available in ASCII (text), Access, .DBF, or Excel format.

Using the SAS or SPSS Version:
For the SPSS version, the files are:
•    “DX GROUPS.SAV” - This file is merged onto your ED data in order to recode and group diagnoses;
•    ‘EDDXS.SAV" - This file lists diagnoses and the proportion of cases that are to be assigned to the classification categories; and
•    “ED Algorithm.sps” - This is the SPSS program that is used to run the algorithm.

For the SAS version (which will run in either SAS 7 or SAS 8), the files are:
•    “ED Macros.sas” - This file contains SAS macros that group or recode diagnoses and classify them into the categories described above;
•    ‘FINDXACS.SD7" - This file lists diagnoses and the proportion of cases that are to be assigned to the classification categories; and
•    “ED Algorithm Sample Program.sas” - This is the SAS program that is used to run the algorithm.
All files are contained in the compressed (zipped) file.
Applying the algorithm involves three simple steps:

    STEP 1:    Put the unzipped files in a directory along with the ED encounter data set you want to classify (containing one record for each ED visit).  The ED data set should be in the appropriate format (SAS 7 or 8, or SPSS) and contain a variable with the principal discharge diagnosis for the ED visit.
    STEP 2:    Set the appropriate names in the LET statements at the top of the program.
        For the SPSS version, specify the following: 1) IN SINGLE QUOTES, the full path name (including a final backslash) of the directory on your computer that contains the files, 2) IN SINGLE QUOTES, the name of the SPSS data set (including the .SAV extension) containing ED records to be classified, 3) the name of the variable in your data set that contains the principal diagnosis, and 4) IN SINGLE QUOTES, the name you want the program to use to write the output data set (including the .SAV extension).
STEP 3:    Run the program and analyze the output data set, which will be written to the same directory the other files are in.

Know About NYU classification

NYU classification or commonly used as ED Classification is the way to classify Emergency utilization. With support from the Commonwealth Fund, the Robert Wood Johnson Foundation, and the United Hospital Fund of New York, the NYU Center for Health and Public Service Research has developed an algorithm to help classify ED utilization.

ED Or NYU categories:
•    Non-emergent - The patient’s initial complaint, presenting symptoms, vital signs, medical history, and age indicated that immediate medical care was not required within 12 hours;
•    Emergent/Primary Care Treatable - Based on information in the record, treatment was required within 12 hours, but care could have been provided effectively and safely in a primary care setting.  The complaint did not require continuous observation, and no procedures were performed or resources used that are not available in a primary care setting (e.g., CAT scan or certain lab tests);
•    Emergent - ED Care Needed - Preventable/Avoidable - Emergency department care was required based on the complaint or procedures performed/resources used, but the emergent nature of the condition was potentially preventable/avoidable if timely and effective ambulatory care had been received during the episode of illness (e.g., the flare-ups of asthma, diabetes, congestive heart failure, etc.); and
•    Emergent - ED Care Needed - Not Preventable/Avoidable - Emergency department care was required and ambulatory care treatment could not have prevented the condition (e.g., trauma, appendicitis, myocardial infarction, etc.).

It is important to recognize that the algorithm is not intended as a triage tool or a mechanism to determine whether ED use in a specific case is “appropriate” (e.g., for reimbursement purposes). Since few diagnostic categories are clear-cut in all cases, the algorithm assigns cases probabilistically on a percentage basis, reflecting this potential uncertainty and variation.

Since the original development of the algorithm, users have expressed an interest in examining separately cases involving a primary diagnosis of injury, mental health problems, alcohol, or substance abuse.