measure sampling

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Specification Manual for the Joint Commission International Library of Measures Version 2.0, effective for January 2013 discharges (1st Quarter 2013) ©2011 Joint Commission International

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Appendix I:

Measure Sampling

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Appendix I: Measure Sampling


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Introduction Sampling is a process of selecting a representative part of the population of interest in order to estimate the organization’s performance, without collecting data for its entire population. Using a statistically valid sample, an organization can measure its performance in an effective and efficient manner. Sampling is a particularly useful technique for performance measures that require primary data collection from a source such as the medical record. Sampling should not be used unless the organization has a large enough number of cases in the measure’s initial eligible population because a fairly large number of sample cases are needed to accurately reflect the organization’s performance. Organizations with large patient volumes may perform data collection on a sample of the total population, but sampling is not required. To obtain statistically valid sample data, the sample size should be carefully determined and the sample cases should be randomly selected such that individual cases in the population have an equal chance of being selected. Only when the sample data truly represent the whole population can the sample-based performance measure data be meaningful and useful. Guidelines for effective sampling procedures follow. Sampling may be used for all measures in the Library of Measures except for I-HBIPS-2, I-HBIPS-3, I-NCS-2, I-NSC-4 and I-NSC-5. These measures cannot be sampled because they are event-based measures.

Sampling Availability If an organization decides to sample a measure’s or measure set’s initial eligible population, sampling should be applied to all monthly discharge medical records identified as part of the inpatient initial eligible population. Initial eligible population criteria are described in each measure set chapter of the specification manual. The initial eligible population should be identified by using available databases or other information repositories that contain monthly patient discharge information, International Classification of Diseases (ICD) diagnosis/procedure codes or patient diagnoses or procedures, and other necessary administrative data (e.g., patient age). Sampling should be undertaken on a monthly basis.

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Sample Size Requirements Hospitals selecting sample cases for Library Measures should ensure that its measure’s or measure set’s initial patient population(s) and sample size(s) meet the following conditions:

The number of discharged cases to be sampled on a monthly basis is determined in accordance with the following sampling table:

Measure Sampling Table

Total Monthly Initial Patient Population Size (N) for the Selected Measure

Required Monthly Sample Size (n)

>= 59 58

<= 58 No sampling; 100% population required

If desired, a hospital may select a larger monthly sample size. Sample Size Examples:

A hospital has 57 discharges (N) in a month for a given measure initial patient population. All 57 cases (100%) should be reviewed. Sampling is not appropriate in this example.

A hospital has 128 discharges (N) in a month for a given measure initial patient population. The sample size for this month would be 58 cases (n).

A hospital has 512 discharges (N) in a month for a given measure initial patient population. The sample size for this month would be 58 cases (n).

A hospital has 905 discharges (N) in a month for a given measure initial patient population. The sample size for this month would be 58 cases (n) (the maximum required number of cases for the monthly sample size).

According to the sampling table, it is possible in the same measure to have “No sampling” (<58 initial patient population discharge cases) in one month and be able to sample with the next month of cases, if there are a sufficient number of initial patient population discharge cases to support the sampling methodology as described.

Systematic Random Sampling Approach Systematic random sampling requires every kth record from a population size of N in

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such a way that a sample size of n is obtained, where k < N/n. The first sample record (i.e., the starting point) must be randomly selected before taking every kth record. This is a two-step process as follows:

1. Select the starting point; and 2. Then select every kth record thereafter until the selection of the sample size is

complete. Random Selection Example: How to apply random sampling For a measure or measure set with an initial patient population size of 360 discharges per month (N), the sample size would be 58 (n) according to the table provided in this section. To select a random sample of 58 cases you would implement the following process:

1. Determine the initial patient population size N (i.e., the total number of discharges associated with the selected measure) for the month.

2. Determine the sample size n using the above table. 3. Divide the population size N by the suggested sample size n and the quotient

is k (i.e., the resulting integer is the sampling interval k). Example: How to get sample interval number

a. The sampling interval k = 360/58 = 6 sampling interval (k) b. Thus, every 6th (k) patient record will be selected from the measure

population until 58 cases have been selected.

4. To ensure that each patient has an equal chance of being selected, the “starting point” must be randomly determined before selecting every 6th record.

Example: How to determine starting point a. Therefore, a simple approach to determine where to start would be to

write the numbers 1,2,3,4,5 on separate pieces of paper and b. Then place the numbers in a container and pull one piece of paper

identifying a number to start counting the “k” sampling interval i. For example, if you draw number 3, start with the 3rd case on

your list and select every 6th case after that until you reach 58 cases.

measure sampling

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