Palliative Care (PAL)

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Set Measures
General Data Elements
Measure Set Specific Data Elements
Palliative Care (PAL) Initial Patient Population
Note – General Data Elements:
Initial Patient Population Algorithm
Sampling / Sample Size Requirements

Set Measures

Set Measure ID	Measure Short Name
PAL-01	Pain Screening
PAL-02	Pain Assessment
PAL-03	Dyspnea Screening
PAL-04	Treatment Preferences and Goals of Care
PAL-05	Treatment Preferences Discharge Document

General Data Elements

Element Name	Collected For
Admission Date	All Records,
Birthdate	All Records,
Discharge Date	All Records, Not collected for HBIPS-2 and HBIPS-3
Sex	All Records,

Measure Set Specific Data Elements

Element Name	Collected For
Discharge Disposition	PAL-05
Dyspnea Severity	PAL-03
Goals of Care	PAL-04
Initial Encounter	PAL
Initial Encounter Date	PAL
Pain Character	PAL-02
Pain Duration	PAL-02
Pain Effect	PAL-02
Pain Factors	PAL-02
Pain Frequency	PAL-02
Pain Location	PAL-02
Pain Severity	PAL-01, PAL-02
Treatment Preferences	PAL-04
Treatment Preferences Document	PAL-05

Palliative Care (PAL) Initial Patient Population

The PAL Measure Set Population (common to all PAL measures) is defined as all patients who have received a consultation with any member of the palliative care service team. “Consultation” indicates that the patient received a face to face encounter visit with any member of the palliative care core interdisciplinary team. The core interdisciplinary team is comprised of the following: Physician(s); Registered nurse(s) or advanced practice nurse(s); Chaplain(s) or, spiritual care professional(s); Social worker(s).

The population of the PAL measure set can be identified by using data elements that are common to all of the performance measures in the set:

Discharge Date
Initial Encounter

While not required for the identification of the initial patient population or the calculation of the measures, the following data elements are collected for purposes of case identification:

Admission Date
Birthdate
Sex

Note – General Data Elements:

The following General Data Elements are optional for the PAL measure set. Collection of these data elements is not currently required for the identification of the initial patient population or the calculation of the measures for purposes of measure submission for certification.

Optional General Data Elements:

Hispanic Ethnicity
ICD-10-CM Other Diagnosis Codes
ICD-10-CM Principal Diagnosis Code
ICD-10-PCS Other Procedure Codes
ICD-10-PCS Principal Procedure Code
Payment Source
Postal Code
Race

Initial Patient Population Algorithm

Sampling / Sample Size Requirements

Sampling Methodology

Sampling is a process of selecting a representative part of a population 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 number of cases in the measure population, because a fairly large number of sample cases is needed to achieve a representative sample of the population of interest. To obtain statistically valid sample data, the sample size should be carefully determined, and the sample cases should be randomly selected in such a way that the 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.

Sampling Approaches

Simple random sampling - selecting a sample size (n) from a population of size (N) in such a way that every case has the same chance of being selected. Systematic random sampling - selecting every kth record from a population of size N in 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: a) Randomly select the starting point by choosing a number between one and k using a table of random numbers or a computer-generated random number; and b) Then select every kth record thereafter until the selection of the sample size is completed. As an example, say the site has 33 cases for the month. These 33 cases would then be put on a list and numbered from 1 to 33. First we calculate the sampling interval k as 33/10 which rounds to 3. The site would then randomly choose a number between 1 and 3 to use as the starting point on the list, sample this case, and then from this point sample every 3rd case on the list until they come to the end of the list to create their sample.

Monthly Sample Size

Monthly Patient Volume (number of discharges)”	Monthly Sample Size (number of medical records)
1 - 9	100%
10 - 49	10 cases
50 - 99	20%
=> 100	20 cases

Specifications Manual for Joint Commission National Quality Measures (v2019A1)
Discharges 10-01-19 through 12-31-19 (4Q19)

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