Translating measures across the continuum of care: Using Rasch analysis to create a crosswalk between the Functional Independence Measure and the Minimum Data Set

Velozo, Craig A.; Byers, Katherine L.; Wang, Ying-Chih; Joseph, Bryttnee Roberts

doi:10.1682/jrrd.2006.06.0068

Cited by 39 publications

(36 citation statements)

References 27 publications

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“…This study evaluated the FIM-MDS crosswalk conversion algorithm developed by Velozo and colleagues [14]. We obtained "mixed" findings from the validity testing of the FIM-MDS motor and cognition crosswalks.…”

Section: Discussionmentioning

confidence: 99%

“…In 2006, Velozo and colleagues developed a FIM-MDS crosswalk conversion algorithm to link the FIM and the MDS [14]. Their intent was to develop an effective, efficient method of tracking and evaluating veterans' functional status changes across IRFs and SNFs.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we performed a follow-up validation analysis based on the FIM-MDS crosswalk conversion algorithm developed by Velozo and colleagues [14]. The validity of the conversion algorithm was tested at three different levels: (1) individual patient, (2) classification, and (3) facility.…”

Section: Introductionmentioning

confidence: 99%

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Validation of FIM-MDS crosswalk conversion algorithm

Wang

Byers²,

Velozo³

2008

JRRD

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Abstract-In this study, we performed a validation analysis of a crosswalk that converts Functional Independence Measure (FIM) scores to Minimum Data Set (MDS) scores and vice versa in order to achieve score compatibility. Data from 2,130 patients were obtained from the Department of Veteran Affairs' Austin Automation Center. The conversion algorithm was tested at the (1) individual patient level, (2) classification level, and (3) facility level. The validity testing resulted in mixed findings. The mean MDS-derived FIM (FIMc) scores were within 1.3 and 0.1 points of the mean actual FIM (FIMa) scores for the motor and cognition scales, respectively. Kappa statistics demonstrated a fair to substantial (0.37-0.66) strength of agreement between functional-related group classifications generated from the FIMa and FIMc scores. Four of the five facilities had an average point difference of 2.4 between the mean FIMa and FIMc scores. While the sample distributions were similar, individual score comparisons fell short of expectations. Only 37% to 67% of the FIMc scores were within 5 points of the FIMa scores. The crosswalk algorithm demonstrated a convenient way to achieve score comparisons across different rehabilitation settings. However, the effectiveness of a single measure or of crosswalk conversions may ultimately depend on the quality of the data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of FIM-MDS crosswalk conversion algorithm

Wang

Byers²,

Velozo³

2008

JRRD

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“…Then, methods based on item response theory (IRT) allow estimation of trait scores (Embretson & Reise, 2000). This approach has been used to construct depression scores at each wave of a longitudinal study when there were different numbers of response categories at different waves (Jones & Fonda, 2004),and to co-calibrate two commonly used measures of functional independence (Velozo, Byers, Wang, & Joseph, 2007). In the absence of existing bridge items in studies being brought together, it is possible to recruit a new linked sample, administer the measures to be harmonized, use IRT to build a measurement crosswalk, and produce a conversion table that can be applied to the original studies.…”

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confidence: 99%

Data Harmonization in Aging Research: Not so Fast

Gatz

Reynolds

Finkel

et al. 2015

Experimental Aging Research

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Background/Study Context-Harmonizing measures in order to conduct pooled data analyses has become a scientific priority in aging research. Retrospective harmonization where different studies lack common measures of comparable constructs presents a major challenge. This study compared different approaches to harmonization with a crosswalk sample who completed multiple versions of the measures to be harmonized.

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“…Investigators are just beginning to develop methods for calibrating functional status by using the different items in the Medicare program 11 and in the Veterans Health Administration. 12 Furthermore, medical acuity has consistently been shown to be an important factor affecting rehabilitation outcomes, yet measurement across settings has been limited to diagnosis and comorbidities. Clinicians in some settings collect data on specific medical conditions or treatments that measure acuity variations, but, like the FIM instrument, these measures are setting specific.…”

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confidence: 99%

Perspectives on the State-of-the-Science in Rehabilitation Medicine and Its Implications for Medicare Postacute Care Policies

Gage

Stineman

Deutsch

et al. 2007

Archives of Physical Medicine and Rehabilitation

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Better measurement of the case-mix complexity of patients receiving rehabilitation services is critical to understanding variations in the outcomes achieved by patients treated in different postacute care (PAC) settings. The Medicare program recognized this issue and is undertaking a major initiative to develop a new patient-assessment instrument that would standardize case-mix measurement in inpatient rehabilitation facilities, long-term care hospitals, skilled nursing facilities, and home health agencies. The new instrument, called the Continuity Assessment Record and Evaluation Tool, builds on the scientific advances in measurement to develop standard measures of medical acuity, functional status, cognitive impairment, and social support related to resource need, outcomes, and continuity of care for use in all PAC settings. KeywordsHealth care reform; Medicare; Prospective payment system; Rehabilitation; Treatment outcomes THE ISSUES DISCUSSED at the State-of-the-Science Symposium held in February 2007 and in the November 2007 issue of Archives are key to the development of sound health policy. We lack an instrument that allows us to compare patient outcomes across postacute care (PAC) settings in an evidence-based manner. This instrument is needed to measure the relative value of rehabilitation and medical services delivered by PAC providers, including inpatient rehabilitation facilities (IRFs), skilled nursing facilities (SNFs), long-term care hospitals (LTCHs), home health agencies (HHAs), and outpatient settings. As the differences in staffing and care practices become blurred across the different types of

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Translating measures across the continuum of care: Using Rasch analysis to create a crosswalk between the Functional Independence Measure and the Minimum Data Set

Cited by 39 publications

References 27 publications

Validation of FIM-MDS crosswalk conversion algorithm

Validation of FIM-MDS crosswalk conversion algorithm

Data Harmonization in Aging Research: Not so Fast

Perspectives on the State-of-the-Science in Rehabilitation Medicine and Its Implications for Medicare Postacute Care Policies

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