Assessing Clinically Important Differences During Rehabilitation for Stroke: A Pilot Study Evaluating Anchor and Distribution Derived Estimates of Physical Function Change in Classically Summed and Rasch Models of Section GG of the Inpatient Rehabilitation Facility Patient Assessment Instrument.

OBJECTIVE: To determine clinically important differences (CIDs) on Section GG physical functioning scores on the Centers for Medicare and Medicaid Services (CMS) Inpatient Rehabilitation Facility Assessment Instrument (IRF-PAI) for patients with stroke, using anchor and distribution-based approaches. DESIGN: Pilot prospective observational cohort study. SETTING: Inpatient rehabilitation facility. PARTICIPANTS: Patients with stroke (N=208). INTERVENTIONS: Physicians assessed improvements during rehabilitation using the modified Rankin scale (mRS). Improvements (≥1 point) on the mRS were used as the anchor for establishing CIDs. MAIN OUTCOME MEASURES: Classically summed and Rasch transformed Section GG change scores associated with clinically important improvements on the mRS. RESULTS: A total of 166 patients (79.8%) improved ≥1 point on the mRS. Change scores of 27, 9, and 16 on Section GG total physical functioning (self-care + mobility), self-care, and mobility/walk scales, respectively, had high sensitivity (0.82-0.85) but low specificity (0.52-0.69) in identifying patients improving on the mRS. Positive predictive values ranged from 0.87 to 0.91, and negative predictive values ranged from 0.42 to 0.52. Total physical functioning and selfcare anchor-derived change scores were similar to the reliable change index (RCI [2.77 × SEM]), calculated as 28 and 10 points, respectively, whereas anchor-derived mobility/walk scale change scores were equivalent to 1.96 × SEM. Exploratory Rasch modeling identified 3 Section GG subscales (R-Self-Care, R-Mobility, and R-Walking). Improvements on the R-Walking subscale were most correlated with mRS improvements (ρ=-0.47); however, accuracy of CID estimates was not improved. CONCLUSIONS: Cut-off scores obtained using the mRS anchor aligned with more robust estimates of change, as estimated by distribution-based measures. While patients achieving anchor-derived cut-offs have a high probability of mRS improvement, change scores may fail to detect clinically meaningful improvements at these same thresholds. Alternative criteria for determining MCID/CIDs, should be explored. Rasch models require further validation.

Entropic measures of complexity in a new medical coding system.

BACKGROUND: Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015. OBJECTIVE: This research aims to introduce entropic measures to help users prepare for the migration to a new medical coding system by identifying and focusing preparation initiatives on clinical concepts with more likelihood of adoption challenges. METHODS: Two entropic measures of coding complexity are introduced. The first measure is a function of the variation in the alphabets of new codes. The second measure is based on the possible number of valid representations of an old code. RESULTS: A demonstration of how to implement the proposed techniques is carried out using the 2015 mappings between ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed in the context of clinical concepts that were likely to pose challenges regarding documentation, coding errors, and longitudinal data comparisons. CONCLUSION: The proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of adoption challenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.

Network analysis of medical care services.

Medical care services can be organized into a network. Understanding the structure of this network cannot only help analyze common clinical protocols but can also help reveal previously unknown patterns of care. The objective of this research is to introduce the concept and methods for constructing and analyzing the network of medical care services. We start by demonstrating how to build the network itself and then develop algorithms, based on principal component analysis and social network analysis, to detect communities of services. Finally, we propose novel graphical techniques for representing and assessing patterns of care. We demonstrate the application of our algorithms using data from an Emergency Department in New York State. One of the implications of our research is that clinical experts could use our algorithms to detect deviations from either existing protocols of care or administrative norms.