Psychometric evaluation of a patient-reported item bank for healthcare engagement

Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters

The application of unidimensional IRT models requires item response data to be unidimensional. Often, however, item response data contain a dominant dimension, as well as one or more nuisance dimensions caused by content clusters. Applying a unidimensional IRT model to multidimensional data causes violations of local independence, which can vitiate IRT applications. To evaluate and, possibly, remedy the problems caused by forcing unidimensional models onto multidimensional data, we consider the creation of a projected unidimensional IRT model, where the multidimensionality caused by nuisance dimensions is controlled for by integrating them out from the model. Specifically, when item response data have a bifactor structure, one can create a unidimensional model based on projecting to the general factor. Importantly, the projected unidimensional IRT model can be used as a benchmark for comparison to a unidimensional model to judge the practical consequences of multidimensionality. Limitations of the proposed approach are detailed.

Changes in healthcare engagement during the COVID-19 pandemic

BACKGROUND

Healthcare engagement, defined as the self-efficacy to enact the behaviors needed to obtain optimal benefit from health services, is an important aspect of healthcare quality. Measuring changes to healthcare engagement is essential to informing current and ongoing adaptations to health service delivery. The objective of the present study was to explore the responsiveness of the recently developed PROMIS® Healthcare Engagement measure (PHE), a patient-reported outcome, through investigating the impact of COVID and COVID-related healthcare disruptions on healthcare engagement from pre- to peri-pandemic.

METHODS

Baseline data (2018-2019) were collected via a national mail survey of Veterans receiving VA care. For follow-up data, a subset of participants was randomly selected to be invited to a follow-up survey. Administrative data was used from the VA's Corporate Data Warehouse (CDW). We used mixed effects linear modeling to compare changes in healthcare engagement from baseline to follow-up between Veterans who reported healthcare disruptions and Veterans who did not report healthcare disruptions, adjusting for covariates.

RESULTS

From baseline to follow-up, healthcare engagement scores increased on average by 2.84 points. Compared to Veterans who reported no disruptions, Veterans who experienced COVID-related healthcare disruptions demonstrated greater decreases to healthcare engagement (difference scores ≥ - 1.98, ps ≤ 0.002) Further, Veterans with more healthcare disruptions showed greater decreases in healthcare engagement relative to those with fewer healthcare disruptions, such that Veterans with 2 healthcare disruptions (difference score = -4.20) significantly differed from Veterans reporting only 1 healthcare disruption, and Veterans reporting 3 or more disruptions (difference score = -3.75) significantly differed from those with 2 disruptions.

CONCLUSION

Our results provide preliminary evidence of the PHE's responsiveness through demonstrating that environmental factors, such as pandemic-related factors, influence healthcare engagement. The COVID-19 pandemic had a complex effect on healthcare engagement, with healthcare engagement scores increasing overall during the pandemic but Veterans reporting COVID-related healthcare disruptions showing decreased changes in healthcare engagement. These findings support the utility of the PHE as a measure of healthcare engagement.

Novel Tools to Approach and Measure Outcomes in Patients with Fractures

Upper extremity fractures are prevalent and pose a great burden to patients and society. In the US alone, the annual incidence of upper extremity fractures is 67.6 fractures per 10,000 persons. While the majority of patients with upper extremity fractures demonstrate satisfactory outcomes when treated appropriately (the details of which are discussed in prior articles), the importance of follow-up and outcome measurement cannot be understated. Outcome measurement allows for accountability and improvement in clinical outcomes and research. The purpose of this article is to describe recent advances in methods and tools for assessing clinical and research outcomes in hand and upper extremity care. Three specific advances that are broadly changing the landscape of follow-up care of our patients include: 1) telemedicine, 2) patient-reported outcome measurement, and 3) wearables/remote patient monitoring.

Clinical Validity of the PROMIS Healthcare Engagement 8-Item Short Form

BACKGROUND

Healthcare engagement is a key measurement target for value-based healthcare, but a reliable and valid patient-reported measure has not yet been widely adopted.

OBJECTIVE

To assess the validity of a newly developed patient-reported measure of healthcare engagement, the 8-item PROMIS Healthcare Engagement (PHE-8a).

DESIGN

Prospective cohort study of the association between healthcare engagement and quality of care over 1 year. We fit mixed effects models of quality indicators as a function of engagement scores, adjusting for age, race/ethnicity, rural residence, and risk scores.

PARTICIPANTS

National stratified random sample of 9552 Veterans receiving Veterans Health Administration care for chronic conditions (hypertension, diabetes) or mental health conditions (depression, post-traumatic stress disorder).

MAIN MEASURES

Patient experience: Consumer Assessment of Health Plans and Systems communication and self-management support composites; no-show rates for primary care and mental health appointments; use of patient portal My HealtheVet; and Healthcare Effectiveness Data and Information Set electronic quality measures: HbA1c poor control, controlling high blood pressure, and hyperlipidemia therapy adherence.

KEY RESULTS

Higher engagement scores were associated with better healthcare quality across all outcomes, with each 5-point increase (1/2 standard deviation) in engagement scores associated with statistically significant and clinically meaningful gains in quality. Across the continuum of low to high engagement scores, we observed a concomitant reduction in primary care no-show rates of 37% and 24% for mental health clinics; an increased likelihood of My HealtheVet use of 15.4%; and a decreased likelihood of poor diabetes control of 44%.

CONCLUSIONS

The PHE-8a is a brief, reliable, and valid patient-reported measure of healthcare engagement. These results confirm previously untested hypotheses that patient engagement can promote healthcare quality.

Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model

As part of a scale development project, we fit a nominal response item response theory model to responses to the Health Care Engagement Measure (HEM). When using the original 5-point response format, categories were not ordered as intended for six of the 23 items. For the remaining, the category boundary discrimination between Categories 0 (not at all true) and 1 (a little bit true) was only weakly discriminating, suggesting uninformative categories. When the lowest two categories were collapsed, psychometric properties improved greatly. Category boundary discriminations within items, however, varied significantly. Specifically, higher response category distinctions, such as responding 3 (very true) versus 2 (mostly true) were considerably more discriminating than lower response category distinctions. Implications for HEM scoring and for improving measurement precision at lower levels of the construct are presented as is the unique role of the nominal response model in category analysis.