Objectives

Knowing which patient-centered medical home (PCMH) care delivery changes and quality improvement (QI) practices further PCMH implementation is essential.

Study design

We used the 2008-2017 National Committee of Quality Assurance (NCQA) PCMH directory of 15,188 primary care practices that received Level 1, 2, or 3 NCQA PCMH recognition to construct a stratified national sample of 105 practices engaged in PCMH transformation. We examined their QI practices and PCMH changes associated with PCMH transformation.

Methods

We derived QI practice and PCMH change variables from semistructured interviews. Practice leaders completed the PCMH Assessment (PCMH-A) measuring the practice's degree of PCMH implementation, which is a proxy for patient-centeredness. Controlling for practice characteristics, we regressed PCMH-A scores on QI practice and PCMH change variables.

Results

Practices undergoing PCMH transformation nationwide most commonly made care delivery changes in access and continuity of care. To improve quality, practices most commonly engaged in discussing and targeting areas of patient experience improvement, trending performance, and conducting targeted QI. However, practices lower in patient-centeredness as measured by the PCMH-A were more likely to engage in efforts to improve patient experiences, such as reviewing patient experience data or engaging in 1-on-1 provider counseling related to patient interactions. Mature PCMH practices focused on changes in continuity of care.

Conclusions

Practices undertake a wide variety of care delivery changes and QI practices simultaneously to meet PCMH requirements. The patient experience-specific QI practices and PCMH care delivery changes that practices make to improve patient-centeredness differ by years of PCMH recognition.

Objective

The purpose of this study was to estimate the significance of individual change using 5 statistical indicators in 2 samples of patients treated for low back pain.

Methods

This secondary analysis used observational and clinical trial data from 2 samples of patients with low back pain to compare 5 ways of estimating significant individual change on the Impact Stratification Score (ISS) administered at the following 2 time points: 3 months apart in an observational study of 1680 patients undergoing chiropractic care, and 6 weeks apart in a randomized trial of 750 active-duty military personnel with low back pain. The following 5 methods were compared: (1) standard deviation index; (2) standard error of measurement (SEM); (3) standard error of estimate (SEE); (4) standard error of prediction (SEP); and (5) the reliable change index (RCI). The ISS is the sum of the Patient-Reported Outcomes Measurement Information System (PROMIS)-29 v2.1 physical function, pain interference, and pain intensity scores and is scored to have a possible range of 8 (least impact) to 50 (greatest impact).

Results

The amount of change on the ISS needed for significant individual change in both samples was 5 for the SEM and for the SEE and 7 for the SEP and RCI.

Conclusions

The results of the current study provide some preliminary support for use of the SEP or the RCI to identify significant individual change and provide estimated thresholds of individual change that can be used for the ISS. The SEP and RCI estimates of significant change were consistent with retrospective ratings of change of at least moderately better in prior research. These 2 were less likely than other methods to classify people with low back pain as responders who have not actually gotten better (false positive). In contrast, the SEM and SEE were less likely to miss real change (false negative).

Objective

To crosswalk the National Institutes of Health (NIH) Pain Consortium's Research Task Force proposed Impact Stratification Score (ISS) to the PEG (Pain Intensity, Interference With Enjoyment of Life, Interference With General Activity) Scale.

Design

Cross-sectional data collected in 2021. Ordinary least squares regression analyses of ISS and PEG.

Setting

Amazon Mechanical Turk workers.

Participants

1931 adults with back pain with an average age of 41 (range, 19-77); 48% were female, 16% Hispanic, 7% non-Hispanic Black, 5% non-Hispanic Asian, and 71% non-Hispanic White (N=1931).

Interventions

Not applicable.

Main outcome measures

The Patient-Reported Outcomes Measurement Information System (PROMIS)-29+2 v2.1 survey that includes the ISS, and the 3-item PEG.

Results

The ISS and PEG had a correlation coefficient of 0.74. The ISS accounted for 55% of the adjusted variance in the PEG and the standardized average deviation between observed and predicted scores (normalized mean absolute error) was 0.53. Likewise, the PEG explained 55% of the variance in the ISS with a normalized mean absolute error of 0.52.

Conclusions

This study provides a crosswalk between the ISS and PEG that can be used to predict one from the other. The regression equations can facilitate comparisons in studies that use different measures.

It is challenging to synthesize findings across studies of pain impact. This study develops a link to estimate the Patient-Reported Outcomes Measurement Information System (PROMIS) global health measure from the 3-item Pain intensity, interference with Enjoyment of life, interference with General activity (PEG) scale. The PROMIS and PEG items were administered to 795 adults (average age = 51; 54% female, 79% White). We estimated correlations among the PEG and PROMIS items and conducted factor analysis to identify the best subset of PROMIS items for linking to the PEG. An item response theory graded response model was estimated to link the PEG with the 4-item PROMIS global physical health scale. A categorical single-factor model and a bifactor model provided support for a single dimension for the PEG and PROMIS global physical health items. The product-moment correlation between estimated PROMIS global physical health scale from the PEG and the actual global physical health score was .74. The mean difference between estimated PROMIS global physical health scale score from the PEG and the observed global physical health score was less than a T-score point. This study makes it possible to estimate the average global physical health for group-level comparisons in research that includes the PEG. PERSPECTIVE: This article describes an empirical link of the PEG to the PROMIS global physical health scale that makes it possible to estimate the average global physical health in studies that include the PEG. This link can facilitate comparisons among studies that have not administered the PEG or the PROMIS global health scale.

Introduction

Shadow coaching improves provider-patient interactions, as measured by CG-CAHPS® overall provider rating (OPR) and provider communication (PC). However, these improvements erode over time.

Aim

Examine whether a second coaching session (re-coaching) improves and sustains patient experience.

Setting

Large, urban Federally Qualified Health Center PROGRAM: Trained providers observed patient care by colleagues and provided suggestions for improvement. Providers with OPRs<90 (0-100-point scale) were eligible.

Evaluation

We used stratified randomization based on provider type and OPR to assign half of the 40 eligible providers to re-coaching. For OPR and PC, we fit mixed-effects regression models with random-effects for provider (level of treatment assignment) and fixed-effects for time (linear spline with knots and possible "jump" at initial coaching and re-coaching), previous OPR, patient characteristics, and sites. We observed a statistically significant medium jump among re-coached providers after re-coaching on OPR (3.7 points) and PC (3.5 points); differences of 1, 3, and ≥5-points for CAHPS measures are considered small, medium, and large. Improvements from re-coaching persisted for 12 months for OPR and 8 months for PC.

Discussion

Re-coaching improved patient experience more than initial coaching, suggesting the reactivation of knowledge from initial coaching. However, re-coaching gains also eroded. Coaching should occur every 6 to 12 months to maintain behaviors and scores.

Purpose

The Patient-Reported Outcomes Measurement Information System® (PROMIS)-16 assesses the same multi-item domains but does not include the pain intensity item in the PROMIS-29. We evaluate how well physical and mental health summary scores estimated from the PROMIS-16 reproduce those estimated using the PROMIS-29.

Methods

An evaluation of data collected from 4130 respondents from the KnowledgePanel. Analyses include confirmatory factor analysis to assess physical and mental health latent variables based on PROMIS-16 scores, reliability estimates for the PROMIS measures, mean differences and correlations of scores estimated by the PROMIS-16 with those estimated by the PROMIS-29, and associations between differences in corresponding PROMIS-16 and PROMIS-29 scores by sociodemographic characteristics.

Results

A two-factor (physical and mental health) model adequately fits the PROMIS-16 scores. Reliability estimates for the PROMIS-16 measures were slightly lower than for the PROMIS-29 measures. There were minimal differences between PROMIS physical and mental health summary scores estimated using the PROMIS-16 or the PROMIS-29. PROMIS-16 and PROMIS-29 score differences by sociodemographic characteristics were small. Using the PROMIS pain intensity item when scoring the PROMIS-16 produced similar estimates of physical and mental health summary scores.

Conclusion

The PROMIS-16 provides similar estimates of the PROMIS-29 physical and mental health summary scores. The high reliability of these scores indicates they are accurate enough for use with individual patients.

Objectives

Quality improvement (QI) requires data, indicators, and national benchmarks. Knowledge about the usefulness of Child Hospital Consumer Assessment of Healthcare Providers and Systems (Child HCAHPS) data are lacking. We examined quality leader and frontline staff perceptions about patient experience measurement and use of Child HCAHPS data for QI.

Methods

We surveyed children's hospital leaders and staff about their use of Child HCAHPS for QI, including measures from other studies. We compared scale and item means for leaders and staff and compared means to other studies.

Results

Almost all leaders, but only one-third of staff, received reports with Child HCAHPS data. Leaders found the data more useful for comparisons to other hospitals than did staff. Both agreed on the validity of Child HCAHPS scores and used these data for improving pediatric care experiences. They agreed the data accurately reflect their hospital's quality of care, provide specific information for QI, and can be used to improve pediatric care experiences. They also agreed on approaches to improve Child HCAHPS scores. Among staff, QI was reported as essential to their daily work and that Child HCAHPS data were integral to QI.

Conclusions

As uptake of the Child HCAHPS survey increases, our study of one medium-sized, urban children's hospital revealed that leaders and staff believe Child HCAHPS provides actionable metrics for improvement. Our study fills a gap in research about the use of Child HCAHPS for pediatric QI. A multisite evaluation would provide further information about how the Child HCAHPS survey can improve care.

Background

Healthcare organizations want to improve patient care experiences. Some use 'shadow coaching' to improve interactions between providers and patients. A Federally Qualified Health Center (FQHC) implemented a half-day observation of individual primary-care providers by a 'shadow coach' during real-time patient visits, including an in-person verbal debrief afterwards and a written report with specific recommendations. Shadow coaching identified areas for improvement. We aimed to characterize lessons and barriers to implementing shadow coaching as a mechanism to improve interactions with patients and change organizational culture.

Methods

We examined provider and coach perceptions of shadow coaching through interviewing coached providers, stratified by provider type and Consumer Assessment of Healthcare Providers and Systems (CAHPS) performance, and the coaches who coached the most providers. We interviewed 19 coached providers and 2 coaches in a large, urban FQHC. Content analysis identified implementation barriers, facilitators and themes.

Results

Coaches reported needing 'buy-in' throughout the organization and the need to be credible and empathize with the providers being coached. Most providers reported behaviour changes based on recommendations. Almost all providers recalled at least one coaching recommendation that was actionable. Providers and coaches highlighted patient-level and practise-level barriers that impeded their ability to implement recommended improvements. CAHPS data was reported as an effective performance management metric for measuring change, counselling providers, and evaluating provider-level efforts but was not always specific enough to yield tangible recommendations.

Conclusions

Regular messaging by leadership about the priority and purpose of shadow coaching was essential for both physician engagement and its mature implementation across the organization. Coaching could be embedded into a long-term strategy of professional development with periodic re-coaching. Re-coaching sessions could target issues raised by providers, such as dealing with difficult patients or specific populations. Research on the timing and content of re-coaching is needed.

Background

Shadow coaching, a type of one-on-one provider counseling by trained peers, is an effective strategy for improving provider behaviors and patient interactions, but its effects on improving patient experience for English- and Spanish-preferring patients is unknown.

Objective

Assess effects of shadow coaching on patient experience for English- and for Spanish-preferring patients.

Design

We analyzed 2012-2019 Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) data (n=46,089) from an urban Federally Qualified Health Center with 44 primary care practices and 320 providers. One-third (n=14,631) were Spanish-preferring patients. We fit mixed-effects regression models with random effects for provider (the level of treatment assignment) and fixed effects for time (a linear spline for time with a knot and "jump" at coaching date), patient characteristics, and site indicators, stratified by preferred language.

Participants

The 74 providers who had a 6-month average top-box score on the CAHPS overall provider rating below 90 (on a 100-point scale) were shadow coached. Similar percentages of English-preferring (45%) and Spanish-preferring patients (43%) were seen by coached providers.

Intervention

Trained providers observed patient care by colleagues and provided suggestions for improvement. Verbal feedback was provided immediately after the observation and the participant received a written report summarizing the comments and recommendations from the coaching session.

Main measures

CG-CAHPS Visit Survey 2.0 provider communication composite and overall provider rating (0-100 scoring).

Key results

We found a statistically significant 2-point (small) jump in CAHPS provider communication and overall provider rating among English-preferring patients of coached providers. There was no evidence of a coaching effect on patient experience for Spanish-preferring patients.

Conclusions

Coaching improved care experiences for English-preferring patients but may not have improved patient experience for Spanish-preferring patients. Selection and training of providers to communicate effectively with Spanish-preferring patients is needed to extend the benefits of shadow coaching to Spanish-preferring patients.