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PURPOSE: Patient outcomes can improve when primary care and behavioral health providers use a collaborative system of care, but integrating these services is difficult. We tested the effectiveness of a practice intervention for improving patient outcomes by enhancing integrated behavioral health (IBH) activities. METHODS: We conducted a pragmatic, cluster randomized controlled trial. The intervention combined practice redesign, quality improvement coaching, provider and staff education, and collaborative learning. At baseline and 2 years, staff at 42 primary care practices completed the Practice Integration Profile (PIP) as a measure of IBH. Adult patients with multiple chronic medical and behavioral conditions completed the Patient-Reported Outcomes Measurement Information System (PROMIS-29) survey. Primary outcomes were the change in 8 PROMIS-29 domain scores. Secondary outcomes included change in level of integration. RESULTS: Intervention assignment had no effect on change in outcomes reported by 2,426 patients who completed both baseline and 2-year surveys. Practices assigned to the intervention improved PIP workflow scores but not PIP total scores. Baseline PIP total score was significantly associated with patient-reported function, independent of intervention. Active practices that completed intervention workbooks (n = 13) improved patient-reported outcomes and practice integration (P ≤ .05) compared with other active practices (n = 7). CONCLUSION: Intervention assignment had no effect on change in patient outcomes; however, we did observe improved patient outcomes among practices that entered the study with greater IBH. We also observed more improvement of integration and patient outcomes among active practices that completed the intervention compared to active practices that did not. Additional research is needed to understand how implementation efforts to enhance IBH can best reach patients.

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OBJECTIVES: The Medicare end-stage kidney disease (ESKD) prospective payment system (PPS) for maintenance dialysis, implemented in 2011, resulted in modestly increased access to both home-based peritoneal dialysis (PD) and home hemodialysis (HHD) treatment modalities, but it is unclear whether regional disparities in home dialysis (PD and HHD) were affected. We compared regional home dialysis use by White and non-White individuals over time. STUDY DESIGN: Retrospective cohort study of dialysis facilities offering home dialysis in 2006-2016 and of 1,098,579 patients with prevalent ESKD in 2006-2016. METHODS: We compared hospital referral region (HRR) utilization rates of home dialysis between White and non-White patients over time using a generalized estimating equation model with a negative binomial distribution adjusting for regional characteristics. RESULTS: The mean number of facilities offering home dialysis operating in each HRR increased from 15.6 in 2006 to 22.1 in 2016. Observed mean HRR home dialysis rates increased overall, but White patients maintained greater home dialysis use than non-White patients: 19.7% in 2006 and 26.2% in 2016 among White patients vs 13.0% in 2006 and 17.8% in 2016 among non-White patients. In adjusted analysis, there was no evidence of changes in White/non-White disparities in home dialysis use over time (P = .84) or after the Medicare ESKD PPS in 2011 (incidence rate ratio, 0.97; 95% CI, 0.92-1.02; P = .29). CONCLUSIONS: Even after modest increases in dialysis facility availability and patient utilization after the implementation of the Medicare ESKD PPS in 2011, significant racial disparities in home dialysis remain.

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OBJECTIVE: To describe a multistage process of designing and evaluating a dashboard that presents data on how equitably health plans provide care for their members. STUDY DESIGN: We designed a dashboard for presenting summative and finer-grained data to health plans for characterizing how well plans are serving individuals who belong to racial/ethnic minority groups and individuals with low income. The data presented in the dashboard were based on CMS' Health Equity Summary Score (HESS) for Medicare Advantage plans. METHODS: Interviews and listening sessions were conducted with health plan representatives and other stakeholders to assess understanding, perceived usefulness, and interpretability of HESS data. Usability testing was conducted with individuals familiar with quality measurement and reporting to evaluate dashboard design efficiency. RESULTS: Listening session participants understood the purpose of the HESS and expressed a desire for this type of information. Usability testing revealed a need to improve dashboard navigability and to streamline content. CONCLUSIONS: The HESS dashboard is a potentially useful tool for presenting data on health equity to health plans. The multistage process of continual testing and improvement used to develop the dashboard could be a model for targeting and deciding upon quality improvement efforts in the domain of health equity.

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BACKGROUND: Health care quality varies by patient factors, including race-and-ethnicity and preferred language. Addressing inequities requires identifying them and incentivizing equity. OBJECTIVES: We apply an approach first implemented in the Medicare Advantage setting to measure equity in patient experiences by race-and-ethnicity [Asian American and Native Hawaiian or Pacific Islander (AA and NHPI), Black, Hispanic, vs. White] and language preference (English-preferring vs. another-language-preferring). We identify characteristics of hospitals providing high-quality equitable care. RESEARCH DESIGN: We estimated, standardized, and combined performance measures into a Health Equity Summary Score (HESS) using 2016-2019 Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey data. The HCAHPS HESS considered current cross-sectional performance, within-hospital improvement, and overall improvement by race-and-ethnicity and language preference. SUBJECTS: A total of 3333 US hospitals with 2019 HCAHPS Star Ratings. RESULTS: The HCAHPS HESS was calculable for 44% of hospitals. High-scoring (4-5 diamonds on a 1-diamond to 5-diamond scale) hospitals tended to be smaller than intermediate-scoring [3 diamonds (14% of high-scoring hospitals had <100 beds vs. 7% of intermediate-scoring hospitals, P <0.001) and were less often for-profit (20% vs. 31%, P <0.001)]. While a significant percentage (29%) of patients served by high-scoring hospitals were AA and NHPI, Black, or Hispanic, and 9% were another-language-preferring, there were smaller proportions of Black and Hispanic patients in high-scoring versus other hospitals. HESS performance was negatively associated with the percentage of patients preferring another language to English. HESS scores were moderately correlated with overall Star Ratings ( r =0.70). CONCLUSIONS: The HCAHPS HESS and practices of high-scoring hospitals could promote more equitable patient experiences.

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Purpose: To determine if pain screening and functional assessment results are associated with new diagnoses and treatment for pain in primary care. Patients and Methods: Observational study at 13 primary care sites of a statewide federally qualified health center that implemented routine screening and functional assessment for all adults in primary care. The study group included 10,091 adults aged 18+ who had an in-person visit between July 2, 2018, and June 1, 2019, where they screened positive for chronic pain and completed a 3-question functional assessment with the PEG (Pain, Enjoyment of Life, General Activity). Multivariate logistic regressions quantified associations between pain frequency, diagnosis and treatment, sociodemographics, comorbidities, and self-reported severe pain impairment with pain diagnoses and treatment documented after screening. Results: Patients were mostly women (60.3%), Latinx (41.1%), English-speaking (80.1%), and Medicaid-insured (62.0%); they averaged 49.1 years old (SD = 13.7 years). Patients with severe pain impairment or who were Latinx were more likely to get a newly documented pain diagnosis (absolute risk difference [ARD]: 13.2% and 8.6%, ps < 0.0001), while patients with mental health/substance use or medical comorbidities were less likely (ARDs: -20.0% to -6.2%, ps < 0.001). Factors most consistently associated with treatment were prior treatment of the same modality (4 of 7 treatments, ARDs = 27.3% to 44.1%, ps <0.0001), new pain diagnosis (5 of 7, ARDs = 3.2% to 27.4%, ps <0.001), and severe impairment (4 of 7, ARDs = 2.6% to 6.5%, ps < 0.0001). A new diagnosis had the strongest association with non-opioid pain analgesics and physical medicine (ARD = 27.0% and 27.4%, p < 0.0001). Latinx patients were less likely to receive opioid analgesics and mental health/substance use medications and counseling (ARDs = -3.3% to 7.5%, ps <0.0001). Conclusion: Screening and assessment with patient-reported tools may influence pain care. Care for Latinx patients differed from non-Latinx white patients.

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Recent value-based payment reforms in the U.S. called for empirical data on how primary care practices of varying characteristics fund their integrated behavioral health services. To describe payment strategies used by U.S. primary care practices to fund behavioral health integration and compare strategies between practices with and without hospital affiliation.Baseline data were used and collected from 44 practices participating in a cluster-randomized, pragmatic trial of behavioral health integration. Data included practice characteristics and payment strategies-fee-for-service payment, pay-for-performance incentives, grants, and graduate medical education funds. Descriptive and comparative analyses using Fisher's exact tests and independent T-tests were conducted. The sample had 26 (59.1%) hospital-affiliated (hospital/health system-owned, academic medical centers and hospital-affiliated practices) and 18 (40.9%) non-hospital-affiliated practices (community health centers/federally qualified health centers and privately-owned practices). Most practices (88.6%) received payments through fee-for-service; 63.6% received pay-for-performance incentives; 31.8% received grant funds. Collaborative Care Management billing (CPT) codes were used in six (13.6%) practices. Over half (53.8%) of hospital-affiliated practices funded their behavioral health services through fee-for-service and pay-for-performance incentives only, as opposed to two-thirds (66.7%) of non-hospital-affiliated practices required additional support from grants and/or general medical education funds. Primary care practices support behavioral health integration through diverse payment strategies. More hospital-affiliated practices compared to non-hospital-affiliated practices funded integrated behavioral health services through fee-for-service and pay-for-performance incentives. Practices without hospital affiliation relied on multiple funding streams including grants and/or general medical education funds, suggesting their approach to financial sustainment may be more precarious or challenging, compared to hospital-affiliated practices.

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Although most health care occurs in the ambulatory setting, limited research examines how providers and patients think about and enact ambulatory patient safety. This multimethod qualitative study seeks to identify perceived challenges and strategies to improve ambulatory safety from the perspectives of clinicians, staff, and patients. Data included interviews (N = 101), focus groups (N = 65), and observations of safety processes (N = 79) collected from 10 patient-centered medical homes. Key safety issues included the lack of interoperability among health information systems, clinician-patient communication failures, and challenges with medication reconciliation. Commonly cited safety strategies leveraged health information systems or involved dedicated resources (eg, providing access to social workers). Patients also identified strategies not mentioned by clinicians, emphasizing the need for their involvement in developing safety solutions. This work provides insight into safety issues of greatest concern to clinicians, staff, and patients and strategies to improve safety in the ambulatory setting.

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BACKGROUND: Assess the feasibility of using goal attainment scaling (GAS) in care planning for older adults with complex needs. GAS is an individualized approach to goal setting and follow up using a quantified scale. To date, little is known about the feasibility of GAS among this population. METHODS: We conducted a qualitative study with a sample of 28 older adults and 23 providers from diverse settings to evaluate the value and challenges of this approach. We conducted semi-structured interviews and iteratively coded and analyzed interview transcripts for themes related to value, challenges, and implementation. RESULTS: Most older adults and providers reported that the GAS approach added value to the care encounter. GAS supported collaboration and patient accountability for their goals, though it could be demotivating to some patients. Some older adults and providers noted that GAS could be confusing and that it was uncomfortable to talk about negative outcomes (i.e., the - 2 and - 1 boxes of the scale). Factors that facilitated implementation included using visual copies of the GAS forms, having an established patient-provider relationship, practicing the approach, and having previous goal-related clinical training. CONCLUSIONS: GAS was feasible to implement across diverse settings, and, despite challenges, both older adults and providers reported that it added value to care planning encounters with the potential to improve delivery of person-centered care. Further efforts to demonstrate the applicability and benefit of this method for older adults are warranted, particularly to address implementation of the approach.

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BACKGROUND: Quality improvement (QI) may be aimed at improving care for all patients, or it may be targeted at only certain patient groups. Health care providers have little guidance when determining when targeted QI may be preferred. OBJECTIVES: The aim was to develop a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which we apply to the performance of health plans for different patient groups. RESEARCH DESIGN AND MEASURES: Retrospective analysis of 7 Health Care Effectiveness Data and Information Set (HEDIS) measures of clinical care quality. SUBJECTS: All Medicare Advantage (MA) beneficiaries eligible for any of 7 HEDIS measures 2015-2018. RESULTS: MA plans with higher overall performance tended to be less inconsistent in their performance (r=-0.2) across groups defined by race-and-ethnicity and low-income status (ie, dual eligibility for Medicaid or receipt of Low-Income Subsidy). Plan characteristics were usually associated with only small differences in inconsistency. The characteristics associated with differences in consistency [eg, size, Health Maintenance Organization (HMO) status] were also associated with differences in overall performance. We identified 9 (of 363) plans that had large inconsistency in performance across groups (>0.8 SD) and investigated the reasons for inconsistency for 2 example plans. CONCLUSIONS: This newly developed inconsistency metric may help those designing and evaluating QI efforts to appropriately determine when targeted QI is preferred. It can be used in settings where performance varies across groups, which can be defined by patient characteristics, geographic areas, hospital wards, etc. Effectively targeting QI efforts is essential in today's resource-constrained health care environment.

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BACKGROUND: Previous studies have identified disparities in readmissions among Medicare beneficiaries hospitalized for the Hospital Readmissions Reduction Program's (HRRP's) priority conditions. Evidence suggests timely follow-up is associated with reduced risk of readmission, but it is unknown whether timely follow-up reduces disparities in readmission. OBJECTIVE: To assess whether follow-up within 7 days after discharge from a hospitalization reduces risk of readmission and mitigates identified readmission disparities. DESIGN: A retrospective cohort study using Cox proportional hazards models to estimate the associations between sociodemographic characteristics (race and ethnicity, dual-eligibility status, rurality, and area social deprivation), follow-up, and readmission. Mediation analysis was used to examine if disparities in readmission were mitigated by follow-up. PARTICIPANTS: We analyzed data from 749,402 Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, or pneumonia, and discharged home between January 1 and December 1, 2018. MAIN MEASURE: All-cause unplanned readmission within 30 days after discharge. KEY RESULTS: Post-discharge follow-up within 7 days of discharge was associated with a substantially lower risk of readmission (HR: 0.52, 95% CI: 0.52-0.53). Across all four HRRP conditions, beneficiaries with dual eligibility and beneficiaries living in areas with high social deprivation had a higher risk of readmission. Non-Hispanic Black beneficiaries had higher risk of readmission after hospitalization for pneumonia relative to non-Hispanic Whites. Mediation analysis suggested that 7-day follow-up mediated 21.2% of the disparity in the risk of readmission between dually and non-dually eligible beneficiaries and 50.7% of the disparity in the risk of readmission between beneficiaries living in areas with the highest and lowest social deprivation. Analysis suggested that after hospitalization for pneumonia, 7-day follow-up mediated nearly all (97.5%) of the increased risk of readmission between non-Hispanic Black and non-Hispanic White beneficiaries. CONCLUSIONS: Improving rates of follow-up could be a strategy to reduce readmissions for all beneficiaries and reduce disparities in readmission based on sociodemographic characteristics.

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OBJECTIVE: We sought to examine the extent to which body mass index (BMI) was available in electronic health records for Florida Medicaid recipients aged 5 to 18 years taking Second-Generation Antipsychotics (SGAP). We also sought to illustrate how clinical data can be used to identify children most at-risk for SGAP-induced weight gain, which cannot be done using process-focused measures. METHODS: Electronic health record (EHR) data and Medicaid claims were linked from 2013 to 2019. We quantified sociodemographic differences between children with and without pre- and post-BMI values. We developed a linear regression model of post-BMI to examine pre-post changes in BMI among 4 groups: 1) BH/SGAP+ children had behavioral health conditions and were taking SGAP; 2) BH/SGAP- children had behavioral health conditions without taking SGAP; 3) children with asthma; and 4) healthy children. RESULTS: Of 363,360 EHR-Medicaid linked children, 18,726 were BH/SGAP+. Roughly 4% of linked children and 8% of BH/SGAP+ children had both pre and post values of BMI required to assess quality of SGAP monitoring. The percentage varied with gender and race-ethnicity. The R2 for the regression model with all predictors was 0.865. Pre-post change in BMI differed significantly (P < .0001) among the groups, with more BMI gain among those taking SGAP, particularly those with higher baseline BMI. CONCLUSION: Meeting the 2030 Centers for Medicare and Medicaid Services goal of digital monitoring of quality of care will require continuing expansion of clinical encounter data capture to provide the data needed for digital quality monitoring. Using linked EHR and claims data allows identifying children at higher risk for SGAP-induced weight gain.

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AIMS AND OBJECTIVES: To describe the development of the Patient Centeredness Index (PCI), evaluate its psychometric characteristics and evaluate the relationships between scores on the PCI and an established measure of empathy. BACKGROUND: Patient centeredness helps patients manage multiple chronic conditions with their providers, nurses and other team members. However, no instrument exists for evaluating patient centeredness within primary care practices treating this population. DESIGN: Multi-site instrument development and validation. STROBE reporting guidelines were followed. METHODS: To identify themes, we consulted literature on patient centeredness and engaged stakeholders who had or were caring for people with multiple chronic conditions (n = 7). We composed and refined items to represent those themes with input from clinicians and researchers. To evaluate reliability and convergent validity, we administered surveys to participants (n = 3622) with chronic conditions recruited from 44 primary care practices for a large-scale cluster randomised clinical trial of the effects of a practice-level intervention on patient and practice-level outcomes. Participants chose to complete the 16-item survey online, on paper or by phone. Surveys assessed demographics, number of chronic conditions and ratings of provider empathy. We conducted exploratory factor analysis to model the interrelationships among items. RESULTS: A single factor explained 93% of total variance. Factor loadings ranged from 0.55-0.85, and item-test correlations were ≥.67. Cronbach's alpha was .93. A moderate, linear correlation with ratings of provider's empathy (r = .65) supports convergent validity. CONCLUSIONS: The PCI is a new tool for obtaining patient perceptions of the patient centeredness of their primary care practice. The PCI shows acceptable reliability and evidence of convergent validity among patients managing chronic conditions. RELEVANCE TO CLINICAL PRACTICE: The PCI rapidly identifies patients' perspectives on patient centeredness of their practice, making it ideal for administration in busy primary care settings that aim to efficiently address patient-identified needs. TRIAL REGISTRATION: Clinicaltrials.org Protocol ID: WLPS-1409-24372. TITLE: Integrating Behavioural Health and Primary Care for Comorbid Behavioural and Medical Problems (IBHPC).

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OBJECTIVES: The patient-centered medical home (PCMH) may provide a key model for ambulatory patient safety. Our objective was to explore which PCMH and patient safety implementation and social network factors may be necessary or sufficient for higher patient safety culture. METHODS: This was a cross-case analysis study in 25 diverse U.S. PCMHs. Data sources included interviews of a clinician and an administrator in each PCMH, surveys of clinicians and staff, and existing data on the PCMHs' characteristics. We used coincidence analysis, a novel method based on set theory and Boolean logic, to evaluate relationships between factors and the implementation outcome of patient safety culture. RESULTS: The coincidence analysis identified 5 equally parsimonious solutions (4 factors), accounting for all practices with higher safety culture. Three solutions contained the same core minimally sufficient condition: the implementation factor leadership priority for patient safety and the social network factor reciprocity in advice-seeking network ties (advice-seeking relationships). This minimally sufficient condition had the highest coverage (5/7 practices scoring higher on the outcome) and best performance across solutions; all included leadership priority for patient safety. Other key factors included self-efficacy and job satisfaction and quality improvement climate. The most common factor whose absence was associated with the outcome was a well-functioning process for behavioral health. CONCLUSIONS: Our findings suggest that PCMH safety culture is higher when clinicians and staff perceive that leadership prioritizes patient safety and when high reciprocity among staff exists. Interventions to improve patient safety should consider measuring and addressing these key factors.

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Initiatives that support and incentivize the integration of behavioral health and general medical care have become a focus of government strategies to achieve the triple aim of improved health, better patient experience, and reduced costs. The authors describe the components of four large-scale national initiatives aimed at integrating care for a wide range of behavioral health needs. Commonalities across these national initiatives highlight health care and social services needs that must be addressed to improve care for people with co-occurring behavioral health and general medical conditions. These findings can inform how to design, test, select, and align the most promising strategies for integrated care in a variety of settings.

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OBJECTIVE: To investigate (a) the magnitude of the independent associations of neighborhood-level and person-level social risk factors (SRFs) with quality, (b) whether neighborhood-level SRF associations may be proxies for person-level SRF associations, and (c) how the association of person-level SRFs and quality varies by neighborhood-level SRFs. DATA SOURCES: 2015-2016 Medicare Advantage HEDIS data, Medicare beneficiary administrative data, and 2016 American Community Survey (ACS). STUDY DESIGN: Mixed effects linear regression models (1) estimated overall inequities by neighborhood-level and person-level SRFs, (2) compared neighborhood-level associations to person-level associations, and (3) tested the interactions of person-level SRFs with corresponding neighborhood-level SRFs. DATA COLLECTION/EXTRACTION METHODS: Beneficiary-level SES and disability administrative data and five-year ACS neighborhood-level SRF information were each linked to HEDIS data. PRINCIPAL FINDINGS: For all or nearly all HEDIS measures, quality was worse in neighborhoods lower in SES and in neighborhoods with higher proportions of residents with a disability. Quality by neighborhood racial and ethnic composition was mixed. Accounting for corresponding person-level SRFs reduced neighborhood SRF associations by 25% for disability, 43% for SES, and 74%-102% for racial and ethnic groups. Person-level SRF coefficients were not consistently reduced in models that added neighborhood-level SRFs. In 19 of 35 instances, there were significant (p < 0.05) interactions between neighborhood-level and corresponding person-level SRFs. Significant interactions were always positive for disability, SES, Black, and Hispanic, indicating more negative neighborhood effects for people with SRFs that did not match their neighborhood and more positive neighborhood effects for people with SRFs that matched their neighborhood. CONCLUSIONS: Relying solely on neighborhood-level SRF models that omit similar person-level SRFs overattributes inequities to neighborhood characteristics. Neighborhood-level characteristics account for much less variation in these measures' scores than similar person-level SRFs. Inequity-reduction programs may be most effective when targeting neighborhoods with a high proportion of people with a given SRF.

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Context: Most patients in need of behavioral health (BH) care are seen in primary care, which often has difficulty responding. Some practices integrate behavioral health care (IBH), with medical and BH providers at the same location, working as a team. However, it is difficult to achieve high levels of integration. Objective: Test the effectiveness of a practice intervention designed to increase BH integration. Study Design: Pragmatic, cluster-randomized controlled trial. Setting: 43 primary care practices with on-site BH services in 13 states. Population: 2,460 adults with multiple chronic medical and behavioral conditions. Intervention: 24-month practice change process including an online curriculum, a practice redesign and implementation workbook, remote quality improvement coaching services, and an online learning community. Outcomes: Primary outcomes were changes in the 8 Patient-Reported Outcomes Measurement Information System (PROMIS-29) domain scores. Secondary outcomes were changes in medication adherence, self-reported healthcare utilization, time lost due to disability, cardiovascular capacity, patient centeredness, provider empathy, and several condition-specific measures. A sample of practice staff completed the Practice Integration Profile at each time point to estimate the degree of BH integration in that site. Practice-level case studies estimated the typical costs of implementing the intervention. Results: The intervention had no significant effect on any of the primary or secondary outcomes. Subgroup analyses showed no convincing patterns of effect in any populations. COVID-19 was apparently not a moderating influence of the effect of the intervention on outcomes. The intervention had a modest effect on the degree of practice integration, reaching statistical significance in the Workflow domain. The median cost of the intervention was $18,204 per practice. In post-hoc analysis, level of BH integration was associated with improved patient outcomes independent of the intervention, both at baseline and longitudinally. Conclusions: The specific intervention tested in this study was inexpensive, but had only a small impact on the degree of BH integration, and none on patient outcomes. However, practices that had more integration at baseline had better patient outcomes, independent of the intervention. Although this particular intervention was ineffective, IBH remains an attractive strategy for improving patient outcomes.

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Background: The research enterprise has embraced patient centeredness in embedded efficient pragmatic trials, but limited data exist on using patient-reported outcomes (PROs) collected as part of usual clinical care for research. Objectives: We sought to assess the performance of different assessment methods for obtaining PROs in a pragmatic cluster randomized trial (HomePal study) designed to compare two models of home-based palliative care (HBPC). Design: Descriptive analytics, comparative trends, and psychometric performance of PROs collected in the HomePal study; measures included Edmonton Symptom Assessment System (ESAS), PROMIS-10, and others administered at baseline, 1, and 6 months. Setting/Subjects: HomePal was conducted in the Southern California and Northwest Kaiser Permanente regions in the United States; subjects were patients receiving HBPC and their caregivers. Measurements: We specifically compared ESAS obtained by research staff with those obtained by clinical HBPC nurses at the time of HBPC enrollment. We also compared ESAS completed by patients versus if done or assisted by a caregiver (proxy). Results: We enrolled 3533 patients and had 2205 ESAS measurements that met the criteria for analysis at baseline and 1447 at the one-month follow-up assessment. Research staff-obtained ESAS at admission to HBPC was higher overall (indicating more symptoms) than the clinically collected measure whether symptoms were reported by patients (31.7 ± 15.4 vs. 26.0 ± 13.4) or by proxies (36.9 ± 15.6 vs. 26.5 ± 13.5). These differences persisted with follow-up ESAS measures. Conclusions: We identified significant variability in PRO responses between different surveyors and whether proxy interaction was needed suggesting complex issues around PRO measure performance for pragmatic embedded trials. ClinicalTrials.gov Identifier: NCT03694431.

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