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Real-world evidence includes all health-related information, such as electronic health records, insurance claims, pharmacy records and wearables that are obtained outside of clinical trials. These data can provide critical insights into the natural history of disease and evaluate the safety and effectiveness of treatment regimens used in clinical practice. Real-world data have been applied to varying degrees by global regulatory agencies to inform and expedite many phases of drug development and help refine the use of therapeutic regimens after marketing, especially in populations that are under-represented in registration trials. For the management of hepatocellular carcinoma, early detection provides the best chance for curative therapies, whose success has been evaluated in numerous cohorts. The availability of novel systemic therapies, including kinase inhibitors and immunotherapies, has provided new treatment options and improved survival in patients with advanced stage hepatocellular carcinoma. Real-world longitudinal observational studies can help understand the long-term safety and effectiveness of these agents.

Background Patients hospitalized for coronavirus disease 2019 (COVID-19) may experience complications following hospitalization and require readmission. In this analysis, we estimated the rate and risk factors associated with COVID-19–related readmission and inpatient mortality. Methods In this retrospective cohort study, we used deidentified chargemaster data from 297 hospitals across 40 US states on patients hospitalized with COVID-19 from 15 February 2020 through 9 June 2020. Demographics, comorbidities, acute conditions, and clinical characteristics of first hospitalization are summarized. Multivariable logistic regression was used to measure risk factor associations with 30-day readmission and in-hospital mortality. Results Among 29 659 patients, 1070 (3.6%) were readmitted. Readmitted patients were more likely to have diabetes, hypertension, cardiovascular disease (CVD), or chronic kidney disease (CKD) vs those not readmitted (P 60 vs 18–40 years (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.48–2.50) and those admitted in the Northeast vs West (OR, 1.43; 95% CI, 1.14–1.79) or South (OR, 1.28; 95% CI, 1.11–1.49). Comorbidities including diabetes (OR, 1.34; 95% CI, 1.12–1.60), CVD (OR, 1.46; 95% CI, 1.23–1.72), CKD stage 1–5 (OR, 1.51; 95% CI, 1.25–1.81), and CKD stage 5 (OR, 2.27; 95% CI, 1.81–2.86) were associated with higher odds of readmission; 12.3% of readmitted patients died during second hospitalization. Conclusions Among this large US population of patients hospitalized with COVID-19, readmission was associated with certain comorbidities and acute conditions during first hospitalization. These findings may inform strategies to mitigate risks of readmission due to COVID-19 complications.

Objectives Promoting patient involvement in managing co-occurring physical and mental health conditions is increasingly recognized as critical to improving outcomes and controlling costs in this growing chronically ill population. The main objective of this study was to conduct an economic evaluation of the Wellness Incentives and Navigation (WIN) intervention as part of a longitudinal randomized pragmatic clinical trial for chronically ill Texas Medicaid enrollees with co-occurring physical and mental health conditions. Methods The WIN intervention used a personal navigator, motivational interviewing, and a flexible wellness expense account to increase patient activation, that is, the patient’s knowledge, skills, and confidence in managing their self-care and co-occurring physical and mental health conditions. Regression models were fit to both participant-level quality-adjusted life years (QALYs) and total costs of care (including the intervention) controlling for demographics, health status, poverty, Medicaid managed care plan, intervention group, and baseline health utility and costs. Incremental costs and QALYs were calculated based on the difference in predicted costs and QALYs under intervention versus usual care and were used to calculate the incremental cost-effectiveness ratios (ICERs). Confidence intervals were calculated using Fieller’s method, and sensitivity analyses were performed. Results The mean ICER for the intervention compared with usual care was $12 511 (95% CI $8971-$16 842), with a sizable majority of participants (70%) having ICERs below $40 000. The WIN intervention also produced higher QALY increases for participants who were sicker at baseline compared to those who were healthier at baseline. Conclusion The WIN intervention shows considerable promise as a cost-effective intervention in this challenging chronically ill population.

BACKGROUND Nonalcoholic steatohepatitis (NASH), advanced fibrosis, and subsequent cirrhosis and hepatocellular carcinoma are becoming the most common etiology for liver failure and liver transplantation; however, they can only be diagnosed at these potentially reversible stages with a liver biopsy, which is associated with various complications and high expenses. Knowing the difference between the more benign isolated steatosis and the more severe NASH and cirrhosis informs the physician regarding the need for more aggressive management. OBJECTIVE We intend to explore the feasibility of using machine learning methods for noninvasive diagnosis of NASH and advanced liver fibrosis and compare machine learning methods with existing quantitative risk scores. METHODS We conducted a retrospective analysis of clinical data from a cohort of 492 patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD), NASH, or advanced fibrosis. We systematically compared 5 widely used machine learning algorithms for the prediction of NAFLD, NASH, and fibrosis using 2 variable encoding strategies. Then, we compared the machine learning methods with 3 existing quantitative scores and identified the important features for prediction using the SHapley Additive exPlanations method. RESULTS The best machine learning method, gradient boosting (GB), achieved the best area under the curve scores of 0.9043, 0.8166, and 0.8360 for NAFLD, NASH, and advanced fibrosis, respectively. GB also outperformed 3 existing risk scores for fibrosis. Among the variables, alanine aminotransferase (ALT), triglyceride (TG), and BMI were the important risk factors for the prediction of NAFLD, whereas aspartate transaminase (AST), ALT, and TG were the important variables for the prediction of NASH, and AST, hyperglycemia (A1c), and high-density lipoprotein were the important variables for predicting advanced fibrosis. CONCLUSIONS It is feasible to use machine learning methods for predicting NAFLD, NASH, and advanced fibrosis using routine clinical data, which potentially can be used to better identify patients who still need liver biopsy. Additionally, understanding the relative importance and differences in predictors could lead to improved understanding of the disease process as well as support for identifying novel treatment options.

Patients with primary biliary cholangitis (PBC) often suffer with pruritus. We describe the impact of pruritus on quality of life and how it is managed in a real-world cohort.TARGET-PBC is a longitudinal observational cohort of patients with PBC across the USA. Data include information from medical records for three years prior to the date of consent up to 5 years of follow-up. Enrolled patients were asked to complete patient-reported outcome surveys: PBC-40, 5-D itch, and the PROMIS fatigue survey. Kruskal-Wallis tests were used to compare differences in symptoms between groups.A total of 211 patients with completed PRO surveys were included in the current study. PRO respondents were compared with non-respondents in the TARGET-PBC population and were broadly similar. Pruritus was reported in 170 patients (81%), with those reporting clinically significant pruritus (30%) scoring worse across each domain of the PBC-40 and 5-D itch, more frequently having cirrhosis, and having significantly greater levels of fatigue. Patients reporting clinically significant pruritus were more likely to receive treatment, but 33% had never received treatment (no itch = 43.9%, mild itch = 38.3%).The prevalence of pruritus was high in this population, and those reporting clinically significant pruritus had a higher likelihood of having advanced disease and worse quality of life. However, this study found that pruritus in PBC is under-treated. This may be due in part to ineffectiveness of current treatments, poor tolerance, or the lack of FDA-approved medications for pruritus.

Purpose PCORnet, the National Patient-Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. Methods We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012-2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD-9/ICD-10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ≥6.5%, or (CP3) an antidiabetic prescription and HbA1c ≥6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t0 . Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident-T2DM CP using electronic health record (EHR) review as reference. Results The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% (n = 1572; CI:95.1-97.0) and was consistently high across sites. The PPV for the incident-T2DM CP was 5.8% (CI:4.5-7.5). Conclusions The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.

We used electronic medical record (EMR) data in the National Patient‐Centered Clinical Research Network (PCORnet) to characterize “real‐world” prescription patterns of Type 2 diabetes (T2D) medications. We identified a retrospective cohort of 613,203 adult patients with T2D from 33 datamarts (median patient number: 12,711) from 2012 through 2017 using a validated computable phenotype. We characterized outpatient T2D prescriptions for each patient in the 90 days before and after cohort entry, as well as demographics, comorbidities, non‐T2D prescriptions, and clinical and laboratory variables in the 730 days prior to cohort entry. Approximately half of the individuals in the cohort were females and 20% Black. Hypertension (60.3%) and hyperlipidemia (50.5%) were highly prevalent. Most patients were prescribed either a single T2D drug class (42.2%) or had no evidence of a T2D prescription in the EMR (42.4%). A smaller percentage was prescribed multiple T2D drug types (15.4%). Among patients prescribed a single T2D drug type, metformin was the most common (42.6%), followed by insulin (18.2%) and sulfonylureas (13.9%). Newer classes represented approximately 13% of single T2D drug type prescriptions (dipeptidyl peptidase‐4 inhibitors [6.6%], glucagon‐like peptide‐1 receptor agonists [2.5%], thiazolidinediones [2.0%], and sodium‐glucose cotransporter‐2 inhibitors [1.6%]). Among patients prescribed multiple T2D drug types, the most common combination was metformin and sulfonylureas (63.5%). Metformin‐based regimens were highly prevalent in PCORnet's T2D population, whereas newer agents were prescribed less frequently. PCORnet is a novel source for the potential conduct of observational studies among patients with T2D.

Background The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing. However, the current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial manual efforts. Objective The purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes and separating T1DM from T2DM in pediatric patients. Methods This retrospective study utilized a data set from the University of Florida Health Integrated Data Repository to identify 300 patients aged 18 or younger with T1DM, T2DM, or that were healthy based on a developed computable phenotype. Three endocrinology residents/fellows manually reviewed medical records of all probable cases to validate diabetes status and type. This refined computable phenotype was then used to identify all cases of T1DM and T2DM in the OneFlorida Clinical Research Consortium. Results A total of 295 electronic health records were manually reviewed; of these, 128 cases were found to have T1DM, 35 T2DM, and 132 no diagnosis. The positive predictive value was 94.7%, the sensitivity was 96.9%, specificity was 95.8%, and the negative predictive value was 97.6%. Overall, the computable phenotype was found to be an accurate and sensitive method to pinpoint pediatric patients with T1DM. Conclusions We developed a computable phenotype for identifying T1DM correctly and efficiently. The computable phenotype that was developed will enable researchers to identify a population accurately and cost-effectively. As such, this will vastly improve the ease of identifying patients for future intervention studies.

Pronounced health disparities exist in type 1 diabetes (T1D) based on socioeconomic status (SES) yet there are a lack of programs designed to promote health equity for vulnerable communities. The All for ONE (Outreach, Networks, and Education) mentoring program was piloted pairing college students and publicly insured teenagers with T1D to assess feasibility as a possible intervention. There were 22 mentors recruited (mean age 20 ± 2 years; 17 [77%] females; mean HbA1c 8.4 ± 1.5%) and matched with mentees based on gender. There were 42 teens randomized to treatment and control groups including 22 teens in the treatment group (age 14 ± 2 years; 17 [77%] females; HbA1c 9.8 ± 2.3%) and 20 teens in the control group (age 14 ± 2 years; 15 [75%] females; HbA1c 8.9 ± 2.0%) followed over 9 months. Outcome measures included HbA1c and the Children's Hope Scale. The intervention included automated text reminders for blood glucose monitoring, text exchanges, social events with education, and clinic visits with mentors/mentees. Mean change in HbA1c for teens was +0.09% in the intervention group, compared with +0.28% in the control group (P = .61); college students had a reduction in HbA1c of -0.22% (P = .38). Treatment group teens had marked improvement in their hope for the future compared to control group teens (P = .04) and were more likely to attend clinic visits (P = .02). This program established feasibility for a model that could be replicated and modified for other types of settings. Additional research is warranted to study the potential long-term benefits of participating in the All for ONE mentoring program.

Background: Understanding the long-term cardiovascular safety of type 2 diabetes (T2D) medications is a high-priority research area. We used electronic medical record (EMR) data in the National Patient Centered Clinical Research Network (PCORnet) to characterize “real-world” use of T2D medications. Methods and Results: We identified a retrospective cohort of patients with T2D using a validated computable phenotype. We queried 4 unique PCORnet sites for data regarding T2D medication use in the 90 days before and after cohort entry. We described patient demographics, comorbidities, non-T2D medication use, and clinical variables in the 730 days prior to cohort entry. We identified 49,654 patients with T2D across the 4 sites (range: 2,276-20,322). Use of T2D medications, characterized by pharmacologic class, and selected characteristics are presented in Table. Among patients prescribed a single class of T2D medication, metformin was the most common (52.5%). Among patients prescribed multiple classes of T2D agents, the most common combination was metformin and sulfonylureas (49%). Conclusions: Across 4 PCORnet sites, metformin-based regimens were very prevalent, and approximately 10% of regimens included newer agents. PCORnet will be a valuable resource for future observational studies of T2D medications and long-term cardiovascular outcomes in “real-world” populations. Disclosure K.N. Bachmann: Stock/Shareholder; Self; Medtronic. C. Roumie: None. A.D. Wiese: None. C.G. Grijalva: None. J.B. Buse: Consultant; Self; Neurimmune AG. Research Support; Self; AstraZeneca, National Center for Advancing Translational Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk A/S, Sanofi, vTv Therapeutics. Stock/Shareholder; Self; Mellitus Health, PhaseBio Pharmaceuticals, Inc., Stability Health. Other Relationship; Self; ADOCIA, AstraZeneca, Dance Biopharm Holdings Inc., Eli Lilly and Company, MannKind Corporation, NovaTarg, Novo Nordisk A/S, Senseonics, vTv Therapeutics, Zafgen, Inc. R. Bradford: None. E.O. Zalimeni: None. P. Knoepp: None. H.L. Morris: None. W.T. Donahoo: None. N. Fanous: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health. B. Katalenich: None. M.M. Cook: None. R.L. Rothman: Consultant; Self; Abbott, edlogics. R.J. Chakkalakal: None. Funding National Institutes of Health (UL1TR002489, UL1TR002489); U.S. Department of Veterans Affairs (IK2CX001678); Patient-Centered Outcomes Research Institute (CDRN-1306-04869)

Background Patient reports of collaborative goal setting have repeatedly been associated with improved health outcomes, and the American Diabetes Association specifically encourages collaborative goal setting as a component of high quality care. Current limitations in our understanding of what needs to transpire for patients to denote goal setting as “collaborative”, remain a barrier to fostering collaborative goal setting in practice. Methods Four focus groups were conducted among 19 patients with diabetes. A semi-structured focus group guide was used to explore patient perceptions of collaborative goal setting and what needed to happen for goals to be considered collaboratively set. Focus group transcripts were coded using thematic analysis. Results Collaborative goal setting was described by patients as occurring within the context of a caring relationship where patients and health care providers: (1) listen and learn from each other; (2) share ideas; (3) agree on a measurable objective; and (4) support goal achievement. Patients also articulated clear responsibilities for themselves and clinicians and described collaborative goal setting as a process that occurs over time. Conclusions Patients perceived collaborative goal setting as a multidimensional process that occurs over time within the context of a caring relationship and encompasses distinct patient and clinician responsibilities.

There are tremendous health disparities in type 1 diabetes (T1D) according to socioeconomic status (SES). Moreover, college students with T1D, regardless of SES, face serious health risks as they transition to independent living. The All for ONE (Outreach, Networks, and Education) program was piloted as a novel intervention where college students with T1D mentored publicly insured teenagers with T1D (n=88, treatment and control). Outcome measures for college students and teens included HbA1c, diabetes knowledge, and the validated College Resiliency and Children’s Hope scales. The intervention included daily text reminders for blood glucose monitoring, weekly text exchanges, social events infused with T1D education, clinic visits with mentors/mentees and a weekly training course for the mentors. Paired t-tests were used to compare changes from baseline to study conclusion among the college students, and ANCOVA was used to compare changes between intervention and control teens adjusting for baseline. Adjusting for baseline, the mean change in HbA1c was +0.09 (9% increase) in the intervention group, compared to +0.28 (31% increase) in the control group (p=0.61). College students had a mean -0.22 (27% decrease) in HbA1c (p=0.38). Treatment group teens were significantly more likely to attend clinic visits during the pilot than control group teens (p = 0.02*). Exit surveys with mentors, mentees and parents expounded on the program’s success with 98% reporting they wanted to participate again if given the opportunity. The main value of the program was listed by mentors as increased networks with other college students with T1D, by mentees as being able to talk to their mentor about the disease, and by parents as giving their child a confidant who understood T1D. This pioneering program concurrently targets two vulnerable populations, establishing critical feasibility for a model that uses routine events of texting, clinic visits, and college curriculum. Disclosure A.F. Walker: None. M.J. Haller: None. M.J. Gurka: None. H.L. Morris: None. C. Anez-Zabala: None. B.S. Bruggeman: None. D. Guiffre: None. H. Rohrs: None. M.A. Atkinson: Other Relationship; Self; Patent Issued. D. Schatz: None.

Among patients with diabetes, racial differences in cardiometabolic risk factor control are common. The extent to which differences in medication adherence contribute to such disparities is not known. We examined whether medication adherence, controlling for treatment intensification, could explain differences in risk factor control between black and white patients with diabetes. We identified three cohorts of black and white patients treated with oral medications and who had poor risk factor control at baseline (2009): those with glycated hemoglobin (HbA1c) >8 % (n = 37,873), low-density lipoprotein cholesterol (LDL-C) >100 mg/dl (n = 27,954), and systolic blood pressure (SBP) >130 mm Hg (n = 63,641). Subjects included insured adults with diabetes who were receiving care in one of nine U.S. integrated health systems comprising the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) consortium. Baseline and follow-up risk factor control, sociodemographic, and clinical characteristics were obtained from electronic health records. Pharmacy-dispensing data were used to estimate medication adherence (i.e., medication refill adherence [MRA]) and treatment intensification (i.e., dose increase or addition of new medication class) between baseline and follow-up. County-level income and educational attainment were estimated via geocoding. Logistic regression models were used to test the association between race and follow-up risk factor control. Models were specified with and without medication adherence to evaluate its role as a mediator. We observed poorer medication adherence among black patients than white patients (p

Patient activation, the perceived capacity to manage one’s health, is positively associated with better health outcomes and lower costs. Underlying characteristics influencing patient activation are not completely understood leading to gaps in intervention strategies designed to improve patient activation. We suggest that variability in executive functioning influences patient activation and ultimately has an impact on health outcomes. To examine this hypothesis, 440 chronically ill Medicaid enrollees completed measures of executive functioning, patient activation, and health-related quality of life. Mediation analyses revealed that executive functioning: (a) directly affected patient activation and mental health-related quality of life, (b) indirectly affected mental health-related quality of life through patient activation, and (c) was unrelated to physical health-related quality of life. These data indicate that further study of the relationships among neurocognitive processes, patient activation, and health-related quality of life is needed and reinforces previous work demonstrating the association between patient activation and self-reported outcomes.

Background: Hydroxyurea (HU) has emerged as an important disease-modifying therapy for children and adults with sickle cell anemia (SCA), but has traditionally been underutilized. Consensus, evidence-based guidelines published by the National Heart, Lung, and Blood Institute (NHLBI) in 2014 recommended broadening the use of HU for SCA, but the impact of these recommendations on HU utilization is unknown. The objective of this abstract is to determine if HU utilization in children and adults with SCA living in Florida increased following publication of the 2014 Guidelines. We hypothesized that limitations in care coordination and implementation resulted in minimal increases in the rates of HU utilization. We further hypothesized that individuals living more than 45 minutes from a comprehensive sickle cell center would be less likely to be prescribed HU in the prior 12 months. Methods: This study is a cross sectional analysis utilizing the OneFlorida Clinical Data Research Network (CDRN), which provides access to electronic health record and claims data for over 15.4 million patient records standardized to the PCORnet Common Data Model v4.1. Possible SCA cases were identified by International Classification of Diseases, 9th (ICD-9) and 10th (ICD-10) revision codes. Patients were eligible if they were at least 9 months of age and had two or more health encounters in which an ICD-9 or ICD-10 code for SCA was used. The primary endpoint was one or more HU prescriptions written or filled in a given calendar year between 2012 and 2017. In order to examine trends in HU utilization, segmented regression analyses of an interrupted time series were conducted. Logistic regression was performed to identify patient characteristics independently associated with HU utilization in 2017. Covariates of interest included age, gender, acute healthcare utilization (emergency department visits and hospitalizations), and distance to comprehensive sickle cell care (defined by zip code+4). Adjusted Odds Ratios (OR) and 95% confidence intervals (CI) were reported. Thirteen comprehensive sickle cell centers were identified based on local expert opinion, review of the American Society of Hematology Find a Hematologist database, and involvement in recent multicenter clinical trials. Results: 9,532 unique patients were identified with a mean age in 2017 of 21 years (SD 17.6). 57.4% were female, 76.2% were Black, 6% were Hispanic, 65.2% had three or more acute healthcare visits, 23% lived more than 45 minutes from a comprehensive sickle cell center, and approximately 73% were insured by Medicaid. Between 2012 and 2017, there was a 4.7% increase in HU utilization (12.7% vs. 17.4%, p Conclusions: These findings suggest there has been a slight, but statistically significant, increase in HU utilization in children and adults with SCA in Florida since publication of the 2014 NHLBI Guidelines. HU is being prescribed to more pediatric patients less than 18 years of age. However, HU remains drastically underutilized and appears to be preferentially prescribed to patients with three or more hospitalizations or emergency department visits per year. Additional research is needed to determine predictors of HU utilization and implementation strategies to improve prescribing rates. The OneFlorida CDRN provides an excellent resource to track quality metrics for SCA in Florida. Figure Disclosures Black: Micelle BioPharma: Research Funding; Prolong Pharmaceuticals: Consultancy; Sanofi: Consultancy; Sancilio and Company: Research Funding; NHLBI: Research Funding; Pfizer: Research Funding; Novartis: Research Funding; HRSA: Research Funding. OffLabel Disclosure: Hydroxyurea for children less than 2 years of age

Little is known about how patient-clinician communication leads to better outcomes. Among patients with diabetes, we describe patient-reported use of collaborative goal setting and evaluate whether perceived competency and physician trust mediate the association between collaborative goal setting and glycemic control.Data from a patient survey administered in 2008 to a cohort of insured patients aged 18+ years with diabetes who initiated oral mono-therapy between 2000 and 2005 were joined with pharmaceutical claims data for the prior 12 months and laboratory data for the prior and subsequent 12 months (N=1065). A structural equation model (SEM) was used to test mediation models controlling for baseline HbA1c.The hypothesized mediation model was supported. Patient-reported use of more collaborative goal setting was associated with greater perceived self-management competency and increased level of trust in the physician (p0.05). In turn, both greater perceived competence and increased trust were associated with increased control (p0.05).Findings indicate that engaging patients in collaborative goal setting during clinical encounters has potential to foster a trusting patient-clinician relationship as well as enhance patient perceived competence, thereby improving clinical control.Fostering collaborative goal setting may yield payoffs in improved clinical outcomes among patients with diabetes.

Objective Despite known benefits of patient-perceived collaborative goal setting, we have a limited ability to monitor this process in practice. We developed the Patient Measure of Collaborative Goal Setting (PM-CGS) to evaluate the use of collaborative goal setting from the patient's perspective. Research design and methods A random sample of 400 patients aged 40 years or older, receiving diabetes care from the Virginia Commonwealth University Health System between 8/2012 and 8/2013, were mailed a survey containing potential PM-CGS items (n=44) as well as measures of patient demographics, perceived self-management competence, trust in their physician, and self-management behaviors. Confirmatory factor analysis was used to evaluate construct validity. External validity was evaluated via a structural equation model (SEM) that tested the association of the PM-CGS with self-management behaviors. The direct and two mediated (via trust and self-efficacy) pathways were tested. Results A total of 259 patients responded to the survey (64% response rate), of which 192 were eligible for inclusion. Results from the factor analysis supported a 37-item measure of patient-perceived CGS spanning five domains: listen and learn; share ideas; caring relationship; measurable objective; and goal achievement support (χ=4366.13, p

Background/Aims Helping patients set and follow up on goals may be an effective way to help patients improve their confidence (self-efficacy) and commitment to improve diabetes self- management. We evaluate associations among patient-reported use of collaborative goal setting with clinicians, patient-reported self-efficacy, and clinical control (measured by HbA1c) among patients with diabetes.