Clinical Data Research Network Research Articles

Bariatric surgery and COVID-19 outcomes: results from the PaTH to Health: Diabetes study

BackgroundObesity and type 2 diabetes mellitus (T2DM) are risk factors for severe COVID-19 infection. Bariatric surgery (BSG) is an effective treatment of obesity through weight loss and may reduce COVID-19 severity. ObjectivesWe examined the effect of BSG on COVID-19 outcomes in patients with or at risk of T2DM. SettingElectronic health record data from the PaTH Clinical Data Research Network, a partnership of 5 health systems reviewed from March 1, 2020, to December 31, 2020. MethodsAmbulatory and in-hospital patient encounters with COVID-19 diagnosis and obesity were identified. We constructed 2 patient groups: BSG and non-BSG (NBSG). The BSG group included patients with at least 1 encounter for the BSG procedure code and/or 1 BSG diagnosis code; the NBSG group included patients with no procedure or diagnosis code for BSG with body mass index (BMI) ≥40 or BMI ≥35 and at least 2 obesity-related co-morbidities. We matched 1 patient in the BSG group to 2 patients in the NBSG group based on age, gender (sex defined at birth), race and ethnicity, group (T2DM and at risk of T2DM), and site. The primary outcome was 30-day outcomes of COVID-19 severity. ResultsAfter matching, we found that patients with BSG had lower odds of respiratory failure (41%) and ventilation/intensive care unit (ICU) admission/death (52%). Patients in the BSG group had lower odds of hospitalization, pneumonia, respiratory failure, and the most severe COVID-19 outcomes combined (ventilation/ICU admission/death). T2DM was identified as a risk factor for COVID-19 severity in the BSG group. ConclusionsThis retrospective, matched-cohort analysis found BSG to have a protective effect against severe COVID-19 outcomes.

Trends in prevalence of esophageal adenocarcinoma: Findings from a statewide database of over 6 million patients.

Esophageal cancer (EC) is a leading cause of cancer-related death in the west 1 . Esophageal squamous cell carcinoma (SCC) is the most common type of EC worldwide. However, in Western countries, including the United States, esophageal adenocarcinoma (EAC) is the most common 2 . EAC is most common in the lower esophagus whereas SCC is most common in the middle and upper esophagus 3 . The incidence of EAC has increased dramatically in western countries over the past few decades. 2 3 The exact reason for this rise in EAC has not been clearly understood. However, an increase in the prevalence of EAC risk factors is postulated as a potential explanation 4 . Although there are many identifiable EAC risk factors, including gastroesophageal reflux disease (GERD), obesity, male sex, White race, and smoking 5 6 7 , Barrett's esophagus (BE) remains the major precursor lesion of esophageal adenocarcinoma. BE develops when there is a change in the normal squamous lining of the esophageal mucosa into intestinal metaplasia 8 9 . The incidence has also increased in the population over the past few decades 10 11 . There is a well-described progression within BE from non-dysplastic BE (NDBE), low-grade dysplasia (LGD), high-grade dysplasia (HGD), intramucosal carcinoma (IMC), to invasive EAC 12 13 . Recent data suggest that the increased incidence of EAC may have plateaued 1 . However, we questioned whether the prevalence of EAC is still increasing, especially at younger ages in lieu of recent trends showing an increase in the prevalence of colorectal cancer in younger patients. These findings resulted in a lowering of the colorectal cancer screening age cutoff to 45 years from 50 years 14 15 16 . Therefore, we aimed to assess the time trends in the prevalence and incidence of EAC and some of its risk factors in a large population of patients in Florida and to assess these trends based on age categories. We hypothesized that the prevalence of EAC and BE has increased over time at younger age groups.

Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network

BackgroundCare plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. Unfortunately, there is a lack of integration of these data in clinical data research networks (CDRN) data trusts, due in large part to nursing care being documented with local vocabulary, resulting in non-standardized data. The absence of high-quality nursing care plan data in data trusts limits the investigation of interdisciplinary care aimed at improving patient outcomes. ObjectiveTo map local nursing care plan terms for patients’ problems and goals in the EHR of one large health system to the standardized nursing terminologies (SNTs), NANDA International (NANDA-I), and Nursing Outcomes Classification (NOC). MethodsWe extracted local problems and goals used by nurses to document care plans from two hospitals. After removing duplicates, the terms were independently mapped to NANDA-I and NOC by five mappers. Four nurses who regularly use the local vocabulary validated the mapping. Results83% of local problem terms were mapped to NANDA-I labels and 93% of local goal terms were mapped to NOC labels. The nurses agreed with 95% of the mapping. Local terms not mapped to labels were mapped to the domains or classes of the respective terminologies. ConclusionMapping local vocabularies used by nurses in EHRs to SNTs is a foundational step to making interoperable nursing data available for research and other secondary purposes in large data trusts. This study is the first phase of a larger project building, for the first time, a pipeline to standardize, harmonize, and integrate nursing care plan data from multiple Florida hospitals into the statewide CDRN OneFlorida+ Clinical Research Network data trust.

Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network

ObjectiveTo assess the application and utility of algorithms designed to detect features of SLE in electronic health record (EHR) data in a multisite, urban data network.MethodsUsing the Chicago Area Patient-Centered...

Computable phenotypes for cohort identification: core content for a new class of FAIR Digital Objects

Computable phenotypes for cohort identification: core content for a new class of FAIR Digital Objects

Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites.

Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D. In two PCORnet networks, PEDSnet and OneFlorida, the study assessed measures of glycemic control, diabetic ketoacidosis admissions, and clinic visits in 2016-2018 among youth 0-20 years of age. The study team developed measure EHR-based specifications, identified institution-specific rates using data stored in the CDM, and assessed agreement with manual chart review. Among 9,740 youth with T1D across 12 institutions, one quarter (26%) had two or more measures of A1c greater than 9% annually (min 5%, max 47%). The median A1c was 8.5% (min site 7.9, max site 10.2). Overall, 4% were hospitalized for diabetic ketoacidosis (min 2%, max 8%). The predictive value of the PCORnet CDM was >75% for all measures and >90% for three measures. Using EHR-derived data to assess comparative quality for T1D is a valid, efficient, and reliable data collection tool for measuring T1D care and outcomes. Wide variations across institutions were observed, and even the best-performing institutions often failed to achieve the American Diabetes Association HbA1C goals (<7.5%).

Rural-urban outcome differences associated with COVID-19 hospitalizations in North Carolina.

People living in rural regions in the United States face more health challenges than their non-rural counterparts which could put them at additional risks during the COVID-19 pandemic. Few studies have examined if rurality is associated with additional mortality risk among those hospitalized for COVID-19. We studied a retrospective cohort of 3,991 people hospitalized with SARS-CoV-2 infections discharged between March 1 and September 30, 2020 in one of 17 hospitals in North Carolina that collaborate as a clinical data research network. Patient demographics, comorbidities, symptoms and laboratory data were examined. Logistic regression was used to evaluate associations of rurality with a composite outcome of death/hospice discharge. Comorbidities were more common in the rural patient population as were the number of comorbidities per patient. Overall, 505 patients died prior to discharge and 63 patients were discharged to hospice. Among rural patients, 16.5% died or were discharged to hospice vs. 13.3% in the urban cohort resulting in greater odds of death/hospice discharge (OR 1.3, 95% CI 1.1, 1.6). This estimate decreased minimally when adjusted for age, sex, race/ethnicity, payer, disease comorbidities, presenting oxygen levels and cytokine levels (adjusted model OR 1.2, 95% CI 1.0, 1.5). This analysis demonstrated a higher COVID-19 mortality risk among rural residents of NC. Implementing policy changes may mitigate such disparities going forward.

Implications of missingness in self-reported data for estimating racial and ethnic disparities in Medicaid quality measures.

To assess the feasibility and implications of imputing race and ethnicity for quality and utilization measurement in Medicaid. 2017 Oregon Medicaid claims from the Oregon Health Authority and electronic health records (EHR) from OCHIN, a clinical data research network, were used. We cross-sectionally assessed Hispanic-White, Black-White, and Asian-White disparities in 22 quality and utilization measures, comparing self-reported race and ethnicity to imputed values from the Bayesian Improved Surname Geocoding (BISG) algorithm. Race and ethnicity were obtained from self-reported data and imputed using BISG. 42.5%/4.9% of claims/EHR were missing self-reported data; BISG estimates were available for >99% of each and had good concordance (0.87-0.95) with Asian, Black, Hispanic, and White self-report. All estimated racial and ethnic disparities were statistically similar in self-reported and imputed EHR-based measures. However, within claims, BISG estimates and incomplete self-reported data yielded substantially different disparities in almost half of the measures, with BISG-based Black-White disparities generally larger than self-reported race and ethnicity data. BISG imputation methods are feasible for Medicaid claims data and reduced missingness to <1%. Disparities may be larger than what is estimated using self-reported data with high rates of missingness.

Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements.

The Greater Plains Collaborative (GPC) and other PCORnet Clinical Data Research Networks capture healthcare utilization within their health systems. Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and electronic health records (EHRs) data with state-wide Medicare and Medicaid claims and assess how claims and clinical data complement each other to identify obesity and related comorbidities in a patient sample. EHR, billing, and tumor registry data from 7 healthcare systems were integrated with Center for Medicare (2011-2016) and Medicaid (2011-2012) services insurance claims to create deidentified databases in Informatics for Integrating Biology & the Bedside and PCORnet Common Data Model formats. We describe technical details of how this federally compliant, cloud-based data environment was built. As a use case, trends in obesity rates for different age groups are reported, along with the relative contribution of claims and EHR data-to-data completeness and detecting common comorbidities. GROUSE contained 73 billion observations from 24 million unique patients (12.9 million Medicare; 13.9 million Medicaid; 6.6 million GPC patients) with 1674134 patients crosswalked and 983450 patients with body mass index (BMI) linked to claims. Diagnosis codes from EHR and claims sources underreport obesity by 2.56 times compared with body mass index measures. However, common comorbidities such as diabetes and sleep apnea diagnoses were more often available from claims diagnoses codes (1.6 and 1.4 times, respectively). GROUSE provides a unified EHR-claims environment to address health system and federal privacy concerns, which enables investigators to generalize analyses across health systems integrated with multistate insurance claims.

S191 Impact of Pandemic on Epidemiology and Management of Acute Colonic Pseudo Obstruction: Nationwide Clinical Data Research Network

Introduction: Acute colonic pseudo-obstruction (ACPO), also known as Ogilvie's syndrome, is characterized by massive acute dilatation of the large intestine without a mechanical cause. Early recognition to identify serious adverse events such as ischemia/ perforation is critical to reducing mortality rates. COVID-19 pandemic led to a significant shift of resources for the treatment of patients with reduced utilization of endoscopy and imaging, which could have impacted outcomes in patients with ACPO. In this study, we aim to identify the change in the clinical outcomes in ACPO patients in 2020. Methods: All adult patients with a new diagnosis of intestinal obstruction without mechanical cause were identified using ICD-10 codes (K59.8, K56.69). Patients diagnosed with megacolon (K59.3) were excluded. Patients were included from TriNetX, a federated cloud-based network that comprises 92 healthcare organizations across the entire US. The primary outcomes were rates of pharmacological therapy (neostigmine use), lower GI endoscopic decompression (Colonoscopy/sigmoidoscopy), surgery (laparotomy), and overall 30 day- mortality. The outcomes were measured after 1:1 propensity matching of the groups based on the baseline demographics and comorbidities. Results: A total of 24,310 patients were diagnosed with ACPO in 2020 compared to 95,014 with a reduced incidence of 74.5%. Comorbidities of these patients at presentation and clinical outcomes are noted in Table 1. Patients with behavioral/neurodevelopmental disorders, obesity, opioid use and laxative were significantly higher in ACPO patients. After matching, rates of neostigmine use (risk ratio [RR]- 0.71 (CI- 0.41 - 1.23)), imaging (CT/MRI) (RR- 0.86 (CI- 0.72 - 1.03)), surgery ((RR- 0.93 (CI- 0.83 - 1.03)) did not differ between the groups. Endoscopic utilization reduced significantly in 2020 (RR- 0.86 (CI- 0.78 - 0.95)). Furthermore, mortality was low in 2020 group (RR- 0.74 (CI- 0.65 - 0.84)). Conclusion: The rates of ACPO diagnosis in 2020 reduced by 74.5% compared to 2019. Although the neostigmine use, cross-sectional imaging use, and surgery rates did not change, endoscopic utilization was reduced by 15%. This was noted despite a need for urgent endoscopic decompression needed in these patients. Interestingly, the mortality was also reduced by 26% in 2020-ACPO groups even after lower utilization rates of endoscopy. (Table Presented).

A model of weight-based stigma in health care and utilization outcomes: Evidence from the learning health systems network.

ObjectiveObesity is stigmatized and people with obesity report experiencing stigmatizing situations when seeking health care. The implications of these experiences are not well understood. This study tests an indirect effects model of negative care experiences as an intermediate variable between obesity and care avoidance/utilization and switching primary care doctors.MethodsA survey was completed by 2380 primary care patients in the Learning Health Systems Network (LHSNet) Clinical Data Research Network with a BMI >25 kg/m2. Measures included scales assessing stigmatizing situations, perceived patient‐centered communication, perceived respect, having delayed needed care, and having looked for a new primary doctor in the past 12 months. Sequential and serial indirect effects of care experiences and respect in the association between BMI and care utilization outcomes was modeled.ResultsThe hypothesized model was supported by findings. The associations between BMI and delaying needed care (OR = 1.06, p < 0.001) and attempting to switch primary doctors (OR = 1.02, p = 0.04) was mediated by both stigmatizing situations experienced in a health care context and lower patient‐centered communication. Lower perceived respect mediated the association between care experiences and utilization outcomes.ConclusionsPeople with higher BMIs may avoid care or switch doctors as a result of stigmatizing experiences and poor communication with doctors. These outcomes may contribute to morbidity in people with obesity if they delay or avoid care for health concerns when symptoms first present.

Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer\u2019s disease

In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer’s disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial’s study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials’ design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.

Predictors of Participation in Clinical Research.

Despite numerous efforts to create more equitable healthcare systems, minority populations face long-standing health disparities compared to White populations. Healthcare research is the necessary foundation for creating equitable health systems and providing patient-centered care. Significant challenges exist, however, with recruiting and engaging underrepresented populations in clinical research. The purpose of this analysis was to determine how research participants' race, trust, and level of education influence participation barriers in clinical research. The study used secondary, cross-sectional survey data that were collected between 2014 and 2016 through the former Mid-South Clinical Data Research Network, currently known as the Stakeholders, Technology, and Research Clinical Research Network. Descriptive statistics and Spearman rank correlations were performed between level of education, level of trust, and each attitude statement for each racial category. A total of 2,190 survey responses were used in the data analysis. The mean age of respondents was 52 years, with majority being women, White, insured, and working full time. Overall, the respondents had favorable attitudes toward research participation. Trust was correlated with agreement in many attitude statements for both White and African American respondents, whereas correlations with education level were more variable depending on racial grouping. Trust level was negatively associated with agreement toward the statement "researchers do not care about me" in White and Native American respondents. The results support the importance of trust to research participation. Generally, education level was not strongly predictive of research participation, although prediction was influenced by race and attitude.

91726 Screening for Obesity related renal damage in adolescent women - Body Surface area matters

ABSTRACT IMPACT: This change will improve primary care physicians and pediatrics ability to identify, intervene and prevent obesity related renal damage in the vulnerable population of young adults OBJECTIVES/GOALS: Obesity related glomerulopathy has a reversible stage manifested as hyperfiltration. Early intervention depends on the ability to identify hyperfiltration. Hyperfiltration prevalence is underestimated using the currently recommended formula We investigated whether calculating BSA-adjusted GFR will more readily identify hyperfiltration. METHODS/STUDY POPULATION: We extracted data from a large urban, multi-institutional Electronic Health Records (EHR) clinical data research network to construct an EHR data base of 60,549 women and girls ages 12-21 years from the New York metropolitan area. EGFR was calculated in two ways, 1) according to age appropriate formula, and 2) according to age appropriate formula and adjusted to body surface area (BSA). BMI-for-age values were classified according to the World Health Organization schema and grouped according to the CDC definitions. BSA was calculated according to the Du-Bois formula. Hyperfiltration was defined by a threshold of 135ml/min. The Bland Altman method assessed the agreement between formulas across the different BMI groups. RESULTS/ANTICIPATED RESULTS: Serum creatinine values were similar across different BMI groups. Comparing eGFR values, hyperfiltration rates were similar across BMI groups, ranging between 4%-6.6%. BSA-adjusted GFR was different across BMI groups: hyperfiltration rates were 0.81% for the underweight group, 2.56% for the normal weight, 12.18% for the overweight and 39% in the obese group. This trend of hyperfiltration paralleled the the rise in urine creatinine across BMI groups. DISCUSSION/SIGNIFICANCE OF FINDINGS: BSA-adjusted GFR more sensitively detects hyperfiltration due to obesity than does eGFR. Calculating BSA-adjusted GFR will improve primary care and pediatric physicians’ ability to identify, intervene and prevent early ORG. Changes in body composition may account for the increasing discordance between BSA-adjusted and eGFR as BMI rises.

A Patient Activities of Daily Living Scale for Amyotrophic Lateral Sclerosis

Background: Motor neuron disorders are rare, progressive neurodegenerative diseases which affect multiple domains of motor function. The ability to assess function from home using an electronic medical record (EMR) would facilitate pragmatic studies.&#x0D; Objective: To develop a Patient Activity of Daily Living scale for Amyotrophic Lateral Sclerosis and other motor neuron disorders (PADL-ALS) to support large pragmatic trials.&#x0D; Methods: The Greater Plains Collaborative Clinical Data Research Network (GPC) developed and tested the feasibility of using the PADL-ALS. We convened patient and caregiver focus groups and in-person meetings to recommend changes to the ALS Functional Rating Scale-Revised (ALSFRS-R), which clarified language and added questions about pseudobulbar affect, pain, and faith. Feasibility was determined by conducting a survey of participants identified using EMR-computable phenotypes and returned via patient-preferred modalities.&#x0D; Results: Surveys were distributed to 1079 participants at nine GPC health systems. The survey response rate was 44.4% (range 12.9-57.66%): male to female ratio 1.56; 84% self-identified as a patient with ALS. Patient respondents used computers or tablets more frequently than caregivers responding on their behalf. The PADL-ALS correlated to clinic-performed ALSFRS-R within 4 weeks of survey completion (n=33, rho=0.93, Kansas only). The pseudobulbar affect question correlated to functional motor burden. Over 80% agreed to be contacted for future research opportunities.&#x0D; Conclusion: We demonstrated the feasibility of determining functional burden with the PADL-ALS using an EMR-computable phenotype. Future directions include implementing the PADL-ALS to answer pragmatic questions about ALS care.

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing. To differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis. We developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network. The classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted). Prevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history.

International Classification of Diseases, Tenth Revision, Clinical Modification social determinants of health codes are poorly used in electronic health records.

There have been increasing calls for clinicians to document social determinants of health (SDOH) in electronic health records (EHRs). One potential source of SDOH in the EHRs is in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Z codes (Z55–Z65). In February 2018, ICD-10-CM Official Guidelines for Coding and Reporting approved that all clinicians, not just the physicians, involved in the care of a patient can document SDOH using these Z codes.To examine the utilization rate of the ICD-10-CM Z codes using data from a large network of EHRs.We conducted a retrospective analysis of EHR data between 2015 to 2018 in the OneFlorida Clinical Research Consortium, 1 of the 13 Clinical Data Research Networks funded by Patient-Centered Outcomes Research Institute. We calculated the Z code utilization rate at both the encounter and patient levels.We found a low rate of utilization for these Z codes (270.61 per 100,000 at the encounter level and 2.03% at the patient level). We also found that the rate of utilization for these Z codes increased (from 255.62 to 292.79 per 100,000) since the official approval of Z code reporting from all clinicians by the American Hospital Association Coding Clinic and ICD-10-CM Official Guidelines for Coding and Reporting became effective in February 2018.The SDOH Z codes are rarely used by clinicians. Providing clear guidelines and incentives for documenting the Z codes can promote their use in EHRs. Improvements in the EHR systems are probably needed to better document SDOH.

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data.

ObjectiveTo synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet).Materials and MethodsWe started with 3 widely cited DQ literature—2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)—and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment methods from these studies, mapped their relationships, and organized a synthesized summarization of existing DQ dimensions and assessment methods. We reviewed the data checks employed by the PCORnet and mapped them to the synthesized DQ dimensions and methods.ResultsWe analyzed a total of 3 reviews, 20 DQ frameworks, and 226 DQ studies and extracted 14 DQ dimensions and 10 assessment methods. We found that completeness, concordance, and correctness/accuracy were commonly assessed. Element presence, validity check, and conformance were commonly used DQ assessment methods and were the main focuses of the PCORnet data checks.DiscussionDefinitions of DQ dimensions and methods were not consistent in the literature, and the DQ assessment practice was not evenly distributed (eg, usability and ease-of-use were rarely discussed). Challenges in DQ assessments, given the complex and heterogeneous nature of real-world data, exist.ConclusionThe practice of DQ assessment is still limited in scope. Future work is warranted to generate understandable, executable, and reusable DQ measures.

Utilization of intensive behavioural treatment for obesity in patients with diabetes.

Obesity is a leading public health concern. The Centers for Medicare and Medicaid Services implemented a healthcare procedure code for intensive behavioural therapy (IBT) in 2012 to facilitate payment for addressing obesity in primary care settings, followed by universal coverage by insurers for all adults. Our objective was to understand utilization of IBT from 2009 to 2017 in patients with a diabetes diagnosis. Leveraging electronic health record data from the PaTH Clinical Data Research Network (CDRN), a partnership of six health systems, utilization of IBT was summarized at a yearly basis. The trend of IBT prevalence was examined for patients with diabetes by gender, race, age (>=65 vs <65) and rurality. A total of 205, 913 patients were included. While utilization of IBT is low (0.24% in 2017), use of IBT increased among patients with commercial insurance and Medicaid (codes S9449 and S9470) in 2011, and among patients with Medicare (code G0447) in 2012. IBT users tended to be less than 65 years of age, female, non-White (Black or Hispanic), and reside in urban areas. Overall, use of IBT in patients with diabetes remains low. Future work is necessary to understand the impact of IBT and, if effective, how to increase use within primary care.

Role of health insurance and neighborhood-level social deprivation on hypertension control following the affordable care act health insurance opportunities.

To understand if neighborhood-level social deprivation moderates the association between gaining health insurance and improved hypertension control. We used electronic health record (EHR) data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) clinical data research network from five states that expanded Medicaid eligibility (CA, OH, OR, WA, WI). We include patients with hypertension aged 19-64. Controlled hypertension was assessed for four groups pre-(1/1/2012-12/31/2013) to post-(1/1/2014-12/31/2017) Affordable Care Act (ACA) Medicaid expansion: (1) newly insured, (2) continuously insured, (3) discontinuously insured, and (4) continuously uninsured. We also used Social Deprivation Index score to derive predicted probability of controlled hypertension using logistic mixed effects. N = 28,485 patients. All groups experienced improved hypertension control: the newly insured saw a greater increase than the other groups (8.6% vs. 0.9% for the continuously uninsured, 1.3% for the continuously and 3.0% for the discontinuously insured). The likelihood of hypertension control rose more for the newly insured (vs. the other insurance groups) for patients living in the most deprived neighborhoods (16% from pre- to post-ACA). Gaining health insurance was related to hypertension control; individuals living in the most disadvantaged communities experienced the greatest benefit. Ensuring health insurance access is important for cardiovascular health, especially among disadvantaged communities.