Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables wasnot at randomand was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.

Malnutrition and dental caries in early childhood remain persistent and intertwined global health challenges, particularly for indigenous and geographically-remote populations. To examine the prevalence and associations between early childhood dental caries, parent-reported mouth pain and malnutrition in the Amazonian region of Ecuador, we conducted a cross-sectional study of the oral health and nutrition status of 1407 children from birth through age 6 in the “Alli Kiru” program (2011–2013). We used multivariate regression analysis to examine relationships between severe caries, parent-reported mouth pain measures, and nutritional status. The prevalence of dental caries was 65.4%, with 44.7% of children having deep or severe caries, and 33.8% reporting mouth pain. The number of decayed, missing and filled teeth (dmft) increased dramatically with age. Malnutrition was prevalent, with 35.9% of children stunted, 1.1% wasted, 7.4% underweight, and 6.8% overweight. As mouth pain increased in frequency, odds for severe caries increased. For each unit increase in mouth pain frequency interfering with sleeping, children had increased odds for being underweight (Adjusted Odds Ratio (AOR): 1.27; 95% CI: 1.02–1.54) and decreased odds for being overweight (AOR: 0.76; 95% CI: 0.58–0.97). This relationship was most pronounced among 3–6 year-olds. Early childhood caries, mouth pain and malnutrition were prevalent in this sample of young children. Parent-reported mouth pain was associated with severe caries, and mouth pain interfering with sleeping was predictive of poor nutritional status. We demonstrate the utility of a parsimonious parent-reported measure of mouth pain to predict young children’s risk for severe early childhood caries and malnutrition, which has implications for community health interventions.

The global nutrition transition has increased the prevalence of childhood dental caries. Greater understanding is needed of the impact of social determinants—including maternal education—on child oral health. This is a cross-sectional analysis of a convenience sample of families of 458 indigenous Ecuadorian children aged 6 months through 6 years from 2011–2013. Data was collected by mother interviews and child dental and anthropometric examinations. Multivariate logistic and Zero-Inflated-Poisson regression analyses assessed associations between years of maternal education and maternal-child oral health practices and child oral health outcomes. Each additional year of maternal education was significantly (p < 0.05) associated with some healthier practices including greater likelihood of mothers and children drinking milk daily (OR 1.20; 95% CI 1.08, 1.34); and less healthy practices including greater likelihood of bottle-feeding children with sugary liquids (OR 1.14; 95% CI 1.06, 1.22) and to older age, giving children sweets daily, calming children with a bottle or sweets, and less likelihood of helping brush their children’s teeth (OR 0.93; 95% CI 0.88, 0.98). Each year of maternal education had a small but statistically non-significant influence on increasing the odds of children being among those who are cavity-free (OR 1.03; 95% CI 0.92, 1.16). Interventions to improve health outcomes should focus not just on maternal education but also address social and commercial determinants of health through nutrition and oral health education, as well as policies to reduce sugar and ensure universal access to oral health care.

The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., no action to close health gaps) to patient- (i.e., failure to retrieve medication or low adherence) or clinician-related (i.e., failure to initiate/titrate medication) behavior. We illustrated how such data can be used to manage health and care gaps for blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), and HbA1c for 240,582 Sutter Health primary care patients. Prevalence of health gaps was 44% for patients with hypertension, 33% with hyperlipidemia, and 57% with diabetes. Failure to retrieve medication was common; this patient-related care gap was highly associated with health gaps (odds ratios (OR): 1.23–1.76). Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6–6.0) and diabetes (OR = 5.7; 95% CI: 5.4–6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps.

Statin use is proven to be effective in lowering low-density lipoprotein cholesterol (LDL-C) levels and reducing risk of recurrent myocardial infarction, stroke, and mortality in individuals with established cardiovascular disease (CVD). We used medication dispensed data (e.g., SureScripts), which has been integrated with the electronic health record (EHR) to examine the factors and outcomes associated with adherence to statins. This study is a secondary data analysis using longitudinal data between 1/1/2010–10/31/2021 (n = 1486,286 over nearly 12 years) from a large community-based health system on all primary care patients aged 35 years or older when diagnosed with the first CVD two years after their first primary care visit and had new statin prescriptions on or after CVD diagnosis. Multivariable logistic regression models were used to identify the factors associated with filling the prescription and statin adherence, respectively. Survival analysis was used to assess the association between statin adherence and LDL-C control. Of the 5155 patients with newly prescribed statins, a total of 3553 (68.9 %) were adherent, with insurance type, online patient portal use, race, age, statin intensity, and cardiologist visits emerging as significant predictors. Specifically, patients with PPO/FFS were less likely to fill statin prescriptions compared to those with HMO. Infrequent online patient portal use is associated with lower adherence. There is a disparity between patients race categories (Non-Hispanic Black (NHB) vs. Non-Hispanic White (NHW)) in filling the prescription and adhering to the filled prescription. Medication adherence is defined as proportion of days covered (PDC) of 80 % or greater. Adherence was positively associated with older age, high-intensity statins, and cardiologist visits. Having a visit with a cardiologist showed better adherence to the prescription and lowering of LDL values. Additionally, adhering to statins has shown a better outcome of lowering patients LDL values. The findings emphasize demographic and healthcare factors in medication adherence and LDL control, suggesting tailored interventions for diverse populations, addressing disparities in insurance type, race, and online portal use, and involving cardiologists in medication management for improved medication adherence and clinical outcomes.

Background: Electronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging. Objective: This study aimed to evaluate a Cardiometabolic Sutter Health Advanced Reengineered Encounter (CM-SHARE), a web-based application to visualize key EHR data, on the EHR use efficiency. Methods: We developed algorithms to identify key clinic workflow measures (eg, total encounter time, total physician time in the examination room, and physician EHR time in the examination room) using audit data, and we validated and calibrated the measures with time-motion data. We used a pre-post parallel design to identify propensity score-matched CM-SHARE users (cases), nonusers (controls), and nested-matched patients. Cardiometabolic encounters from matched case and control patients were used for the workflow evaluation. Outcome measures were compared between the cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases. Results: Time-motion observation was conducted on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference (<0.8 minutes) between the audit data-derived workflow measures and the time-motion observation. Two key unobservable times from audit data, physician entry into and exiting the examination room, were imputed based on time-motion studies. CM-SHARE was launched with 6 pilot PCPs in April 2016. During the prestudy period (April 1, 2015, to April 1, 2016), 870 control patients with 2845 encounters were matched with 870 case patients and encounters, and 727 case patients with 852 encounters were matched with 727 control patients and 3754 encounters in the poststudy period (June 1, 2016, to June 30, 2017). Total encounter time was slightly shorter (mean -2.7, SD 1.4 minutes, 95% CI -4.7 to -0.9; mean -1.6, SD 1.1 minutes, 95% CI -3.2 to -0.1) for cases than controls for both periods. CM-SHARE saves physicians approximately 2 minutes EHR time in the examination room (mean -2.0, SD 1.3, 95% CI -3.4 to -0.9) compared with prestudy period and poststudy period controls (mean -1.9, SD 0.9, 95% CI -3.8 to -0.5). In the spread phase, 48 CM-SHARE spread PCPs were matched with 84 control PCPs and 1272 cases with 3412 control patients, having 1119 and 4240 encounters, respectively. A significant reduction in total encounter time for the CM-SHARE group was observed for short appointments (<= 20 minutes; 5.3-minute reduction on average) only. Total physician EHR time was significantly reduced for both longer and shorter appointments (17%-33% reductions). Conclusions: Combining EHR audit log files and clinical information, our approach offers an innovative and scalable method and new measures that can be used to evaluate clinical EHR efficiency of digital tools used in clinical settings.