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Objective We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research. Materials and Methods Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements. Results We received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data. Discussion Based on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries. Conclusion We are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.

Background The heterogeneity of migraine has been reported extensively, with identified subgroups usually based on symptoms. Grouping individuals with migraine and similar comorbidity profiles has been suggested, however such segmentation methods have not been tested using real-world clinical data. Objective To gain insights into natural groupings of patients with migraine using latent class analysis based on electronic health record-determined comorbidities. Methods Retrospective electronic health record data analysis of primary-care patients at Sutter Health, a large open healthcare system in Northern California, USA. We identified migraine patients over a five-year time period (2015–2019) and extracted 29 comorbidities. We then applied latent class analysis to identify comorbidity-based natural subgroups. Results We identified 95,563 patients with migraine and found seven latent classes, summarized by their predominant comorbidities and population share: fewest comorbidities (61.8%), psychiatric (18.3%), some comorbidities (10.0%), most comorbidities – no cardiovascular (3.6%), vascular (3.1%), autoimmune/joint/pain (2.2%), and most comorbidities (1.0%). We found minimal demographic differences across classes. Conclusion Our study found groupings of migraine patients based on comorbidity that have the potential to be used to guide targeted treatment strategies and the development of new therapies.

Objective To characterize patients who utilize services for migraine in a large integrated health care network, and describe patterns of care and utilization. Background Within health care systems, migraine is a common reason for seeking primary and neurology care, but relatively little is documented about who seeks care and the factors that explain variation in utilization. Methods We conducted a retrospective cohort study using electronic health record (EHR) data from Sutter Health primary care (PC) patients who had at least one office visit to a PC clinic between 2013 and 2017. Migraine status was ascertained from diagnosis codes and medication orders. Control status was assigned to those with no evidence of care for any type of headache. We divided the primary care migraine cohort into two groups: those who received all their care for migraine from PC (denoted PC‐M) and those who had ≥1 encounter with a neurologist for migraine (denoted N‐M). Migraine cases were also designated as having preexisting migraine if they had an encounter with a migraine diagnosis within (±) 6 months of their first study period PC visit and, otherwise, designated as first migraine consult. Two levels of contrasts included: patients with migraine and controls; and within the group of patients with migraine, PC‐M and N‐M groups. Comorbid conditions were determined from EHR encounter diagnosis codes. Results We identified 94,149 patients with migraine (including 21,525 N‐M and 72,624 PC‐M) and 1,248,763 controls. Comorbidities: Proportions of psychiatric [29.8% (n = 28,054) vs. 11.8% (n = 147,043)], autoimmune [(4.4% (n = 4162) vs. 2.6% (n = 31,981)], pain [13.2% (n = 12,439) vs. 5.8% (n = 72,049)], respiratory [24.6% (n = 23,186) vs. 12.3% (n = 153,692)], neurologic [2.9% (n = 2688) vs. 0.9% (n = 11,321)], and cerebrovascular [1.0% (n = 945) vs. 0.6% (n = 7500)] conditions were higher in the migraine group compared to controls, all p

Frequent emergency department (ED) utilization is an indicator of unmet health and social needs, especially among patients with mental and physical health problems. The authors aimed to characterize frequent ED utilizers and drivers of multiple ED use, including presence of serious mental illness (SMI), across 2 large health care systems in Northern California. Using electronic health records and a data-sharing platform, a cross-sectional analysis was conducted of patients aged 18+ years with ≥10 ED visits in 2016. Logistic regression was used to determine factors associated with multiple ED use versus single ED use. Among the 8036 patients who met inclusion criteria, the mean age was 55.9 years (95% CI = 55.5-56.4), 53% were female, 54% were non-Hispanic white, and 38% had any SMI. Overall, 51% of patients were single ED utilizers. Patients ages ≥65 years were less likely to use multiple EDs compared to younger patients (ages 18-23) (OR = 0.3, 95% CI = 0.2-0.4). African Americans exhibited more than 3 times the likelihood of multi-ED use compared to non-Hispanic whites (OR = 3.8, 95% CI = 3.3-4.3). A diagnosis of any SMI (OR = 2.3 [95% CI: 2.1-2.6]), major depressive disorder (OR = 1.3, 95% CI = 1.1-1.4), schizophrenia (OR = 2.1, 95% CI = 1.6-2.6), or suicidal attempts/ideation (OR = 2.7, 95% CI = 2.1-3.6) was significantly associated with increased likelihood of multi-ED use. Findings indicate heterogeneity in regional utilization patterns among frequent ED utilizers, with mental illness increasing the likelihood of multi-ED use.

Introduction The purpose of this study was to develop and validate a predictive model for the early identification of nonresponders to a 12-month lifestyle change program in clinical practice. Methods Investigators identified lifestyle change program participants in the electronic health records of a large healthcare delivery system between 2010 and 2017. Nonresponse was defined as weight gain or no weight loss at 12 months from the program initiation (baseline). Logistic regression with percentage weight change at 2–12 weeks from baseline was used as an independent predictor of nonresponse. Baseline demographics and clinical characteristics were also tested as potential predictors. The authors performed ten-fold cross-validation for model assessment and examined model performance with the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values. The analyses were conducted in 2019. Results Among 947 program participants, 30% were classified as nonresponders at 12 months. The model with the best discrimination of responders from nonresponders included weight change at 12 weeks from baseline as the sole predictor (area under the receiver operating characteristic curve, 0.789). Sensitivity and positive predictive value were maximized at 0.56 (specificity and negative predictive value, 0.81 each). Conclusions In a cohort of lifestyle change program participants from clinical practice, percentage weight change at 12 weeks from baseline can serve as a single indicator of nonresponse at the completion of the 12-month program. Clinicians can easily apply this algorithm to identify and assess participants in potential need of adjunctive or alternative therapy to maximize treatment outcomes.

BackgroundIn the United States (US), urinary tract infections (UTI) lead to more than 10 million office visits each year. Temperature and season are potentially important risk factors for UTI, particularly in the context of climate change.MethodsWe examined the relationship between ambient temperature and outpatient UTI diagnoses among patients followed from 2015 to 2017 in two California healthcare systems: Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. We identified UTI diagnoses in adult patients using diagnostic codes and laboratory records from electronic health records. We abstracted patient age, sex, season of diagnosis, and linked community-level Index of Concentration at the Extremes (ICE-I, a measure of wealth and poverty concentration) based on residential address. Daily county-level average ambient temperature was assembled from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). We implemented distributed lag nonlinear models (DLNM) to assess the association between UTI and lagged daily temperatures. Main analyses were confined to women. In secondary analyses, we stratified by season, healthcare system, and community-level ICE-I.ResultsWe observed 787,186 UTI cases (89% among women). We observed a threshold association between ambient temperature and UTI among women: an increase in daily temperature from the 5th percentile (6.0 ˚C) to the mean (16.2 ˚C) was associated with a 3.2% (95% CI: 2.4, 3.9%) increase in same-day UTI diagnosis rate, whereas an increase from the mean to 95th percentile was associated with no change in UTI risk (0.0%, 95% CI: -0.7, 0.6%). In secondary analyses, we observed the clearest monotonic increase in the rate of UTI diagnosis with higher temperatures in the fall. Associations did not differ meaningfully by healthcare system or community-level ICE-I. Results were robust to alternate model specifications.DiscussionIncreasing temperature was related to higher rate of outpatient UTI, particularly in the shoulder seasons (spring, autumn).

Study objective We examine racial and ethnic differences in opioid prescribing and dosing for long bone fractures at emergency department (ED) discharge. Methods We conducted an electronic health records–based cross-sectional study of adults with long bone fractures who presented to the ED across 22 sites from a health care delivery system (2016 to 2017). We examined differences in opioid prescribing at ED discharge and, among patients with a prescription, differences in opioid dosing (measured as morphine milligram equivalents) by race/ethnicity, using regression modeling with statistical adjustment for patient, fracture, and prescriber characteristics. Results A total of 11,576 patients with long bone fractures were included in the study; 64.4% were non-Hispanic white; 16.4%, 7.3%, 5.8%, and 5.1%, respectively, were Hispanic, Asian, black, and of other or unknown race; and 65.6% received an opioid at discharge. After adjusting for other factors, rates of opioid prescribing were not different by race/ethnicity; however, among patients with an opioid prescription, total morphine milligram equivalent units prescribed were 4.3%, 6.0%, and 8.1% less for Hispanics, blacks, and Asians relative to non-Hispanic whites. Conclusion Racial and ethnic minority groups with long bone fractures receive similar frequencies of opioid prescriptions at discharge, with a small potency difference. How this affects pain relief and why it happens is unclear.

The objective of this study was to assess the relationship between public protests and county-level, novel coronavirus disease (COVID-19) hospitalization rates across California. Publicly available data were included in the analysis from 55 of 58 California state counties (29 March–14 October 2020). Mixed-effects negative binomial regression models were used to examine the relationship between daily county-level COVID-19 hospitalizations and two main exposure variables: any vs. no protests and 1 or &gt, 1 protest vs. no protests on a given county-day. COVID-19 hospitalizations were used as a proxy for viral transmission since such rates are less sensitive to temporal changes in testing access/availability. Models included covariates for daily county mobility, county-level characteristics, and time trends. Models also included a county-population offset and a two-week lag for the association between exposure and outcome. No significant associations were observed between protest exposures and COVID-19 hospitalization rates among the 55 counties. We did not find evidence to suggest that public protests were associated with COVID-19 hospitalization within California counties. These findings support the notion that protesting during a pandemic may be safe, ostensibly, so long as evidence-based precautionary measures are taken.

Background Urinary tract infections (UTIs) occur commonly, but recent data on UTI rates are scarce. It is unknown how the growth of virtual healthcare delivery affects outpatient UTI management and trends in the United States. Methods From 1 January 2008 to 31 December 2017, UTIs from outpatient settings (office, emergency, and virtual visits) were identified from electronic health records at Kaiser Permanente Southern California using multiple UTI definitions. Annual rates estimated by Poisson regression were stratified by sex, care setting, age, and race/ethnicity. Annual trends were estimated by linear or piecewise Poisson regression. Results UTIs occurred in 1 065 955 individuals. Rates per 1000 person-years were 53.7 (95% confidence interval [CI], 50.6–57.0) by diagnosis code with antibiotic and 25.8 (95% CI, 24.7–26.9) by positive culture. Compared to office and emergency visits, UTIs were increasingly diagnosed in virtual visits, where rates by diagnosis code with antibiotic increased annually by 21.2% (95% CI, 16.5%–26.2%) in females and 29.3% (95% CI, 23.7%–35.3%) in males. Only 32% of virtual care diagnoses had a culture order. Overall, UTI rates were highest and increased the most in older adults. Rates were also higher in Hispanic and white females and black and white males. Conclusions Outpatient UTI rates increased from 2008 to 2017, especially in virtual care and among older adults. Virtual care is important for expanding access to health services, but strategies are needed in all outpatient care settings to ensure accurate UTI diagnosis and reduce inappropriate antibiotic treatment.

Background Migraine is one of the most common neurological disorders in clinical practice and is a substantial cause of disability worldwide. Current approaches to therapy are primarily based on medication but are often limited by inadequate effectiveness and common side effects. Newer, more effective medications are expensive. Mindfulness-based stress reduction (MBSR), an 8-week classroom-based meditation intervention, is inexpensive, has no known side effects, and has demonstrated clinically meaningful effectiveness for several chronic-pain syndromes. In addition, MBSR has shown promising results for migraine therapy in a few small case studies and pilot studies. We present here the protocol for a two-arm randomized controlled pilot trial of MBSR for moderate-to-severe episodic migraine, which, if successful, will form the basis for a fully powered clinical trial. Methods/design This study, set in Northern California, is a two-arm parallel-comparison single-blinded randomized controlled pilot trial with the goal of recruiting approximately 60 participants with moderate-to-severe episodic migraine. The feasibility outcomes include ability and time required to recruit, adherence to the MBSR treatment, and ability to measure outcomes using 31-day headache diaries and patient-reported questionnaire data. The active treatment arm consists of an 8-week community-based MBSR class plus usual care, and the wait-list control group is usual care. Recruitment is underway and expected to be complete by the end of 2018. Discussion To our knowledge, this is the first pragmatic trial in the U.S. of MBSR for migraine using community-based classes, and if it proves viable, we plan to conduct a fully powered trial to determine the effectiveness of the intervention for reducing headache days for moderate-to-severe episodic migraineurs. Trial registration Clinicaltrials.gov, NCT02824250. Registered on 6 July 2016. Electronic supplementary material The online version of this article (10.1186/s13063-019-3355-y) contains supplementary material, which is available to authorized users.

As the novel coronavirus disease (COVID-19) pandemic spreads throughout the United States, evidence is mounting that racial and ethnic minorities and socioeconomically disadvantaged groups are bearing a disproportionate burden of illness and death. We conducted a retrospective cohort analysis of COVID-19 patients at Sutter Health, a large integrated health system in northern California, to measure potential disparities. We used Sutter's integrated electronic health record to identify adults with suspected and confirmed COVID-19, and we used multivariable logistic regression to assess risk of hospitalization, adjusting for known risk factors, such as race/ethnicity, sex, age, health, and socioeconomic variables. We analyzed 1,052 confirmed cases of COVID-19 from the period January 1-April 8, 2020. Among our findings, we observed that compared with non-Hispanic white patients, non-Hispanic African American patients had 2.7 times the odds of hospitalization, after adjustment for age, sex, comorbidities, and income. We explore possible explanations for this, including societal factors that either result in barriers to timely access to care or create circumstances in which patients view delaying care as the most sensible option. Our study provides real-world evidence of racial and ethnic disparities in the presentation of COVID-19.

Introduction Translational lifestyle change programs for community and clinical settings have been available for a decade, yet there are limited data on their comparative effectiveness. This study examines the effectiveness of a Centers for Disease Control and Prevention–aligned lifestyle change program relative to usual care in clinical practice. Methods This was an electronic health record–based retrospective cohort study conducted in a community-based healthcare system. Investigators identified adult program participants and usual-care patients in the electronic health record between 2010 and 2018 and defined their index date (baseline) as the first lifestyle change program encounter or a random encounter date, respectively. Participants were matched 1:2 to usual-care patients on baseline demographics and clinical characteristics using propensity-score methods. Changes in body weight and blood pressure were examined from baseline through 24 months. Results The authors identified 2,833 program participants and 438,432 usual-care patients meeting study eligibility criteria. A total of 2,833 program participants were matched to 4,776 usual-care patients; the average age was 54 years, and 80% of the participants were female. Program participation was associated with a 1.9- and 1.6-fold higher prevalence of clinically meaningful (≥5%) weight loss at 12- and 24-month follow-up than usual care and a higher prevalence of blood pressure control at 12 months but not at 24 months. Patients without type 2 diabetes at baseline had more pronounced outcomes than those with type 2 diabetes. Conclusions This study demonstrates the effectiveness of an evidence-based, Centers for Disease Control and Prevention–aligned lifestyle change program in reducing cardiometabolic risk factors compared with usual care in clinical practice, with long-term reductions in weight and transient reductions in blood pressure.

Purpose The purpose of this electronic health record (EHR)–based retrospective cohort study was to characterize a population of patients participating in a 12-month, lifestyle change program in a community-based health system and to examine longitudinal weight outcomes. Methods Program participants were identified in the EHRs of a health care delivery system across 18 sites between 2010 and 2017. Outcomes were mean weight change and proportion of patients with ≥5% weight loss through 24 months from program initiation. Results Among 4463 program participants, 3156 met study eligibility criteria, with a mean ± SD age of 53.5 ± 13.1 years; 77.7% were women. Mean baseline weight ± SD was 101.3 ± 23.8 kg. Three main cardiometabolic risk groups were identified: prediabetes/high risk for diabetes (47.3%), overweight/obese in the absence of elevated diabetes risk (27.2%), and existing diabetes (23.9%). Maximal mean weight loss was 3.9% at 6 months from baseline. At 12 and 24 months from baseline, mean weight loss was 3.2% and 2.3%, respectively, with 31% and 29% of participants attaining ≥5% weight loss. Long-term weight outcomes were similar across risk groups. Conclusions A lifestyle change program in a clinical practice setting is associated with modest weight loss, sustained through 24 months, among participants with a range of cardiometabolic risk factors. More than one-quarter of participants achieve ≥5% weight loss, regardless of cardiometabolic risk.

Graphical abstract, Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.

Background: Home-based care coordination and support programs for people with advanced illness work alongside usual care to promote personal care goals, which usually include a preference for home-based end-of-life care. More research is needed to confirm the efficacy of these programs, especially when disseminated on a large scale. Advanced Illness Management is one such program, implemented within a large open health system in northern California, USA. Aim: To evaluate the impact of Advanced Illness Management on end-of-life resource utilization, cost of care, and care quality, as indicators of program success in supporting patient care goals. Design: A retrospective-matched observational study analyzing medical claims in the final 3 months of life. Setting/participants: Medicare fee-for-service 2010–2014 decedents in northern California, USA. Results: Final month total expenditures for Advanced Illness Management enrollees ( N = 1352) were reduced by US$4824 (US$3379, US$6268) and inpatient payments by US$6127 (US$4874, US$7682). Enrollees also experienced 150 fewer hospitalizations/1000 (101, 198) and 1361 fewer hospital days/1000 (998, 1725). The percentage of hospice enrollees increased by 17.9 percentage points (14.7, 21.0), hospital deaths decreased by 8.2 percentage points (5.5, 10.8), and intensive care unit deaths decreased by 7.1 percentage points (5.2, 8.9). End-of-life chemotherapy use and non-inpatient expenditures in months 2 and 3 prior to death did not differ significantly from the control group. Conclusion: Advanced Illness Management has a positive impact on inpatient utilization, cost of care, hospice enrollment, and site of death. This suggests that home-based support programs for people with advanced illness can be successful on a large scale in supporting personal end-of-life care choices.

Context Relatively few patients with gout receive appropriate treatment. Objective To determine whether a pharmacist-staffed gout management program is more effective than usual care in achieving target serum uric acid (sUA) levels in gout patients. Design A parallel-group, randomized controlled trial of a pharmacist-staffed, telephone-based program for managing hyperuricemia vs usual care. Trial duration was 26 weeks. Main outcome measures Primary outcome measure was achieving sUA levels at or below 6 mg/dL at the 26-week visit. Secondary outcome was mean change in sUA levels in the control and intervention groups. Participants were adults with recurrent gout and sUA levels above 6.0 mg/dL. Participants were randomly assigned to management by a clinical pharmacist following protocol or to monitoring of sUA levels but management of their gout by their usual treating physician. Results Of 102 patients who met eligibility criteria, 77 subjects obtained a baseline sUA measurement and were entered into the trial. Among 37 participants in the intervention group, 13 (35%) had sUA levels at or below 6.0 mg/dL at 26 weeks, compared with 5 (13%) of 40 participants in the control group (risk ratio = 2.8, 95% confidence interval [CI] = 1.1 to 7.1, p = 0.03). The mean change in sUA levels among controls was +0.1 mg/dL compared with -1.5 mg/dL in the intervention group (sUA difference = -1.6, 95% CI = -0.9 to -2.4, p Conclusions A structured pharmacist-staffed program was more effective than usual care for achieving target sUA levels. These results suggest a structured program could greatly improve gout management.

To evaluate the utility of the Fracture Risk Calculator (FRC, Foundation for Osteoporosis Research and Education) for predicting 10-year hip fracture risk within a "real world" population.Retrospective cohort study.We identified female members of Kaiser Permanente Northern California aged ≥50 years with bone mineral density (BMD) measured during 1997-2003. Hospitalization for hip fracture was ascertained up to 10 years following the BMD date, and 10-year observed hip fracture probabilities were calculated. Baseline data for fracture risk calculation were extracted from health plan databases, including age, race/ethnicity, smoking, body mass index, prior fracture, rheumatoid arthritis, glucocorticoid use, disorders associated with bone loss, and femoral neck BMD. Predicted 10-year FRC hip fracture probabilities were compared with observed 10-year hip fracture probabilities.Among 94,489 women (mean age 62.8 +/- 8.6 years, average femoral neck Z-score +0.1),the median duration of follow-up was 6.6 years, during which 1579 (1.7%) hip fractures occurred. Using the FRC, 23% met or exceeded the National Osteoporosis Foundation's 3% hip fracture threshold. The FRC somewhat underestimated observed hip fracture probabilities; across 10-year risk categories1%, 1% to 2.9%, and 3% to 4.9%, ratios of observed to median predicted probabilities ranged from 1.3 to 1.4.The FRC tool can be applied to assess fracture risk in large populations using data from administrative databases. Despite some underestimation, this relatively simple tool may assist targeting of at-risk populations for more complete fracture risk assessment.