Prevalence of Select New Symptoms and Conditions Among Persons Aged Younger Than 20 Years and 20 Years or Older at 31 to 150 Days After Testing Positive or Negative for SARS-CoV-2.

Importance: New symptoms and conditions can develop following SARS-CoV-2 infection. Whether they occur more frequently among persons with SARS-CoV-2 infection compared with those without is unclear. Objective: To compare the prevalence of new diagnoses of select symptoms and conditions between 31 and 150 days after testing among persons who tested positive vs negative for SARS-CoV-2. Design, Setting, and Participants: This cohort study analyzed aggregated electronic health record data from 40 health care systems, including 338 024 persons younger than 20 years and 1 790 886 persons aged 20 years or older who were tested for SARS-CoV-2 during March to December 2020 and who had medical encounters between 31 and 150 days after testing. Main Outcomes and Measures: International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes were used to capture new symptoms and conditions that were recorded 31 to 150 days after a SARS-CoV-2 test but absent in the 18 months to 7 days prior to testing. The prevalence of new symptoms and conditions was compared between persons with positive and negative SARS-CoV-2 tests stratified by age (20 years or older and young than 20 years) and care setting (nonhospitalized, hospitalized, or hospitalized and ventilated). Results: A total of 168 701 persons aged 20 years or older and 26 665 younger than 20 years tested positive for SARS-CoV-2, and 1 622 185 persons aged 20 years or older and 311 359 younger than 20 years tested negative. Shortness of breath was more common among persons with a positive vs negative test result among hospitalized patients (≥20 years: prevalence ratio [PR], 1.89 [99% CI, 1.79-2.01]; <20 years: PR, 1.72 [99% CI, 1.17-2.51]). Shortness of breath was also more common among nonhospitalized patients aged 20 years or older with a positive vs negative test result (PR, 1.09 [99% CI, 1.05-1.13]). Among hospitalized persons aged 20 years or older, the prevalence of new fatigue (PR, 1.35 [99% CI, 1.27-1.44]) and type 2 diabetes (PR, 2.03 [99% CI, 1.87-2.19]) was higher among those with a positive vs a negative test result. Among hospitalized persons younger than 20 years, the prevalence of type 2 diabetes (PR, 2.14 [99% CI, 1.13-4.06]) was higher among those with a positive vs a negative test result; however, the prevalence difference was less than 1%. Conclusions and Relevance: In this cohort study, among persons hospitalized after a positive SARS-CoV-2 test result, diagnoses of certain symptoms and conditions were higher than among those with a negative test result. Health care professionals should be aware of symptoms and conditions that may develop after SARS-CoV-2 infection, particularly among those hospitalized after diagnosis.

PCORnet® 2020: current state, accomplishments, and future directions.

OBJECTIVE: To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale. STUDY DESIGN AND SETTING: Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet. RESULTS: Within the Clinical Research Networks, electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least one visit from 2009 to 2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009 to 2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up. CONCLUSION: PCORnet's infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases.

Smoking-Cessation Assistance: Before and After Stage 1 Meaningful Use Implementation.

INTRODUCTION: Brief smoking-cessation interventions in primary care settings are effective, but delivery of these services remains low. The Centers for Medicare and Medicaid Services' Meaningful Use (MU) of Electronic Health Record (EHR) Incentive Program could increase rates of smoking assessment and cessation assistance among vulnerable populations. This study examined whether smoking status assessment, cessation assistance, and odds of being a current smoker changed after Stage 1 MU implementation. METHODS: EHR data were extracted from 26 community health centers with an EHR in place by June 15, 2009. AORs were computed for each binary outcome (smoking status assessment, counseling given, smoking-cessation medications ordered/discussed, current smoking status), comparing 2010 (pre-MU), 2012 (MU preparation), and 2014 (MU fully implemented) for pregnant and non-pregnant patients. RESULTS: Non-pregnant patients had decreased odds of current smoking over time; odds for all other outcomes increased except for medication orders from 2010 to 2012. Among pregnant patients, odds of assessment and counseling increased across all years. Odds of discussing or ordering of cessation medications increased from 2010 compared with the other 2 study years; however, medication orders alone did not change over time, and current smoking only decreased from 2010 to 2012. Compared with non-pregnant patients, a lower percentage of pregnant patients were provided counseling. CONCLUSIONS: Findings suggest that incentives for MU of EHRs increase the odds of smoking assessment and cessation assistance, which could lead to decreased smoking rates among vulnerable populations. Continued efforts for provision of cessation assistance among pregnant patients is warranted.

Assessing Trends in Tobacco Cessation in Diverse Patient Populations.

INTRODUCTION: This study examined change in tobacco use over 4 years among the general population of patients in six diverse health care organizations using electronic medical record data. METHODS: The study cohort (N = 34 393) included all patients age 18 years or older who were identified as smokers in 2007, and who then had at least one primary care visit in each of the following 4 years. RESULTS: In the 4 years following 2007, this patient cohort had a median of 13 primary care visits, and 38.6% of the patients quit smoking at least once. At the end of the fourth follow-up year, 15.4% had stopped smoking for 1 year or more. Smokers were more likely to become long-term quitters if they were 65 or older (OR = 1.32, 95% CI = [1.16, 1.49]), or had a diagnoses of cancer (1.26 [1.12, 1.41]), cardiovascular disease (1.22 [1.09, 1.37]), asthma (1.15 [1.06, 1.25]), or diabetes (1.17 [1.09, 1.27]). Characteristics associated with lower likelihood of becoming a long-term quitter were female gender (0.90 [0.84, 0.95]), black race (0.84 [0.75, 0.94]) and those identified as non-Hispanic (0.50 [0.43, 0.59]). CONCLUSIONS: Among smokers who regularly used these care systems, one in seven had achieved long-term cessation after 4 years. This study shows the practicality of using electronic medical records for monitoring patient smoking status over time. Similar methods could be used to assess tobacco use in any health care organization to evaluate the impact of environmental and organizational programs.

CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data.

OBJECTIVES: Comparative effectiveness research (CER) requires the capture and analysis of data from disparate sources, often from a variety of institutions with diverse electronic health record (EHR) implementations. In this paper we describe the CER Hub, a web-based informatics platform for developing and conducting research studies that combine comprehensive electronic clinical data from multiple health care organizations. METHODS: The CER Hub platform implements a data processing pipeline that employs informatics standards for data representation and web-based tools for developing study-specific data processing applications, providing standardized access to the patient-centric electronic health record (EHR) across organizations. RESULTS: The CER Hub is being used to conduct two CER studies utilizing data from six geographically distributed and demographically diverse health systems. These foundational studies address the effectiveness of medications for controlling asthma and the effectiveness of smoking cessation services delivered in primary care. DISCUSSION: The CER Hub includes four key capabilities: the ability to process and analyze both free-text and coded clinical data in the EHR; a data processing environment supported by distributed data and study governance processes; a clinical data-interchange format for facilitating standardized extraction of clinical data from EHRs; and a library of shareable clinical data processing applications. CONCLUSION: CER requires coordinated and scalable methods for extracting, aggregating, and analyzing complex, multi-institutional clinical data. By offering a range of informatics tools integrated into a framework for conducting studies using EHR data, the CER Hub provides a solution to the challenges of multi-institutional research using electronic medical record data.

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study.

Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the 'emrAdapter' tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.

Documentation of the 5 as for smoking cessation by PCPs across distinct health systems.

OBJECTIVES: Physicians can help patients quit smoking using the 5 As of smoking cessation. This study aimed to (1) identify the proportion of known smokers that receive smoking cessation services in the course of routine clinical practice; (2) describe demographic and comorbidity characteristics of patients receiving the 5 As in these systems; and (3) evaluate differences in performance of the 5 As across health systems, gender, and age categories. STUDY DESIGN: Electronic medical records of 200 current smokers from 6 unique health systems (N = 1200) were randomly selected from 2006 to 2010. Primary care encounter progress notes were hand coded for occurrences of the 5 As. METHODS: Bivariate comparisons of delivery of the 3 smoking-cessation services by site, gender, and age category were analyzed using χ² tests. RESULTS: About 50% of smokers were advised to quit smoking, 39% were assessed for their readiness to quit, and 54% received some type of assistance to help them quit smoking. Only 2% had a documented plan for follow-up regarding their quitting efforts (arrange). Significant differences were found among sites for documentation of receiving the 5 As and between age groups receiving assistance with quitting. There was no statistically significant difference between genders in receipt of the 5 As. CONCLUSIONS: Documentation of adherence to the 5 As varied by site and some demographics. Adjustments to protocols for addressing cessation and readiness to quit may be warranted. Health systems could apply the methodology described in this paper to assess their own performance, and then use that as a basis to guide improvement initiatives.

Automating care quality measurement with health information technology.

OBJECTIVES: To assess the performance of a health information technology platform that enables automated measurement of asthma care quality using comprehensive electronic medical record (EMR) data, including providers' free-text notes. STUDY DESIGN: Retrospective data study of outpatient asthma care in Kaiser Permanente Northwest (KPNW), a midsized health maintenance organization (HMO), and OCHIN, Inc, a group of Federally Qualified Health Centers. METHODS: We created 22 automated quality measures addressing guideline-recommended outpatient asthma care. We included EMRs of asthma patients aged >12 years during a 3-year observation window and narrowed this group to those with persistent asthma (13,918 KPNW; 1825 OCHIN). We validated our automated quality measures using chart review for 818 randomly selected patients, stratified by age and sex for each health system. In both health systems, we compared the performance of these measures against chart review. RESULTS: Most measures performed well in the KPNW system, where accuracy averaged 88% (95% confidence interval [CI] 82%-93%). Mean sensitivity was 77% (95% CI 62%-92%) and mean specificity was 84% (95% CI 75%-93%). The automated analysis was less accurate at OCHIN, where mean accuracy was 80% (95% CI 72%-89%) with mean sensitivity and specificity 52% (95% CI 35%-69%) and 82% (95% CI 69%-95%) respectively. CONCLUSIONS: To achieve comprehensive quality measurement in many clinical domains, the capacity to analyze text clinical notes is required. The automated measures performed well in the HMO, where practice is more standardized. The measures need to be refined for health systems with more diversity in clinical practice, patient populations, and setting.

Receipt of diabetes preventive care among safety net patients associated with differing levels of insurance coverage.

BACKGROUND: Patients receive care in safety net clinics regardless of insurance status; however, receipt of diabetes preventive care might vary among patients with differing levels of insurance continuity. METHODS: In a retrospective cohort study, using electronic health record data from adults with diabetes who were receiving care in 50 safety net clinics in Oregon in 2005 to 2007, we conducted adjusted logistic regressions to model the associations between amount of time with insurance and rates of receipt of lipid screening, influenza vaccination, nephropathy screening (urine microalbumin), and HbA1c (glycohemoglobin) screening. RESULTS: Of 3384 adults with diabetes, 711 were partially insured (covered 1% to 99% of the 3-year study period), 909 had no coverage, and 1764 were continuously insured. In adjusted models, persons with partial or no coverage during the 3-year study period were less likely to receive most preventive services compared with those with continuous coverage. We found no evidence of a dose-response relationship with increasing duration of coverage, nor of a threshold amount of partial coverage, associated with better receipt of care. CONCLUSIONS: Safety net clinic patients need both access to primary care and continuous insurance. All patients with partial coverage, regardless of the extent of time with insurance, had lower odds of receiving preventive care.