Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility

ObjectiveThe US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data.Materials and MethodsIn this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic^® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects.ResultsOver 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001).DiscussionMissing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening.ConclusionThe use of longitudinal EHR smoking data could improve lung cancer screening.     

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

ObjectiveThe aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies.Materials and MethodsWe searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures.ResultsOne hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles.DiscussionVendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported.ConclusionThe number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.     

Effects of technology-supported exercise programs on the knee pain,physical function,and quality of life of individuals with knee osteoarthritis and/or chronic knee pain: A systematic review and meta-analysis of randomized controlled trials

ObjectiveThe study sought to examine the effects of technology-supported exercise programs on the knee pain, physical function, and quality of life of individuals with knee osteoarthritis and/or chronic knee pain by a systematic review and meta-analysis of randomized controlled trials.Materials and MethodsWe searched MEDLINE, EMBASE, CINAHL Plus, and the Cochrane Library from database inception to August 2020. A meta-analysis and subgroup analyses, stratified by technology type and program feature, were conducted.ResultsTwelve randomized controlled trials were reviewed, all of which implemented the programs for 4 weeks to 6 months. Telephone, Web, mobile app, computer, and virtual reality were used to deliver the programs. The meta-analysis showed that these programs were associated with significant improvements in knee pain (standardized mean difference [SMD] = −0.29; 95% confidence interval [CI], −0.48 to −0.10; P = .003) and quality of life (SMD = 0.25; 95% CI, 0.04 to 0.46; P = .02) but not with significant improvement in physical function (SMD = 0.22; 95% CI, 0 to 0.43; P = .053). Subgroup analyses showed that some technology types and program features were suggestive of potential benefits.ConclusionsUsing technology to deliver the exercise programs appears to offer benefits. The technology types and program features that were associated with health values have been identified, based on which suggestions are discussed for the further research and development of such programs.     

Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank

ObjectiveThe objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank.Materials and MethodsWe built a fully automated image curation and labeling technique using deep learning and distributive computing to identify subcutaneous and visceral abdominal fat compartments from 52,844 computed tomography scans in 13,502 patients in the Penn Medicine Biobank (PMBB). A classification network identified the inferior and superior borders of the abdomen, and a segmentation network differentiated visceral and subcutaneous fat. Following technical evaluation of our method, we conducted studies to validate known relationships with visceral and subcutaneous fat.ResultsWhen compared with 100 manually annotated cases, the classification network was on average within one 5-mm slice for both the superior (0.4 ± 1.1 slice) and inferior (0.4 ± 0.6 slice) borders. The segmentation network also demonstrated excellent performance with intraclass correlation coefficients of 1.00 (P < 2 × 10^-16) for subcutaneous and 1.00 (P < 2 × 10^-16) for visceral fat on 100 testing cases. We performed integrative analyses of abdominal fat with the phenome extracted from the electronic health record and found highly significant associations with diabetes mellitus, hypertension, and renal failure, among other phenotypes.ConclusionsThis work presents a fully automated and highly accurate method for the quantification of abdominal fat that can be applied to routine clinical imaging studies to fuel translational scientific discovery.     

Can decision support combat incompleteness and bias in routine primary care data?

ObjectiveRoutine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.Materials and MethodsWe used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.ResultsSupported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = −0.08 [−0.11, −0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.ConclusionsWe provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.     

The correlation between fear of falling and upper extremity muscle strength

Objectives:To determine the relationship between fear of falling (FOF) and upper extremity muscle strength.Methods:This cross-sectional study included 112 hospitalized, mobile patients. Forty-seven (42%) were males and 65 (58%) were females, and the mean age was 72.3. The study was carried out between September 2018 and September 2019 at Balikli Rum Hospital Nursing Homes, Istanbul, Turkey. Patients were tested using geriatric tools (such as Mini-Mental State Examination) and physical tests such as handgrip, key pinch and 6-meter up and go tests.Results:The average annual falling number of elderly people with FOF was statistically significantly higher than that in those without FOF (p=0.001). Right handgrip, left handgrip, right key pinch, and left key-pinch mean values in elderly individuals with FOF were statistically significantly lower than those without FOF (p< 0.001, p< 0.001, p< 0.001, p< 0.001, respectively).Conclusion:The measurement of upper extremity strength could be a predicting parameter of FOF.     

Association of CYP1A1 MspI polymorphism in the esophageal cancer risk: a meta-analysis in the Chinese population

Background

Although many epidemiologic studies have investigated the CYP1A1 MspI gene polymorphisms and their associations with esophageal cancer (EC), definite conclusions cannot be drawn. To clarify the effects of CYP1A1 MspI polymorphisms on the risk of EC, a meta-analysis was performed in Chinese population.

Methods

Related studies were identified from PubMed, Springer Link, Ovid, Chinese Wanfang Data Knowledge Service Platform, Chinese National Knowledge Infrastructure (CNKI), and Chinese Biology Medicine (CBM) till October 2014. Pooled ORs and 95% CIs were used to assess the strength of the associations.

Results

A total of 13 studies including 1,519 EC cases and 1,962 controls were involved in this meta-analysis. Overall, significant association was found between CYP1A1 MspI polymorphism and EC risk when all studies in the Chinese population pooled into this meta-analysis (C vs. T: OR = 1.25, 95% CI = 1.04 to 1.51; CC + CT vs. TT: OR = 1.35, 95% CI = 1.06 to 1.72; CC vs. TT + CT: OR = 1.35, 95% CI = 1.03 to 1.76). When we performed stratified analyses by geographical locations, histopathology type, and source of control, significantly increased risks were found in North China (C vs. T: OR = 1.38, 95% CI = 1.12 to 1.70; CC vs. TT: OR = 1.72, 95% CI = 1.16 to 2.56; CC + CT vs. TT: OR = 1.52, 95% CI = 1.14 to 2.02; CC vs. TT + CT: OR = 1.55, 95% CI = 1.17 to 2.06), in the population-based studies (C vs. T: OR = 1.22, 95% CI = 1.05 to 1.42; CC vs. TT: OR = 1.38, 95% CI = 1.02 to 1.88; CC + CT vs. TT: OR = 1.36, 95% CI = 1.10 to 1.69; CC vs. TT + CT: OR = 1.43, 95% CI = 1.13 to 1.81) and ESCC (C vs. T: OR = 1.17, 95% CI = 1.04 to 1.32; CC + CT vs. TT: OR = 1.28, 95% CI = 1.08 to 1.52).

Conclusions

This meta-analysis provides the evidence that CYP1A1 MspI polymorphism may contribute to the EC development in the Chinese population.     

A theory-based meta-regression of factors influencing clinical decision support adoption and implementation

ObjectiveThe purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results.Materials and MethodsWe leveraged search results from a previous systematic literature review and updated the search to screen articles published from January 2017 to January 2020. We included randomized controlled trials and cluster randomized controlled trials that compared a CDS intervention with and without specific factors. We used random effects meta-regression procedures to analyze clinician behavior for the aggregate effects. The theoretical model was the Unified Theory of Acceptance and Use of Technology (UTAUT) model with motivational control.ResultsThirty-four studies were included. The meta-regression models identified the importance of effort expectancy (estimated coefficient = −0.162; P = .0003); facilitating conditions (estimated coefficient = 0.094; P = .013); and performance expectancy with motivational control (estimated coefficient = 1.029; P = .022). Each of these factors created a significant impact on clinician behavior. The meta-regression model with the multivariate analysis explained a large amount of the heterogeneity across studies (R2 =88.32%).DiscussionThree positive factors were identified: low effort to use, low controllability, and providing more infrastructure and implementation strategies to support the CDS. The multivariate analysis suggests that passive CDS could be effective if users believe the CDS is useful and/or social expectations to use the CDS intervention exist.ConclusionsOverall, a modified UTAUT model that includes motivational control is an appropriate model to understand psychological factors associated with CDS effectiveness and to guide CDS design, implementation, and optimization.     

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records

ObjectiveDeveloping algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs.Materials and MethodsPheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center''s BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. ResultsIn this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online.ConclusionsPheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.     

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)

ObjectiveThis study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses.Materials and MethodsUsing an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated.ResultsIn general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased.DiscussionAnalyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression.ConclusionIn general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression—an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.     

Integrity of clinical information in radiology reports documenting pulmonary nodules

ObjectiveQuantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations.Materials and MethodsThis Institutional Review Board-approved, retrospective cohort study was performed at an academic medical center. Natural language processing was performed on radiology reports of CT scans of chest, abdomen, or spine completed in 2016 to assess presence of pulmonary nodules, excluding patients with lung cancer, of which 300 reports were randomly sampled to form the study cohort. Documentation of nodule characteristics were manually extracted from reports by 2 authors with 20% overlap. CT images corresponding to 60 randomly selected reports were further reviewed by a thoracic radiologist to record nodule characteristics. Documentation completeness for all characteristics were reported in percentage and compared using χ² analysis. Concordance with a thoracic radiologist was reported as percentage agreement; impact on making follow-up recommendations was assessed using kappa.ResultsDocumentation completeness for pulmonary nodule characteristics differed across variables (range = 2%–90%, P < .001). Concordance with a thoracic radiologist was 75% for documenting nodule laterality and 29% for size. Follow-up recommendations were in agreement in 67% and 49% of reports when there was lack of completeness and concordance in documenting nodule size, respectively.DiscussionEssential pulmonary nodule characteristics were under-reported, potentially impacting recommendations for pulmonary nodule follow-up.ConclusionLack of documentation of pulmonary nodule characteristics in radiology reports is common, with potential for compromising patient care and clinical decision support tools.     

Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program

ObjectiveTo give providers a better understanding of how to use the electronic health record (EHR), improve efficiency, and reduce burnout.Materials and MethodsAll ambulatory providers were offered at least 1 one-on-one session with an “optimizer” focusing on filling gaps in EHR knowledge and lack of customization. Success was measured using pre- and post-surveys that consisted of validated tools and homegrown questions. Only participants who returned both surveys were included in our calculations.ResultsOut of 1155 eligible providers, 1010 participated in optimization sessions. Pre-survey return rate was 90% (1034/1155) and post-survey was 54% (541/1010). 451 participants completed both surveys. After completing their optimization sessions, respondents reported a 26% improvement in mean knowledge of EHR functionality (P < .01), a 19% increase in the mean efficiency in the EHR (P < .01), and a 17% decrease in mean after-hours EHR usage (P < .01). Of the 401 providers asked to rate their burnout, 32% reported feelings of burnout in the pre-survey compared to 23% in the post-survey (P < .01). Providers were also likely to recommend colleagues participate in the program, with a Net Promoter Score of 41.DiscussionIt is possible to improve provider efficiency and feelings of burnout with a personalized optimization program. We ascribe these improvements to the one-on-one nature of our program which provides both training as well as addressing the feeling of isolation many providers feel after implementation.ConclusionIt is possible to reduce burnout in ambulatory providers with personalized retraining designed to improve efficiency and knowledge of the EHR.     

Human-centered development of an electronic health record-embedded,interactive information visualization in the emergency department using fast healthcare interoperability resources

ObjectiveDevelop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards.Materials and MethodsWe applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient’s asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users.ResultsUsability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction.DiscussionFollowing HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented.ConclusionCompared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches.     

The quality of social determinants data in the electronic health record: a systematic review

ObjectiveThe aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).Materials and MethodsWe conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process.ResultsThe most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully.DiscussionThe type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups.ConclusionConsideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.     

Impact of a problem-oriented view on clinical data retrieval

ObjectiveThe electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV).MethodsSimple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem.ResultsWith POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001).DiscussionThe study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load.ConclusionEHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.