EGEMS (Washington, DC)最新文献

{"title":"Denominators Matter: Understanding Medical Encounter Frequency and Its Impact on Surveillance Estimates Using EHR Data","authors":"N. Cocoros, Aileen Ochoa, Karen Eberhardt, Bob Zambarano, M. Klompas","doi":"10.5334/egems.292","DOIUrl":"https://doi.org/10.5334/egems.292","url":null,"abstract":"Background: There is scant guidance for defining what denominator to use when estimating disease prevalence via electronic health record (EHR) data. Objectives: Describe the intervals between medical encounters to inform the selection of denominators for population-level disease rates, and evaluate the impact of different denominators on the prevalence of chronic conditions. Methods: We analyzed the EHRs of three practices in Massachusetts using the Electronic medical record Support for Public Health (ESP) system. We identified adult patients’ first medical encounter per year (2011–2016) and counted days to next encounter. We estimated the prevalence of asthma, hypertension, obesity, and smoking using different denominators in 2016: ≥1 encounter in the past one year or two years and ≥2 encounters in the past one year or two years. Results: In 2011–2016, 1,824,011 patients had 28,181,334 medical encounters. The median interval between encounters was 46, 56, and 66 days, depending on practice. Among patients with one visit in 2014, 82–84 percent had their next encounter within 1 year; 87–91 percent had their next encounter within two years. Increasing the encounter interval from one to two years increased the denominator by 23 percent. The prevalence of asthma, hypertension, and obesity increased with successively stricter denominators – e.g., the prevalence of obesity was 24.1 percent among those with ≥1 encounter in the past two years, 26.3 percent among those with ≥1 encounter in the last one year, and 28.5 percent among those with ≥2 encounters in the past one year. Conclusions: Prevalence estimates for chronic conditions can vary by >20 percent depending upon denominator. Understanding such differences will inform which denominator definition is best to be used for the need at hand.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47556666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular Health Trends in Electronic Health Record Data (2012-2015): A Cross-Sectional Analysis of The Guideline Advantage™.","authors":"Joyce E Rudy,&nbsp;Yosef Khan,&nbsp;Julie K Bower,&nbsp;Sejal Patel,&nbsp;Randi E Foraker","doi":"10.5334/egems.268","DOIUrl":"https://doi.org/10.5334/egems.268","url":null,"abstract":"<p><strong>Background: </strong>Electronic health record (EHR) data can measure cardiovascular health (CVH) of patient populations, but have limited generalizability when derived from one health care system.</p><p><strong>Objective: </strong>We used The Guideline Advantage™ (TGA) data repository, comprising EHR data of patients from 8 diverse health care systems, to describe CVH of adult patients and progress towards the American Heart Association's (AHA's) 2020 Impact Goals.</p><p><strong>Methods: </strong>Our analysis included 203,488 patients with 677,733 encounters recorded in TGA from 2012 to 2015. Five measures from EHRs [cigarette smoking status, body mass index (BMI), blood pressure (BP), cholesterol, and diabetes mellitus (DM)] were categorized as poor/intermediate/ideal according to AHA's Life's Simple 7 algorithm. We presented distributions and trends of CVH for each metric over time, first using all available data, and then in a subsample (n = 1,890) of patients with complete data on all metrics.</p><p><strong>Results: </strong>Among all patients, the greatest stride towards ideal CVH attainment from 2012 to 2015 was for cigarette smoking (50.6 percent to 65 percent), followed by DM (17.3 percent to 20.7 percent) and BP (21.1 percent to 23.2 percent). Overall, prevalence of ideal CVH did not increase for any metric in the subsample. Males slightly improved in ideal CVH for BMI and cholesterol; meanwhile, females saw no improvement in ideal CVH for any metric. As ideal CVH for BP and cholesterol increased slightly among white patients, ideal CVH for BP, cholesterol, BMI, and DM worsened for non-whites.</p><p><strong>Conclusion: </strong>Despite improvements in some CVH metrics in the outpatient setting, more tangible progress is needed to meet AHA's 2020 Impact Goals.</p>","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":"7 1","pages":"30"},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41221982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The NQRN Registry Maturational Framework: Evaluating the Capability and Use of Clinical Registries","authors":"Seth E. Blumenthal","doi":"10.5334/egems.278","DOIUrl":"https://doi.org/10.5334/egems.278","url":null,"abstract":"Clinical registries are increasingly used as national performance measurement platforms. In 2018, nearly 70 percent of the more than 50 specialty society registries in the United States were used by the Centers for Medicare & Medicaid Services (CMS) to measure the quality of clinical care. Private payers and evaluating organizations also use or desire to use registry information to inform quality improvement programs and value-based payment models. The requirements for an entity to become a CMS Qualified Clinical Data Registry (QCDR) constitute a minimum set of standards for the purpose of reporting to the CMS Quality Payment Program. Models and frameworks exist that can help classify registries by purpose and use, and maturity models are available for evaluating health IT systems generally. However, there is currently no framework that describes the capability that should be expected from a registry at different phases of its development and maturity. In response, the National Quality Registry Network (NQRN) has developed a registry maturational framework. The framework models early, intermediate and mature development phases, the capabilities anticipated during these phases and 17 domains across which registry programs support those capabilities. The framework was developed and refined by NQRN registry stewards, users and other stakeholders between 2013–2018. It is intended to be used as a developmental guide or for registry evaluation. The successful use of registry information to execute value-based payment models is a critical need in U.S. health care. The NQRN framework can help ensure that our national system of registries is rising to the occasion.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44941739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Segmenting Medicare Expenditures to Inform Cost Effective Care Delivery for Older Adults","authors":"C. Crowley, T. Kent, Liane Wardlow, M. Twaddle","doi":"10.5334/egems.272","DOIUrl":"https://doi.org/10.5334/egems.272","url":null,"abstract":"Introduction: Faced with growing populations of older, medically complex patients, health systems are now incentivized to deliver cost-effective, high-value care. We evaluated a new method that builds upon existing Medicare spending concentration studies to further segment these expenditures, revealing use patterns to inform care redesign. Methods: We obtained monthly Medicare expenditure data and derived baseline comparison data using typical methods for identifying a yearly high-cost subpopulation. We then applied the new methodology, ordering monthly patient expenditures from highest to lowest to more extensively segment the baseline data. Our evaluation examined the following within the new more extensive segmentation: monthly expenditure distribution, corresponding patient counts, and occupancy of specific patient subgroups within the extended segmentation of baseline data. Results: Compared to the baseline data, we found further spending concentration, with 16.7 percent of high-cost patients being responsible for about two-thirds of baseline expenditures. The remaining 83.3 percent of the high-cost subpopulation exhibited lower spending, collectively accounting for about one third of baseline expenditures. Additionally, we found that unique patient subgroups occupied different segments over time, with specific subgroups comprising 8.3 percent of the study subpopulation patients migrating into and out of each highest spending segment, accounting for almost half of monthly baseline expenditures. Conclusions: With monthly health care expenditures concentrated among small numbers of migrating patients, our evaluation suggested potential cost-effectiveness in tiered care delivery models, where small subgroups receive direct, active care interactions, while the remainder experience surveillance-level care, designed to both address ongoing medical needs and to detect emergent migration.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43367055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controlling Confounding in a Study of Oral Anticoagulants: Comparing Disease Risk Scores Developed Using Different Follow-Up Approaches","authors":"J. Bohn, S. Schneeweiss, R. Glynn, S. Toh, R. Wyss, R. Desai, J. Gagne","doi":"10.5334/egems.254","DOIUrl":"https://doi.org/10.5334/egems.254","url":null,"abstract":"Purpose: Little is known about how disease risk score (DRS) development should proceed under different pharmacoepidemiologic follow-up strategies. In an analysis of dabigatran vs. warfarin and risk of major bleeding, we compared the results of DRS adjustment when models were developed under “intention-to-treat” (ITT) and “as-treated” (AT) approaches. Methods: We assessed DRS model discrimination, calibration, and ability to induce prognostic balance via the “dry run analysis”. AT treatment effects stratified on each DRS were compared with each other and with a propensity score (PS) stratified reference estimate. Bootstrap resampling of the historical cohort at 10 percent–90 percent sample size was performed to assess the impact of sample size on DRS estimation. Results: Historically-derived DRS models fit under AT showed greater decrements in discrimination and calibration than those fit under ITT when applied to the concurrent study population. Prognostic balance was approximately equal across DRS models (–6 percent to –7 percent “pseudo-bias” on the hazard ratio scale). Hazard ratios were between 0.76 and 0.78 with all methods of DRS adjustment, while the PS stratified hazard ratio was 0.83. In resampling, AT DRS models showed more overfitting and worse prognostic balance, and led to hazard ratios further from the reference estimate than did ITT DRSs, across sample sizes. Conclusions: In a study of anticoagulant safety, DRSs developed under an AT principle showed signs of overfitting and reduced confounding control. More research is needed to determine if development of DRSs under ITT is a viable solution to overfitting in other settings.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43828133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying an Electronic Health Records Data Quality Framework Across Service Sectors: A Case Study of Juvenile Justice System Data","authors":"M. Aalsma, K. Schwartz, K. Haight, G. Jarjoura, A. Dir","doi":"10.5334/egems.258","DOIUrl":"https://doi.org/10.5334/egems.258","url":null,"abstract":"Context: Integrating electronic health records (EHR) with other sources of administrative data is key to identifying factors affecting the long-term health of traditionally underserved populations, such as individuals involved in the justice system. Linking existing administrative data from multiple sources overcomes many of the limitations of traditional prospective studies of population health, but the linking process assumes high levels of data quality and consistency within administrative data. Studies of EHR, unlike other types of administrative data, have provided guidance to evaluate the utility of big data for population health research. Case Description: Here, an established EHR data quality framework was applied to identify and describe the potential shortcomings of administrative juvenile justice system data collected by one of four case management systems (CMSs) across 12 counties in a Midwest state. The CMS data were reviewed for logical inconsistencies and compared along the data quality dimensions of plausibility and completeness. Major Themes: After applying the data quality framework, several patterns of logical inconsistencies within the data were identified. To resolve these inconsistencies, recommendations regarding data entry, review, and extraction are offered. Conclusion: The recommendations related to achieving quality justice system data can be applied to future efforts to link administrative databases from multiple sources. Increasing trust in administrative data quality related to vulnerable populations ultimately improves knowledge of pressing public health concerns.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48939027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Symptom Burden in Long-Term Survivors of Head and Neck Cancer: Patient-Reported Versus Clinical Data","authors":"G. Pocobelli, Rebecca A. Ziebell, M. Fujii, K. Hutcheson, Steven S. Chang, J. McClure, Jessica Chubak","doi":"10.5334/egems.271","DOIUrl":"https://doi.org/10.5334/egems.271","url":null,"abstract":"Introduction: The symptom burden faced by long-term head and neck cancer survivors is not well understood. In addition, the accuracy of clinical data sources for symptom ascertainment is not clear. Objective: To 1) describe the prevalence of symptoms in 5-year survivors of head and neck cancer, and 2) to evaluate agreement between symptoms obtained via self-report and symptoms obtained from clinical data sources. Methods: We recruited 5-year survivors of head and neck cancer enrolled at Kaiser Permanente Washington (n = 54). Symptoms were assessed using the MD Anderson Symptom Inventory head and neck cancer module. For each symptom, we assessed the agreement of the patient’s survey response (“gold standard”) with the 1) medical chart and 2) administrative health care claims data. We computed the sensitivity, specificity, positive predictive value (PPV), and negative predictive value, along with their 95 percent confidence intervals, for each clinical data source. Results: Eighty percent of patients responded. Nearly all participants (95 percent) reported experiencing at least one symptom from the MDASI-HN, and 93 percent reported two or more symptoms. Among patients reporting a given symptom, there was generally no evidence of the symptom from either clinical data source (i.e., sensitivity was generally no greater than 40 percent). The specificity and PPV of the clinical data sources were generally higher than the sensitivity. Conclusion: Relying only on medical chart review and/or administrative health data would substantially underestimate symptom burden in long-term head and neck cancer survivors.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47911341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Information Technology and Accountable Care Organizations: A Systematic Review and Future Directions","authors":"Casey P. Balio, Nate C. Apathy, Robin Danek","doi":"10.5334/egems.261","DOIUrl":"https://doi.org/10.5334/egems.261","url":null,"abstract":"Background: Since the inception of Accountable Care Organizations (ACOs), many have acknowledged the potential synergy between ACOs and health information technology (IT) in meeting quality and cost goals. Objective: We conducted a systematic review of the literature in order to describe what research has been conducted at the intersection of health IT and ACOs and identify directions for future research. Methods: We identified empirical studies discussing the use of health IT via PubMed search with subsequent snowball reference review. The type of health IT, how health IT was included in the study, use of theory, population, and findings were extracted from each study. Results: Our search resulted in 32 studies describing the intersection of health IT and ACOs, mainly in the form of electronic health records and health information exchange. Studies were divided into three streams by purpose; those that considered health IT as a factor for ACO participation, health IT use by current ACOs, and ACO performance as a function of health IT capabilities. Although most studies found a positive association between health IT and ACO participation, studies that address the performance of ACOs in terms of their health IT capabilities show more mixed results. Conclusions: In order to better understand this emerging relationship between health IT and ACO performance, we propose future research should consider more quasi-experimental studies, the use of theory, and merging health, quality, cost, and health IT use data across ACO member organizations.","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41625722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease.","authors":"Celena B Peters,&nbsp;Jared L Hansen,&nbsp;Ahmad Halwani,&nbsp;Monique E Cho,&nbsp;Jianwei Leng,&nbsp;Tina Huynh,&nbsp;Zachary Burningham,&nbsp;John Caloyeras,&nbsp;Tara Matsuda,&nbsp;Brian C Sauer","doi":"10.5334/egems.257","DOIUrl":"https://doi.org/10.5334/egems.257","url":null,"abstract":"<p><strong>Background: </strong>The goal of this study was to compare the performance of several database algorithms designed to identify red blood cell (RBC) Transfusion Related hospital Admissions (TRAs) in Veterans with end stage renal disease (ESRD).</p><p><strong>Methods: </strong>Hospitalizations in Veterans with ESRD and evidence of dialysis between 01/01/2008 and 12/31/2013 were screened for TRAs using a clinical algorithm (CA) and four variations of claims-based algorithms (CBA 1-4). Criteria were implemented to exclude patients with non-ESRD-related anemia (e.g., injury, surgery, bleeding, medications known to produce anemia). Diagnostic performance of each algorithm was delineated based on two clinical representations of a TRA: RBC transfusion required to treat ESRD-related anemia on admission regardless of the reason for admission (labeled as TRA) and hospitalization for the primary purpose of treating ESRD-related anemia (labeled TRA-Primary). The performance of all algorithms was determined by comparing each to a reference standard established by medical records review. Population-level estimates of classification agreement statistics were calculated for each algorithm using inverse probability weights and bootstrapping procedures. Due to the low prevalence of TRAs, the geometric mean was considered the primary measure of algorithm performance.</p><p><strong>Results: </strong>After application of exclusion criteria, the study consisted of 12,388 Veterans with 26,672 admissions. The CA had a geometric mean of 90.8% (95% Confidence Interval: 81.8, 95.6) and 94.7% (95% CI: 80.5, 98.7) for TRA and TRA-Primary, respectively. The geometric mean for the CBAs ranged from 60.3% (95% CI: 53.2, 66.9) to 91.8% (95% CI: 86.9, 95) for TRA, and from 80.7% (95% CI: 72.9, 86.7) to 96.7% (95% CI: 94.1, 98.2) for TRA-Primary. The adjusted proportions of admissions classified as TRAs was 3.2% (95% CI: 2.8, 3.8) and TRA-Primary was 1.3% (95% CI: 1.1, 1.7).</p><p><strong>Conclusions: </strong>The CA and select CBAs were able to identify TRAs and TRA-primary with high levels of accuracy and can be used to examine anemia management practices in ESRD patients.</p>","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":"7 1","pages":"23"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37415716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving Ahead: What's Next for the eGEMs Community.","authors":"Paul Wallace","doi":"10.5334/egems.313","DOIUrl":"https://doi.org/10.5334/egems.313","url":null,"abstract":"<p><p><i>eGEMs</i>, in close partnership with our key sponsor and publisher, AcademyHealth, has provided a window to the transformational impact of electronic health data (EHD) on how we pursue health and deliver healthcare. This commentary traces key milestones in that journey and announces the next chapter for this community and the critical work it produces.</p>","PeriodicalId":72880,"journal":{"name":"EGEMS (Washington, DC)","volume":"7 1","pages":"22"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37281028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}