scholarly journals Multi-ancestry gene-trait connection landscape using electronic health record (EHR) linked biobank data

2021 ◽  
Author(s):  
Binglan Li ◽  
Yogasudha Veturi ◽  
Anastasia Lucas ◽  
Yuki Bradford ◽  
Shefali Setia Verma ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Association Study ◽  
Complex Disease ◽  
The United States ◽  
European Ancestry ◽  
Health Record ◽  
Summary Statistics ◽  
A Genome ◽  
Disease Associations ◽  
Electronic Health

Understanding genetic factors of complex traits across ancestry groups holds a key to improve the overall health care quality for diverse populations in the United States. In recent years, multiple electronic health record-linked (EHR-linked) biobanks have recruited participants of diverse ancestry backgrounds; these biobanks make it possible to obtain phenome-wide association study (PheWAS) summary statistics on a genome-wide scale for different ancestry groups. Moreover, advancement in bioinformatics methods provide novel means to accelerate the translation of basic discoveries to clinical utility by integrating GWAS summary statistics and expression quantitative trait locus (eQTL) data to identify complex trait-related genes, such as transcriptome-wide association study (TWAS) and colocalization analyses. Here, we combined the advantages of multi-ancestry biobanks and data integrative approaches to investigate the multi-ancestry, gene-disease connection landscape. We first performed a phenome-wide TWAS on Electronic Medical Records and Genomics (eMERGE) III network participants of European ancestry (N = 68,813) and participants of African ancestry (N = 12,658) populations, separately. For each ancestry group, the phenome-wide TWAS tested gene-disease associations between 22,535 genes and 309 curated disease phenotypes in 49 primary human tissues, as well as cross-tissue associations. Next, we identified gene-disease associations that were shared across the two ancestry groups by combining the ancestry-specific results via meta-analyses. We further applied a Bayesian colocalization method, fastENLOC, to prioritize likely functional gene-disease associations with supportive colocalized eQTL and GWAS signals. We replicated the phenome-wide gene-disease analysis in the analogous Penn Medicine BioBank (PMBB) cohorts and sought additional validations in the PhenomeXcan UK Biobank (UKBB) database, PheWAS catalog, and systematic literature review. Phenome-wide TWAS identified many proof-of-concept gene-disease associations, e.g. FTO-obesity association (p = 7.29e-15), and numerous novel disease-associated genes, e.g. association between GATA6-AS1 with pulmonary heart disease (p = 4.60e-10). In short, the multi-ancestry, gene-disease connection landscape provides rich resources for future multi-ancestry complex disease research. We also highlight the importance of expanding the size of non-European ancestry datasets and the potential of exploring ancestry-specific genetic analyses as these will be critical to improve our understanding of the genetic architecture of complex disease.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

scholarly journals A genome-first approach to aggregating rare genetic variants in LMNA for association with electronic health record phenotypes

2019 ◽  
Vol 22 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Joseph Park ◽  
◽  
Michael G. Levin ◽  
Christopher M. Haggerty ◽  
Dustin N. Hartzel ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Genetic Variants ◽  
Health Record ◽  
A Genome ◽  
Rare Genetic Variants ◽  
Electronic Health

scholarly journals The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

2021 ◽  
Vol 12 (01) ◽  
pp. 153-163
Author(s):  
Zoe Co ◽  
A. Jay Holmgren ◽  
David C. Classen ◽  
Lisa P. Newmark ◽  
Diane L. Seger ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medication Safety ◽  
Medication Reconciliation ◽  
The United States ◽  
Health Record ◽  
Evaluation Tool ◽  
Safety Test ◽  
Electronic Health ◽  
The Impact

Abstract Background Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. Objective To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. Methods The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. Results For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug–drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. Conclusion Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

scholarly journals Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
Author(s):  
Adam Wright ◽  
Angela Ai ◽  
Joan Ash ◽  
Jane F Wiesen ◽  
Thu-Trang T Hickman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Quantitative Methods ◽  
Clinical Decision ◽  
The United States ◽  
Health Record ◽  
Qualitative And Quantitative Methods ◽  
Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

scholarly journals 517. Treatment Patterns of Hospitalized Adults with Infections Due to Carbapenem Non-Susceptible Gram-Negative Organisms in a Large Electronic Health Record Database in the United States

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S249-S249
Author(s):  
Tanya Burton ◽  
Amy Anderson ◽  
Jerry Seare ◽  
Ryan J Dillon ◽  
Eilish McCann
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Electronic Health Record ◽  
Infection Type ◽  
Treatment Options ◽  
Complicated Urinary Tract Infection ◽  
The United States ◽  
Treatment Patterns ◽  
Health Record ◽  
Gram Negative ◽  
Electronic Health

Abstract Background Infections caused by carbapenem non-susceptible (C-NS) Gram-negative (GN) organisms pose a major threat, due in part to limited treatment options. The aim of this study was to assess treatment patterns for these infections in a large US electronic health record database. Methods A retrospective cohort study of hospitalized adults with complicated intra-abdominal infection (cIAI), complicated urinary tract infection (cUTI), bacterial pneumonia (BP), or bacteremia (BAC) due to C-NS (resistant/intermediate susceptibility to carbapenem) GN organisms from January 2013 to March 2018. Patients with inherently C-NS organisms (e.g., Pseudomonas aeruginosa to ertapenem) were only included if resistance to another carbapenem was identified. The index date was the date of first C-NS culture in a qualifying hospitalization (±3 days from admission/discharge). Clinical characteristics and administered treatments were assessed from admission to discharge with variables summarized descriptively and stratified by infection type. Results 7,702 patients met inclusion criteria: 31% cUTI ± BAC, 24% BP ± BAC, 21% cUTI + BP ± BAC, 17% cIAI ± BAC, cUTI, or BP, 7% BAC only. The median age was 66 years, ranging from 60 (BAC) to 69 (cUTI) years; male, 57%. The most common pathogens were Pseudomonas aeruginosa (64%) and Klebsiella pneumoniae (15%). Antibiotics were administered to the majority of patients (87%); of which, 79% received combination therapy (median classes: 3, maximum: 7), the remainder received monotherapy. For antibiotic-treated patients, 93% initiated an antibiotic before the non-susceptibility status of the underlying organism was known. The most common classes given during the index hospitalization were: penicillin (49%), fluoroquinolone (44%), carbapenem (40%), cephalosporin (39%), aminoglycoside (28%) (by infection type, Figure 1). Eleven percent of patients received colistin/polymyxin B. Conclusion Varied antibiotic use was observed in this cohort, with carbapenems frequently detected despite the C-NS nature of the underlying GN organisms. The use of antibiotics to which organisms are non-susceptible could lead to poor health outcomes, supporting the need for new targeted therapies to treat C-NS infections. Disclosures All authors: No reported disclosures.

scholarly journals The Use of Electronic Health Record Systems During COVID-19 at a Local Community Hospital on the West Coast

2021 ◽  
pp. 53-58
Author(s):  
Malini Krishnamurthi, Ph.D.
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Patient Care ◽  
Data Privacy ◽  
Local Community ◽  
Quality Care ◽  
The United States ◽  
Health Record ◽  
Privacy And Security ◽  
Electronic Health

The United States Federal government looks toward information technology to curtail health care costs while increasing the quality of patient care through the adoption of electronic health record (EHR)systems. This paper examined the experience of a hospital with its EHR system in the context of the pandemic. Results showed that the hospital maintains a state-of-the-art health care system to provide quality care to its community and was responsive to the recent crisis. The results were consistent with other comparable hospitals examined in this study. The hospitals were successful in adopting EHR systems. They were able to identify gaps that could be filled with technology add-ons from different software vendors to improve their functionality and thereby provide better & timely patient care. Managing large volumes of data generated in the normal process of EHR operation and ensuring data privacy and security were the significant challenges faced and are likely to continue in the future.

scholarly journals Implementation and adoption of a health insurance support tool in the electronic health record: A mixed methods analysis within a randomized trial

2020 ◽  
Author(s):  
Brigit Hatch ◽  
Carrie Tillotson ◽  
Nathalie Huguet ◽  
Miguel Marino ◽  
Andrea Baron ◽  
...  
Keyword(s):  
Health Insurance ◽  
Electronic Health Record ◽  
Health Center ◽  
The United States ◽  
Audit And Feedback ◽  
Research Network ◽  
Relative Advantage ◽  
Health Record ◽  
Clinic Staff ◽  
Electronic Health

Abstract Background: In addition to delivering vital health care to millions of patients in the United States, community health centers (CHCs) provide needed health insurance outreach and enrollment support to their communities. We developed a health insurance enrollment tracking tool integrated within the electronic health record (EHR) and conducted a hybrid implementation-effectiveness trial in a CHC-based research network to assess tool adoption using two implementation strategies. Methods: CHCs were recruited from the OCHIN practice-based research network. Seven health center systems (23 CHC clinic sites) were recruited and randomized to receive basic educational materials alone (Arm 1), or these materials plus facilitation (Arm 2) during the 18-month study period, September 2016-April 2018. Facilitation consisted of monthly contacts with clinic staff and utilized audit and feedback and guided improvement cycles. We measured total and monthly tool utilization from the EHR. We conducted structured interviews of CHC staff to assess factors associated with tool utilization. Qualitative data were analyzed using an immersion-crystallization approach with barriers and facilitators identified using the Consolidated Framework for Implementation Research. Results: The majority of CHCs in both study arms adopted the enrollment tool. The rate of tool utilization was, on average, higher in Arm 2 compared to Arm 1 (20.0% versus 4.7%, p <0.01). However, by the end of the study period, the rate of tool utilization was similar in both arms; and observed between-arm differences in tool utilization were largely driven by a single, large health center in Arm 2. Perceived relative advantage of the tool was the key factor identified by clinic staff as driving tool utilization. Implementation climate and leadership engagement were also associated with tool utilization. Conclusions: Using basic education materials and low-intensity facilitation, CHCs quickly adopted an EHR-based tool to support critical outreach and enrollment activities aimed at improving access to health insurance in their communities. Though facilitation carried some benefit, a CHC’s perceived relative advantage of the tool was the primary driver of decisions to implement the tool. Trial Registration: ClinicalTrials.gov: NCT02355262, Posted February 4, 2015

Sign in / Sign up

Export Citation Format

Share Document