scholarly journals Finding Meaning in Medication Reconciliation Using Electronic Health Records: Qualitative Analysis in Safety Net Primary and Specialty Care (Preprint)

2018 ◽  
Author(s):  
George Yaccoub Matta ◽  
Elaine C Khoong ◽  
Courtney R Lyles ◽  
Dean Schillinger ◽  
Neda Ratanawongsa
Keyword(s):  
Electronic Health Records ◽  
Qualitative Analysis ◽  
Medication Reconciliation ◽  
Safety Net ◽  
Specialty Care ◽  
Health Records ◽  
Finding Meaning ◽  
Electronic Health

scholarly journals Finding Meaning in Medication Reconciliation Using Electronic Health Records: Qualitative Analysis in Safety Net Primary and Specialty Care

10.2196/10167 ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. e10167 ◽  
Author(s):  
George Yaccoub Matta ◽  
Elaine C Khoong ◽  
Courtney R Lyles ◽  
Dean Schillinger ◽  
Neda Ratanawongsa
Keyword(s):  
Electronic Health Records ◽  
Qualitative Analysis ◽  
Medication Reconciliation ◽  
Safety Net ◽  
Specialty Care ◽  
Health Records ◽  
Finding Meaning ◽  
Electronic Health

scholarly journals 1665. Using Electronic Health Records to Describe TB in Community Health Settings: a Cohort Analysis in a Large Safety-Net Population

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Electronic Health Records ◽  
Safety Net ◽  
The United States ◽  
Research Network ◽  
Health Records ◽  
Treatment Regimens ◽  
Data Source ◽  
Electronic Health

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures

scholarly journals Creating a foundation for implementing an electronic health records (EHR)-integrated Social Knowledge Networking (SKN) system on medication reconciliation

2018 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Rangachari P. ◽  
Dellsperger K.C. ◽  
Fallaw D. ◽  
Davis I. ◽  
Sumner M. ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Medication Reconciliation ◽  
Phase 1 ◽  
Transitions Of Care ◽  
Social Knowledge ◽  
Importance Rating ◽  
Health Records ◽  
Professional Affiliation ◽  
Inpatient Medicine ◽  
Electronic Health

Background: In fall 2016, Augusta University received a two-year grant from AHRQ, to implement a Social Knowledge Networking (SKN) system for enabling its health system, AU-Health, to progress from “limited use” of electronic health records (EHR) Medication Reconciliation (MedRec) Technology, to “meaningful use”. Phase 1 sought to identify a comprehensive set of issues related to EHR MedRec encountered by practitioners at AU-Health. These efforts helped develop a Reporting Tool, which, along with a Discussion Tool, was incorporated into the AU-Health EHR, at the end of Phase 1. Phase 2 (currently underway), comprises a 52-week pilot of the EHR-integrated SKN system in outpatient and inpatient medicine units. The purpose of this paper is to describe the methods and results of Phase 1.Methods: Phase 1 utilized an exploratory mixed-method approach, involving two rounds of data collection. This included 15 individual interviews followed by a survey of 200 practitioners, i.e., physicians, nurses, and pharmacists, based in the outpatient and inpatient medicine service at AU Health.Results: Thematic analysis of interviews identified 55 issue-items related to EHR MedRec under 9 issue-categories. The survey sought practitioners’ importance-rating of all issue-items identified from interviews. A total of 127 (63%) survey responses were received. Factor analysis served to validate the following 6 of the 9 issue-categories, all of which, were rated “Important” or higher (on average), by over 70% of all respondents: 1) Care-Coordination (CCI); 2) Patient-Education (PEI); 3) Ownership-and-Accountability (OAI); 4) Processes-of-Care (PCI); 5) IT-Related (ITRI); and 6)Workforce-Training (WTI). Significance-testing of importance-rating by professional affiliation revealed no statistically significant differences for CCI and PEI; and some statistically significant differences for OAI, PCI, ITRI, and WTI.Conclusions: There were two key gleanings from the issues related to EHR MedRec unearthed by this study: 1) there was an absence of shared understanding among practitioners, of the value of EHR MedRec in promoting patient safety, which contributed to workarounds, and suboptimal use of the EHR MedRec system; and 2) there was a socio-technical dimension to many of the issues, creating an added layer of complexity. These gleanings in turn, provide insights into best practices for managing both clinical transitions-of-care in the EHR MedRec process; and socio-technical challenges encountered in EHR MedRec implementation.

scholarly journals Electronic Health Records in Specialty Care: A Time-Motion Study

2007 ◽  
Vol 14 (5) ◽  
pp. 609-615 ◽  
Author(s):  
H. G. Lo ◽  
L. P. Newmark ◽  
C. Yoon ◽  
L. A. Volk ◽  
V. L. Carlson ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Specialty Care ◽  
Health Records ◽  
Time Motion ◽  
Motion Study ◽  
Electronic Health

scholarly journals The use of open source electronic health records within the federal safety net

2014 ◽  
Vol 21 (2) ◽  
pp. 280-284 ◽  
Author(s):  
Jason C Goldwater ◽  
Nancy J Kwon ◽  
Ashley Nathanson ◽  
Alison E Muckle ◽  
Alexa Brown ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Open Source ◽  
Safety Net ◽  
Health Records ◽  
Electronic Health

scholarly journals “It’s like texting at the dinner table”: A qualitative analysis of the impact of electronic health records on patient-physician interaction in hospitals

2017 ◽  
Vol 24 (2) ◽  
pp. 216 ◽  
Author(s):  
Kimberly D Pelland ◽  
Rosa R Baier ◽  
Rebekah L Gardner
Keyword(s):  
Electronic Health Records ◽  
Qualitative Analysis ◽  
Rhode Island ◽  
Rank Order ◽  
Health Records ◽  
Patient Interactions ◽  
Inpatient Settings ◽  
Prior Literature ◽  
Electronic Health ◽  
The Impact

Background: Electronic health records (EHRs) may reduce medical errors and improve care, but can complicate clinical encounters.Objective: To describe hospital-based physicians’ perceptions of the impact of EHRs on patient-physician interactions and contrast these findings against office-based physicians’ perceptionsMethods: We performed a qualitative analysis of comments submitted in response to the 2014 Rhode Island Health Information Technology Survey. Office- and hospital-based physicians licensed in Rhode Island, in active practice, and located in Rhode Island or neighboring states completed the survey about their Electronic Health Record use.Results: The survey’s response rate was 68.3% and 2,236 (87.1%) respondents had EHRs. Among survey respondents, 27.3% of hospital-based and 37.8% of office-based physicians with EHRs responded to the question about patient interaction. Five main themes emerged for hospital-based physicians, with respondents generally perceiving EHRs as negatively altering patient interactions. We noted the same five themes among office-based physicians, but the rank-order of the top two responses differed by setting: hospital-based physicians commented most frequently that they spend less time with patients because they have to spend more time on computers; office-based physicians commented most frequently on EHRs worsening the quality of their interactions and relationships with patients.Conclusion: In our analysis of a large sample of physicians, hospital-based physicians generally perceived EHRs as negatively altering patient interactions, although they emphasized different reasons than their office-based counterparts. These findings add to the prior literature, which focuses on outpatient physicians, and can shape interventions to improve how EHRs are used in inpatient settings.

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