Brief Report: Identifying common pharmacotherapies associated with reduced COVID-19 morbidity using electronic health records

AbstractImportanceAbsent a vaccine or any established treatments for the novel and highly infectious coronavirus-19 (COVID-19), rapid efforts to identify potential therapeutics are required.ObjectiveTo identify commonly-prescribed medications that may be associated with lesser risk of morbidity with COVID-19 across 5 Eastern Massachusetts hospitals.DesignIn silico cohort using electronic health records between 7/1/2019 and 4/07/2020. Setting: Outpatient, emergency department and inpatient settings from 2 academic medical centers and 3 community hospitals.ParticipantsAll individuals presenting to a clinical site and undergoing COVID-19 testing.Main Outcome or MeasureInpatient hospitalization; documented requirement for mechanical ventilation.ResultsAmong 12,818 individuals with COVID-19 testing results available, 2271 (17.7%) were test-positive, and 707/2271 (31.1%) were hospitalized in one of 5 hospitals. Based on a comparison of ranked electronic prescribing frequencies, medications enriched among test-positive individuals not requiring hospitalization included ibuprofen, valacyclovir, and naproxen. Among individuals who were hospitalized, mechanical ventilation was documented in 213 (30.1%); ibuprofen and naproxen were also more commonly prescribed among individuals not requiring ventilation.Conclusions and RelevanceThese preliminary findings suggest that electronic health records may be applied to identify medications associated with lower risk of morbidity with COVID-19, but larger cohorts will be required to address confounding by indication. Larger scale efforts at repositioning may help to identify FDA-approved medications meriting study for prevention of COVID-19 morbidity and mortality.Fundingnone.Key PointsQuestionCan electronic health records identify medications that may be associated with diminished risk of COVID-19 morbidity?FindingsThis cohort study across 5 hospitals identified medications enriched among individuals who did not require hospitalization for COVID-19 despite a positive test.MeaningWhile preliminary and subject to confounding, our results suggest that electronic health records may complement efforts to identify novel therapeutics for COVID-19 by identifying FDA-approved compounds with potential benefit in reducing COVID-19-associated morbidity.

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Identifying Heart Failure from Electronic Health Records: A Systematic Evidence Review

10.1101/2021.02.01.21250933 ◽

2021 ◽

Author(s):

Rebecca T. Levinson ◽

Jennifer R. Malinowski ◽

Suzette J. Bielinski ◽

Luke V. Rasmussen ◽

Quinn S. Wells ◽

...

Keyword(s):

Heart Failure ◽

Electronic Health Records ◽

Academic Medical Centers ◽

Significant Heterogeneity ◽

Health Records ◽

Algorithm Development ◽

Medical Centers ◽

Academic Medical ◽

Evidence Review ◽

Electronic Health

ABSTRACTBackgroundHeart failure (HF) is a complex syndrome associated with significant morbidity and healthcare costs. Electronic health records (EHRs) are widely used to identify patients with HF and other phenotypes. Despite widespread use of EHRs for phenotype algorithm development, it is unclear if the characteristics of identified populations mirror those of clinically observed patients and reflect the known spectrum of HF phenotypes.MethodsWe performed a subanalysis within a larger systematic evidence review to assess the different methods used for HF algorithm development and their application to research and clinical care. We queried PubMed for articles published up to November 2020. Out of 318 studies screened, 25 articles were included for primary analysis and 15 studies using only International Classification of Diseases (ICD) codes were evaluated for secondary analysis. Results are reported descriptively.ResultsHF algorithms were most often developed at academic medical centers and the V.A. One health system was responsible for 8 of 10 HF algorithm studies. HF and congestive HF were the most frequent phenotypes observed and less frequently, specific HF subtypes and acute HF. Diagnoses were the most common data type used to identify HF patients and echocardiography was the second most frequent. The majority of studies used rule-based methods to develop their algorithm. Few studies used regression or machine learning methods to identify HF patients. Validation of algorithms varied considerably: only 52.9% of HF and 44.4% of HF subtype algorithms were validated, but 75% of acute HF algorithms were. Demographics of any study population were reported in 68% of algorithm studies and 53% of ICD-only studies. Fewer than half reported demographics of their HF algorithm-identified population. Of those reporting, most identified majority male (>50%) populations, including both algorithms for HF with preserved ejection fraction.ConclusionThere is significant heterogeneity in phenotyping methodologies used to develop HF algorithms using EHRs. Validation of algorithms is inconsistent but largely relies on manual review of patient records. The concentration of algorithm development at one or two sites may reduce potential generalizability of these algorithms to identify HF patients at non-academic medical centers and in populations from underrepresented regions. Differences between the reported demographics of algorithm-identified HF populations those expected based on HF epidemiology suggest that current algorithms do not reflect the full spectrum of HF patient populations.

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Identification of patients with hemoglobin SS/Sβ0 thalassemia disease and pain crises within electronic health records

Blood Advances ◽

10.1182/bloodadvances.2018017541 ◽

2018 ◽

Vol 2 (11) ◽

pp. 1172-1179 ◽

Cited By ~ 2

Author(s):

Ashima Singh ◽

Javier Mora ◽

Julie A. Panepinto

Keyword(s):

Electronic Health Records ◽

Acute Care ◽

Sensitivity And Specificity ◽

Data Model ◽

High Sensitivity ◽

Common Data Model ◽

Health Records ◽

Key Points ◽

Electronic Health

Key Points The algorithms have high sensitivity and specificity to identify patients with hemoglobin SS/Sβ0 thalassemia and acute care pain encounters. Codes conforming to common data model are provided to facilitate adoption of algorithms and standardize definitions for EHR-based research.

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The portability of natural language processing methods to detect suicidality from unstructured clinical text in US and UK electronic health records

10.21203/rs.3.rs-155430/v1 ◽

2021 ◽

Author(s):

Marika Cusick ◽

Sumithra Velupillai ◽

Johnny Downs ◽

Thomas Campion ◽

Rina Dutta ◽

...

Keyword(s):

Natural Language Processing ◽

Electronic Health Records ◽

Natural Language ◽

Language Processing ◽

Healthcare Systems ◽

Risk Identification ◽

Health Records ◽

Clinical Text ◽

Academic Medical ◽

Electronic Health

Abstract In the global effort to prevent death by suicide, many academic medical institutions are implementing natural language processing (NLP) approaches to detect suicidality from unstructured clinical text in electronic health records (EHRs), with the hope of targeting timely, preventative interventions to individuals most at risk of suicide. Despite the international need, the development of these NLP approaches in EHRs has been largely local and not shared across healthcare systems. In this study, we developed a process to share NLP approaches that were individually developed at King’s College London (KCL), UK and Weill Cornell Medicine (WCM), US - two academic medical centers based in different countries with vastly different healthcare systems. After a successful technical porting of the NLP approaches, our quantitative evaluation determined that independently developed NLP approaches can detect suicidality at another healthcare organization with a different EHR system, clinical documentation processes, and culture, yet do not achieve the same level of success as at the institution where the NLP algorithm was developed (KCL approach: F1-score 0.85 vs. 0.68, WCM approach: F1-score 0.87 vs. 0.72). Shared use of these NLP approaches is a critical step forward towards improving data-driven algorithms for early suicide risk identification and timely prevention.

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E-Prescribing: Benefit, Barrier, and Adopting Challenge in Electronic Prescribing

Journal of Medicine ◽

10.3329/jom.v21i2.50213 ◽

2020 ◽

Vol 21 (2) ◽

pp. 98-101

Author(s):

Rasmi Zakiah Oktarlina

Keyword(s):

Patient Safety ◽

Electronic Health Records ◽

Health Workers ◽

Health Facilities ◽

Electronic Prescribing ◽

Health Records ◽

Electronic Health ◽

Barriers And Challenges

E-Prescribing system provides prescribing drugs electronically that can be a stand-alone system or be integrated with Electronic Health Records (EHRs) system. When implementing an E-Prescribing system, we need to consider it’s benefits and barriers, also adopting factors that can affect the success of the implementation. The main advantage of using an E-Prescribing system is to increase patient safety, meanwhile, the biggest barriers and challenges are it’s cost-related and adaptation by health facilities and related health workers. This article review provides information about E-prescribing and it’s elements to help for a better understanding of the system and to give some insights before adopting the e-prescribing system. J MEDICINE JUL 2020; 21 (2) : 98-101

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Study of electronic prescribing rates and barriers identified among providers using electronic health records in New York City

Journal of Innovation in Health Informatics ◽

10.14236/jhi.v19i2.800 ◽

2011 ◽

Vol 19 (2) ◽

pp. 91-97

Author(s):

Sam Amirfar ◽

Sheila Anane ◽

Michael Buck ◽

Rachel Cohen ◽

Steve DiLonardo ◽

...

Keyword(s):

New York ◽

New York City ◽

Electronic Health Records ◽

York City ◽

Electronic Prescribing ◽

Health Records ◽

Electronic Health

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Electronic Health Records, Electronic Prescribing and Medication Errors: A Systematic Review of Literature, 2000-2014

British Journal of Medicine and Medical Research ◽

10.9734/bjmmr/2015/13490 ◽

2015 ◽

Vol 5 (5) ◽

pp. 672-704 ◽

Cited By ~ 2

Author(s):

Naseem Qureshi ◽

Dalal Al-Dossari ◽

Ibrahim Al-Zaagi ◽

Abdullah Al-Bedah ◽

Abdulrahman Abudalli ◽

...

Keyword(s):

Systematic Review ◽

Electronic Health Records ◽

Medication Errors ◽

Electronic Prescribing ◽

Review Of Literature ◽

Health Records ◽

Electronic Health

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

Journal of Innovation in Health Informatics ◽

10.14236/jhi.v24i2.894 ◽

2017 ◽

Vol 24 (2) ◽

pp. 216 ◽

Cited By ~ 4

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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Using Electronic Health Records to Enhance Predictions of Fall Risk in Inpatient Settings

The Joint Commission Journal on Quality and Patient Safety ◽

10.1016/j.jcjq.2020.01.009 ◽

2020 ◽

Vol 46 (4) ◽

pp. 199-206

Author(s):

Gil Moskowitz ◽

Natalia N. Egorova ◽

Ariela Hazan ◽

Robert Freeman ◽

David L. Reich ◽

...

Keyword(s):

Electronic Health Records ◽

Fall Risk ◽

Health Records ◽

Inpatient Settings ◽

Electronic Health

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A Qualitative Study of Physician Perspectives on Adaptation to Electronic Health Records

10.21203/rs.2.13132/v3 ◽

2019 ◽

Author(s):

Cynthia Judine Sieck ◽

Nicole Pearl ◽

Tiffani J. Bright ◽

Po-Yin Yen

Keyword(s):

Qualitative Study ◽

Electronic Health Records ◽

Medical Center ◽

Academic Medical Center ◽

Opportunity To Learn ◽

Structured Interviews ◽

Health Records ◽

Physician Perspectives ◽

Academic Medical ◽

Electronic Health

Abstract Background Electronic Health Records (EHRs) have the potential to improve many aspects of care and their use has increased in the last decade. Because of this, acceptance and adoption of EHRs is less of a concern than adaptation to use. To understand this issue more deeply, we conducted a qualitative study of physician perspectives on EHR use to identify factors that facilitate adaptation.Methods We conducted semi-structured interviews with 9 physicians across a range of inpatient disciplines at a large Academic Medical Center. Interviews were conducted by phone, lasting approximately 30 minutes, and were transcribed verbatim for analysis. We utilized inductive and deductive methods in our analysis.Results We identified 4 major themes related to EHR adapation: impact of EHR changes on physicians, how physicians managed these changes, factors that facilitated adapation to using the EHR and adapting to using the EHR in the patient encounter. Within these themes, physicians felt that a positive mindset toward change, providing upgrade training that was tailored to their role, and the opportunity to learn from colleagues were important facilitators of adaption.Conclusions As EHR use moves beyond implementation, physicians continue to be required to adapt to the technology and to its frequent changes. Our study provides actionable findings that allow healthcare systems to focus on factors that facilitate the adaptation process for physicians.

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