scholarly journals Development of CancerLinQ, a Health Information Learning Platform From Multiple Electronic Health Record Systems to Support Improved Quality of Care

2020 ◽  
pp. 929-937
Author(s):  
Danielle Potter ◽  
Raven Brothers ◽  
Andrej Kolacevski ◽  
Jacob E. Koskimaki ◽  
Amy McNutt ◽  
...  
Keyword(s):  
United States ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Health System ◽  
Real World ◽  
The United States ◽  
Health Records ◽  
Patients With Cancer ◽  
Learning Health System ◽  
Electronic Health

PURPOSE ASCO, through its wholly owned subsidiary, CancerLinQ LLC, developed CancerLinQ, a learning health system for oncology. A learning health system is important for oncology patients because less than 5% of patients with cancer enroll in clinical trials, leaving evidence gaps for patient populations not enrolled in trials. In addition, clinical trial populations often differ from the overall cancer population with respect to age, race, performance status, and other clinical parameters. MATERIALS AND METHODS Working with subscribing practices, CancerLinQ accepts data from electronic health records and transforms the local representation of a patient’s care into a standardized representation on the basis of the Quality Data Model from the National Quality Forum. CancerLinQ provides this information back to the subscribing practice through a series of tools that support quality improvement. CancerLinQ also creates de-identified data sets for secondary research use. RESULTS As of March 2020, CancerLinQ includes data from 63 organizations across the United States that use nine different electronic health records. The database includes 1,426,015 patients with a primary cancer diagnosis, of which 238,680 have had additional information abstracted from unstructured content. CONCLUSION As CancerLinQ continues to onboard subscribing practices, the breadth of potential applications for a learning health care system widen. Future practice-facing tools could include real-world data visualization, recommendations for treatment of patients with actionable genetic variations, and identification of patients who may be eligible for clinical trials. Feeding these insights back into oncology practice ensures that we learn how to treat patients with cancer not just on the basis of the selective experience of the 5% that enroll in clinical trials, but from the real-world experience of the entire spectrum of patients with cancer in the United States.

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 Adoption of Electronic Health Records and Perceptions of Financial and Clinical Outcomes Among Ophthalmologists in the United States

2018 ◽  
Vol 136 (2) ◽  
pp. 164 ◽  
Author(s):  
Michele C. Lim ◽  
Michael V. Boland ◽  
Colin A. McCannel ◽  
Arvind Saini ◽  
Michael F. Chiang ◽  
...  
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
Clinical Outcomes ◽  
The United States ◽  
Health Records ◽  
Electronic Health

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

scholarly journals Impact of Obesity on Outcomes of Patients With Coronavirus Disease 2019 in the United States: A Multicenter Electronic Health Records Network Study

2020 ◽  
Vol 159 (6) ◽  
pp. 2221-2225.e6 ◽  
Author(s):  
Shailendra Singh ◽  
Mohammad Bilal ◽  
Haig Pakhchanian ◽  
Rahul Raiker ◽  
Gursimran S. Kochhar ◽  
...  
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
The United States ◽  
Health Records ◽  
Electronic Health

scholarly journals Understanding optimisation processes of electronic health records (EHRs) in select leading hospitals: a qualitative study

2018 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Mark Chun Moon ◽  
Rebecca Hills ◽  
George Demiris
Keyword(s):  
United States ◽  
Qualitative Study ◽  
Electronic Health Records ◽  
Hospital Setting ◽  
The United States ◽  
Theory Approach ◽  
Health Records ◽  
High Performing ◽  
Advisory Groups ◽  
Electronic Health

BackgroundLittle is known about optimisation of electronic health records (EHRs) systems in the hospital setting while adoption of EHR systems continues in the United States.ObjectiveTo understand optimisation processes of EHR systems undertaken in leading healthcare organisations in the United States.MethodsInformed by a grounded theory approach, a qualitative study was undertaken that involved 11 in-depth interviews and a focus group with the EHR experts from the high performing healthcare organisations across the United States.ResultsThe study describes EHR optimisation processes characterised by prioritising exponentially increasing requests with predominant focus on improving efficiency of EHR, building optimisation teams or advisory groups and standardisation. The study discusses 16 types of optimisation that interdependently produced 16 results along with identifying 11 barriers and 20 facilitators to optimisation.ConclusionsThe study describes overall experiences of optimising EHRs in select high performing healthcare organisations in the US. The findings highlight the importance of optimising the EHR after, and even before, go-live and dedicating resources exclusively for optimisation.

scholarly journals A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
World Population ◽  
Health Records ◽  
Target Populations ◽  
Electronic Health

SummaryObjective: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.Methods: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generaliz-ability of clinical trials and for defining population-representative clinical trial eligibility criteria.Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105

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