scholarly journals Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0159227 ◽  
Author(s):  
Rose Gabert ◽  
Blake Thomson ◽  
Emmanuela Gakidou ◽  
Gregory Roth
Keyword(s):  
High Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Diabetes Prevention ◽  
Population Based ◽  
Treatment Interventions ◽  
Prevention And Treatment

Perspectives on Electronic Prescribing and Terminologies

2013 ◽  
pp. e370-e373
Author(s):  
David Liebovitz
Keyword(s):  
Patient Safety ◽  
Electronic Medical Records ◽  
Medical Records ◽  
International Classification Of Diseases ◽  
Population Based ◽  
Electronic Prescribing ◽  
Medication History ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Potential Benefits

Electronic medical records provide potential benefits and also drawbacks. Potential benefits include increased patient safety and efficiency. Potential drawbacks include newly introduced errors and diminished workflow efficiency. In the patient safety context, medication errors account for significant patient harm. Electronic prescribing (e-prescribing) offers the promise of automated drug interaction and dosage verification. In addition, the process of enabling e-prescriptions also provides access to an often unrecognized benefit, that of viewing the dispensed medication history. This information is often critical to understanding patient symptoms. Obtaining significant value from electronic medical records requires use of standardized terminology for both targeted decision support and population-based management. Further, generating documentation for a billable encounter requires usage of proper codes. The emergence of International Classification of Diseases (ICD)-10 holds promise in facilitating identification of a more precise patient code while also presenting drawbacks given its complexity. This article will focus on elements of e-prescribing and use of structured chart content, including diagnosis codes as they relate to physician office practices.

scholarly journals Characterizing telemedicine use in clinical immunology and allergy in Canada before the COVID-19 pandemic

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Erika Yue Lee ◽  
Christine Song ◽  
Peter Vadas ◽  
Matthew Morgan ◽  
Stephen Betschel
Keyword(s):  
Rural Communities ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Population Based ◽  
Administrative Database ◽  
Single Centre ◽  
Remote Communities ◽  
Common Diagnosis ◽  
Average Potential

Abstract Rationale There exists a geographic barrier to access CIA care for patients who live in rural communities; telemedicine may bridge this gap in care. Herein we characterized the use of telemedicine in CIA at a population-based level and single centre. Methods Before the COVID-19 pandemic, telemedicine care was provided via the Ontario Telemedicine Network (OTN) in Ontario, Canada. Descriptive data were collected from the OTN administrative database and from electronic medical records at a single academic centre during 2014 to 2019. The potential distance travelled and time saved by telemedicine visits were calculated using postal codes. Results A total of 1298 telemedicine visits was conducted over OTN, with an average of 216 visits per year. Only 11% of the allergists/immunologists used telemedicine to provide care before the COVID-19 pandemic. In the single centre that provided the majority of the telemedicine care, 66% patients were female and the overall mean age was 46. The most common diagnosis was immunodeficiency (40%), followed by asthma (13%) and urticaria (11%). Most patients required at least one follow-up via telemedicine. The average potential two-way distance travelled per visit was 718 km and the average potential time travelled in total was 6.6 h. Conclusion Telemedicine was not widely used by allergists/immunologists in Ontario, Canada before the COVID-19 pandemic. It could offer a unique opportunity to connect patients who live in remote communities and allergists/immunologists who practice in urban centres in Canada. Independent of the current pandemic, our study further highlights the need for more physicians to adopt and continue telemedicine use as well as for healthcare agencies to support its use as a strategic priority once the pandemic is over.

scholarly journals Comparison of Efficacy and Safety of Rosuvastatin, Atorvastatin and Pravastatin among Dyslipidemic Diabetic Patients

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Lolwa Barakat ◽  
Amin Jayyousi ◽  
Abdulbari Bener ◽  
Bilal Zuby ◽  
Mahmoud Zirie
Keyword(s):  
Renal Function ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Population Based ◽  
Diabetic Patients ◽  
Population Based Study ◽  
Number Of Patients ◽  
Second Year ◽  
New Onset

Objectives. To investigate the efficacy and the safety of the three most commonly prescribed statins (rosuvastatin, atorvastatin, and pravastatin) for managing dyslipidemia among diabetic patients in Qatar. Subjects and Methods. This retrospective observational population-based study included 350 consecutive diabetes patients who were diagnosed with dyslipidemia and prescribed any of the indicated statins between September 2005 and September 2009. Data was collected by review of the Pharmacy Database, the Electronic Medical Records Database (EMR viewer), and the Patient's Medical Records. Comparisons of lipid profile measurements at baseline and at first- and second-year intervals were taken. Results. Rosuvastatin (10 mg) was the most effective at reducing LDL-C (29.03%). Atorvastatin reduced LDL-C the most at a dose of 40 mg (22.8%), and pravastatin reduced LDL-C the most at a dose of 20 mg (20.3%). All three statins were safe in relation to muscular and hepatic functions. In relation to renal function, atorvastatin was the safest statin as it resulted in the least number of patients at the end of 2 years of treatment with the new onset of microalbuminuria (10.9%) followed by rosuvastatin (14.3%) and then pravastatin (26.6%). Conclusion. In the Qatari context, the most effective statin at reducing LDL-C was rosuvastatin 10 mg. Atorvastatin was the safest statin in relation to renal function. Future large-scale prospective studies are needed to confirm these results.

scholarly journals Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 528-537 ◽  
Author(s):  
Albee Y Ling ◽  
Allison W Kurian ◽  
Jennifer L Caswell-Jin ◽  
George W Sledge ◽  
Nigam H Shah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Natural Language Processing ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Cancer Diagnosis ◽  
Medical Records ◽  
De Novo ◽  
Metastatic Breast ◽  
Population Based

Abstract Objectives Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Materials and Methods We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients. Results There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870. Discussion and Conclusion To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.

scholarly journals Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

2020 ◽  
Vol 10 (11) ◽  
pp. 784
Author(s):  
Peihao Fan ◽  
Xiaojiang Guo ◽  
Xiguang Qi ◽  
Mallika Matharu ◽  
Ravi Patel ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
High Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Mathematic Model ◽  
Clinical Decision ◽  
Predictive Values ◽  
High Risk Populations ◽  
One Year

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.

scholarly journals Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer

2020 ◽  
Author(s):  
Naveen K. Singh ◽  
Partha Ray ◽  
Aaron F. Carlin ◽  
Celestine Magallanes ◽  
Sydney C. Morgan ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Point Of Care ◽  
Low Cost ◽  
Population Based ◽  
Medical Providers ◽  
Viral Loads ◽  
Percent Agreement ◽  
Antigen Assay ◽  
Antigen Testing

AbstractSignificant barriers to the diagnosis of latent and acute SARS-CoV-2 infection continue to hamper population-based screening efforts required to contain the COVID-19 pandemic in the absence of effective antiviral therapeutics or vaccines. We report an aptamer-based SARS-CoV-2 salivary antigen assay employing only low-cost reagents ($3.20/test) and an off-the-shelf glucometer. The test was engineered around a glucometer as it is quantitative, easy to use, and the most prevalent piece of diagnostic equipment globally making the test highly scalable with an infrastructure that is already in place. Furthermore, many glucometers connect to smartphones providing an opportunity to integrate with contract tracing apps, medical providers, and electronic medical records. In clinical testing, the developed assay detected SARS-CoV-2 infection in patient saliva across a range of viral loads - as benchmarked by RT-qPCR - within one hour, with 100% sensitivity (positive percent agreement) and distinguished infected specimens from off-target antigens in uninfected controls with 100% specificity (negative percent agreement). We propose that this approach can provide an inexpensive, rapid, and accurate diagnostic for distributed screening of SARS-CoV-2 infection at scale.

scholarly journals Validation of fragility fractures in primary care electronic medical records: A population-based study

2019 ◽  
Vol 15 (5) ◽  
pp. e1-e4 ◽  
Author(s):  
Daniel Martinez-Laguna ◽  
Alberto Soria-Castro ◽  
Cristina Carbonell-Abella ◽  
Pilar Orozco-López ◽  
Pilar Estrada-Laza ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Fragility Fractures ◽  
Population Based ◽  
Population Based Study
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