scholarly journals What Results Should Be Returned from Opportunistic Screening in Translational Research?

2020 ◽  
Vol 10 (1) ◽  
pp. 13 ◽  
Author(s):  
Colin M. E. Halverson ◽  
Sarah H. Jones ◽  
Laurie Novak ◽  
Christopher Simpson ◽  
Digna R. Velez Edwards ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Primary Care Physicians ◽  
Ethical Issues ◽  
Opportunistic Screening ◽  
Test Results ◽  
Undue Burden ◽  
Positive Results

Increasingly, patients without clinical indications are undergoing genomic tests. The purpose of this study was to assess their appreciation and comprehension of their test results and their clinicians’ reactions. We conducted 675 surveys with participants from the Vanderbilt Electronic Medical Records and Genomics (eMERGE) cohort. We interviewed 36 participants: 19 had received positive results, and 17 were self-identified racial minorities. Eleven clinicians who had patients who had participated in eMERGE were interviewed. A further 21 of these clinicians completed surveys. Participants spontaneously admitted to understanding little or none of the information returned to them from the eMERGE study. However, they simultaneously said that they generally found testing to be “helpful,” even when it did not inform their health care. Primary care physicians expressed discomfort in being asked to interpret the results for their patients and described it as an undue burden. Providing genetic testing to otherwise healthy patients raises a number of ethical issues that warrant serious consideration. Although our participants were enthusiastic about enrolling and receiving their results, they express a limited understanding of what the results mean for their health care. This fact, coupled the clinicians’ concern, urges greater caution when educating and enrolling participants in clinically non-indicated testing.

Clinical features, treatment, and outcome of dogs with Coccidioides osteomyelitis

2022 ◽  
pp. 1-8
Author(s):  
Stephanie L. Shaver ◽  
Daniel S. Foy ◽  
Todd D. Carter
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Clinical Signs ◽  
Test Results ◽  
Serologic Test ◽  
Rapid Improvement ◽  
Radiographic Appearance ◽  
One Year ◽  
Positive Results

Abstract OBJECTIVE To describe signalment, clinical signs, serologic test results, treatment, and outcome of dogs with Coccidioides osteomyelitis (COM) and to compare those findings with findings for dogs with osteosarcoma (OSA). ANIMALS 14 dogs with COM and 16 dogs with OSA. PROCEDURES Data were retrospectively gathered from electronic medical records. RESULTS Dogs with COM were younger and weighed less than dogs with OSA. Six dogs with COM had appendicular lesions, 5 had axial lesions, and 3 had both appendicular and axial lesions; 9 had monostotic disease, and 5 had polyostotic disease. Axial lesions and nonadjacent polyostotic disease were more common in dogs with COM than in dogs with OSA, but radiographic appearance was not different between the 2 groups. Median IgG titer at diagnosis of COM was 1:48 and was significantly decreased after 6 and 12 months of treatment. Percentage of dogs with COM that had clinical signs was significantly decreased after 1, 3, 6, and 12 months of treatment. One year after initiation of treatment, 9 of 9 dogs were still receiving fluconazole and 8 of 9 dogs had positive results for serum IgG titer testing. CLINICAL RELEVANCE Dogs with COM typically had a rapid improvement in clinical signs after initiating treatment with fluconazole but required long-term antifungal treatment. Dogs with COM differed from dogs with OSA, but radiographic features had a great degree of overlap between groups, confounding the ability to make a diagnosis on the basis of diagnostic imaging alone.

scholarly journals An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation (Preprint)

2018 ◽  
Author(s):  
Cheng-Yi Yang ◽  
Ray-Jade Chen ◽  
Wan-Lin Chou ◽  
Yuarn-Jang Lee ◽  
Yu-Sheng Lo
Keyword(s):  
Health Care ◽  
Infectious Diseases ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Predictive Power ◽  
Influenza Surveillance ◽  
Early Prediction ◽  
Influenza Like Illness ◽  
Reporting Systems ◽  
Positive Results

BACKGROUND Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. OBJECTIVE We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. METHODS First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. RESULTS Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. CONCLUSIONS Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.

scholarly journals Improving Compliance with Screening of Diabetic Patients for Microalbuminuria in Primary Care Practice

2013 ◽  
Vol 2013 ◽  
pp. 1-3 ◽  
Author(s):  
Abeer Anabtawi ◽  
L. Mary Mathew
Keyword(s):  
Primary Care ◽  
Quality Control ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Primary Care Physicians ◽  
Primary Care Practice ◽  
Compliance Rate ◽  
Care Practice ◽  
Diabetic Patients ◽  
Modest Improvement

Studies showed suboptimal compliance rate of primary care physicians with microalbuminuria screening. This study evaluated impact of electronic medical records (EMR) and computerized physicians reminders on compliance rate and showed small to modest improvement. Combining EMR with quality control monitoring has significantly improved compliance [OR 1.556, 95% CI 1.251–1.935, ].

scholarly journals Factors affecting the mature use of electronic medical records by primary care physicians: a systematic review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rana Melissa Rahal ◽  
Jay Mercer ◽  
Craig Kuziemsky ◽  
Sanni Yaya
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Electronic Medical Records ◽  
User Satisfaction ◽  
Medical Records ◽  
Financial Incentives ◽  
Primary Care Physicians ◽  
Transition Planning ◽  
Narrative Synthesis ◽  
Factors Influencing

Abstract Background Despite a substantial increase in the adoption of electronic medical records (EMRs) in primary health care settings, the use of advanced EMR features is limited. Several studies have identified both barriers and facilitating factors that influence primary care physicians’ (PCPs) use of advanced EMR features and the maturation of their EMR use. The purpose of this study is to explore and identify the factors that impact PCPs’ mature use of EMRs. Methods A systematic review was conducted in accordance with the Cochrane Handbook. The MEDLINE, Embase, and PsycINFO electronic databases were searched from 1946 to June 13, 2019. Two independent reviewers screened the studies for eligibility; to be included, studies had to address factors influencing PCPs’ mature use of EMRs. A narrative synthesis was conducted to collate study findings and to report on patterns identified across studies. The quality of the studies was also appraised. Results Of the 1893 studies identified, 14 were included in this study. Reported factors that influenced PCPs’ mature use of EMRs fell into one of the following 5 categories: technology, people, organization, resources, and policy. Concerns about the EMR system’s functionality, lack of physician awareness of EMR functionality, limited physician availability to learn more about EMRs, the habitual use of successfully completing clinical tasks using only basic EMR features, business-oriented organizational objectives, lack of vendor training, limited resource availability, and lack of physician readiness were reported as barriers to PCPs’ mature use of EMRs. The motivation of physicians, user satisfaction, coaching and peer mentoring, EMR experience, gender, physician perception, transition planning for changes in roles and work processes, team-based care, adequate technical support and training, sharing resources, practices affiliated with an integrated delivery system, financial incentives, and policies to increase EMR use all had a favorable impact on PCPs’ use of advanced EMR features. Conclusions By using a narrative synthesis to synthesize the evidence, we identified interrelated factors influencing the mature use of EMRs by PCPs. The findings underline the need to provide adequate training and policies that facilitate the mature use of EMRs by PCPs. Trial registration: PROSPERO CRD42019137526.

scholarly journals Clinical Features Observed in General Practice Associated With the Subsequent Diagnosis of Progressive Supranuclear Palsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Mary J. Kwasny ◽  
Denise M. Oleske ◽  
Jorge Zamudio ◽  
Robert Diegidio ◽  
Günter U. Höglinger
Keyword(s):  
Primary Care ◽  
General Practice ◽  
Electronic Medical Records ◽  
Clinical Features ◽  
Random Forests ◽  
Medical Records ◽  
Primary Care Physicians ◽  
Case Control Study ◽  
Case Control ◽  
Control Study

Background: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder that is difficult for primary care physicians to recognize due to its progressive nature and similarities to other neurologic disorders. This case-control study aimed to identify clinical features observed in general practice associated with a subsequent diagnosis of PSP.Methods: We analyzed a de-identified dataset of 152 PSP cases and 3,122 matched controls from electronic medical records of general practices in Germany. We used a random forests algorithm based on machine learning techniques to identify clinical features (medical conditions and treatments received) associated with pre-diagnostic PSP without using an a priori hypothesis. We then assessed the relative effects of the features with the highest importance scores and generated multivariate models using clustered logistic regression analyses to identify a subset of clinical features associated with subsequent PSP diagnosis.Results: Using the random forests approach, we identified 21 clinical features associated with pre-diagnostic PSP (odds ratio ≥2.0 in univariate analyses). From these, we constructed a multivariate model comprising 9 clinical features with ~90% likelihood of identifying a subsequent PSP diagnosis. These features included known PSP symptoms, common misdiagnoses, and 2 novel associations, diabetes mellitus and cerebrovascular disease, which are possible modifiable risk factors for PSP.Conclusion: In this case-control study using data from electronic medical records, we identified 9 clinical features, including 2 previously unknown factors, associated with the pre-diagnostic stage of PSP. These may be used to facilitate recognition of PSP and reduce time to referral by primary care physicians.

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