Confirmation of Dermatophytes in Nail Specimens Using In-Office Dermatophyte Test Medium Cultures

2003 ◽  
Vol 93 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Maureen B. Jennings ◽  
Michael G. Rinaldi
Keyword(s):  
Primary Care ◽  
Convenient Method ◽  
Primary Care Physicians ◽  
Laboratory Tests ◽  
Test Results ◽  
Central Laboratory ◽  
Test Medium ◽  
Laboratory Cultures ◽  
Special Expertise ◽  
Using Data

Using data from a multicenter nationwide multispecialty survey, the authors investigated the efficacy of in-office dermatophyte test medium (DTM) and central laboratory cultures used to confirm onychomycosis across samples collected by podiatric, dermatologic, and primary-care physicians. The samples collected by podiatric physicians were both positive or both negative in 43% and 27% of patients, respectively. Samples harvested by dermatologists were both positive in 37% of patients and both negative in 32%, while the samples collected by primary-care physicians were both positive in 28% of patients and both negative in 38%. The accuracy of DTM and central laboratory tests is dependent on the proper collection of nail samples, and the accuracy of mycologic test results varied significantly across nail specimens harvested by podiatric, dermatologic, and primary-care physicians. DTM culture was found to be an effective and convenient method of confirming dermatophyte infections in patients with signs of onychomycosis. The data presented here indicate that the special expertise of podiatric physicians in treating foot-related illnesses translates into more accurate mycologic testing. (J Am Podiatr Med Assoc 93(3): 195-202, 2003)

scholarly journals Optimising Seniors’ Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug–Drug and Drug–Drug–Gene Interactions

2020 ◽  
Vol 10 (3) ◽  
pp. 84
Author(s):  
Roger E. Thomas
Keyword(s):  
Primary Care ◽  
Primary Care Physicians ◽  
Adverse Drug Events ◽  
Meta Analysis ◽  
Gene Interactions ◽  
Rural And Remote ◽  
Remote Areas ◽  
Rural And Remote Areas ◽  
Enormous Variation ◽  
Using Data

Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.

scholarly journals Patient-Care Questions that Physicians Are Unable to Answer

2007 ◽  
Vol 14 (4) ◽  
pp. 407-414 ◽  
Author(s):  
John W. Ely ◽  
Jerome A. Osheroff ◽  
Saverio M. Maviglia ◽  
Marcy E. Rosenbaum
Keyword(s):  
Primary Care ◽  
Patient Care ◽  
Primary Care Physicians ◽  
Clinical Information ◽  
Clinical Findings ◽  
Information Resources ◽  
Clinical Information Systems ◽  
Unanswered Question ◽  
Test Results ◽  
Clinical Questions

Abstract Objective: To describe the characteristics of unanswered clinical questions and propose interventions that could improve the chance of finding answers. Design: In a previous study, investigators observed primary care physicians in their offices and recorded questions that arose during patient care. Questions that were pursued by the physician, but remained unanswered, were grouped into generic types. In the present study, investigators attempted to answer these questions and developed recommendations aimed at improving the success rate of finding answers. Measurements: Frequency of unanswered question types and recommendations to increase the chance of finding answers. Results: In an earlier study, 48 physicians asked 1062 questions during 192 half-day office observations. Physicians could not find answers to 237 (41%) of the 585 questions they pursued. The present study grouped the unanswered questions into 19 generic types. Three types accounted for 128 (54%) of the unanswered questions: (1) “Undiagnosed finding” questions asked about the management of abnormal clinical findings, such as symptoms, signs, and test results (What is the approach to finding X?); (2) “Conditional” questions contained qualifying conditions that were appended to otherwise simple questions (What is the management of X, given Y? where “given Y” is the qualifying condition that makes the question difficult.); and (3) “Compound” questions asked about the association between two highly specific elements (Can X cause Y?). The study identified strategies to improve clinical information retrieval, listed below. Conclusion: To improve the chance of finding answers, physicians should change their search strategies by rephrasing their questions and searching more clinically oriented resources. Authors of clinical information resources should anticipate questions that may arise in practice, and clinical information systems should provide clearer and more explicit answers.

scholarly journals Human- Versus Machine Learning–Based Triage Using Digitalized Patient Histories in Primary Care: Comparative Study (Preprint)

2020 ◽  
Author(s):  
Artin Entezarjou ◽  
Anna-Karin Edstedt Bonamy ◽  
Simon Benjaminsson ◽  
Pawel Herman ◽  
Patrik Midlöv
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Physical Examination ◽  
Medical History ◽  
Primary Care Physicians ◽  
Interrater Reliability ◽  
Reference Group ◽  
Majority Vote ◽  
Medical Histories ◽  
Using Data

BACKGROUND Smartphones have made it possible for patients to digitally report symptoms before physical primary care visits. Using machine learning (ML), these data offer an opportunity to support decisions about the appropriate level of care (triage). OBJECTIVE The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method. METHODS After testing several models, a naïve Bayes triage model was created using data from digital medical histories, capable of classifying digital medical history reports as either in need of urgent physical examination or not in need of urgent physical examination. The model was tested on 300 digital medical history reports and classification was compared with the majority vote of an expert panel of 5 primary care physicians (PCPs). Reliability between raters was measured using both Cohen κ (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement). RESULTS Interrater reliability as measured by Cohen κ was 0.17 when comparing the majority vote of the reference group with the model. Agreement was 74% (138/186) for cases judged not in need of urgent physical examination and 42% (38/90) for cases judged to be in need of urgent physical examination. No specific features linked to the model’s triage decision could be identified. Between physicians within the panel, Cohen κ was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen κ of 0.55. CONCLUSIONS Low interrater and intrarater agreement in triage decisions among PCPs limits the possibility to use human decisions as a reference for ML to automate triage in primary care.

scholarly journals Integrating population-wide laboratory testing data with physician audit-and-feedback reports to improve glycemic and cholesterol control among Ontarians with diabetes

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Michael Campitelli ◽  
Michael Paterson ◽  
Mahmoud Azimaee ◽  
Anna Greenberg ◽  
P. Alison Paprica ◽  
...  
Keyword(s):  
Primary Care ◽  
Laboratory Test ◽  
Primary Care Physicians ◽  
Audit And Feedback ◽  
Fiscal Year ◽  
Test Results ◽  
Laboratory Test Results ◽  
Patients With Diabetes ◽  
Cholesterol Control ◽  
Diabetes Patients

IntroductionImproving the care and management of patients with diabetes, particularly those with extreme blood glucose and/or cholesterol levels, has been identified as a key priority area for healthcare in Ontario. A multi-organizational collaboration produces audit-and-feedback reports distributed to consenting primary care physicians across the province for quality improvement purposes. Objectives and ApproachWe examined the feasibility of linking the Ontario Laboratory Information System (OLIS), a large and nearly population-wide database of laboratory test results in Ontario, with the existing provincial audit-and-feedback reporting structure to integrate aggregated, physician-level measures of glycemic and cholesterol control among patients with diabetes. All Ontario residents alive on March 31, 2014, attached to a primary care physician, and diagnosed with diabetes for at least two years were included. These patients were linked to OLIS to extract laboratory test orders and results for glycated hemoglobin (HbA1C) and low-density lipoproteins (LDL) between April 1, 2013 and March 31, 2014. ResultsThere were 1,108,530 diabetes patients included who were assigned to 10,085 primary care physicians. During fiscal year (FY) 2013, 70%, 64%, and 59% of diabetes patients were tested for HbA1C, LDL, and both measures, respectively. Among the 648,238 diabetes patients with at least one of each test in FY2013, 13% had a HbA1C test exceeding a threshold of 9%, 4% had a LDL test exceeding a threshold of 4 mmol/L, and 0.8% exceeded both thresholds. At the physician-level, the median (Interquartile Range) proportions of diabetes patients exceeding the testing thresholds were 12% (9%-16%) for HbA1c and 4% (2%-6%) for LDL. In a multilevel logistic regression model, there was significant between-physician variability in the proportions of diabetes patients exceeding the HbA1C (p Conclusion/ImplicationsWe developed a mechanism for integrating population-wide, clinical laboratory test results into physician audit-and-feedback reports to improve diabetes care in Ontario. Significant variation observed in the aggregated, physician-level proportions of diabetes patients testing above clinical thresholds for HbA1C and LDL highlights the importance of reporting such information to physicians.

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.

scholarly journals Regulatory focus affects physician risk tolerance

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Peter J. Veazie ◽  
Scott McIntosh ◽  
Benjamin P. Chapman ◽  
James G. Dolan
Keyword(s):  
Primary Care ◽  
Decision Making ◽  
Regulatory Focus ◽  
Primary Care Physicians ◽  
Randomization Test ◽  
Practice Variation ◽  
Risk Tolerance ◽  
Medical Decision ◽  
Test Results ◽  
Physician Decision Making

Risk tolerance is a source of variation in physician decision-making. This variation, if independent of clinical concerns, can result in mistaken utilization of health services. To address such problems, it will be helpful to identify nonclinical factors of risk tolerance, particularly those amendable to intervention – regulatory focus theory suggests such a factor. This study tested whether regulatory focus affects risk tolerance among primary care physicians. Twenty-seven primary care physicians were assigned to promotion-focused or prevention-focused manipulations and compared on the Risk Taking Attitudes in Medical Decision Making scale using a randomization test. Results provide evidence that physicians assigned to the promotion-focus manipulation adopted an attitude of greater risk tolerance than the physicians assigned to the prevention-focused manipulation (P=0.01). The Cohen’s d statistic was conventionally large at 0.92. Results imply that situational regulatory focus in primary care physicians affects risk tolerance and may thereby be a nonclinical source of practice variation. Results also provide marginal evidence that chronic regulatory focus is associated with risk tolerance (P=0.05), but the mechanism remains unclear. Research and intervention targeting physician risk tolerance may benefit by considering situational regulatory focus as an explanatory factor.

scholarly journals Sources of Information on Medicinal Products Among Physicians – A Survey Conducted Among Primary Care Physicians in Poland

2022 ◽  
Vol 12 ◽  
Author(s):  
Magdalena Zielińska ◽  
Tomasz Hermanowski
Keyword(s):  
Primary Care ◽  
Clinical Guidelines ◽  
Primary Care Physicians ◽  
Demographic Data ◽  
Sources Of Information ◽  
Medicinal Products ◽  
Using Data ◽  
Credible Information ◽  
Source Of Information ◽  
Credible Source

Introduction: Primary care physicians need to have access to up-to-date knowledge in various fields of medicine and high-quality information sources, but little is known about the use and credibility of sources of information on medicinal products among Polish doctors. The main goal of this study was to analyze the sources of information on medicinal products among primary care physicians in Poland.Methods: A survey was conducted among 316 primary care physicians in Poland. The following information was collected: demographic data of participants, type and frequency of using data sources on medicinal products, barriers to access credible information, assessment of the credibility of the sources used, impact of a given source and other factors on prescription decisions.Results: The most frequently mentioned sources of information were medical representatives (79%), medical journals (78%) and congresses, conventions, conferences, and training (76%). The greatest difficulty in finding the latest information about medicinal products was the lack of time. The surveyed doctors considered clinical guidelines to be the most credible source of information, and this source also had the greatest impact on the choice of prescribed medicinal products.Conclusion: The study showed that clinicians consider clinical guidelines as the most credible source of information with the greatest impact on prescribing medicinal products. However, it is not the source most often mentioned by doctors for obtaining knowledge about medicinal products. There is a need to develop strategies and tools to provide physicians with credible sources of information.

scholarly journals Primary Care Physicians' Challenges in Ordering Clinical Laboratory Tests and Interpreting Results

2014 ◽  
Vol 27 (2) ◽  
pp. 268-274 ◽  
Author(s):  
J. Hickner ◽  
P. J. Thompson ◽  
T. Wilkinson ◽  
P. Epner ◽  
M. Shaheen ◽  
...  
Keyword(s):  
Primary Care ◽  
Primary Care Physicians ◽  
Laboratory Tests ◽  
Clinical Laboratory

scholarly journals Human- Versus Machine Learning–Based Triage Using Digitalized Patient Histories in Primary Care: Comparative Study

10.2196/18930 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18930
Author(s):  
Artin Entezarjou ◽  
Anna-Karin Edstedt Bonamy ◽  
Simon Benjaminsson ◽  
Pawel Herman ◽  
Patrik Midlöv
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Physical Examination ◽  
Medical History ◽  
Primary Care Physicians ◽  
Interrater Reliability ◽  
Reference Group ◽  
Majority Vote ◽  
Medical Histories ◽  
Using Data

Background Smartphones have made it possible for patients to digitally report symptoms before physical primary care visits. Using machine learning (ML), these data offer an opportunity to support decisions about the appropriate level of care (triage). Objective The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method. Methods After testing several models, a naïve Bayes triage model was created using data from digital medical histories, capable of classifying digital medical history reports as either in need of urgent physical examination or not in need of urgent physical examination. The model was tested on 300 digital medical history reports and classification was compared with the majority vote of an expert panel of 5 primary care physicians (PCPs). Reliability between raters was measured using both Cohen κ (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement). Results Interrater reliability as measured by Cohen κ was 0.17 when comparing the majority vote of the reference group with the model. Agreement was 74% (138/186) for cases judged not in need of urgent physical examination and 42% (38/90) for cases judged to be in need of urgent physical examination. No specific features linked to the model’s triage decision could be identified. Between physicians within the panel, Cohen κ was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen κ of 0.55. Conclusions Low interrater and intrarater agreement in triage decisions among PCPs limits the possibility to use human decisions as a reference for ML to automate triage in primary care.

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