Publications by authors named "Richard J Medford"

10 Publications

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Managing Pandemics with Health Informatics: Successes and Challenges.

Yearb Med Inform 2021 Apr 21. Epub 2021 Apr 21.

Clinical Informatics Center, UT Southwestern, Medical Center, Dallas, TX, USA.

Introduction: The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic.

Methods: The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals.

Results: An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic.

Conclusion: COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.
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http://dx.doi.org/10.1055/s-0041-1726478DOI Listing
April 2021

Early Crowdfunding Response to the COVID-19 Pandemic: Cross-sectional Study.

J Med Internet Res 2021 02 9;23(2):e25429. Epub 2021 Feb 9.

Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States.

Background: As the number of COVID-19 cases increased precipitously in the United States, policy makers and health officials marshalled their pandemic responses. As the economic impacts multiplied, anecdotal reports noted the increased use of web-based crowdfunding to defray these costs.

Objective: We examined the web-based crowdfunding response in the early stage of the COVID-19 pandemic in the United States to understand the incidence of initiation of COVID-19-related campaigns and compare them to non-COVID-19-related campaigns.

Methods: On May 16, 2020, we extracted all available data available on US campaigns that contained narratives and were created between January 1 and May 10, 2020, on GoFundMe. We identified the subset of COVID-19-related campaigns using keywords relevant to the COVID-19 pandemic. We explored the incidence of COVID-19-related campaigns by geography, by category, and over time, and we compared the characteristics of the campaigns to those of non-COVID-19-related campaigns after March 11, when the pandemic was declared. We then used a natural language processing algorithm to cluster campaigns by narrative content using overlapping keywords.

Results: We found that there was a substantial increase in overall GoFundMe web-based crowdfunding campaigns in March, largely attributable to COVID-19-related campaigns. However, as the COVID-19 pandemic persisted and progressed, the number of campaigns per COVID-19 case declined more than tenfold across all states. The states with the earliest disease burden had the fewest campaigns per case, indicating a lack of a case-dependent response. COVID-19-related campaigns raised more money, had a longer narrative description, and were more likely to be shared on Facebook than other campaigns in the study period.

Conclusions: Web-based crowdfunding appears to be a stopgap for only a minority of campaigners. The novelty of an emergency likely impacts both campaign initiation and crowdfunding success, as it reflects the affective response of a community. Crowdfunding activity likely serves as an early signal for emerging needs and societal sentiment for communities in acute distress that could be used by governments and aid organizations to guide disaster relief and policy.
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http://dx.doi.org/10.2196/25429DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879716PMC
February 2021

Derivation With Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.

Acad Emerg Med 2021 02 22;28(2):206-214. Epub 2020 Dec 22.

From the Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Objectives: The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19.

Methods: All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease-specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient-boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold.

Results: A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15-0.3) for the gradient-boosted method.

Conclusion: The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X-ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.
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http://dx.doi.org/10.1111/acem.14182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753649PMC
February 2021

An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak.

Open Forum Infect Dis 2020 Jul 30;7(7):ofaa258. Epub 2020 Jun 30.

University of Texas Southwestern Medical Center, Clinical Informatics Center, Dallas, Texas, USA.

Background: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.

Methods: We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.

Results: We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.

Conclusions: Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.
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http://dx.doi.org/10.1093/ofid/ofaa258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337776PMC
July 2020

A Comparison of Online Medical Crowdfunding in Canada, the UK, and the US.

JAMA Netw Open 2020 10 1;3(10):e2021684. Epub 2020 Oct 1.

Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas.

Importance: Despite major differences in their health care systems, medical crowdfunding is increasingly used to finance personal health care costs in Canada, the UK, and the US. However, little is known about the campaigns designed to raise monetary donations for medical expenses, the individuals who turn to crowdfunding, and their fundraising intent.

Objective: To examine the demographic characteristics of medical crowdfunding beneficiaries, campaign characteristics, and their association with funding success in Canada, the UK, and the US.

Design, Setting, And Participants: This cross-sectional study extracted and manually reviewed data from GoFundMe campaigns discoverable between February 2018 and March 2019. All available campaigns on each country domain's GoFundMe medical discovery webpage that benefitted a unique patient(s) were included from Canada, the UK, and the US. Data analysis was performed from March to December 2019.

Exposures: Campaign and beneficiary characteristics.

Main Outcomes And Measures: Log-transformed amount raised in US dollars.

Results: This study examined 3396 campaigns including 1091 in Canada, 1082 in the UK, and 1223 in the US. Campaigns in the US (median [IQR], $38 204 [$31 200 to $52 123]) raised more funds than campaigns in Canada ($12 662 [$9377 to $19 251]) and the UK ($6285 [$4028 to $12 348]). In the overall cohort per campaign, Black individuals raised 11.5% less (95% CI, -19.0% to -3.2%; P = .006) than non-Black individuals, and male individuals raised 5.9% more (95% CI, 2.2% to 9.7%; P = .002) than female individuals. Female (39.4% of campaigns vs 50.8% of US population; difference, 11.3%; 95% CI, 8.6% to 14.1%; P < .001) and Black (5.3% of campaigns vs 13.4% of US population; difference, 8.1%; 95% CI, 6.8% to 9.3%; P < .001) beneficiaries were underrepresented among US campaigns. Campaigns primarily for routine treatment expenses were approximately 3 times more common in the US (77.9% [272 of 349 campaigns]) than in Canada (21.9% [55 of 251 campaigns]; difference, 56.0%; 95% CI, 49.3-62.7%; P < .001) or the UK (26.6% [127 of 478 campaigns]; difference, 51.4%; 95% CI, 45.5%-57.3%; P < .001). However, campaigns for routine care were less successful overall. Approved, inaccessible care and experimental care raised 35.7% (95% CI, 25.6% to 46.7%; P < .001) and 20.9% (95% CI, 13.3% to 29.1%; P < .001), respectively, more per campaign than routine care. Campaigns primarily for alternative treatment expenses (16.1% [174 of 1079 campaigns]) were nearly 4-fold more common for cancer (23.5% [144 of 614 campaigns]) vs noncancer (6.5% [30 of 465 campaigns]) diagnoses.

Conclusions And Relevance: Important differences were observed in the reasons individuals turn to medical crowdfunding in the 3 countries examined that suggest racial and gender disparities in fundraising success. More work is needed to understand the underpinnings of these findings and their implications on health care provision in the countries examined.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.21684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588935PMC
October 2020

Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter.

Infect Control Hosp Epidemiol 2021 Feb 6;42(2):131-138. Epub 2020 Aug 6.

Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.

Objective: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter.

Design: Retrospective cross-sectional study.

Methods: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments.

Results: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0-0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise).

Conclusions: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.
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http://dx.doi.org/10.1017/ice.2020.406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450231PMC
February 2021

Non-typeable and purpura fulminans.

BMJ Case Rep 2020 Jul 8;13(7). Epub 2020 Jul 8.

Department of Infectious Disease and Geographic Medicine, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, USA.

typically causes illness and infection in the paediatric population. We report a case of a 53-year-old man who developed invasive non-typeable infection associated with purpura fulminans and multiorgan failure. On review of the literature, this is the first reported case of non-typeable causing purpura fulminans. The patient was treated with intravenous ceftriaxone 2 g/day and was eventually discharged from the hospital almost 2 months after admission. We discuss the role that infection/sepsis plays in disturbances to the coagulation cascade leading to purpura fulminans and the virulence factors that make non-typeable unique. Finally, we review other cases of associated with purpura fulminans and discuss the similarities with our case.
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http://dx.doi.org/10.1136/bcr-2020-234880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348642PMC
July 2020

Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:108-115. Epub 2020 May 30.

Stanford University, Stanford, California.

Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision. Further, we build models that predict the likelihood of bacterial susceptibility to sets of antibiotics. These models contain decision thresholds that separate subgroups of patients whose susceptibility rates to narrow-spectrum antibiotics equal overall susceptibility rates to broader-spectrum drugs. Retroactively analyzing these thresholds on our one year test set, we find that 14% of patients infected with Escherichia coli and empirically treated with piperacillin/tazobactam could have been treated with ceftriaxone with coverage equal to the overall susceptibility rate ofpiperacillin/tazobactam. Similarly, 13% of the same cohort could have been treated with cefazolin - a first generation cephalosporin.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233062PMC
May 2020

Anal cancer and intraepithelial neoplasia: epidemiology, screening and prevention of a sexually transmitted disease.

CMAJ 2015 Feb 15;187(2):111-115. Epub 2014 Dec 15.

Division of Infectious Diseases (Medford), University of Ottawa, Ottawa, Ont.; Division of Infectious Diseases (Salit), University Health Network, University of Toronto, Toronto, Ont.

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http://dx.doi.org/10.1503/cmaj.140476DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312150PMC
February 2015