Accuracy and self correction of information received from an internet breast cancer list: content analysis.

OBJECTIVES: To determine the prevalence of false or misleading statements in messages posted by internet cancer support groups and whether these statements were identified as false or misleading and corrected by other participants in subsequent postings. DESIGN: Analysis of content of postings. SETTING: Internet cancer support group Breast Cancer Mailing List. MAIN OUTCOME MEASURES: Number of false or misleading statements posted from 1 January to 23 April 2005 and whether these were identified and corrected by participants in subsequent postings. RESULTS: 10 of 4600 postings (0.22%) were found to be false or misleading. Of these, seven were identified as false or misleading by other participants and corrected within an average of four hours and 33 minutes (maximum, nine hours and nine minutes). CONCLUSIONS: Most posted information on breast cancer was accurate. Most false or misleading statements were rapidly corrected by participants in subsequent postings

Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 respiratory diseases, and 17 genitourinary diseases. We find multi-task pre-training increases learning efficiency and achieves consistently high performance across the majority of phenotypes. Most important, the multi-task pre-training is almost always either the best-performing model or performs tolerably close to the best-performing model, a property we refer to as robust. All these results lead us to conclude that this multi-task transfer learning architecture is a robust approach for developing generalized and transferable patient language representations for numerous phenotypes.Comment: Journal of Biomedical Informatics (in press

Comparing clinician knowledge and online information regarding Alli (Orlistat)

BACKGROUND: Many consumers join online communities focused on health. Online forums are a popular medium for the exchange of health information between consumers, so it is important to determine the accuracy and completeness of information posted to online forums. OBJECTIVE: We compared the accuracy and completeness of information regarding the FDA-approved over-the counter weight-loss drug Alli (Orlistat) from forums and from clinicians. METHODS: We identified Alli-related questions posted on online forums and then posed the questions to 11 primary care providers. We then compared the clinicians\u27 answers to the answers given on the forums. A panel of blinded experts evaluated the accuracy and completeness of the answers on a scale of 0-4. Another panel of blinded experts categorized questions as being best answered based on clinical experience versus review of the literature. RESULTS: The accuracy and completeness of clinician responses was slightly better than forum responses, but there was no significant difference (2.3 vs. 2.1, p=0.5). Only one forum answer contained information that could potentially cause harm if the advice was followed. CONCLUSIONS: Forum answers were comparable to clinicians\u27 answers with respect to accuracy and completeness, but answers from both sources were unsatisfactory

Confidence-based laboratory test reduction recommendation algorithm

BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs. METHODS: We collected internal patient data from a teaching hospital in Houston and external patient data from the MIMIC III database. The study used a conservative definition of unnecessary laboratory tests, which was defined as stable (i.e., stability) and below the lower normal bound (i.e., normality). Considering that machine learning models may yield less reliable results when trained on noisy inputs containing low-quality information, we estimated prediction confidence to assess the reliability of predicted outcomes. We adopted a select and predict design philosophy to maximize prediction performance by selectively considering samples with high prediction confidence for recommendations. Our model accommodated irregularly sampled observational data to make full use of variable correlations (i.e., with other laboratory test values) and temporal dependencies (i.e., previous laboratory tests performed within the same encounter) in selecting candidates for training and prediction. RESULTS: The proposed model demonstrated remarkable Hgb prediction performance, achieving a normality AUC of 95.89% and a Hgb stability AUC of 95.94%, while recommending a reduction of 9.91% of Hgb tests that were deemed unnecessary. Additionally, the model could generalize well to external patients admitted to another hospital. CONCLUSIONS: This study introduces a novel deep learning model with the potential to significantly reduce healthcare costs and improve patient outcomes by identifying unnecessary laboratory tests for hospitalized patients

Predicting multiple sclerosis disease severity with multimodal deep neural networks

Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale (EDSS), composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) creates opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to the data insufficiency or model simplicity. In this paper, we proposed an idea of using patients' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity at the hospital visit. This work has two important contributions. First, we describe a pilot effort to leverage structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS disease severity. The proposed pipeline demonstrates up to 25% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes

Quality of weight loss advice on internet forums.

BACKGROUND: Adults use the Internet for weight loss information, sometimes by participating in discussion forums. Our purpose was to analyze the quality of advice exchanged on these forums. METHODS: This was a retrospective analysis of messages posted to 18 Internet weight loss forums during 1 month in 2006. Advice was evaluated for congruence with clinical guidelines; potential for causing harm; and subsequent correction when it was contradictory to guidelines (erroneous) or potentially harmful. Message- and forum-specific characteristics were evaluated as predictors of advice quality and self-correction. RESULTS: Of 3368 initial messages, 266 (7.9%) were requests for advice. Of 654 provisions of advice, 56 (8.6%) were erroneous and 19 of these 56 (34%) were subsequently corrected. Forty-three (6.6%) provisions of advice were harmful, and 12 of these 43 (28%) were subsequently corrected. Messages from low-activity forums (fewer messages) were more likely than those from high-activity forums to be erroneous (10.6% vs 2.4%, P \u3c .001) or harmful (8.4% vs 1.2%, P \u3c .001). In high-activity forums, 2 of 4 (50%) erroneous provisions of advice and 2 of 2 (100%) potentially harmful provisions of advice were corrected by subsequent postings. Compared with general weight loss advice, medication-related advice was more likely to be erroneous (P = .02) or harmful (P = .01). CONCLUSIONS: Most advice posted on highly active Internet weight loss forums is not erroneous or harmful. However, clinical and research strategies are needed to address the quality of medication-related advice

Screening for obstructive sleep apnea on the internet: randomized trial.

BACKGROUND: Obstructive sleep apnea is underdiagnosed. We conducted a pilot randomized controlled trial of an online intervention to promote obstructive sleep apnea screening among members of an Internet weight-loss community. METHODS: Members of an Internet weight-loss community who have never been diagnosed with obstructive sleep apnea or discussed the condition with their healthcare provider were randomized to intervention (online risk assessment+feedback) or control. The primary outcome was discussing obstructive sleep apnea with a healthcare provider at 12 weeks. RESULTS: Of 4700 members who were sent e-mail study announcements, 168 (97% were female, age 39.5 years [standard deviation 11.7], body mass index 30.3 [standard deviation 7.8]) were randomized to intervention (n=84) or control (n=84). Of 82 intervention subjects who completed the risk assessment, 50 (61%) were low risk and 32 (39%) were high risk for obstructive sleep apnea. Intervention subjects were more likely than control subjects to discuss obstructive sleep apnea with their healthcare provider within 12 weeks (11% [9/84] vs 2% [2/84]; P=.02; relative risk=4.50; 95% confidence interval, 1.002-20.21). The number needed to treat was 12. High-risk intervention subjects were more likely than control subjects to discuss obstructive sleep apnea with their healthcare provider (19% [6/32] vs 2% [2/84]; P=.004; relative risk=7.88; 95% confidence interval, 1.68-37.02). One high-risk intervention subject started treatment for obstructive sleep apnea. CONCLUSION: An online screening intervention is feasible and likely effective in encouraging members of an Internet weight-loss community to discuss obstructive sleep apnea with their healthcare provider

DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data

The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge\u27s main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability

Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database.

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% were female. A total of 83% smoked within the prior year, 61% were overweight/obese, 83% had immunocompromising conditions, and 96% had ≥2 comorbidities. The median interval between the two positive tests was 77 days. Among the 19 patients with 60 to 89 days between positive tests, 17 (89%) exhibited symptoms or clinical manifestations consistent with COVID-19 at the time of the second positive test and 14 (74%) were hospitalized at the second positive test. Of the four patients with ≥90 days between two positive tests (patient 2 [PT2], PT8, PT14, and PT19), two had mild or no symptoms at the second positive test and one, an immunocompromised patient, had a brief hospitalization at the first diagnosis, followed by intensive care unit (ICU) admission at the second diagnosis 3 months later. Our study demonstrated a high prevalence of compromised immune systems, comorbidities, obesity, and smoking among patients with repeatedly positive SARS-CoV-2 tests. Despite limitations, including a lack of semiquantitative estimates of viral load, these data may help prioritize suspected cases of reinfection for investigation and continued surveillance

Patient and Provider Perspectives on Medication Non-adherence Among Patients with Depression and/or Diabetes in Diverse Community Settings - A Qualitative Analysis.

BACKGROUND: Diabetes and depression affect a significant percentage of the world\u27s total population, and the management of these conditions is critical for reducing the global burden of disease. Medication adherence is crucial for improving diabetes and depression outcomes, and research is needed to elucidate barriers to medication adherence, including the intentionality of non-adherence, to intervene effectively. The purpose of this study was to explore the perspectives of patients and health care providers on intentional and unintentional medication adherence among patients with depression and diabetes through a series of focus groups conducted across clinical settings in a large urban area. METHODS: This qualitative study utilized a grounded theory approach to thematically analyze qualitative data using the framework method. Four focus groups in total were conducted, two with patients and two with providers, over a one-year period using a semi-structured facilitation instrument containing open-ended questions about experiences, perceptions and beliefs about medication adherence. RESULTS: Across the focus groups, communication difficulties between patients and providers resulting in medication non-adherence was a primary theme that emerged. Concerns about medication side effects and beliefs about medication effectiveness were identified as perceptual barriers related to intentional medication non-adherence. Practical barriers to medication adherence, including medication costs, forgetting to take medications and polypharmacy, emerged as themes related to unintentional medication non-adherence. CONCLUSION: The study findings contribute to a growing body of research suggesting health system changes are needed to improve provider education and implement multicomponent interventions to improve medication adherence among patients with depression and/or diabetes, both chronic illnesses accounting for significant disease burden globally