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Background and Objectives: Genomic information is increasingly relevant for disease prevention and risk management at the individual and population levels. Screening healthy adults for Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia using a population-based approach can help identify the 1–2% of the US population at increased risk of developing diseases associated with these conditions and tailor prevention strategies. Our objective is to report findings from an implementation science study that evaluates multi-level facilitators and barriers to implementation of the In Our DNA SC population-wide genomic screening initiative. Methods: We established an IMPACTeam (IMPlementAtion sCience for In Our DNA SC Team) to evaluate the pilot phase using principles of implementation science. We used a parallel convergent mixed methods approach to assess the Reach, Implementation, and Effectiveness outcomes from the RE-AIM implementation science framework during the pilot phase of In Our DNA SC. Quantitative assessment included the examination of frequencies and response rates across demographic categories using chi-square tests. Qualitative data were audio-recorded and transcribed, with codes developed by the study team based on the semi-structured interview guide. Results: The pilot phase (8 November 2021, to 7 March 2022) included recruitment from ten clinics throughout South Carolina. Reach indicators included enrollment rate and representativeness. A total of 23,269 potential participants were contacted via Epic's MyChart patient portal with 1976 (8.49%) enrolled. Black individuals were the least likely to view the program invitation (28.9%) and take study-related action. As a result, there were significantly higher enrollment rates among White (10.5%) participants than Asian (8.71%) and Black (3.46%) individuals (p < 0.0001). Common concerns limiting reach and participation included privacy and security of results and the impact participation would have on health or life insurance. Facilitators included family or personal history of a Tier 1 condition, prior involvement in genetic testing, self-interest, and altruism. Assessment of implementation (i.e., adherence to protocols/fidelity to protocols) included sample collection rate (n = 1104, 55.9%) and proportion of samples needing recollection (n = 19, 1.7%). There were no significant differences in sample collection based on demographic characteristics. Implementation facilitators included efficient collection processes and enthusiastic clinical staff. Finally, we assessed the effectiveness of the program, finding low dropout rates (n = 7, 0.35%), the identification of eight individuals with Tier 1 conditions (0.72% positive), and high rates of follow-up genetic counseling (87.5% completion). Conclusion: Overall, Asian and Black individuals were less engaged, with few taking any study-related actions. Strategies to identify barriers and promoters for the engagement of diverse populations are needed to support participation. Once enrolled, individuals had high rates of completing the study and follow-up engagement with genetic counselors. Findings from the pilot phase of In Our DNA SC offer opportunities for improvement as we expand the program and can provide guidance to organizations seeking to begin efforts to integrate population-wide genomic screening. [ABSTRACT FROM AUTHOR]

Background: The expanding role of technology to augment diabetes care and management highlights the need for clinicians to learn about these new tools. As these tools continue to evolve and enhance improved outcomes, it is imperative that clinicians consider the role of telemonitoring, or remote monitoring, in patient care. This article describes a successful telemonitoring project in Utah. Subjects and Methods: This was a nonrandomized prospective observational preintervention-postintervention study, using a convenience sample. Patients with uncontrolled diabetes and/or hypertension from four rural and two urban primary care clinics and one urban stroke center participated in a telemonitoring program. The primary clinical outcome measures were changes in hemoglobin A1C (A1C) and blood pressure. Other outcomes included fasting lipids, weight, patient engagement, diabetes knowledge, hypertension knowledge, medication adherence, and patient perceptions of the usefulness of the telemonitoring program. Results: Mean A1C decreased from 9.73% at baseline to 7.81% at the end of the program ( P<0.0001). Systolic blood pressure also declined significantly, from 130.7 mm Hg at baseline to 122.9 mm Hg at the end ( P=0.0001). Low-density lipoprotein content decreased significantly, from 103.9 mg/dL at baseline to 93.7 mg/dL at the end ( P=0.0263). Other clinical parameters improved nonsignificantly. Knowledge of diabetes and hypertension increased significantly ( P<0.001 for both). Patient engagement and medication adherence also improved, but not significantly. Per questionnaires at study end, patients felt the telemonitoring program was useful. Conclusions: Telemonitoring improved clinical outcomes and may be a useful tool to help enhance disease management and care of patients with diabetes and/or hypertension. [ABSTRACT FROM AUTHOR]

The Health Information Technology for Economic and Clinical Health Act is intended to enhance reimbursement of health care providers for meaningful use of electronic health records systems. This presents both opportunities and challenges for public health departments. To earn incentive payments, clinical providers must exchange specified types of data with the public health system, such as immunization and syndromic surveillance data and notifiable disease reporting. However, a crisis looms because public health's information technology systems largely lack the capabilities to accept the types of data proposed for exchange. Cloud computing may be a solution for public health information systems. Through shared computing resources, public health departments could reap the benefits of electronic reporting within federal funding constraints. [ABSTRACT FROM AUTHOR]

The United States faces tremendous challenges with its healthcare system. By any standard, it is expensive and performs poorly in most measures of health and thus, is in great need of reform. But how do we reform things without making the situation worse? Some of the more fundamental problems arise from the combination of a fee-for-service payment system for physicians with insurance-based financing care. This combination results in conflicts among the interests of patients, physicians and payers. This paper examines this issue from a decision analytic perspective, starting with a definition of the patient-centered view, and an assessment of the practicality of controlling costs by making healthcare more patient-centric. It then illustrates how fee-for-service models corrupt decision-making and other solutions designed to reign in the abuses of the fee-for-service model and also negatively impacts the quality of decision making for individual patients. Whatever the strategies for health reform, the degree of patient-centeredness of care is a benchmark that allows policy makers to understand how far they have had to deviate from optimal to achieve the desired ends of cost control. [ABSTRACT FROM AUTHOR]

The United States faces tremendous challenges with its healthcare system. By any standard, it is expensive and performs poorly in most measures of health and thus, is in great need of reform. But how do we reform things without making the situation worse? Some of the more fundamental problems arise from the combination of a fee-for-service payment system for physicians with insurance-based financing care. This combination results in conflicts among the interests of patients, physicians and payers. This paper examines this issue from a decision analytic perspective, starting with a definition of the patient-centered view, and an assessment of the practicality of controlling costs by making healthcare more patient-centric. It then illustrates how fee-for-service models corrupt decision-making and other solutions designed to reign in the abuses of the fee-for-service model and also negatively impacts the quality of decision making for individual patients. Whatever the strategies for health reform, the degree of patient-centeredness of care is a benchmark that allows policy makers to understand how far they have had to deviate from optimal to achieve the desired ends of cost control. [ABSTRACT FROM AUTHOR]

The Internet provides a medium to administer and evaluate evidence-based interventions for highly prevalent public health problems worldwide. The authors report a series of four Internet smoking cessation studies conducted in English and Spanish. These studies examined both outcome (self-reported 7-day abstinence) and mechanisms related to outcome (the impact of major depressive episodes [MDEs] on the likelihood of quitting). Over 4,000 smokers from 74 countries entered the studies. Studies 1 and 2 evaluated a standard smoking cessation guide (the “ Guía ”). Studies 3 and 4 were randomized trials comparing the Guía +ITEMs (individually timed educational messages) to the Guía +ITEMs+a mood management course. ITEMs were E-mails inviting participants back to the site at specific times. Online follow-up assessments resulted in completion rates of 44%–54% at 1 month and 26%–30% at 6 months in studies 1 and 2. Incentives and follow-up phone calls increased these rates to 70%, 66%, 65%, and 62% at 1, 3, 6, and 12 months in study 4. At 6 months, self-reported 7-day abstinence rates using missing = smoking data were 6% in studies 1 and 2, 10%–14% in study 3, and 20%–26% in study 4. The Guía +ITEMs condition tended to have higher quit rates, which reached significance at the 12-month follow-up in study 3 and at the 3-month follow-up in study 4. Smokers with past (but not current) MDEs tended to be the most likely to abstain and those with current MDEs the least likely. This trend reached significance in studies 1 and 4. [ABSTRACT FROM AUTHOR]

Background: Most tools for estimating utilities use clinical trial data from general health status models, such as the 36-Item Short-Form Health Survey (SF-36). A disease-specific model may be more appropriate. The objective of this study was to apply a disease-specific utility mapping function for schizophrenia to data from a large, 1-year, open-label study of long-acting risperidone and to compare its performance with an SF-36-based utility mapping function. Methods: Patients with schizophrenia or schizoaffective disorder by DSM-IV criteria received 25, 50, or 75 mg long-acting risperidone every 2 weeks for 12 months. The Positive and Negative Syndrome Scale (PANSS) and SF-36 were used to assess efficacy and health-related quality of life. Movement disorder severity was measured using the Extrapyramidal Symptom Rating Scale (ESRS); data concerning other common adverse effects (orthostatic hypotension, weight gain) were collected. Transforms were applied to estimate utilities. Results: A total of 474 patients completed the study. Long-acting risperidone treatment was associated with a utility gain of 0.051 using the disease-specific function. The estimated gain using an SF-36-based mapping function was smaller: 0.0285. Estimates of gains were only weakly correlated (r = 0.2). Because of differences in scaling and variance, the requisite sample size for a randomized trial to confirm observed effects is much smaller for the disease-specific mapping function (156 versus 672 total subjects). Conclusion: Application of a disease-specific mapping function was feasible. Differences in scaling and precision suggest the clinically based mapping function has greater power than the SF-36-based measure to detect differences in utility. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is a common severe syndrome with a highly variable pattern of symptoms. The medications used to treat this disorder are expensive and may cause severe adverse effects. Little is known about how the public perceives health outcomes in schizophrenia or the potential adverse effects of antipsychotic medication. This complicates the use of standard cost-effectiveness analysis to set priorities for health resource allocation.Objective: In this study, we measured utility weights for a set of health states derived from Positive and Negative Symptom Scale (PANSS) scores, and a set of health states that included the presence of common adverse effects of medication, thus creating utility mapping function for clinical data.Methods: We presented a convenience sample of members of a large commercial Internet survey panel with digital video materials portraying eight different patterns of schizophrenia with varying levels of positive, negative and cognitive symptoms, and five common adverse effects of antipsychotic medications. We then elicited their standard gamble (SG) and visual analog scale (VAS) ratings using iMPACT3 computer program, with an automated error repair feature, deliberately over-sampling minority ethnic groups in the panel. We censored subjects with uncorrected errors in ratings and estimated utilities for each state by re-weighting responses to match United States population demographics.Subjects: 620 well-educated, ethnically diverse volunteers (54% Caucasian), who spanned a broad range of age groups and geographical regions of the US participated in this study.Results: Because of evidence of bias in ratings, 175 (29%) subjects with internal inconsistencies in ratings were censored from estimates of mean population utilities. In the remaining 441 subjects, SG utilities, re-weighted to match US population demographic profiles, ranged from 0.88 for mild schizophrenia to 0.47 extremely severe schizophrenia. Variability in the types of symptoms exhibited (positive, negative or cognitive) was less important to participants than the overall severity of each state. Modest reductions in symptoms (for example, from severe to moderate or moderate to mild) were associated with relatively large changes in utility (0.12-0.19). Adverse effects decreased utilities for states by amounts ranging from 0.09 (pseudo-parkinsonism) to 0.05 (obesity).Conclusions: The public views schizophrenia is a very disabling syndrome and treatments that reduce symptoms produce important gains in utility, even if they do not induce a complete remission. Adverse effects significantly reduce utilities for states; therefore, cost-effectiveness analyses for this disorder should take adverse effects of treatment into consideration. [ABSTRACT FROM AUTHOR]

The Internet is a rich multimodal communication environment where researchers can often recruit large numbers of participants for survey studies at low cost; however, there are many unanswered questions about the use of this technology in medical research. An important question is whether the results obtained in such surveys are generalizable to other populations. In this article, we describe different approaches for recruitment of participants on the Internet and how specific approaches may limit generalizability. Another issue clouding use of the Web in research is the essential anonymity of participants. We discuss the risk and benefits of anonymous surveys and technical methods that minimize the risks while preserving anonymity and confidentiality. A related problem may be accidental or deliberate enrollment of unqualified individuals in studies. Researchers can use survey design and statistical methods described herein to partially address threats to validity arising from such individuals; however, in some situations, confirmation of eligibility, by manual review of medical records, may be necessary. [ABSTRACT FROM AUTHOR]

Objective.—While it is technically feasible to conduct migraine research on the Internet, can one be sure that the subjects who participate actually have the disorder? We examined this issue in 109 sequential subjects in an Internet study of migraineurs' symptoms. Background.—The increasing use of the Internet by the US population provides an opportunity for using this medium in clinical research, but, to be useful, the results of such research must be valid and generalizable. Methods.—Using postings to Internet newsgroups of migraineurs, we recruited subjects to a migraine research site. We examined reported symptoms by comparing them with the International Headache Society's criteria for diagnosis of migraine and by subjective review by a neurologist. We also attempted to contact the subject's primary care physicians to confirm their diagnosis. Results.—We found considerable evidence for the validity of the participants' self-reported diagnoses. Most subjects with professed migraine reported quality of pain (97%) and associated symptoms (92%) consistent with that diagnosis, and review of their symptoms and questionnaire responses by the neurologist suggested that almost all subjects (97%) had migraine. Personal physicians confirmed a diagnosis of migraine in 90% of con- senting subjects (n=49). Conclusions.—The validity of self-reported diagnosis of migraine does not appear to be an obstacle to conducting research in subject populations on the Internet. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to assess the reliability and internal consistency of measurements of utilities performed with a computer program (iMPACT2) designed for Internet surveys and Internet patient decision-support systems.Methods: We implemented the Internet Multimedia Preference Assessor Construction Tool, version 2 (iMPACT2) program using the combination of a web server, HTML files, and a web-accessible database. The program randomized subjects, screened their responses for missing data and failures of internal consistency, assisted patients with resolving certain inconsistencies, and, upon a subject's completion of the protocol, provided a report of results to the research assistant administering the program. To validate the iMPACT2 program, we recruited 60 healthy community volunteers and elicited preferences in a research-lab setting using a visual analog scale (VAS) and the standard gamble (SG) for subject's current health and three hypothetical states. For purposes of comparison, we also administered a Short Form-12 (SF-12) health-assessment questionnaire. Subjects used the computer software on two occasions separated by 2-4 weeks of time.Results: Visual analog scale and standard gamble ratings for subjects' current health were reliable (intraclass correlation coefficient (ICC) of 0.82 and 0.84 (two outliers excluded -0.60 without exclusions), respectively) were comparable with the reliability of the Physical and Mental Component scales of the SF-12 (ICCs of 0.84 and 0.75, respectively). Subjects could easily discriminate between hypothetical states (D scores 0.74 for SG and 0.90 for VAS), and 94% had a completely internally consistent ordering of preference ratings for states.Conclusions: iMPACT2 produces measurements of standard gamble utilities that are reliable and have a high degree of internal consistency. Procedures for assessment of utilities developed for desktop computer programs can be translated to software designed for the Internet, facilitating the use of utilities and endpoints in clinical trials and development of web-based decision-support applications for patients. However, further testing, including direct comparisons with traditional interviewer administered utility elicitation protocols, is needed. [ABSTRACT FROM AUTHOR]

Purpose: Although previous studies have examined race and sex differences in health care, few studies have investigated the possible role of physician bias. We evaluated the influence of race and sex on medical students' perceptions of patients' symptoms to determine if there are differences in these perceptions early in medical training.Subjects and Methods: One-hundred sixty-four medical students were randomly assigned to view a video of a black female or white male actor portraying patients with identical symptoms of angina. We evaluated students' perceptions of the actors' health state (based on their assessment of quality of life) using a visual analog scale and a standard rating technique, as well as the type of chest pain diagnosis.Results: Students assigned a lower value (indicating a less desirable health state) to the black woman than to the white man with identical symptoms [visual scale (mean +/- SD): 72 +/- 13 vs 67 +/- 12, P <0.02; standard gamble: 87 +/- 10 vs 80 +/- 15, P < 0.001). Nonminority students reported higher mean values for the white male patient (standard gamble: 89 +/- 8 vs 81 +/- 14 for the black female patient), whereas minority students' assessments did not differ by patient. Male students assigned a slightly lower value to the black female patient (standard gamble: 76 +/- 16 vs 87 +/- 10 for the white male patient). Students were less likely to characterize the black female patient's symptoms as angina (46% vs 74% for the white male patient, P = 0.001).Conclusions: The way that medical students perceive patient symptoms appears to be affected by nonmedical factors. [ABSTRACT FROM AUTHOR]

Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer's Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) ( χ 2 = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses. [ABSTRACT FROM AUTHOR]

Presents a study to determine if potentially ineffective care is delivered less often to Medicare patients enrolled in health maintenance organizations than those in traditional fee-for-service plans. Patients; Outcome; Methods; Results; Conclusions.

Objectives: In hospitalized patients, cigarette smoking is linked to increased readmission rates, emergency department visits, and overall mortality. Smoking cessation reduces these risks, but many patients who smoke are unsuccessful in quitting. Nicotine replacement therapy (NRT) is an effective tool that assists patients who smoke with quitting. This study evaluates NRT prescriptions during and after hospitalization at a large health system for patients who smoke. Methods: A retrospective cohort study was conducted to determine the number of patients who were prescribed NRT during an inpatient admission and at time of discharge from a network of nine hospitals across South Carolina between January 1, 2019 and January 1, 2023. Results: This study included 20,757 patients identified as actively smoking with at least one hospitalization during the study period. Of the cohort, 34.9% were prescribed at least one prescription for NRT during their admission to the hospital. Of the patients identified, 12.6% were prescribed NRT upon discharge from the hospital. Conclusions: This study identified significantly low rates of NRT prescribed to smokers during hospitalization and at discharge. Although the management of chronic conditions is typically addressed in the outpatient setting, hospitalization may provide an opportunity for patients to initiate health behavior changes. The low rates of prescriptions for NRT present an opportunity to improve tobacco treatment during hospitalization and beyond. [ABSTRACT FROM AUTHOR]

Following publication of the original article [1], the authors reported an error in one of the authors' names. [ABSTRACT FROM AUTHOR]

Background: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. This study explores the feasibility and effectiveness of natural language processing (NLP) strategy for identifying patients who are socially isolated from clinical narratives.Method: We used data from the Medical University of South Carolina (MUSC) Research Data Warehouse. Patients 18 years-of-age or older who were diagnosed with prostate cancer between January 1, 2014 and May 31, 2017 were eligible for this study. NLP pipelines identifying social isolation were developed via extraction of notes on progress, history and physical, consult, emergency department provider, telephone encounter, discharge summary, plan of care, and radiation oncology. Of 4195 eligible prostate cancer patients, we randomly sampled 3138 patients (75%) as a training dataset. The remaining 1057 patients (25%) were used as a test dataset to evaluate NLP algorithm performance. Standard performance measures for the NLP algorithm, including precision, recall, and F-measure, were assessed by expert manual review using the test dataset.Results: A total of 55,516 clinical notes from 3138 patients were included to develop the lexicon and NLP pipelines for social isolation. Of those, 35 unique patients (1.2%) had social isolation mention(s) in 217 notes. Among 24 terms relevant to social isolation, the most prevalent were "lack of social support," "lonely," "social isolation," "no friends," and "loneliness". Among 1057 patients in the test dataset, 17 patients (1.6%) were identified as having social isolation mention(s) in 40 clinical notes. Manual review identified four false positive mentions of social isolation and one false negatives in 154 notes from randomly selected 52 controls. The NLP pipeline demonstrated 90% precision, 97% recall, and 93% F-measure. The major reasons for a false positive included the ambiguities of the experiencer of social isolation, negation, and alternate meaning of words.Conclusions: Our NLP algorithms demonstrate a highly accurate approach to identify social isolation. [ABSTRACT FROM AUTHOR]

Background: Homework, or between-session practice of skills learned during therapy, is integral to effective youth mental health TREATMENTS. However, homework is often under-utilized by providers and patients due to many barriers, which might be mitigated via mHealth solutions. Methods: Semi-structured qualitative interviews were conducted with nationally certified trainers in Trauma Focused Cognitive Behavioral Therapy (TF-CBT; n = 21) and youth TF-CBT patients ages 8–17 (n = 15) and their caregivers (n = 12) to examine barriers to the successful implementation of homework in youth mental health treatment and potential mHealth solutions to those barriers. Results: The results indicated that many providers struggle to consistently develop, assign, and assess homework exercises with their patients. Patients are often difficult to engage and either avoid or have difficulty remembering to practice exercises, especially given their busy/chaotic home lives. Trainers and families had positive views and useful suggestions for mHealth solutions to these barriers in terms of functionality (e.g., reminders, tracking, pre-made homework exercises, rewards) and user interface (e.g., easy navigation, clear instructions, engaging activities). Conclusions: This study adds to the literature on homework barriers and potential mHealth solutions to those barriers, which is largely based on recommendations from experts in the field. The results aligned well with this literature, providing additional support for existing recommendations, particularly as they relate to treatment with youth and caregivers. [ABSTRACT FROM AUTHOR]

We use the implementation science framework RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) to describe outcomes of In Our DNA SC, a population-wide genomic screening (PWGS) program. In Our DNA SC involves participation through clinical appointments, community events, or at home collection. Participants provide a saliva sample that is sequenced by Helix, and those with a pathogenic variant or likely pathogenic variant for CDC Tier 1 conditions are offered free genetic counseling. We assessed key outcomes among the first cohort of individuals recruited. Over 14 months, 20,478 participants enrolled, and 14,053 samples were collected. The majority selected at-home sample collection followed by clinical sample collection and collection at community events. Participants were predominately female, White (self-identified), non-Hispanic, and between the ages of 40–49. Participants enrolled through community events were the most racially diverse and the youngest. Half of those enrolled completed the program. We identified 137 individuals with pathogenic or likely pathogenic variants for CDC Tier 1 conditions. The majority (77.4%) agreed to genetic counseling, and of those that agreed, 80.2% completed counseling. Twelve clinics participated, and we conducted 108 collection events. Participants enrolled at home were most likely to return their sample for sequencing. Through this evaluation, we identified facilitators and barriers to implementation of our state-wide PWGS program. Standardized reporting using implementation science frameworks can help generalize strategies and improve the impact of PWGS. We evaluated In Our DNA SC, a population-wide genomic screening program, through the RE-AIM framework. In 14 months, over 20,000 individuals enrolled, providing 14,053 samples. Pathogenic or likely pathogenic variants for a CDC Tier 1 condition were found in 137 individuals, with the majority of individuals agreeing to genetic counseling. [ABSTRACT FROM AUTHOR]

Key Points: Question: Does 100 mg of fluvoxamine twice daily for 13 days, compared with placebo, shorten symptom duration among outpatient adults (aged ≥30 years) with symptomatic mild to moderate COVID-19? Findings: In this platform randomized clinical trial with 1175 US participants enrolled during the time that Omicron COVID-19 subvariants were circulating, there was no reportable difference in the time to sustained recovery between fluvoxamine and placebo groups (adjusted hazard ratio, 0.99 [95% credible interval, 0.89-1.09]; P for efficacy =.40). Median time to sustained recovery was 10 days (95% CI, 10-11) in both the intervention and placebo group. Meaning: Fluvoxamine, 100 mg twice daily, does not shorten the duration of symptoms in outpatient adults with mild to moderate COVID-19. Importance: The effect of higher-dose fluvoxamine in reducing symptom duration among outpatients with mild to moderate COVID-19 remains uncertain. Objective: To assess the effectiveness of fluvoxamine, 100 mg twice daily, compared with placebo, for treating mild to moderate COVID-19. Design, Setting, and Participants: The ACTIV-6 platform randomized clinical trial aims to evaluate repurposed medications for mild to moderate COVID-19. Between August 25, 2022, and January 20, 2023, a total of 1175 participants were enrolled at 103 US sites for evaluating fluvoxamine; participants were 30 years or older with confirmed SARS-CoV-2 infection and at least 2 acute COVID-19 symptoms for 7 days or less. Interventions: Participants were randomized to receive fluvoxamine, 50 mg twice daily on day 1 followed by 100 mg twice daily for 12 additional days (n = 601), or placebo (n = 607). Main Outcomes and Measures: The primary outcome was time to sustained recovery (defined as at least 3 consecutive days without symptoms). Secondary outcomes included time to death; time to hospitalization or death; a composite of hospitalization, urgent care visit, emergency department visit, or death; COVID-19 clinical progression scale score; and difference in mean time unwell. Follow-up occurred through day 28. Results: Among 1208 participants who were randomized and received the study drug, the median (IQR) age was 50 (40-60) years, 65.8% were women, 45.5% identified as Hispanic/Latino, and 76.8% reported receiving at least 2 doses of a SARS-CoV-2 vaccine. Among 589 participants who received fluvoxamine and 586 who received placebo included in the primary analysis, differences in time to sustained recovery were not observed (adjusted hazard ratio [HR], 0.99 [95% credible interval, 0.89-1.09]; P for efficacy =.40]). Additionally, unadjusted median time to sustained recovery was 10 (95% CI, 10-11) days in both the intervention and placebo groups. No deaths were reported. Thirty-five participants reported health care use events (a priori defined as death, hospitalization, or emergency department/urgent care visit): 14 in the fluvoxamine group compared with 21 in the placebo group (HR, 0.69 [95% credible interval, 0.27-1.21]; P for efficacy =.86) There were 7 serious adverse events in 6 participants (2 with fluvoxamine and 4 with placebo) but no deaths. Conclusions and Relevance: Among outpatients with mild to moderate COVID-19, treatment with fluvoxamine does not reduce duration of COVID-19 symptoms. Trial Registration: ClinicalTrials.gov Identifier: NCT04885530 This randomized study examines the effect of higher-dose fluvoxamine on time to sustained recovery from mild to moderate COVID-19 or progression to severe disease in nonhospitalized adults. [ABSTRACT FROM AUTHOR]

Background: Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN. Strategies that summarize EHR data to identify CAN and convey this to providers has potential to mitigate CAN-related sequelae. This study used expert review/consensus and Natural Language Processing (NLP) to develop and test a lexicon to characterize children who have experienced or are at risk for CAN and compared machine learning methods to the lexicon + NLP approach to determine the algorithm's performance for identifying CAN. Methods: Study investigators identified 90 CAN terms and invited an interdisciplinary group of child abuse experts for review and validation. We then used NLP to develop pipelines to finalize the CAN lexicon. Data for pipeline development and refinement were drawn from a randomly selected sample of EHR from patients seen at pediatric primary care clinics within a U.S. academic health center. To explore a machine learning approach for CAN identification, we used Support Vector Machine algorithms. Results: The investigator-generated list of 90 CAN terms were reviewed and validated by 25 invited experts, resulting in a final pool of 133 terms. NLP utilized a randomly selected sample of 14,393 clinical notes from 153 patients to test the lexicon, and.03% of notes were identified as CAN positive. CAN identification varied by clinical note type, with few differences found by provider type (physicians versus nurses, social workers, etc.). An evaluation of the final NLP pipelines indicated 93.8% positive CAN rate for the training set and 71.4% for the test set, with decreased precision attributed primarily to false positives. For the machine learning approach, SVM pipeline performance was 92% for CAN + and 100% for non-CAN, indicating higher sensitivity than specificity. Conclusions: The NLP algorithm's development and refinement suggest that innovative tools can identify youth at risk for CAN. The next key step is to refine the NLP algorithm to eventually funnel this information to care providers to guide clinical decision making. [ABSTRACT FROM AUTHOR]

Importance: The effectiveness of ivermectin to shorten symptom duration or prevent hospitalization among outpatients in the US with mild to moderate symptomatic COVID-19 is unknown.Objective: To evaluate the efficacy of ivermectin, 400 μg/kg, daily for 3 days compared with placebo for the treatment of early mild to moderate COVID-19.Design, Setting, and Participants: ACTIV-6, an ongoing, decentralized, double-blind, randomized, placebo-controlled platform trial, was designed to evaluate repurposed therapies in outpatients with mild to moderate COVID-19. A total of 1591 participants aged 30 years and older with confirmed COVID-19, experiencing 2 or more symptoms of acute infection for 7 days or less, were enrolled from June 23, 2021, through February 4, 2022, with follow-up data through May 31, 2022, at 93 sites in the US.Interventions: Participants were randomized to receive ivermectin, 400 μg/kg (n = 817), daily for 3 days or placebo (n = 774).Main Outcomes and Measures: Time to sustained recovery, defined as at least 3 consecutive days without symptoms. There were 7 secondary outcomes, including a composite of hospitalization or death by day 28.Results: Among 1800 participants who were randomized (mean [SD] age, 48 [12] years; 932 women [58.6%]; 753 [47.3%] reported receiving at least 2 doses of a SARS-CoV-2 vaccine), 1591 completed the trial. The hazard ratio (HR) for improvement in time to recovery was 1.07 (95% credible interval [CrI], 0.96-1.17; posterior P value [HR >1] = .91). The median time to recovery was 12 days (IQR, 11-13) in the ivermectin group and 13 days (IQR, 12-14) in the placebo group. There were 10 hospitalizations or deaths in the ivermectin group and 9 in the placebo group (1.2% vs 1.2%; HR, 1.1 [95% CrI, 0.4-2.6]). The most common serious adverse events were COVID-19 pneumonia (ivermectin [n = 5]; placebo [n = 7]) and venous thromboembolism (ivermectin [n = 1]; placebo [n = 5]).Conclusions and Relevance: Among outpatients with mild to moderate COVID-19, treatment with ivermectin, compared with placebo, did not significantly improve time to recovery. These findings do not support the use of ivermectin in patients with mild to moderate COVID-19.Trial Registration: ClinicalTrials.gov Identifier: NCT04885530. [ABSTRACT FROM AUTHOR]

The article presents a medical research study which examined the potential survival benefit of anterior temporal lobe resection for patients with pharmacoresistant epilepsy in comparison to medical management of the disorder. A decision analysis using a Monte Carlo simulation model populated with quality-of-life data obtained directly from the patients and data from medical literature was used for the study. Life expectancy and quality-adjusted life expectancy were the main outcome measures. Researchers concluded that substantial gains in life expectancy and quality-adjusted life expectancy would be achieved with anterior temporal lobe resection compared with medical management .

Objective: Assessment of the feasibility of conducting clinical research with patients using electronic mail and the World Wide Web.Design: We re-contacted 463 patients with ulcerative colitis (UC) and 154 benign prostatic hyperplasia (BPH) patients who had provided us with e-mail addresses as part of a Web-based study. In an electronic mail message, we informed patients of a web site with a new study and invited them to participate. We then examined the factors associated with patients' participation in the new study.Results: Completion rates were 28% for UC patients (single mailing) and 48% for BPH patients (up to four mailings in a two-month period). Some patients could not be contacted due to invalid e-mail addresses (23%). Those who completed the new study tended to be older, and less time had elapsed since their participation in the previous study. Furthermore, their health-related quality-of-life had significantly improved since the previous study.Conclusion: It is possible to use direct electronic mail contact to conduct follow-up research with patients. Response rates appear to be related to the number of messages sent, age of the recipients, and time since the initial contact. [ABSTRACT FROM AUTHOR]

Background: Clinical trials are key to ensuring high-quality, effective, and safe health care interventions, but there are many barriers to their successful and timely implementation. Difficulties with participant recruitment and enrollment are largely affected by difficulties with obtaining informed consent. Teleconsent is a telemedicine- based approach to obtaining informed consent and offers a unique solution to limitations of traditional consent approaches. Methods: We conducted a survey among 134 clinical trial researchers in academic/university-, industry-, and clinically based settings. The survey addressed important aspects of teleconsent, potential teleconsent enhancements, and other telehealth capabilities to support clinical research. Results: The majority of respondents viewed teleconsent as an important approach for obtaining informed consent and indicated that they would likely use teleconsent if available. Consenting participants at remote sites, increasing access to clinical trials, and consenting participants in their homes were viewed as the greatest opportunities for teleconsent. Features for building, validating, and assessing understanding of teleconsent forms, mobile capabilities, three-way teleconsent calls, and direct links to forms via recruitment websites were viewed as important teleconsent enhancements. Other telehealth capabilities to support clinical research, including surveys, file transfer, three-way video, screenshare, and photo capture during telemedicine visits, and proposed telemedicine capabilities such as video call recording, ID information capture, and integration of medical devices, were also viewed as important. Conclusions: Teleconsent and telemedicine are promising solutions to some common challenges to clinical trials. Many barriers to study recruitment and enrollment might be overcome by investing time and resources and further evaluating this technology. [ABSTRACT FROM AUTHOR]

Background and purpose: This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes. Materials and methods: All brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using “quanteda” NLP package, all text data were parsed into tokens to create the data frequency matrix. Ten-fold cross-validation was applied for bias correction of the training set. Labeling for AIS was performed manually, identifying clinical notes. We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. We also assessed how n-grams or term frequency-inverse document frequency weighting affected the performance of the algorithms. Results: Of all 3,204 brain MRI documents, 432 (14.3%) were labeled as AIS. AIS documents were longer in character length than those of non-AIS (median [interquartile range]; 551 [377–681] vs. 309 [164–396]). Of all ML algorithms, single decision tree had the highest F1-measure (93.2) and accuracy (98.0%). Adding bigrams to the ML model improved F1-mesaure of naïve Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements. Conclusions: Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. Single decision tree was the best classifier to identify brain MRI reports with AIS. [ABSTRACT FROM AUTHOR]

Due to recently proposed changes in the Common Rule regarding the collection of research preferences, there is an increased need for efficient methods to document opt-in research preferences at a population level. Previously, our institution developed an opt-out paper-based workflow that could not be utilized for research in a scalable fashion. This project was designed to demonstrate the feasibility of implementing an electronic health record (EHR)-based active opt-in research preferences program. The first phase of implementation required creating and disseminating a patient questionnaire through the EHR portal to populate discreet fields within the EHR indicating patients’ preferences for future research study contact (contact) and their willingness to allow anonymised use of excess tissue and fluid specimens (biobank). In the second phase, the questionnaire was presented within a clinic nurse intake workflow in an obstetrical clinic. These permissions were tabulated in registries for use by investigators for feasibility studies and recruitment. The registry was also used for research patient contact management using a new EHR encounter type to differentiate research from clinical encounters. The research permissions questionnaire was sent to 59,670 patients via the EHR portal. Within four months, 21,814 responses (75% willing to participate in biobanking, and 72% willing to be contacted for future research) were received. Each response was recorded within a patient portal encounter to enable longitudinal analysis of responses. We obtained a significantly lower positive response from the 264 females who completed the questionnaire in the obstetrical clinic (55% volunteers for biobank and 52% for contact). We demonstrate that it is possible to establish a research permissions registry using the EHR portal and clinic-based workflows. This patient-centric, population-based, opt-in approach documents preferences in the EHR, allowing linkage of these preferences to health record information. [ABSTRACT FROM AUTHOR]

Objectives: Health information exchanges (HIEs) make possible the construction of databases to characterize patients as multisystem users (MSUs), those visiting emergency departments (EDs) of more than one hospital system within a region during a 1-year period. HIE data can inform an algorithm highlighting patients for whom information is more likely to be present in the HIE, leading to a higher yield HIE experience for ED clinicians and incentivizing their adoption of HIE. Our objective was to describe patient characteristics that determine which ED patients are likely to be MSUs and therefore have information in an HIE, thereby improving the efficacy of HIE use and increasing ED clinician perception of HIE benefit.Methods: Data were extracted from a regional HIE involving four hospital systems (11 EDs) in the Charleston, South Carolina area. We used univariate and multivariable regression analyses to develop a predictive model for MSU status.Results: Factors associated with MSUs included younger age groups, dual-payer insurance status, living in counties that are more rural, and one of at least six specific diagnoses: mental disorders; symptoms, signs, and ill-defined conditions; complications of pregnancy, childbirth, and puerperium; diseases of the musculoskeletal system; injury and poisoning; and diseases of the blood and blood-forming organs. For patients with multiple ED visits during 1 year, 43.8% of MSUs had ≥4 visits, compared with 18.0% of non-MSUs (P < 0.0001).Conclusions: This predictive model accurately identified patients cared for at multiple hospital systems and can be used to increase the likelihood that time spent logging on to the HIE will be a value-added effort for emergency physicians. [ABSTRACT FROM AUTHOR]

Objectives: A small but significant number of patients make frequent emergency department (ED) visits to multiple EDs within a region. We have a unique health information exchange (HIE) that includes every ED encounter in all hospital systems in our region. Using our HIE we were able to characterize all frequent ED users in our region, regardless of hospital visited or payer class. The objective of our study was to use data from an HIE to characterize patients in a region who are frequent ED users (FEDUs).Methods: We constructed a database from a cohort of adult patients (18 years old or older) with information in a regional HIE for a 1-year period beginning in April 2012. Patients were defined as FEDUs (those who made four or more visits during the study period) and non-FEDUs (those who made fewer than four ED visits during the study period). Predictor variables included age, race, sex, payer class, county of residence, and International Classification of Diseases, Ninth Revision codes. Bivariate (χ(2)) and multivariate (logistic regression) analyses were performed to determine associations between predictor variables and the outcome of being a FEDU.Results: The database contained 127,672 patients, 12,293 (9.6%) of whom were FEDUs. Logistic regression showed the following patient characteristics to be significantly associated with the outcome of being a FEDU: age 35 to 44 years; African American race; Medicaid, Medicare, and dual-pay payer class; and International Classification of Diseases, Ninth Revision codes 630 to 679 (complications of pregnancy, childbirth, and puerperium), 780 to 799 (ill-defined conditions), 280 to 289 (diseases of the blood), 290-319 (mental disorders), 680 to 709 (diseases of the skin and subcutaneous tissue), 710 to 739 (musculoskeletal and connective tissue disease), 460 to 519 (respiratory disease), and 520 to 579 (digestive disease). No significant differences were noted between men and women.Conclusions: Data from an HIE can be used to describe all of the patients within a region who are FEDUs, regardless of the hospital system they visited. This information can be used to focus care coordination efforts and link appropriate patients to a medical home. Future studies can be designed to learn the reasons why patients become FEDUs, and interventions can be developed to address deficiencies in health care that result in frequent ED visits. [ABSTRACT FROM AUTHOR]

Learning Health Systems (LHS) require accessible, usable health data and a culture of collaboration--a challenge for any single system, let alone disparate organizations, with macro- and micro-systems. Recently, the National Science Foundation described this important setting as a cyber-social ecosystem. In 2004, in an effort to create a platform for transforming health in South Carolina, Health Sciences South Carolina (HSSC) was established as a research collaboration of the largest health systems, academic medical centers and research intensive universities in South Carolina. With work beginning in 2010, HSSC unveiled an integrated Clinical Data Warehouse (CDW) in 2013 as a crucial anchor to a statewide LHS. This CDW integrates data from independent health systems in near-real time, and harmonizes the data for aggregation and use in research. With records from over 2.7 million unique patients spanning 9 years, this multi-institutional statewide clinical research repository allows integrated individualized patient-level data to be used for multiple population health and biomedical research purposes. In the first 21 months of operation, more than 2,800 de-identified queries occurred through i2b2, with 116 users. HSSC has developed and implemented solutions to complex issues emphasizing anti-competitiveness and participatory governance, and serves as a recognized model to organizations working to improve healthcare quality by extending the traditional borders of learning health systems. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the application of a chronic disease model (CDM) for prostate cancer to visual analog scale (VAS) and time trade-off (TTO) decision tools. Methods: A total of 138 men (mean age 58 years) viewed a CDM module for prostate cancer with and without prostate specific antigen (PSA) screening. Participants rated their hypothetical quality of life with potential prostate cancer treatment complications using a CDM-based VAS decision tool. They were then asked to estimate how many years they would be willing to trade to be free of treatment complications using a CDM-based TTO decision tool. The consistency between VAS and TTO scores and the relationship between scores and preferences for PSA screening test and hypothetical treatment choice for prostate cancer were then evaluated. Results: There was a significant relationship between the VAS and TTO ratings (regression P < 0.001). The TTO tool was sensitive to age. Mean scores with standard deviations for those less than 58 years compared to those 58 years and more were 7.78 (1.75) and 8.41 (1.52), respectively ( P = 0.04). Using the VAS tool, men who chose PSA screening had higher quality of life ratings compared to men who did not choose PSA screening: 7.73 (1.78) and 6.59 (2.39), respectively ( P = 0.01). Similar results were found with the TTO decision tool: 8.33 (1.45) and 7.04 (2.00), respectively ( P = 0.005). Men who would hypothetically prefer treatment for moderately differentiated prostate cancer also had higher TTO scores compared to men who preferred watchful waiting: 8.54 (1.39) and 7.85 (1.73), respectively ( P = 0.04). Conclusion: CDM-based for prostate cancer, VAS and TTO ratings were consistent and were concordant with patient preferences for screening; TTO ratings were also concordant with treatment choice. The use of the CDM-based TTO ratings to adjust for quality of life in decision analytic modeling needs to be explored. [ABSTRACT FROM AUTHOR]

Context: Patients with pharmacoresistant epilepsy have increased mortality compared with the general population, but patients with pharmacoresistant temporal lobe epilepsy who meet criteria for surgery and who become seizure-free after anterior temporal lobe resection have reduced excess mortality vs those with persistent seizures.Objective: To quantify the potential survival benefit of anterior temporal lobe resection for patients with pharmacoresistant temporal lobe epilepsy vs continued medical management.Design: Monte Carlo simulation model that incorporates possible surgical complications and seizure status, with 10,000 runs. The model was populated with health-related quality-of-life data obtained directly from patients and data from the medical literature. Insufficient data were available to assess gamma-knife radiosurgery or vagal nerve stimulation.Main Outcome Measures: Life expectancy and quality-adjusted life expectancy.Results: Compared with medical management, anterior temporal lobe resection for a 35-year-old patient with an epileptogenic zone identified in the anterior temporal lobe would increase survival by 5.0 years (95% CI, 2.1-9.2) with surgery preferred in 100% of the simulations. Anterior temporal lobe resection would increase quality-adjusted life expectancy by 7.5 quality-adjusted life-years (95%, CI, -0.8 to 17.4) with surgery preferred in 96.5% of the simulations, primarily due to increased years spent without disabling seizures, thereby reducing seizure-related excess mortality and improving quality of life. The results were robust to sensitivity analyses.Conclusion: The decision analysis model suggests that on average anterior temporal lobe resection should provide substantial gains in life expectancy and quality-adjusted life expectancy for surgically eligible patients with pharmacoresistant temporal lobe epilepsy compared with medical management. [ABSTRACT FROM AUTHOR]

Previous Presentation: Some of the contents of this paper have been previously presented at the 16th Annual Meeting of the International Society for Technology Assessment in Health Care June 20, 2000 in the Hague, Netherlands and at the 21st Annual Meeting of the Society for Medical Decision Making as a poster on October 3, 1999 in Reno, NV.Background: Studies of schizophrenia treatment often oversimplify the array of health outcomes among patients. Our objective was to derive a set of disease states for schizophrenia using the Positive and Negative Symptom Assessment Scale (PANSS) that captured the heterogeneity of symptom responses.Methods: Using data from a 1-year clinical trial that collected PANSS scores and costs on schizophrenic patients (N=663), we conducted a k-means cluster analyses on PANSS scores for items in five factor domains. Results of the cluster analysis were compared with a conceptual framework of disease states developed by an expert panel. Final disease states were defined by combining our conceptual framework with the empirical results. We tested its utility by examining the influence of disease state on treatment costs and prognosis.Results: Analyses led to an eight-state framework with varying levels of positive, negative, and cognitive impairment. The extent of hostile/aggressive symptoms and mood disorders correlated with severity of disease states. Direct treatment costs for schizophrenia vary significantly across disease states (F=27.47, df=7, p<0.0001), and disease state at baseline was among the most important predictors of treatment outcomes.Conclusion: The disease states we describe offer a useful paradigm for understanding the links between symptom profiles and outcomes. [ABSTRACT FROM AUTHOR]