Hospitalization as an opportunity to improve lung cancer screening in high-risk patients.

BACKGROUND: Lung cancer screening with annual low-dose computed tomography (LDCT) in high-risk patients with exposure to smoking reduces lung cancer-related mortality, yet the screening rate of eligible adults is low. As hospitalization is an opportune moment to engage patients in their overall health, it may be an opportunity to improve rates of lung cancer screening. Prior to implementing a hospital-based lung cancer screening referral program, this study assesses the association between hospitalization and completion of lung cancer screening. METHODS: A retrospective cohort study of evaluated completion of at least one LDCT from 2014 to 2021 using electronic health record data using hospitalization as the primary exposure. Patients aged 55-80 who received care from a university-based internal medicine clinic and reported cigarette use were included. Univariate analysis and logistic regression evaluated the association of hospitalization and completion of LDCT. Cox proportional hazard model examined the time relationship between hospitalization and LDCT. RESULTS: Of the 1935 current smokers identified, 47% had at least one hospitalization, and 21% completed a LDCT during the study period. While a higher proportion of patients with a hospitalization had a LDCT (24%) compared to patients without a hospitalization (18%, p<0.001), there was no association between hospitalization and completion of a LDCT after adjusting for potentially confounding covariates (95%CI 0.680 - 1.149). There was an association between hospitalization time to event and LDCT completion, with hospitalized patients having a lower probability of competing LDCT compared to non-hospitalized patients (HR 0.747; 95% CI 0.611 - 0.914). CONCLUSIONS: In a cohort of patients at risk for lung cancer and established within a primary care clinic, only 1 in 4 patients who had been hospitalized completed lung cancer screening with LDCT. Hospitalization events were associated with a lower probability of LDCT completion. Hospitalization is a missed opportunity to refer at-risk patients to lung cancer screening.

Is hospitalization a missed opportunity to intervene on tobacco cessation?

BACKGROUND: Although tobacco use is associated with elevated morbidity and mortality, its use remains widespread among adults within the United States. Nicotine Replacement Therapy (NRT) products are effective aids that improve rates of tobacco cessation. Many smokers interact with the medical system, such as during hospitalization, without their tobacco use addressed. Hospitalization is a teachable moment for patients to make health-related changes, including tobacco cessation. METHODS: Retrospective cohort study of adult patients in a university-based patient-centered medical home from 2012 to 2021 evaluating the proportion of adults who smoke who received at least one prescription for NRT. Logistic regression models were used to analyze the association of being hospitalized and receipt of a NRT prescription. RESULTS: Of the 4,072 current smokers identified, 1,182 (29%) received at least one prescription for NRT during the study period. Hospitalization was associated with increased odds of receiving a NRT prescription (OR 1.68). Of 1,844 current smokers with a hospitalization during the study period, 1,078 (58%) never received a prescription for NRT at any point. Only 87 (5%) of the smokers received a prescription for NRT during hospitalization or at the time of hospital discharge. CONCLUSIONS: Despite hospitalization being associated with NRT prescribing, most patients who use tobacco and are hospitalized are not prescribed NRT. Hospitalization is an underutilized opportunity for both hospitalists and primary care physicians to intervene on smoking cessation through education and prescription of tobacco cessation aids.

Higher-Dose Fluvoxamine and Time to Sustained Recovery in Outpatients With COVID-19: The ACTIV-6 Randomized Clinical Trial.

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.

Predicting Mammogram Screening Follow Through with Electronic Health Record and Geographically Linked Data.

Cancer is the second leading cause of death in the United States, and breast cancer is the fourth leading cause of cancer-related death, with 42,275 women dying of breast cancer in the United States in 2020. Screening is a key strategy for reducing mortality from breast cancer and is recommended by various national guidelines. This study applies machine learning classification methods to the task of predicting which patients will fail to complete a mammogram screening after having one ordered, as well as understanding the underlying features that influence predictions. The results show that a small group of patients can be identified that are very unlikely to complete mammogram screening, enabling care managers to focus resources. SIGNIFICANCE: The motivation behind this study is to create an automated system that can identify a small group of individuals that are at elevated risk for not following through completing a mammogram screening. This will enable interventions to boost screening to be focused on patients least likely to complete screening.

Could an artificial intelligence approach to prior authorization be more human?

Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has become an "informatics issue" with the rise of automated methods for PA review, championed in the Health Level 7 International's (HL7's) DaVinci Project. DaVinci proposes using rule-based methods to automate PA, a time-tested strategy with known limitations. This article proposes an alternative that may be more human-centric, using artificial intelligence (AI) methods for the computation of authorization decisions. We believe that by combining modern approaches for accessing and exchanging existing electronic health data with AI methods tailored to reflect the judgments of expert panels that include patient representatives, and refined with "few shot" learning approaches to prevent bias, we could create a just and efficient process that serves the interests of society as a whole. Efficient simulation of human appropriateness assessments from existing data using AI methods could eliminate burdens and bottlenecks while preserving PA's benefits as a tool to limit inappropriate care.

The effectiveness of a noninterruptive alert to increase prescription of take-home naloxone in emergency departments.

OBJECTIVE: Opioid-related overdose (OD) deaths continue to increase. Take-home naloxone (THN), after treatment for an OD in an emergency department (ED), is a recommended but under-utilized practice. To promote THN prescription, we developed a noninterruptive decision support intervention that combined a detailed OD documentation template with a reminder to use the template that is automatically inserted into a provider's note by decision rules. We studied the impact of the combined intervention on THN prescribing in a longitudinal observational study. METHODS: ED encounters involving an OD were reviewed before and after implementation of the reminder embedded in the physicians' note to use an advanced OD documentation template for changes in: (1) use of the template and (2) prescription of THN. Chi square tests and interrupted time series analyses were used to assess the impact. Usability and satisfaction were measured using the System Usability Scale (SUS) and the Net Promoter Score. RESULTS: In 736 OD cases defined by International Classification of Disease version 10 diagnosis codes (247 prereminder and 489 postreminder), the documentation template was used in 0.0% and 21.3%, respectively (P < .0001). The sensitivity and specificity of the reminder for OD cases were 95.9% and 99.8%, respectively. Use of the documentation template led to twice the rate of prescribing of THN (25.7% vs 50.0%, P < .001). Of 19 providers responding to the survey, 74% of SUS responses were in the good-to-excellent range and 53% of providers were Net Promoters. CONCLUSIONS: A noninterruptive decision support intervention was associated with higher THN prescribing in a pre-post study across a multiinstitution health system.

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.

Background: Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods: We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results: A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions: Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.

Effect of Ivermectin vs Placebo on Time to Sustained Recovery in Outpatients With Mild to Moderate COVID-19: A Randomized Clinical Trial.

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.

Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT network focused on 4 activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Overall, initial results suggest that tailoring of existing multipurpose federated research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data. The initial output of the project was a new approach to decision support for the prescription of naloxone for home use in the ED, which is under further study within the network.

Automatically identifying opioid use disorder in non-cancer patients on chronic opioid therapy.

Background: Using the International Classification of Diseases (ICD) codes alone to record opioid use disorder (OUD) may not completely document OUD in the electronic health record (EHR). We developed and evaluated natural language processing (NLP) approaches to identify OUD from the clinal note. We explored the concordance between ICD-coded and NLP-identified OUD.Methods: We studied EHRs from 13,654 (female: 8223; male: 5431) adult non-cancer patients who received chronic opioid therapy (COT) and had at least one clinical note between 2013 and 2018. Of eligible patients, we randomly selected 10,218 (75%) patients as the training set and the remaining 3436 patients (25%) as the test dataset for NLP approaches.Results: We generated 539 terms representing OUD mentions in clinical notes (e.g., "opioid use disorder," "opioid abuse," "opioid dependence," "opioid overdose") and 73 terms representing OUD medication treatments. By domain expert manual review for the test dataset, our NLP approach yielded high performance: 98.5% for precision, 100% for recall, and 99.2% for F-measure. The concordance of these NLP and ICD identified OUD was modest (Kappa = 0.63).Conclusions: Our NLP approach can accurately identify OUD patients from clinical notes. The combined use of ICD diagnostic code and NLP approach can improve OUD identification.

Barriers Associated with the Implementation of Homework in Youth Mental Health Treatment and Potential Mobile Health Solutions.

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.

Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research.

OBJECTIVE: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. METHODS: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. RESULTS: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. CONCLUSIONS: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

Informatics for public health and health system collaboration: Applications for the control of the current COVID-19 pandemic and the next one.

Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for immunization data, and computable electronic quality measures. Rather than think of these components individually, a comprehensive approach to rapidly adaptable tools for collaboration is needed.

Automated Production of Research Data Marts from a Canonical Fast Healthcare Interoperability Resource (FHIR) Data Repository: Applications to COVID-19 Research.

OBJECTIVE: Objective: The COVID-19 pandemic has enhanced the need for timely real-world data (RWD) for research. To meet this need, several large clinical consortia have developed networks for access to RWD from electronic health records (EHR), each with its own common data model (CDM) and custom pipeline for extraction, transformation, and load operations for production and incremental updating. However, the demands of COVID-19 research for timely RWD (e.g., 2-week delay) make this less feasible. METHODS AND MATERIALS: We describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical model for representation of clinical data for automated transformation to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs and the near automated production of linked clinical data repositories (CDRs) for COVID-19 research using the FHIR subscription standard. The approach was applied to healthcare data from a large academic institution and was evaluated using published quality assessment tools. RESULTS: Six years of data (1.07M patients, 10.1M encounters, 137M laboratory results), were loaded into the FHIR CDR producing 3 linked real-time linked repositories: FHIR, PCORnet, and OMOP. PCORnet and OMOP databases were refined in subsequent post processing steps into production releases and met published quality standards. The approach greatly reduced CDM production efforts. CONCLUSIONS: FHIR and FHIR CDRs can play an important role in enhancing the availability of RWD from EHR systems. The above approach leverages 21 st Century Cures Act mandated standards and could greatly enhance the availability of datasets for research.

Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research.

OBJECTIVE: Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems. MATERIALS AND METHODS: In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes. RESULTS: Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting. DISCUSSION: Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart. CONCLUSION: By applying data warehousing principles to federate data at the "study" level, the RINS project reduced data fragmentation and promoted research systems integration.

Each patient is a research biorepository: informatics-enabled research on surplus clinical specimens via the living BioBank.

The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu.

Leveraging health system telehealth and informatics infrastructure to create a continuum of services for COVID-19 screening, testing, and treatment.

OBJECTIVES: We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care. MATERIALS AND METHODS: Our health system deployed 4 COVID-19 telehealth programs and 4 biomedical informatics innovations to screen and care for COVID-19 patients. Using programmatic and electronic health record data, we describe the implementation and initial utilization. RESULTS: Through collaboration across multidisciplinary teams and strategic planning, 4 telehealth program initiatives have been deployed in response to COVID-19: virtual urgent care screening, remote patient monitoring for COVID-19-positive patients, continuous virtual monitoring to reduce workforce risk and utilization of personal protective equipment, and the transition of outpatient care to telehealth. Biomedical informatics was integral to our institutional response in supporting clinical care through new and reconfigured technologies. Through linking the telehealth systems and the electronic health record, we have the ability to monitor and track patients through a continuum of COVID-19 services. DISCUSSION: COVID-19 has facilitated the rapid expansion and utilization of telehealth and health informatics services. We anticipate that patients and providers will view enhanced telehealth services as an essential aspect of the healthcare system. Continuation of telehealth payment models at the federal and private levels will be a key factor in whether this new uptake is sustained. CONCLUSIONS: There are substantial benefits in utilizing telehealth during the COVID-19, including the ability to rapidly scale the number of patients being screened and providing continuity of care.

An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.

OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits. MATERIALS AND METHODS: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms. RESULTS: Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. CONCLUSIONS: Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.