Interventional Radiologists Achieve Equivalent Outcomes and Lower Costs for Totally Implantable Venous Access Device Placement Compared to Operating Room Placement.

PURPOSE: To compare the cost and outcomes of surgical and interventional radiology (IR) placement of totally implantable venous access devices (TIVADs) within a large regional health system to determine the service line with better outcomes and lower costs to the health system. MATERIALS AND METHODS: A retrospective review of all chest port placements performed in the operating room (OR) and IR suite over 12 months was conducted at a large, integrated health system with 6 major hospitals. Secondary electronic health record and cost data were used to identify TIVAD placements, follow-up procedures indicating port malfunction, early adverse events (within 1 month after the surgery), late adverse events (2-12 months after the procedure), and health system cost of TIVAD placement and management. RESULTS: For 799 total port placements included in this analysis, the rate of major adverse events was 1.3% and 1.9% for the IR and OR groups, respectively, during the early follow-up (P = .5655) and 4.9% and 2.8% for the IR and OR groups, respectively, during the late follow-up (P = .5437). Malfunction-related follow-up procedure rates were 1.8% and 2.6% for the IR and OR groups, respectively, during the early follow-up (P = .4787) and 12.4% and 10.5% for the IR and OR groups, respectively, during the late follow-up (P = .4354). The mean cost of port placement per patient was $4,509 and $5,247 for the IR and OR groups, respectively. The difference in per-patient cost of port placement was $1,170 greater for the OR group (P = .0074). CONCLUSIONS: The similar rates of adverse events and follow-up procedures and significant differences in insertion cost suggest that IR TIVAD placement may be more cost effective than surgical placement without affecting the quality.

Health Indicators as Measures of Individual Health Status and Their Public Perspectives: Cross-sectional Survey Study.

BACKGROUND: Disease status (eg, cancer stage) has been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and population group life expectancy, have also been used. However, few studies have specifically focused on the comprehensive and objective measures of individual health status. OBJECTIVE: The aim of this study was to analyze the perspectives of the public toward 29 health indicators obtained from a literature review to provide evidence for further prioritization of the indicators. The difference between health status and disease status should be considered. METHODS: This study used a cross-sectional design. Online surveys were administered through Ohio University, ResearchMatch, and Clemson University, resulting in three samples. Participants aged 18 years or older rated the importance of the 29 health indicators. The rating results were aggregated and analyzed as follows (in each case, the dependent variables were the individual survey responses): (1) to determine the agreement among the three samples regarding the importance of each indicator, where the independent variables (IVs) were the three samples; (2) to examine the mean differences between the retained indicators with agreement across the three samples, where the IVs were the identified indicators; and (3) to rank the groups of indicators into various levels after grouping the indicators with no mean differences, where the IVs were the groups of indicators. RESULTS: In total, 1153 valid responses were analyzed. Descriptive statistics revealed that the top five-rated indicators were drug or substance abuse, smoking or tobacco use, alcohol abuse, major depression, and diet and nutrition. Among the 29 health indicators, the three samples agreed upon the importance of 13 indicators. Inferential statistical analysis indicated that some of the 13 indicators held equal importance. Therefore, the 13 indicators were categorized by rank into seven levels: level 1 included blood sugar level and immunization and vaccination; level 2 included LDL cholesterol; level 3 included HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol; level 4 included health literacy rate; level 5 included personal care needs and air quality index greater than 100; level 6 included self-rated health status and HIV testing; and level 7 included the supply of dentists. Levels 1 to 3 were rated significantly higher than levels 4 to 7. CONCLUSIONS: This study provides a baseline for prioritizing 29 health indicators, which can be used by electronic health record or personal health record system designers or developers to determine what can be included in the systems to capture an individual's health status. Currently, self-rated health status is the predominantly used health indicator. Additionally, this study provides a foundation for tracking and measuring preventive health care services more accurately and for developing an individual health status index.

Enhancing Patient Activation and Self-Management Activities in Patients With Type 2 Diabetes Using the US Department of Defense Mobile Health Care Environment: Feasibility Study.

BACKGROUND: Past mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information. OBJECTIVE: The primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense's Mobile Health Care Environment (MHCE) in a patient-centered medical home setting. METHODS: A multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes. RESULTS: The final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA1c) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA1c (control mean -0.53, intervention mean -0.11; P=.006), and low-density lipoprotein cholesterol (control mean -7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA1c (control mean -0.09, intervention mean -0.52; P=.04), BMI (control mean 0.58, intervention mean -1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean -4.86, intervention mean 3.56; P<.001). Significant improvements were seen in AM scores, SDSCA, and waist size for both groups and in diastolic and systolic blood pressure for the control group; the between-group differences were not statistically significant. The percentage of participants who were engaged with MHCE for ≥50% of days period was 60.7% (68/112; months 0-3), 57.4% (62/108; months 3-6), 49.5% (51/103; months 6-9), and 43% (42/98; months 9-12). CONCLUSIONS: Our study produced mixed results with improvement in PAM scores and outcomes in both the intervention and control groups. Structural design issues may have hampered the influence of tailored behavioral messaging within the intervention group. TRIAL REGISTRATION: ClinicalTrials.gov NCT02949037; https://clinicaltrials.gov/ct2/show/NCT02949037. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.6993.

Effect of clinical decision rules, patient cost and malpractice information on clinician brain CT image ordering: a randomized controlled trial.

BACKGROUND: The frequency of head computed tomography (CT) imaging for mild head trauma patients has raised safety and cost concerns. Validated clinical decision rules exist in the published literature and on-line sources to guide medical image ordering but are often not used by emergency department (ED) clinicians. Using simulation, we explored whether the presentation of a clinical decision rule (i.e. Canadian CT Head Rule - CCHR), findings from malpractice cases related to clinicians not ordering CT imaging in mild head trauma cases, and estimated patient out-of-pocket cost might influence clinician brain CT ordering. Understanding what type and how information may influence clinical decision making in the ordering advanced medical imaging is important in shaping the optimal design and implementation of related clinical decision support systems. METHODS: Multi-center, double-blinded simulation-based randomized controlled trial. Following standardized clinical vignette presentation, clinicians made an initial imaging decision for the patient. This was followed by additional information on decision support rules, malpractice outcome review, and patient cost; each with opportunity to modify their initial order. The malpractice and cost information differed by assigned group to test the any temporal relationship. The simulation closed with a second vignette and an imaging decision. RESULTS: One hundred sixteen of the 167 participants (66.9%) initially ordered a brain CT scan. After CCHR presentation, the number of clinicians ordering a CT dropped to 76 (45.8%), representing a 21.1% reduction in CT ordering (P = 0.002). This reduction in CT ordering was maintained, in comparison to initial imaging orders, when presented with malpractice review information (p = 0.002) and patient cost information (p = 0.002). About 57% of clinicians changed their order during study, while 43% never modified their imaging order. CONCLUSION: This study suggests that ED clinician brain CT imaging decisions may be influenced by clinical decision support rules, patient out-of-pocket cost information and findings from malpractice case review. TRIAL REGISTRATION: NCT03449862 , February 27, 2018, Retrospectively registered.

Implementation of a mobile health clinic framework for Hepatitis C virus screening and treatment: a descriptive study.

Background: Although treatment for Hepatitis C Virus (HCV) is effective, individuals face access barriers. The utility of mobile health clinics (MHC), effective mechanisms for providing healthcare to underserved populations, is understudied for HCV-related interventions. We aimed to describe implementation of, and factors associated with, screening and treatment via MHCs. Methods: Clemson Rural Health implemented a novel MHC program to reach and treat populations at-risk for HCV with a focus on care for uninsured individuals. We examined HCV screening and treatment initiation/completion indicators between May 2021 and January 2023. Findings: Among 607 individuals screened across 31 locations, 94 (15.5%) tested positive via antibody and viral load testing. Treatment initiation and completion rates were 49.6% and 86.0%, respectively. Among those screened, the majority were male (57.5%), White (61.3%; Black/Hispanic: 28.2%/7.7%), and without personal vehicle as primary transportation mode (54.4%). Injection drug use (IDU) was 27.2% and uninsured rate was 42.8%. Compared to HCV-negative, those infected included more individuals aged 30-44 (52.1% vs. 36.4%, p = 0.023), male (70.2% vs. 55.2%, p = 0.009), White (78.5% vs. 60.2%, p < 0.0001), without personal vehicle (58.5% vs. 43.5%, p = 0.028), IDU (83.7% vs. 21.0%, p < 0.0001), and uninsured (61.2% vs. 48.8%, p = 0.050). Uninsured rates were higher among those initiating compared to not initiating treatment (74.5% vs. 45.3%, p = 0.004). Interpretation: The MHC framework successfully reaching its target population: at-risk individuals with access barriers to healthcare. The high HCV screening and treatment initiation/completion rates demonstrate the utility of MHCs as effective and acceptable intervention settings among historically difficult-to-treat populations. Funding: Gilead Sciences, Inc., and SC Center for Rural and Primary Healthcare.

Radiation exposure and cost influence physician medical image decision making: a randomized controlled trial.

BACKGROUND: It is estimated that 20%-40% of advanced medical imaging in the United States is unnecessary, resulting in patient overexposure to radiation and increasing the cost of care. Previous imaging utilization studies have focused on clinical appropriateness. An important contributor to excessive use of advanced imaging may be a physician "knowledge gap" regarding the safety and cost of the tests. OBJECTIVES: To determine whether safety and cost information will change physician medical image decision making. RESEARCH DESIGN: Double-blinded, randomized controlled trial. Following standardized case presentation, physicians made an initial imaging choice. This was followed by the presentation of guidelines, radiation exposure and health risk, and cost information. RESULTS: Approximately half (57 of 112, 50.9%) of participants initially selected computed tomography (CT). When presented with guideline recommendations, participants did not modify their initial imaging choice (P=0.197). A significant reduction (56.3%, P<0.001) in CT ordering occurred after presentation of radiation exposure/health risk information; ordering changed to magnetic resonance imaging or ultrasound (US). A significant reduction (48.3%, P<0.001) in CT and magnetic resonance imaging ordering occurred after presentation of Medicare reimbursement information; ordering changed to US. The majority of physicians (31 of 40, 77.5%) selecting US never modified their ordering. No significant relationship between physician demographics and decision making was observed. CONCLUSIONS: This study suggests that physician decision making can be influenced by safety and cost information and the order in which information is provided to physicians can affect their decisions.

Ontologies Applied in Clinical Decision Support System Rules: Systematic Review.

BACKGROUND: Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. OBJECTIVE: Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. METHODS: The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. RESULTS: CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. CONCLUSIONS: Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.

The association of urbanicity and travel time with lung cancer screening utilization.

BACKGROUND: To examine 1) the rate of lung cancer screening (LCS) utilization in a large healthcare system in South Carolina; 2) associations of urbanicity and travel time with LCS utilization. METHODS: LCS-eligible patients from 2019 were identified. The outcome was LCS utilization. The exposures were zip-code level urbanicity and travel time from the centroid of zip-code area to the nearest screening site (<10,10-<20, ≥20 min). Covariates included age, sex, race, marital status, insurance, body mass index, chronic obstructive pulmonary disease, Charlson Comorbidity Index (0, 1, 2, ≥3), and zip-code level median income. Chi-square tests and logistic regressions were employed. RESULTS: The analysis included 6930 patients, among whom 1432 (20.66%) received LCS. After adjusting for covariates, living in a non-metropolitan area (adjusted odds ratio: 0.32; 95% confidence interval: 0.26-0.40) and having longer travel time (0.80 [0.65-0.98] and 0.68 [0.54-0.86] for 10-<20 and ≥20 min travel time, respectively, compared to <10 min travel time) were significantly associated with lower odds of LCS utilization. CONCLUSIONS: The LCS utilization rate of a healthcare system was about 20% in 2019. Living in non-metropolitan areas or having longer travel time to LCS site were associated with lower LCS utilization.

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning.

Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for data labeling requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised KP identification framework using minimal labeled data based on hierarchical attention over the documents and domain adaptation. Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited gold standard label data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify KPs, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging, and light-weighted deep learning models play a role in real-time KP identification as a complementary approach to human experts' effort.

Quality of outpatient clinical notes: a stakeholder definition derived through qualitative research.

BACKGROUND: There are no empirically-grounded criteria or tools to define or benchmark the quality of outpatient clinical documentation. Outpatient clinical notes document care, communicate treatment plans and support patient safety, medical education, medico-legal investigations and reimbursement. Accurately describing and assessing quality of clinical documentation is a necessary improvement in an increasingly team-based healthcare delivery system. In this paper we describe the quality of outpatient clinical notes from the perspective of multiple stakeholders. METHODS: Using purposeful sampling for maximum diversity, we conducted focus groups and individual interviews with clinicians, nursing and ancillary staff, patients, and healthcare administrators at six federal health care facilities between 2009 and 2011. All sessions were audio-recorded, transcribed and qualitatively analyzed using open, axial and selective coding. RESULTS: The 163 participants included 61 clinicians, 52 nurse/ancillary staff, 31 patients and 19 administrative staff. Three organizing themes emerged: 1) characteristics of quality in clinical notes, 2) desired elements within the clinical notes and 3) system supports to improve the quality of clinical notes. We identified 11 codes to describe characteristics of clinical notes, 20 codes to describe desired elements in quality clinical notes and 11 codes to describe clinical system elements that support quality when writing clinical notes. While there was substantial overlap between the aspects of quality described by the four stakeholder groups, only clinicians and administrators identified ease of translation into billing codes as an important characteristic of a quality note. Only patients rated prioritization of their medical problems as an aspect of quality. Nurses included care and education delivered to the patient, information added by the patient, interdisciplinary information, and infection alerts as important content. CONCLUSIONS: Perspectives of these four stakeholder groups provide a comprehensive description of quality in outpatient clinical documentation. The resulting description of characteristics and content necessary for quality notes provides a research-based foundation for assessing the quality of clinical documentation in outpatient health care settings.

A Systematic Approach to Configuring MetaMap for Optimal Performance.

BACKGROUND: MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. OBJECTIVE: To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. METHODS: MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (ß =1) were calculated. RESULTS: The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively. CONCLUSION: We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Intervention to Reduce Interobserver Variability in Computed Tomographic Measurement of Cancer Lesions Among Experienced Radiologists.

While a growing number of research studies have reported the inter-observer variability in computed tomographic (CT) measurements, there are very few interventional studies performed. We aimed to assess whether a peer benchmarking intervention tool may have an influence on reducing interobserver variability in CT measurements and identify possible barriers to the intervention. In this retrospective study, 13 board-certified radiologists repeatedly reviewed 10 CT image sets of lung lesions and hepatic metastases during 3 noncontiguous time periods (T1, T2, T3). Each preselected case contained normal anatomy cephalad and caudal to the lesion of interest. Lesion size measurement under RECISTS 1.1 guidelines, choice of CT slice, and time spent on measurement were captured. Prior to their final measurements, the participants were exposed to the intervention designed to reduce the number of measurements deviating from the median. Chi-square test was performed to identify radiologist-dependent factors associated with the variability. The percent of deviating measurements during T1 and T2 were 20.0% and 23.1%, respectively. There was no statistically significant change in the number of deviating measurements upon the presentation of the intervention despite the decrease in percent from 23.1% to 17.7%. The identified barriers to the intervention include clinical disagreements among radiologists. Specifically, the inter-observer variability was associated with the controversy over the choice of CT image slice (P = 0.045) and selection of start-point, axis, and end-point (P = 0.011). Clinical disagreements rather than random errors were barriers to reducing interobserver variability in CT measurement among experienced radiologists. Future interventions could aim to resolve the disagreement in an interactive approach.

Deep learning for semi-automated unidirectional measurement of lung tumor size in CT.

BACKGROUND: Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement. PURPOSE: The aim of this study is to develop and evaluate deep learning (DL) algorithm for semi-automated unidirectional CT measurement of lung lesions. METHODS: This retrospective study included 1617 lung CT images from 8 publicly open datasets. A convolutional neural network was trained using 1373 training and validation images annotated by two radiologists. Performance of the DL algorithm was evaluated 244 test images annotated by one radiologist. DL algorithm's measurement consistency with human radiologist was evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman plotting. Bonferroni's method was used to analyze difference in their diagnostic behavior, attributed by tumor characteristics. Statistical significance was set at p < 0.05. RESULTS: The DL algorithm yielded ICC score of 0.959 with human radiologist. Bland-Altman plotting suggested 240 (98.4 %) measurements realized within the upper and lower limits of agreement (LOA). Some measurements outside the LOA revealed difference in clinical reasoning between DL algorithm and human radiologist. Overall, the algorithm marginally overestimated the size of lesion by 2.97 % compared to human radiologists. Further investigation indicated tumor characteristics may be associated with the DL algorithm's diagnostic behavior of over or underestimating the lesion size compared to human radiologist. CONCLUSIONS: The DL algorithm for unidirectional measurement of lung tumor size demonstrated excellent agreement with human radiologist.

Diagnosis-to-surgery interval and survival for different histologies of stage I-IIA lung cancer.

BACKGROUND: Guidelines on timeliness of lung cancer surgery are inconsistent. Lung cancer histologic subtypes have different prognosis and treatment. It is important to understand the consequences of delayed surgery for each lung cancer histologic subtype. This study aimed to examine the association between diagnosis-to-surgery time interval and survival for early stage lung cancer and selected histologic subtypes. METHODS: Patients diagnosed with stage I-IIA lung cancer between 2004 and 2015 receiving definitive surgery and being followed up until Dec. 31, 2018, were identified from Surveillance, Epidemiology, and End Results database. Histologic subtypes included adenocarcinoma, squamous or epidermoid carcinoma, bronchioloalveolar carcinoma, large cell carcinoma, adenosquamous carcinoma, carcinoid carcinoma, and small cell carcinoma. Diagnosis-to-surgery interval was treated as multi-categorical variables (<1, 1-2, 2-3, and ≥3 months) and binary variables (≥1 vs. <1 month, ≥2 vs. <2 months, and ≥3 vs. <3 months). Outcomes included cancer-specific and overall survival. Covariates included age at diagnosis, sex, race, marital status, tumor size, grade, surgery type, chemotherapy, radiotherapy, and study period. Kaplan-Meier survival curves and Cox proportional hazards regression models were applied to examine the survival differences. RESULTS: With a median follow-up time of 51 months, a total of 40,612 patients were analyzed, including 40.1% adenocarcinoma and 24.5% squamous or epidermoid carcinoma. The proportion of patients receiving surgery <1, 1-2, 2-3, and ≥3 months from diagnosis were 34.2%, 33.9%, 19.8%, and 12.1%, respectively. Delayed surgery was associated with worse cancer-specific and overall survival for all lung cancers, adenocarcinoma, squamous or epidermoid, bronchioloalveolar, and large cell carcinoma (20-40% increased risk). Dose-dependent effects (longer delay, worse survival) were observed in all lung cancers, adenocarcinoma, and squamous and epidermoid carcinoma. No significant association between surgery delay and survival was observed in adenosquamous, carcinoid, and small cell carcinoma. CONCLUSIONS: Our findings support the guidelines of undertaking surgery within 1 month from diagnosis in patients with stage I-IIA lung cancer. The observed dose-dependent effects emphasize the clinical importance of early surgery. Future studies with larger sample size of less frequent histologic subtypes are warranted to provide more evidence for histology-specific lung cancer treatment guidelines.

Diabetes Patient Surveillance in the Emergency Department: Proof of Concept and Opportunities.

INTRODUCTION: The purpose of this study was to characterize the at-risk diabetes and prediabetes patient population visiting emergency department (ED) and urgent care (UC) centers in upstate South Carolina. METHODS: We conducted this retrospective study at the largest non-profit healthcare system in South Carolina, using electronic health record (EHR) data of patients who had an ED or UC visit between February 2, 2016-July 31, 2018. Key variables including International Classification of Diseases, 10th Revision codes, laboratory test results, family history, medication, and demographic characteristics were used to classify the patients as healthy, having prediabetes, having diabetes, being at-risk for prediabetes, or being at-risk for diabetes. Patients who were known to have diabetes were classified further as having controlled diabetes, management challenged, or uncontrolled diabetes. Population analysis was stratified by the patient's annual number of ED/UC visits. RESULTS: The risk stratification revealed 4.58% unique patients with unrecognized diabetes and 10.34% of the known patients with diabetes considered to be suboptimally controlled. Patients identified as diabetes management challenged had more ED/UC visits. Of note, 33.95% of the patients had unrecognized prediabetes/diabetes risk factors identified during their ED/UC with 87.95% having some form of healthcare insurance. CONCLUSION: This study supports the idea that a single ED/UC unscheduled visit can identify individuals with unrecognized diabetes and an at-risk prediabetes population using EHR data. A patient's ED/UC visit, regardless of their primary reason for seeking care, may be an opportunity to provide early identification and diabetes disease management enrollment to augment the medical care of our community.

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.

BACKGROUND: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. OBJECTIVE: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. METHODS: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. RESULTS: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P<.001). CONCLUSIONS: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation.

America's "undiscovered" laboratory for health services research.

Debate over reforming the nation's healthcare system has stimulated a need for health services research (HSR) models that are nationally applicable. Toward this end, the authors identify the Military Health System (MHS) as America's "undiscovered" laboratory for HSR. Although many may confuse the MHS with the Department of Veterans Affairs (VA), the 2 systems vary dramatically with respect to their beneficiary populations, access to care, and other important attributes. In this article, the authors describe key characteristics of the MHS including its large beneficiary base, its direct care operating environment, its dedicated medical school and graduate education programs, and its fully operational integrated health information system. Although a few health systems (eg, Kaiser Permanente, Partners Healthcare, and Department of Veterans Affairs) possess some characteristics, no other has all of these components in place. This article sets the stage for contemporary HSR studies with broad applicability to current issues in American healthcare that could be performed within the MHS. Inclusion of the MHS environment in HSR studies of health services delivery modalities, adoption of health information technology, access to care, relationship of medical education to effective safe care delivery, health disparities, child health, and behavioral health would provide strong underpinnings for proposed changes in American healthcare delivery. Finally, the article highlights current regulatory barriers to research within the MHS whereas suggesting steps to minimize their impact in conducting HSR.

Retrospective comparison of approaches to evaluating inter-observer variability in CT tumour measurements in an academic health centre.

BACKGROUND: A growing number of research studies have reported inter-observer variability in sizes of tumours measured from CT scans. It remains unclear whether the conventional statistical measures correctly evaluate the CT measurement consistency for optimal treatment management and decision-making. We compared and evaluated the existing measures for evaluating inter-observer variability in CT measurement of cancer lesions. METHODS: 13 board-certified radiologists repeatedly reviewed 10 CT image sets of lung lesions and hepatic metastases selected through a randomisation process. A total of 130 measurements under RECIST 1.1 (Response Evaluation Criteria in Solid Tumors) guidelines were collected for the demonstration. Intraclass correlation coefficient (ICC), Bland-Altman plotting and outlier counting methods were selected for the comparison. The each selected measure was used to evaluate three cases with observed, increased and decreased inter-observer variability. RESULTS: The ICC score yielded a weak detection when evaluating different levels of the inter-observer variability among radiologists (increased: 0.912; observed: 0.962; decreased: 0.990). The outlier counting method using Bland-Altman plotting with 2SD yielded no detection at all with its number of outliers unchanging regardless of level of inter-observer variability. Outlier counting based on domain knowledge was more sensitised to different levels of the inter-observer variability compared with the conventional measures (increased: 0.756; observed: 0.923; improved: 1.000). Visualisation of pairwise Bland-Altman bias was also sensitised to the inter-observer variability with its pattern rapidly changing in response to different levels of the inter-observer variability. CONCLUSIONS: Conventional measures may yield weak or no detection when evaluating different levels of the inter-observer variability among radiologists. We observed that the outlier counting based on domain knowledge was sensitised to the inter-observer variability in CT measurement of cancer lesions. Our study demonstrated that, under certain circumstances, the use of standard statistical correlation coefficients may be misleading and result in a sense of false security related to the consistency of measurement for optimal treatment management and decision-making.

Factors influencing lung cancer screening completion following participation in shared decision-making: A retrospective study in a U.S. academic health system.

PURPOSE: Shared decision making (SDM) between patients and designated health professionals is recommended by several professional organizations prior to lung cancer screening by low dose CT (LDCT). This study seeks to identify factors, including characteristics of patients and referring clinicians, that influence LDCT screening completion following participation in SDM. MATERIALS AND METHODS: This retrospective study consisted of n = 171 patients eligible for LDCT screening and who participated in SDM between 2016 and 2017 in one of two sites in Prisma Health, an academic health care delivery system in South Carolina. Patient characteristics included age, sex, race, body mass index, marital status, insurance, smoking status and history, family history of lung cancer, SDM site, and distance to screening site. Characteristics of referred clinicians included age, sex, race, specialty, years of practice, education, and residency. Descriptive statistics and multivariable generalized linear mixed models were used to compare effects of patient and referring clinician characteristics on LDCT completion. RESULTS: A total of 152 patients (89%) completed LDCT screening after participation in SDM. SDM site (p = 0.02), longer distances to the screening site (p = 0.03), referrals from internal medicine clinicians (p = 0.03), and referrals from younger clinicians (p = 0.01) and from those with less years of experience (p = 0.02) were significantly associated with a lower likelihood of screening completion. CONCLUSIONS: Several factors significantly associated with screening completion were identified. This information can assist with development of interventions to improve communication and decision-making between patients, clinicians, and SDM health professionals, and inform design of targeted decision aids embedded into SDM procedures.

Assessing the legal and ethical preparedness of master of public health graduates.

OBJECTIVES: We explored the relationship between the preparedness of master of public health (MPH) graduates in public health law and ethics and their completion of courses in these areas. METHODS: We reviewed accredited public health schools and programs to assess the supply of required and elective courses in law and ethics. In addition, we conducted an Internet-based scenario survey of MPH graduates. Survey results were analyzed, and relationships between scenario responses and completion of law and ethics courses were assessed. RESULTS: Of the 93 programs and schools reviewed, 14% required a course in ethics and 16% required a course in law. The majority (range = 55%-76%) of the survey respondents indicated being "prepared" or "very prepared" for each of the 9 public health scenarios. There were no significant relationships between scenario responses and completion of an ethics course. Responses to 2 scenarios (one involving food code violations and one involving a prison population) were significantly related to participants' completion of a course in law. CONCLUSIONS: Few public health schools and programs require graduate courses in ethics and law. Most MPH graduates report being prepared to address public health challenges. Additional research is necessary to improve techniques for measuring preparedness.