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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A Patient-Centered Mobile Phone App (iHeartU) With a Virtual Human Assistant for Self-Management of Heart Failure: Protocol for a Usability Assessment Study.

BACKGROUND: Heart failure (HF) causes significant economic and humanistic burden for patients and their families, especially those with a low income, partly due to high hospital readmission rates. Optimal self-care is considered an important nonpharmacological aspect of HF management that can improve health outcomes. Emerging evidence suggests that self-management assisted by smartphone apps may reduce rehospitalization rates and improve the quality of life of patients. We developed a virtual human-assisted, patient-centered mobile health app (iHeartU) for patients with HF to enhance their engagement in self-management and improve their communication with health care providers and family caregivers. iHeartU may help patients with HF in self-management to reduce the technical knowledge and usability barrier while maintaining a low cost and natural, effective social interaction with the user. OBJECTIVE: With a standardized systematic usability assessment, this study had two objectives: (1) to determine the obstacles to effective and efficient use of iHeartU in patients with HF and (2) to evaluate of HF patients' adoption, satisfaction, and engagement with regard to the of iHeartU app. METHODS: The basic methodology to develop iHeartU systems consists of a user-centric design, development, and mixed methods formative evaluation. The iterative design and evaluation are based on the guidelines of the American College of Cardiology Foundation and American Heart Association for the management of heart failure and the validated "Information, Motivation, and Behavioral skills" behavior change model. Our hypothesis is that this method of a user-centric design will generate a more usable, useful, and easy-to-use mobile health system for patients, caregivers, and practitioners. RESULTS: The prototype of iHeartU has been developed. It is currently undergoing usability testing. As of September 2018, the first round of usability testing data have been collected. The final data collection and analysis are expected to be completed by the end of 2019. CONCLUSIONS: The main contribution of this project is the development of a patient-centered self-management system, which may support HF patients' self-care at home and aid in the communication between patients and their health care providers in a more effective and efficient way. Widely available mobile phones serve as care coordination and "no-cost" continuum of care. For low-income patients with HF, a mobile self-management tool will expand their accessibility to care and reduce the cost incurred due to emergency visits or readmissions. The user-centered design will improve the level of engagement of patients and ultimately lead to better health outcomes. Developing and testing a novel mobile system for patients with HF that incorporates chronic disease management is critical for advancing research and clinical practice of care for them. This research fills in the gap in user-centric design and lays the groundwork for a large-scale population study in the next phase. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13502.

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.

Enhancing mHealth Technology in the Patient-Centered Medical Home Environment to Activate Patients With Type 2 Diabetes: A Multisite Feasibility Study Protocol.

BACKGROUND: The potential of mHealth technologies in the care of patients with diabetes and other chronic conditions has captured the attention of clinicians and researchers. Efforts to date have incorporated a variety of tools and techniques, including Web-based portals, short message service (SMS) text messaging, remote collection of biometric data, electronic coaching, electronic-based health education, secure email communication between visits, and electronic collection of lifestyle and quality-of-life surveys. Each of these tools, used alone or in combination, have demonstrated varying degrees of effectiveness. Some of the more promising results have been demonstrated using regular collection of biometric devices, SMS text messaging, secure email communication with clinical teams, and regular reporting of quality-of-life variables. In this study, we seek to incorporate several of the most promising mHealth capabilities in a patient-centered medical home (PCMH) workflow. OBJECTIVE: We aim to address underlying technology needs and gaps related to the use of mHealth technology and the activation of patients living with type 2 diabetes. Stated differently, we enable supporting technologies while seeking to influence patient activation and self-care activities. METHODS: This is a multisite phased study, conducted within the US Military Health System, that includes a user-centered design phase and a PCMH-based feasibility trial. In phase 1, we will assess both patient and provider preferences regarding the enhancement of the enabling technology capabilities for type 2 diabetes chronic care management. Phase 2 research will be a single-blinded 12-month feasibility study that incorporates randomization principles. Phase 2 research will seek to improve patient activation and self-care activities through the use of the Mobile Health Care Environment with tailored behavioral messaging. The primary outcome measure is the Patient Activation Measure scores. Secondary outcome measures are Summary of Diabetes Self-care Activities Measure scores, clinical measures, comorbid conditions, health services resource consumption, and technology system usage statistics. RESULTS: We have completed phase 1 data collection. Formal analysis of phase 1 data has not been completed. We have obtained institutional review board approval and began phase 1 research in late fall 2016. CONCLUSIONS: The study hypotheses suggest that patients can, and will, improve their activation in chronic care management. Improved activation should translate into improved diabetes self-care. Expected benefits of this research to the scientific community and health care services include improved understanding of how to leverage mHealth technology to activate patients living with type 2 diabetes in self-management behaviors. The research will shed light on implementation strategies in integrating mHealth into the clinical workflow of the PCMH setting. TRIAL REGISTRATION: ClinicalTrials.gov NCT02949037. https://clinicaltrials.gov/ct2/show/NCT02949037. (Archived by WebCite at http://www.webcitation.org/6oRyDzqei).

Outdoor Environment and Pediatric Asthma: An Update on the Evidence from North America.

Introduction. The evidence about the association between asthma and outdoor environmental factors has been inadequate for certain allergens. Even less is known about how these associations vary across seasons and climate regions. We reviewed recent literature from North America for research related to outdoor environmental factors and pediatric asthma, with attention to spatial-temporal variations of these associations. Method. We included indexed literature between years 2010 and 2015 on outdoor environmental factors and pediatric asthma, by searching PubMed. Results. Our search resulted in 33 manuscripts. Studies about the link between pediatric asthma and traffic-related air pollutants (TRAP) consistently confirmed the correlation between TRAP and asthma. For general air pollution, the roles of PM2.5 and CO were consistent across studies. The link between asthma and O3 varied across seasons. Regional variation exists in the role of SO2. The impact of pollen was consistent across seasons, whereas the role of polycyclic aromatic hydrocarbon was less consistent. Discussion. Recent studies strengthened the evidence about the roles of PM2.5, TRAP, CO, and pollen in asthma, while the evidence for roles of PM10-2.5, PM10, O3, NO2, SO2, and polycyclic aromatic hydrocarbon in asthma was less consistent. Spatial-temporal details of the environment are needed in future studies of asthma and environment.