Study objective:We conducted a systematic literature review to identify and to update patient characteristics and contextual factors for adult frequent emergency department users (FEDUs) compared with non-FEDU in an era where the US health care system underwent substantial changes. Methods:We searched MEDLINE, CINAHL, and EMBASE to identify all relevant articles after 2010 through July 2018 that describe FEDU. We included US studies on adult FEDU only and excluded studies on specific subgroups of FEDU. We included demographic, clinical, and health care utilization information, and two reviewers independently evaluated the studies using the Joanna Briggs Institute Critical Appraisal tool. Results:The 11 studies included in the review indicated that FEDU were 4% to 16% of total ED users but accounted for 14% to 47% of ED visits, with six to nine visits per year on average. The majority of FEDU were young or middle-aged adults, females, of low socioeconomic status and high school or less education, with public insurance, multiple primary care provider visits, and chronic conditions. Fair or poor self-perceived health status, unemployment, unmet needs from primary care providers (PCPs), mental health, and substance abuse were predictors of FEDU. Conclusion:FEDUs are disproportionally sicker and are also heavy users of non-ED health care service providers. The limited data for non-ED health services use in facility-specific studies of FEDU may contribute to findings in such studies that complex and unmet needs from PCPs contributed to ED visits. This suggests the need for more comprehensive data analysis beyond a few sites that can inform systemic management approaches.

Background & Aims: Hepatocellular carcinoma (HCC) screening of patients with cirrhosis is recommended by professional societies to increase detection of early-stage tumors and survival but is underused in clinical practice. Methods: We conducted a retrospective cohort study of 13,714 patients diagnosed with HCC from 2003 through 2013 included in the Surveillance, Epidemiology, and End Results Program-Medicare database. We characterized receipt of HCC screening in the 3 years before HCC diagnosis using mutually exclusive categories (consistent vs inconsistent vs no screening) and proportion of time covered with screening. Correlates for screening receipt were assessed using a multivariable 2-part regression model. We examined the association between screening receipt and early detection of tumors using multivariable logistic regression. We evaluated associations

BackgroundDiabetes self-management involves adherence to healthy daily habits typically involving blood glucose monitoring, medication, exercise, and diet. To support self-management, some providers have begun testing remote interventions for monitoring and assisting patients between clinic visits. Although some studies have shown success, there are barriers to widespread adoption.ObjectiveThe objective of our study was to identify and classify barriers to adoption of remote health for management of type 2 diabetes.MethodsThe following 6 electronic databases were searched for articles published from 2010 to 2015: MEDLINE (Ovid), Embase (Ovid), CINAHL, Cochrane Central, Northern Light Life Sciences Conference Abstracts, and Scopus (Elsevier). The search identified studies involving remote technologies for type 2 diabetes self-management. Reviewers worked in teams of 2 to review and extract data from identified papers. Information collected included study characteristics, outcomes, dropout rates, technologies used, and barriers identified.ResultsA total of 53 publications on 41 studies met the specified criteria. Lack of data accuracy due to input bias (32%, 13/41), limitations on scalability (24%, 10/41), and technology illiteracy (24%, 10/41) were the most commonly cited barriers. Technology illiteracy was most prominent in low-income populations, whereas limitations on scalability were more prominent in mid-income populations. Barriers identified were applied to a conceptual model of successful remote health, which includes patient engagement, patient technology accessibility, quality of care, system technology cost, and provider productivity. In total, 40.5% (60/148) of identified barrier instances impeded patient engagement, which is manifest in the large dropout rates cited (up to 57%).ConclusionsThe barriers identified represent major challenges in the design of remote health interventions for diabetes. Breakthrough technologies and systems are needed to alleviate the barriers identified so far, particularly those associated with patient engagement. Monitoring devices that provide objective and reliable data streams on medication, exercise, diet, and glucose monitoring will be essential for widespread effectiveness. Additional work is needed to understand root causes of high dropout rates, and new interventions are needed to identify and assist those at the greatest risk of dropout. Finally, future studies must quantify costs and benefits to determine financial sustainability.

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Sequential pattern mining is an important data mining task with broad applications. However, conventional methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns approximately shared by many sequences. To attack these problems, in this paper, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. We present an efficient and effective algorithm, ApproxMAP (for APPROXimate Multiple Alignment Pattern mining), to mine consensus patterns from large sequence databases. The method works in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. A novel structure called weighted sequence is used to compress the alignment result. For each cluster, the longest consensus pattern best representing the cluster is generated from its weighted sequence. Our extensive experimental results on both synthetic and real data sets show that ApproxMAP is robust to noise and both effective and efficient in mining approximate sequential patterns from noisy sequence databases with lengthy sequences. In particular, we report a successful case of mining a real data set which triggered important investigations in welfare services.

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Effective development and implementation of strategies to improve screening rates should aim at improving access to health care, taking into account demographic characteristics such as rural versus urban residence.

BackgroundPosttraumatic stress disorder (PTSD) is a prevalent mental health issue among veterans. Access to PTSD treatment is influenced by geographic (ie, travel distance to facilities), temporal (ie, time delay between services), financial (ie, eligibility and cost of services), and cultural (ie, social stigma) barriers.ObjectiveThe emergence of mobile health (mHealth) apps has the potential to bridge many of these access gaps by providing remote resources and monitoring that can offer discrete assistance to trauma survivors with PTSD and enhance patient-clinician relationships. In this study, we investigate the current mHealth capabilities relevant to PTSD.MethodsThis study consists of two parts: (1) a review of publicly available PTSD apps designed to determine the availability of PTSD apps, which includes more detailed information about three dominant apps and (2) a scoping literature review performed using a systematic method to determine app usage and efforts toward validation of such mHealth apps. App usage relates to how the end users (eg, clinicians and patients) are interacting with the app, whereas validation is testing performed to ensure the app’s purpose and specifications are met.ResultsThe results suggest that though numerous apps have been developed to aid in the diagnosis and treatment of PTSD symptoms, few apps were designed to be integrated with clinical PTSD treatment, and minimal efforts have been made toward enhancing the usability and validation of PTSD apps.ConclusionsThese findings expose the need for studies relating to the human factors evaluation of such tools, with the ultimate goal of increasing access to treatment and widening the app adoption rate for patients with PTSD.