330,119 research outputs found

    Extending remote patient monitoring with mobile real time clinical decision support

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    Large scale implementation of telemedicine services such as telemonitoring and teletreatment will generate huge amounts of clinical data. Even small amounts of data from continuous patient monitoring cannot be scrutinised in real time and round the clock by health professionals. In future huge volumes of such data will have to be routinely screened by intelligent software systems. We investigate how to make m-health systems for ambulatory care more intelligent by applying a Decision Support approach in the analysis and interpretation of biosignal data and to support adherence to evidence-based best practice such as is expressed in treatment protocols and clinical practice guidelines. The resulting Clinical Decision Support Systems must be able to accept and interpret real time streaming biosignals and context data as well as the patient’s (relatively less dynamic) clinical and administrative data. In this position paper we describe the telemonitoring/teletreatment system developed at the University of Twente, based on Body Area Network (BAN) technology, and present our vision of how BAN-based telemedicine services can be enhanced by incorporating mobile real time Clinical Decision Support. We believe that the main innovative aspects of the vision relate to the implementation of decision support on a mobile platform; incorporation of real time input and analysis of streaming\ud biosignals into the inferencing process; implementation of decision support in a distributed system; and the consequent challenges such as maintenance of consistency of knowledge, state and beliefs across a distributed environment

    The use of decision support to measure documented adherence to a national imaging quality measure

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    RATIONALE AND OBJECTIVES: Present methods for measuring adherence to national imaging quality measures often require a resource-intensive chart review. Computerized decision support systems may allow for automated capture of these data. We sought to determine the feasibility of measuring adherence to a national quality measure (NQM) regarding computed tomography pulmonary angiograms (CTPAs) for pulmonary embolism using measure-targeted clinical decision support and whether the associated increased burden of data captured required by this system would affect the use and yield of CTs. MATERIALS AND METHODS: This institutional review board-approved prospective cohort study enrolled patients from September 1, 2009, through November 30, 2011, in the emergency department (ED) of a 776-bed quaternary-care adults-only academic medical center. Our intervention consisted of an NQM-targeted clinical decision support tool for CTPAs, which required mandatory input of the Wells criteria and serum D-dimer level. The primary outcome was the documented adherence to the quality measure prior and subsequent to the intervention, and the secondary outcomes were the use and yield of CTPAs. RESULTS: A total of 1209 patients with suspected PE (2.0% of 58,795 ED visits) were imaged by CTPA during the 12-month control period, and 1212 patients were imaged in the 12 months after the quarter during which the intervention was implemented (2.0% of 59,478 ED visits, P = .84). Documented baseline adherence to the NQM was 56.9% based on a structured review of the provider notes. After implementation, documented adherence increased to 75.6% (P \u3c .01). CTPA yield remained unchanged and was 10.4% during the control period and 10.1% after the intervention (P = .88). CONCLUSIONS: Implementation of a clinical decision support tool significantly improved documented adherence to an NQM, enabling automated measurement of provider adherence to evidence without the need for resource-intensive chart review. It did not adversely affect the use or yield of CTPAs

    Predicting asthma exacerbations employing remotely monitored adherence

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    This Letter investigated the efficacy of a decision-support system, designed for respiratory medicine, at predicting asthma exacerbations in a multi-site longitudinal randomised control trial. Adherence to inhaler medication was acquired over 3 months from patients with asthma employing a dose counter and a remote monitoring adherence device which recorded participant\u27s inhaler use: n = 184 (23,656 audio files), 61% women, age (mean ± sd) 49.3 ± 16.4. Data on occurrence of exacerbations was collected at three clinical visits, 1 month apart. The relative risk of an asthma exacerbation for those with good and poor adherence was examined employing a univariate and multivariate modified Poisson regression approach; adjusting for age, gender and body mass index. For all months dose counter adherence was significantly (p \u3c 0.01) higher than remote monitoring adherence. Overall, those with poor adherence had a 1.38 ± 0.34 and 1.42 ± 0.39 (remotely monitored) and 1.25 ± 0.32 and 1.18 ± 0.31 (dose counter) higher relative risk of an exacerbation in model 1 and model 2, respectively. However, this was not found to be statistically significantly different. Remotely monitored adherence holds important clinical information and future research should focus on refining adherence and exacerbation measures. Decision-support systems based on remote monitoring may enhance patient-physician communication, possibly reducing preventable adverse events

    Improving Medication Adherence for Chronic Disease Using Integrated e-Technologies

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    Diabetes mellitus (DM) is a chronic disease affecting more than 285 people worldwide and the fourth leading cause of death. Increasing evidence suggests that many DM patients have poor adherence with prescribed medication therapies, impacting clinical outcomes. Patients' barriers to medication adherence and the extent to which barriers contribute to poor outcomes, however, are not routinely assessed. We designed a dashboard for an electronic health record system to integrate DM disease and medication data, including patient-reported barriers to adherence. Processes to support routine capture of data from patients are also being explored. The dashboard is being evaluated at multiple ambulatory clinics to examine whether integrated electronic tools can support patient-centered decision-making processes involving complex medication regimens for DM and other chronic diseases

    Master of Science

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    thesisAsthma, diabetes, and depression are chronic diseases managed through the Primary Care Clinical Program at Intermountain Healthcare. Primary Care Providers (PCPs) receive monthly reports on their patients with these conditions. The reporting paradigm focuses on individual diseases. PCPs have asked for a consolidated view of chronic disease, one that is patient-centric rather than disease-centric. A clinical decision support tool was developed using data from Intermountain's enterprise data warehouse. A cube was built to report on asthma, diabetes, and depression patients simultaneously. 183, 000 patients were included in the study. The tool measures PCP's adherence to best practices for chronic disease management. It also allows ad-hoc analysis of large data sets as well as actionable reports for PCPs to identify gaps in adherence to best practices. Primary care providers can view their patient populations with asthma, diabetes and depression in a consolidated report. The decision support tool was successfully built as a prototype for chronic disease management. The tool has the potential to scale and include many chronic conditions for reporting. It was demonstrated to executives, directors, and PCPs at Intermountain. Chronic disease management should be done with a patient focus rather than a disease focus. Information technology has an important role to play in the support of iv primary care and the medical home. Clinical decision support tools can be built to improve population-level and patient-level chronic disease management

    Adherence to medication in adults with Cystic Fibrosis: An investigation using objective adherence data and the Theoretical Domains Framework

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    Objectives Adherence to nebulizer treatment in adults with Cystic Fibrosis (CF) is poor, and interventions are needed. This research aimed to identify the factors affecting nebulizer adherence using the Theoretical Domains Framework (TDF) and to compare these for participants with different levels of adherence. Design Data‐prompted interviews using the TDF. Methods Eighteen semi‐structured interviews were conducted with adults with CF during which objectively measured adherence data were discussed. Framework analysis was used to code the data into TDF domains, and inductive qualitative content analysis was used to code different beliefs and experiences. Aspects of the TDF that differed between participants with different adherence levels were explored. Results Factors influencing adherence to treatment included all 14 domains of the TDF, 10 of which appeared to vary by adherence level: Skills; Memory and decision‐making; and Behavioural regulation; Environmental context and resources; Social influences; Beliefs about consequences; Beliefs about capability; Reinforcement; Social role and identify; Intentions; Optimism; and Emotions. Conclusions This study is the first to use objectively measured adherence data in a data‐prompted interview using the TDF framework to systematically assess the full range of factors potentially influencing adherence. The results highlighted that interventions need to consider issues of capability, opportunity, and motivation. Interventions that challenge dysfunctional beliefs about adherence and which support the development of routines or habits and problem‐solving may be particularly useful for adults with CF

    An ECOOP web portal for visualising and comparing distributed coastal oceanography model and in situ data

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    As part of a large European coastal operational oceanography project (ECOOP), we have developed a web portal for the display and comparison of model and in situ marine data. The distributed model and in situ datasets are accessed via an Open Geospatial Consortium Web Map Service (WMS) and Web Feature Service (WFS) respectively. These services were developed independently and readily integrated for the purposes of the ECOOP project, illustrating the ease of interoperability resulting from adherence to international standards. The key feature of the portal is the ability to display co-plotted timeseries of the in situ and model data and the quantification of misfits between the two. By using standards-based web technology we allow the user to quickly and easily explore over twenty model data feeds and compare these with dozens of in situ data feeds without being concerned with the low level details of differing file formats or the physical location of the data. Scientific and operational benefits to this work include model validation, quality control of observations, data assimilation and decision support in near real time. In these areas it is essential to be able to bring different data streams together from often disparate locations

    Preliminary support for the construct of health care empowerment in the context of treatment for human immunodeficiency virus

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    BackgroundThe Model of Health Care Empowerment (HCE) defines HCE as the process and state of being engaged, informed, collaborative, committed, and tolerant of uncertainty regarding health care. We examined the hypothesized antecedents and clinical outcomes of this model using data from ongoing human immunodeficiency virus (HIV)-related research. The purpose of this paper is to explore whether a new measure of HCE offers direction for understanding patient engagement in HIV medical care. Using data from two ongoing trials of social and behavioral aspects of HIV treatment, we examined preliminary support for hypothesized clinical outcomes and antecedents of HCE in the context of HIV treatment.MethodsThis was a cross-sectional analysis of 12-month data from study 1 (a longitudinal cohort study of male couples in which one or both partners are HIV-seropositive and taking HIV medications) and 6-month data from study 2, a randomized controlled trial of HIV-seropositive persons not on antiretroviral therapy at baseline despite meeting guidelines for treatment. From studies 1 and 2, 254 and 148 participants were included, respectively. Hypothesized antecedents included cultural/social/environmental factors (demographics, HIV-related stigma), personal resources (social problem-solving, treatment knowledge and beliefs, treatment decision-making, shared decision-making, decisional balance, assertive communication, trust in providers, personal knowledge by provider, social support), and intrapersonal factors (depressive symptoms, positive/negative affect, and perceived stress). Hypothesized clinical outcomes of HCE included primary care appointment attendance, antiretroviral therapy use, adherence self-efficacy, medication adherence, CD4+ cell count, and HIV viral load.ResultsAlthough there was no association observed between HCE and HIV viral load and CD4+ cell count, there were significant positive associations of HCE scores with likelihood of reporting a recent primary care visit, greater treatment adherence self-efficacy, and higher adherence to antiretroviral therapy. Hypothesized antecedents of HCE included higher beliefs in the necessity of treatment and positive provider relationships

    PATHway: decision support in exercise programmes for cardiac rehabilitation

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    Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach

    Just Negotiation

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    This Article argues that the procedural justice—that is, fairness of process—plays a critical and largely unexamined role in legal negotiation, encouraging the acceptance of and adherence to negotiated agreements. An economic focus has dominated prior work on legal negotiation and has largely touted the importance of negotiated outcome rather than process. This Article marshals theoretical support for the role that procedural justice may play in bilateral legal negotiation and supports the theoretical case with empirical data from social psychology. A robust empirical literature has established that procedural justice has a significant effect on individuals’ perceptions of their outcomes in third party decision-making systems, encouraging acceptance of and adherence to outcomes and fostering a perception that decision-making systems are legitimate. Recently, such empirical work has begun to consider the effects of procedural justice in a setting without a third-party decision maker. These newest empirical findings support an increased role for fairness of process in negotiation. The Article concludes by exploring the complexities of taking procedural justice effects in negotiation seriously in light of the fact that legal negotiation is conducted by agent (the attorney), rather than principal (the client)
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