12,158 research outputs found

    An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

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    The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

    The Delivery System Design of a Community Mental Health Center and Provision of Quality: Cardiometabolic Screening for Persons with a Severe Mental Illness Prescribed Atypical Antipsychotic Medication

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    Background: Persons with a severe mental illness (SMI) prematurely lose up to 25 years of life when compared to the general population. This patient population has increased morbidity and mortality due to higher than normal rates of obesity, hypertension, diabetes, and cardiovascular disease. Treatment of SMI often includes the use of atypical antipsychotic (AA) medication which has been associated with the development of cardiometabolic illnesses. In response to the higher rates of co-morbid, chronic physical illness, monitoring guidelines for cardiometabolic illness have been published. Despite these guidelines, screening rates for cardiometabolic illness in this population remain low. Neither community mental health nor primary care systems address the physical health concerns of persons with a severe mental illness, thus widening the quality gap for this at risk, vulnerable population. The Chronic Care Model provides a systems framework for addressing the wide range of health needs for chronically ill populations and has successfully been used in improving the quality of care for persons with chronic physical health conditions. Few published studies have used the Chronic Care Model as a framework to guide improving the quality of care for persons with a SMI. Objective: The purpose of this study was to better understand how the delivery system design of a community mental health center affects quality outcomes for persons with a SMI treated on an AA medication that are at high risk for developing cardiometabolic illness. Methods: This cross-sectional study used baseline patient health data of persons with a SMI to analyze cardiometabolic screening rates, based on the American Diabetes Association (ADA), American Psychiatric Association (APA), Association of Clinical Endocrinologists, and North American Association for the Study of Obesity second generation antipsychotic monitoring guideline. The guideline included history of cardiovascular disease and biologic monitoring at baseline, 12 weeks, and both baseline and 12 weeks. This retrospective study used existing data from an electronic health record. A member of the clinic data team electronically extracted study demographic variables. All other study variables were manually extracted by the study investigator. The theoretical basis for this study was supported by the Care Model, an adapted version of the Chronic Care Model. Results: The study sample consisted of 190 patients. The mean patient age was 37.13 years with a SD ± 11.7 years and a range of 19 - 70 years. The majority of patients were men (58.4%) and most patients were single (90.5%). More than one-half of the patients (53.7%) represented a minority race, though most patients were not Hispanic (95.3%). Most patients were not currently employed (88.9%) and nearly one-half of the patients lived below the federal poverty guidelines (47.4%). Ninety percent of the patients were enrolled in the Medicare or State Medical Assistance program. More patients in the study were diagnosed with a mood disorder (72.1%) than a thought disorder (27.9%). Most patients (61.6%) did not schedule their baseline or followup visit, but rather “walked” into the clinic without prior notice. The average number of visits during the initial treatment phase was 3.7 ± 1.4 and more than one-third of patients had the same provider at baseline and follow-up (36.3%). No patients received all recommended screening measures per the ADA and APA monitoring guideline. Biological measures (excluding history of cardiovascular disease) were evaluated for ten patients at baseline, three patients at follow-up and one patient at both baseline and follow-up. At baseline, rates for each screening measure were as follows: weight or BMI (64.2%), blood pressure (62.1%), fasting plasma glucose or hemoglobin A1c (27.9%), fasting lipid profile (8.4%) and family or personal history of cardiovascular disease (34.7%). At followup, rates of each cardiometabolic screening measure were as follows: weight or BMI (63.2%), blood pressure (61.6%), fasting plasma glucose or hemoglobin A1c (13.2%), fasting lipid profile (9.5%). Summaries of the unadjusted (r) and adjusted (beta) associations between combined delivery system design candidate variables and each of the quality outcome variables at baseline revealed associations between being a current smoker (r = .15, p = .041), having a clinic primary care provider (r = .21, p = .003), being a walk-in at baseline (r = .14, p - .048), and the number of screening measures. At follow-up, no statistically significant associations were observed. Conclusion: Data suggest that the delivery system design of a community mental health center inadequately addresses screening for cardiometabolic symptoms of persons with SMI. Findings show that adherence to the full panel of ADA and APA recommended cardiometabolic screening measures for persons treated on an AA medication is abysmal. Even rates of common screening measures, such as blood pressure, are poor. The Care Model was a useful theoretical framework to guide the study. Results of the study indicate that SMI patients may interact with the health care system differently than patients with chronic medical conditions. It is feasible that the high rate of unscheduled visits, or “walk-in” visits and number of different providers caring for patients during the initial treatment phase contributes to poor quality care. Subsequent recommendations include developing an intervention study to evaluate quality outcomes using a) an integrated care delivery design specifically for SMI patients and b) expanding the Care Model components to include the health system organization, decision support, self-management support, and clinical information systems. It is critically important that care delivery systems for persons with SMI be integrated for optimal health outcomes

    Clinical applications of personalized medicine: a new paradigm and challenge

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    The personalized medicine is an emergent and rapidly developing method of clinical practice that uses new technologies to provide decisions in regard to the prediction, prevention, diagnosis and treatment of disease. The continue evolution of technology and the developments in molecular diagnostics and genomic analysis increased the possibility of an even more understanding and interpretation of the human genome and exome, allowing a "personalized" approach to clinical care, so that the concepts of "Systems Medicine" and "System Biology" are increasingly actual. The purpose of this study is to evaluate the personalized medicine about its indications and benefits, actual clinical applications and future perspectives as well as its issues and health care implications. It was made a careful review of the scientific literature on this field that highlighted the applicability and usefulness of this new medical approach as well as the fact that personalized medicine strategy is even more increasing in numerous fields of applications

    Definition of patient complexity in adults: A narrative review.

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    Background: Better identification of complex patients could help to improve their care. However, the definition of patient complexity itself is far from obvious. We conducted a narrative review to identify, describe, and synthesize the definitions of patient complexity used in the last 25 years. Methods: We searched PubMed for articles published in English between January 1995 and September 2020, defining patient complexity. We extended the search to the references of the included articles. We assessed the domains presented in the definitions, and classified the definitions as based on (1) medical aspects (e.g., number of conditions) or (2) medical and/or non-medical aspects (e.g., socio-economic status). We assessed whether the definition was based on a tool (e.g., index) or conceptual model. Results: Among 83 articles, there was marked heterogeneity in the patient complexity definitions. Domains contributing to complexity included health, demographics, behavior, socio-economic factors, healthcare system, medical decisionmaking, and environment. Patient complexity was defined according to medical aspects in 30 (36.1%) articles, and to medical and/or non-medical aspects in 53 (63.9%) articles. A tool was used in 36 (43.4%) articles, and a conceptual model in seven (8.4%) articles. Conclusion: A consensus concerning the definition of patient complexity was lacking. Most definitions incorporated nonmedical factors in the definition, underlining the importance of accounting not only for medical but also for non-medical aspects, as well as for their interrelationship

    The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

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    Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges

    Impact of Chronic Conditions on Treatment, Cancer-and Non-Cancer Outcomes among Elderly Men with Incident Prostate Cancer

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    Prostate Cancer is the most commonly observed non-skin cancer among the elderly men aged 65 years and older in the United States. Nearly one third of elderly men diagnosed with incident prostate cancer have pre-existing chronic conditions. Therefore, among elderly men with prostate cancer, management for cancer and chronic conditions should be optimized to improve healthcare outcomes. Previous literature majorly focused on the risk and management of cancer in the presence of number of conditions, although, it is known that more than 70% of chronic conditions among men diagnosed with prostate cancer were either cardio-metabolic, respiratory or mental health conditions. Lack of evidence persists regarding the impact of common types of chronic conditions and their conditions among elderly men with prostate cancer and vice-versa. The current study is an attempt to shrink the knowledge gap to provide actionable strategies to better management of chronic conditions and prostate cancer among elderly men. The three specific aims of the study were to: (1) examine the associations between the types of pre-existing chronic conditions and cancer stage at diagnosis, initial cancer treatment and clinical outcomes after initial cancer treatment; 2) examine the relationship between metformin use and cancer stage at diagnosis, and the initial cancer-treatment; 3) analyze the impact of cancer diagnosis on the risk of non-cancer hospitalizations and evaluate whether the impact of cancer diagnosis on the risk of non-cancer hospitalizations vary by the types of pre-existing chronic conditions among fee-for-service elderly Medicare beneficiaries with incident prostate cancer. The study used a retrospective cohort design, using multiple years (2002-2010) of the cancer registry data from the Surveillance, Epidemiology and End Results (SEER) program linked with the Medicare administrative claims data and the Area Health Resource Files (AHRF). In the first aim, among elderly men with incident prostate cancer (N = 103,820), the cardio-vascular conditions were the most common chronic condition. 1 in 10 elderly men had advanced prostate cancer at diagnosis. Elderly men without cardio-metabolic, respiratory or mental health conditions were more likely to be diagnosed with advanced prostate cancer as compared to those with all the three types of chronic conditions. 3 in 4 elderly men with localized prostate cancer received either radical prostatectomy (RP), radiation therapy (RT) or hormone therapy during the first six-month after cancer diagnosis. As compared to all three types of chronic conditions, those with single types of chronic conditions were less likely to develop bowel, and urinary dysfunctions. In the second aim, the use of metformin was associated with a reduction in the risk of advanced prostate cancer among elderly men diagnosed with prostate cancer and pre-existing diabetes (N=2, 652). In the third study, elderly men diagnosed with prostate cancer had an increase in the risk of non-cancer hospitalizations during the post-cancer period as compared to the pre-cancer period in both unadjusted and adjusted analyses. The highest rates of non-cancer hospitalizations were observed during first four months after the diagnosis of prostate cancer. To summarize, our study confirms that elderly men with incident prostate cancer and multiple types of pre-existing chronic conditions would pose a different degree of risk for the development of advanced prostate cancer. Although the management of chronic conditions such diabetes with metformin may reduce the risk of advanced prostate cancer among elderly men. An overuse of RT/RP in men with different types of chronic conditions and an increase in the non-cancer hospitalizations in the initial period after diagnosis of prostate cancer suggest the scope of optimum use of RT and RP and improvement in the care of chronic conditions

    The Impact of Big Data on Chronic Disease Management

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    Introduction: Population health management – and specifically chronic disease management – depend on the ability of providers to identify patients at high risk of developing costly and harmful conditions such as diabetes, heart failure, and chronic kidney disease (CKD). The advent of big data analytics could help identify high-risk patients which is really beneficial to healthcare practitioners and patients to make informed decisions in a timelier manner with much more evidence in hand. It would allow doctors to extend effective treatment but also reduces the costs of extending improved care to patients. Purpose: The purpose of this study was to identify current applications of big data analytics in healthcare for chronic disease management and to determine its real-world effectiveness in improving patient outcomes and lessening financial burdens. Methodology: The methodology for this study was a literature review. Six electronic databases were utilized and a total of 49 articles were referenced for this research. Results: Improvement in diagnostic accuracy and risk prediction and reduction of hospital readmissions has resulted in significant decrease in health care cost. Big data analytic studies regarding care management and wellness programs have been largely positive. Also, Big data analytics guided better treatment leading to improved patient outcomes. Discussion/Conclusion: Big data analytics shows initial positive impact on quality of care, patient outcomes and finances, and could be successfully implemented in chronic disease management

    Integrated out-of-hours care arrangements in England: observational study of progress towards single call access via NHS Direct and impact on the wider health system

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    Objectives: To assess the extent of service integration achieved within general practice cooperatives and NHS Direct sites participating in the Department of Health’s national “Exemplar Programme” for single call access to out-of-hours care via NHS Direct. To assess the impact of integrated out-of-hours care arrangements upon general practice cooperatives and the wider health system (use of emergency departments, 999 ambulance services, and minor injuries units). Design: Observational before and after study of demand, activity, and trends in the use of other health services. Setting: Thirty four English general practice cooperatives with NHS Direct partners (“exemplars”) of which four acted as “case exemplars”. Also 10 control cooperatives for comparison. Main Outcome Measures: Extent of integration achieved (defined as the proportion of hours and the proportion of general practice patients covered by integrated arrangements), patterns of general practice cooperative demand and activity and trends in use of the wider health system in the first year. Results: Of 31 distinct exemplars 21 (68%) integrated all out-of-hours call management by March 2004. Nine (29%) established single call access for all patients. In the only case exemplar where direct comparison was possible, cooperative nurse telephone triage before integration completed a higher proportion of calls with telephone advice than did NHS Direct afterwards (39% v 30%; p<0.0001). The proportion of calls completed by NHS Direct telephone advice at other sites was lower. There is evidence for transfer of demand from case exemplars to 999 ambulance services. A downturn in overall demand for care seen in two case exemplars was also seen in control sites. Conclusion: The new model of out-of-hours care was implemented in a variety of settings across England by new partnerships between general practice cooperatives and NHS Direct. Single call access was not widely implemented and most patients needed to make at least two telephone calls to contact the service. In the first year, integration may have produced some reduction in total demand, but this may have been accompanied by shifts from one part of the local health system to another. NHS Direct demonstrated capability in handling calls but may not currently have sufficient capacity to support national implementation

    Jefferson Digital Commons quarterly report: October-December 2018

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    This quarterly report includes: Articles Dissertations From the Archives Grand Rounds and Lectures Industrial Design Capstones Journals and Newsletters LabArchives Launch Masters of Public Health Capstones Posters Reports Videos What People are Saying About the Jefferson Digital Common

    Sleep Quality and the Effect on Functional Outcomes

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    abstract: Introduction: Sleep disorders can go undiagnosed if a provider is not asking the right questions; they can be characterized by loud snoring with apneic episodes that never fully wake the person, difficulty falling asleep or daytime fatigue. Poor sleep can affect activities of daily living, job performance and personal relationships. Poor sleep can be difficult to detect because some may consider it a symptom because of their lifestyle. The purpose of this study is to assess participants sleep quality and functional outcomes of poor sleep. Methods: Primary care providers have an opportunity to screen for sleep disorders as part of the intake process during an office visit. The Functional Outcomes of Sleep Questionnaire (FOSQ), has been proposed as guide to determine if a sleep disorder is affecting quality of life. This descriptive study randomly recruited 20 participants from a community health center. A 10-question survey was given to individuals over the age of 18 who can write and speak English and either have a body mass index (BMI) over 30, hypertension (HTN) or diabetes type II (DMII). Demographic information evaluated included age, gender, HTN, DMII, BMI>30, marital status, sleeping alone, employment type, race, type of insurance, how many times do they wake up at night, the average number of hours slept per night and does the person work night shift. Results: The study used a qualitative approach with a descriptive methodology; statistical analysis consisted of proportions, means and standard deviation to describe the study population. Participant age ranged from 33 to 72 years (M=50.1, SD= 11.32). Sixty percent were both female and married/living with partner. Despite being married/living with partner, 50% slept alone. A Mann-Whitney U test showed that there was a significant difference in four of the questions in the FOSQ-10 in which functional outcomes were not affected by being sleepy or tired. Conclusion: The FOSQ-10 may serve a role in identifying patients who might benefit from a sleep study. The inclusion of a sleep disorder screening tool may increase the specificity and sensitivity of the intervention and the ability to yield data that will objectively measure disordered sleep
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