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QIC MOBILE EBOOK ER

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Dropbox is a service that helps you keep files synchronized on your PC and your mobile devices. The ebook is sent to the Apps/gutenberg. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales Collecting and Analyzing Mobile Device Evidence. Car Owner 'Pgfir 'Prajii' Business Phone Date SZIZOS Address lQB COVER (Front) SPOILER Q I C/I-I / 'PART 1*I - -'Bi (Sill/er) 4 third party content may be suppressed from the eBook and/or eChapter(s). ily.


Qic Mobile Ebook Er

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ppti.info: All eBooks on the topic „Olg“. Discover, download and read free and low-priced eBooks on the subject of „Olg“. Mobile GeoSocial Intelligence Mobile advancement has become a giant in the technology field, The "Always ON- Always Connected " technology allows. MeLearning Solutions er í/á TorranceLearning. . Create your own mobile project – an interactive ebook. QIC is getting the #mobile #design / # development low-down from the one and only Sarah Mercier, of MeLearning Solutions, in a.

Group C Read about Yuet Tung. J Answer the questions. Compare the three people. S Answer the questions with your group. I What do the people have in common? What do you think?

What do you like best about Hving in your country? What would yOll miss if you lived abroad?

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What do they like about it? At first he missed everything - the sunshine, the food, his girlfriend.

But now he has a successful business with his three brothers and his sister. They run a soccer store in New Brunswick. Roberto's girlfriend is now his wife, and they have two children who go to American schools. When asked why he came to the US, Roberto says without hesitation, 'Because I want to work hard and be successful.

He's at the store all day, then works as a driver in the evening. It was so cold! There was snow! Now nearly all my family are here, not only in New York, but also in California, and in Texas.

We meet about once a month and have a huge Mexican meal that takes about five hours! He came from Budapest thirteen years ago. Everything is so big here,' he says. Now I go everywhere by car.

In Hungary, we only use the car at weekends, but here your car is part of your life.

Nobody walks anywhere. The first question everybody asks you is "Where are you from? I like the fact that there are people from all over the world. Nobody tells me what to do. Here you can do what you want, so you learn to make decisions for yourself. I feel in control. She came to the US eight years ago and studied fine art. Now she works on Madison Avenue for a publisher.

She married a Vietnamese American three years ago, and they live in long Island. They don't have any children yet. What does she think of living in New York? It's a bUsy city, very exciting, and people walk very fast! I like the stores here. They're huge, and it's cheaper than Hong Kong.

But you need a car here. In Hong Kong everyone uses public transportation, because it's good and it's cheap. At first I hated driving here, but it's OK now. Here I live in a house with a yard. In Hong Kong it is so crowded.

And the people are friendly. When I go jogging, everyone says "Hit" And the food is from every country in the world. Tell a partner. Discuss with your partner. Two couples, Carol and Mike, and Dave and Alison, talk about their partner's annoying habits. Look at the pictures below. What are their annoyi.

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DB Listen and write the correct names under each picture below. I or false X? Correct the false sentences. Predictive modeling and machine learning techniques, discussed in Chap.

This method has also been called hotspotting [ 15 ]. In the U. Data on health service utilization can be used to identify super-utilizers, or patients who disproportionately utilize high-cost services, such as emergency room visits and hospitalizations, or have high care needs. For example, one study of Camden health centers in New Jersey, where the hotspotting approach originated from, utilized hospital claims data from three facilities to perform a cluster analysis and classify pediatric patients into five subgroups of risk according to their asthma-related emergency department visits and hospitalizations [ 16 ].

The aim of this classification was to identify cohorts and potentially guide interventions tailored to each subgroup to optimally reduce asthma-related hospitalizations. More generally, cohort identification based on service utilization aims to guide the design of multidisciplinary and community-based services, self-management support, and health care that can address medical and non-medical needs of patients, thereby reducing the need to utilize higher-cost services.

These are ways to integrate care delivery and expand excellent service across geography, according to the value agenda summarized in Fig. In another study, patients were identified by their provider as a patient with high-frequency healthcare system access or complex unresolved needs; these patients were then referred to a complex care center within the organization [ 17 ]. In recent years, there is a greater shift towards measuring what matters to patients. Patient-reported outcomes , which aim to be both evidence-based and patient-centered, offer an opportunity to engage the patient in measuring what matters to them, but also requires robust and lengthy processes to develop, validate, and also implement them in a non-invasive manner [ 18 ].

Typically, there is an evidence basis that guides the development of patient-reported outcomes and their validation [ 19 ]. Patient-reported outcome measures PROMs are the tools, such as surveys or questionnaires, used to collect patient-reported outcomes. One international initiative to develop standardized patient outcome measures, the International Consortium of Healthcare Outcomes Measurement ICHOM , is a large, multi-institutional effort that draws from international registries and provider best practices to implement PROMs in alignment with the value agenda [ 20 ].

Patient-reported experience measures PREMs , including patient satisfaction, are intended to ensure accountability for healthcare service provision that is appropriate, equitable, accessible, affordable, appropriate, and efficient [ 21 ]. As already noted, outcome measurement is complex, easily extending well beyond the structure-process-outcomes approach. Scientific literature, medical knowledge, clinical practice guidelines, and outcome measurement specifications are constantly evolving, resulting in rapidly growing volume and variety of data and information.

For example,evidence-based clinical outcomes often are derived from the results of randomized control trial results, if available. Otherwise, outcomes may originate from other study types or expert consensus, and then selected and synthesized into clinical practice guidelines, with an indeterminate timeline or process for revision as new scientific and medical knowledge becomes available.

When a single disease clinical guideline is implemented, the quantity of data and information needed to adhere to guideline recommendations is enormous. For example, consider a guideline update on early management of acute ischemic stroke published in , in which recommendations were made, citing published references [ 24 , 25 ]. Clinical comorbidities may further complicate translation and implementation of such guidelines; for example, a guideline on transient ischemic attack recommends aspirin to prevent ischemic stroke, but in a patient with peptic ulcer disease, this guideline recommends avoiding aspirin, which is a conflict between two concurrently applied guidelines.

This clinical scenario is one example of a use case in which each clinical guideline was transformed into a computer-interpretable format, then conflicts were resolved using a computational method of conflict resolution [ 26 , 27 ]. As a result, accurate quality measurement that adequately accounts for such cases can become challenging. Overall, the measurement of high-value health care should be able to account for clinical complexity, social determinants of health, and patient preferences.

Multimorbidity is a classic example of clinical complexity Box In this case, the clinical complexity of multiple comorbid conditions arises from the numerous possible combinations of disease and types of relationships e. In settings involving clinical complexity, risk adjustment through case mix indices or comorbidity indices, such as the Charlson Index [ 30 ], can be applied, although the latter are more often used in clinical research rather than implemented in outcome measurements.

The simplest approaches to account for clinical complexity can be designed as alerts in an electronic health record EHR to allow for exclusion of a particular patient from a cohort; for example, if a patient has an incurable and terminal illness, an EHR may allow for this patient to be easily identified in a manner that would acknowledge that even though she may meet eligibility criteria for certain preventive services, such as cancer screening, these would be low-value services in this patient context.

Knowledge management, discussed later in this chapter, and dealing with alert fatigue, discussed in Chap. In fact, application of single-disease guidelines to patients with multimorbidity can increase treatment and self-management complexity, risk of interactions between guideline recommendations, potential adverse events, hospitalization and poorer health outcomes [ 32 , 33 , 34 , 35 ].

Consequently, quality measurement in the setting of multimorbidity is challenging—multimorbidity is not simply a count of conditions [ 36 ] and co-occurring conditions can be interrelated in a variety of ways [ 33 ], even in chronology [ 28 , 37 ]. Intelligent information systems, given reliable data, could better be able to handle the complexity and probabilistic nature of potential outcomes for patients with multimorbidity, and thereby measure care quality in a more nuanced manner representative of the population.

Social determinants of health are also important contextual factors in determining an outcome even if not explicitly measured.

Moving away from solely a biomedical approach to medicine, a biopsychosocial model of medicine, first introduced by psychiatrist George Engel in , centralizes the important roles of social, psychological and behavioral determinants of health [ 38 ].

Numerous social determinants of health are now known, including sociodemographic factors e. However, few are documented and in fact a subset of sociodemographic characteristics and social determinants in the behavioral domain are typically the most commonly documented in a structured manner in EHRs [ 39 ].

Additional determinants are usually not documented in a structured format that could enable cohort identification or other data analytical activities that would be supportive of a value agenda. For example, adverse childhood experiences, such as psychological, physical or sexual abuse, or exposure to violence against their mother the original study did not investigate exposure to violence against all types of parents , can be important determinants and risk factors for certain mental health and chronic diseases [ 40 ].

Other patient characteristics that could be important determinants of health, such as positive intimate partner violence screening or undocumented migrant status, may be purposefully left undocumented by clinicians in the electronic health record due to potential legal and social consequences.

Finally, patient preferences are essential to consider in shared decision making, as is a frank discussion of uncertainty in medicine. A probabilistic approach is often more appropriate approach to decision-making than a deterministic one, but such interpretation may be challenging to communicate and dependent on clinician knowledge and skills or numeracy or numerical literacy of the patient.

Outcome measures should appropriately consider a variety of influencing factors, which may be difficult to measure or may not be formally registered in an electronic record or information system, to provide the best representation of true outcomes for a given patient or population.

Costing analyses are conducted to estimate the cost of providing healthcare services. While there are many costing analysis methods, a popular approach coupled with the value-based healthcare framework is time-driven activity-based costing TDABC. Traditional activity-based costing is typically isolated to an individual department, which becomes inadequate for cost estimation that involves further complexity, such as across multiple departments involved in a care pathway [ 11 , 42 ].

Objects may also be time-dependent, for example, there is also a cost per hour of usage for an operating room. A shorter operating time that offers similar outcomes as longer operating times would also be value-added. In TDABC, the intent is to capture all costs incurred by the institution to provide care services in an entire care pathway, including costs of equipment, information technology, space, human labor in the form of health professionals, and additional supportive services Fig.

The methodology specifically distinguishes between these costs versus other costs, such as prices charged to insurers or patients for services rendered and reimbursed costs for those services. Adapted from Kaplan and Porter [ 11 ] Expert interviews, focus groups, process mapping and mining, or event log data can be combined with accurate financial data, including itemized prices and labor costs including benefits to appropriately estimate true costs.

Such approaches are intended to map the care pathway and value streams, highlighting key processes for improvement and points of care as well as care inefficiencies.

Process mining and event logs were described in Chap. Further description of how to perform a costing analysis, such as TDABC, is beyond the scope of this chapter and additional reading is supplied in the references at the end of this chapter.

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Innovation is a key component of healthcare transformation centered on increasing value for patients. A broad definition of innovation would encompass several domains, including the development and implementation of new information technologies that enable remote disease monitoring or self-management care, as well as service delivery innovation or re-design that integrates traditionally disparate services in a manner that increases value for patients.

Further, device and information technology innovations e. E-Health is defined as any activity in which an electronic means is used to deliver information, resources and services related to health; domains include EHRs, telehealth, mobile health e. With the explosive growth of e-Health, more technologies and platforms offer greater opportunities for data collection, management and processing.

Information technology should be designed in support of increasing healthcare value for patients by enabling data capture and consumption in a manner that allows for outcomes and cost measurement, but e-health is not mandatory to increase value Box At Maastricht University Medical Center, a multidisciplinary clinical research group developed MijnIBDcoach, a software platform that enables home monitoring and patient-provider communication about health status, tracking and response to disease activity, medication adherence, side effects, nutrition, fatigue, quality of life, life events, and behavioral health such as stress and anxiety levels [ 44 ].

For example, alerts were created to notify the care team of indicators for a possible disease flare; and during a disease flare, the platform allowed for intensified home monitoring.

E-learning is also available to educate and engage patients in their care. In a randomized controlled trial, patients in the intervention group had a statistically significantly lower mean number of outpatient office visits and mean number of hospitalizations compared to patients who received standard of care [ 45 ].

Patients with the intervention also demonstrated improved patient-reported outcomes, as measured by the My IBD At Home questionnaire, and quality of life, as measured by the Short Inflammatory Bowel Disease Questionnaire, although both without statistical significance. This intervention demonstrates potential added value to health care services offered for a specific patient population, enabled by information and communication technologies. This results in allocations of financial resources towards prevention and out-of-hospital management services, including in-house care management and longer primary care visits.

Added-value services can even include transportation between home and primary care visits. Team-based primary care is also coupled with patient classification into four risk-based cohorts, or tiers, with re-evaluation as patients may transition between tiers throughout the course of their care; these tiers guide primary care visit cadence and allocation of care management resources.

In this case, innovation in the form of service delivery and design with appropriate financial incentives drives increased healthcare value for patients.For example, you might convene in the location of an historical tribal meeting or at an old fort.

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