Hand Held Devices In Hospital Setting And Efficiency 2013 To 2019 Pdf

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The company's focus has always been to fulfill its social responsibility to provide for society in the best possible way and to help its global customers protect, support and save lives. KGaA achieved significant order intake and net sales growth in the first nine months of net of currency effects. The delivery of the order will start in and will stretch until the end of The expected net sales are roughly EUR million.

Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies

Shown are hospitalized and randomized patients in the observation period. CDSS indicates clinical decision support system. Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response. Not all submitted comments are published. Please see our commenting policy for details. The CDSS led to a change in practice in approximately 4 of patients, an effect that was maintained over time. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, , and December 31, , patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown.

Data were analyzed between February 1 and July 31, Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. Of these, The mean SD age of patients was These messages led to a change in practice in approximately 4 of patients.

The resolution rate was In-hospital all-cause mortality also did not differ between groups odds ratio, 0. Alert fatigue did not differ between early and late study periods. The medical information market has evolved rapidly over the past decade, with innovative products eg, point-of-care information summaries gaining popularity among physicians.

Accessibility to high-quality, well-summarized evidence-based information can help physicians identify the best therapeutic or diagnostic options for patients. The use of CDSSs has been proposed as a potential remedy for improving the overall efficiency and quality of health care.

The arrival of the latest generation of CDSSs on the market represents an opportunity for hospitals equipped with EHRs to adopt CDSSs, implementing literature surveillance systems and evidence appraisal performed by international publishing groups. These systems may better support clinical decision-making across multiple specialties and are accessible at competitive costs. However, acceptance of tools aimed at global use should rely on evidence of better care and improved patient outcomes.

To our knowledge, these variables have never been empirically assessed in this setting. The aim of the present study was to evaluate the effectiveness of a vendor-based multispecialty CDSS that generates patient-specific reminders based on high-quality point-of-care information services on clinical practice and quality of care in a general hospital Vimercate Hospital, Vimercate, Italy.

Reminders primarily targeted prescription medications. Patients were admitted to the wards either from the emergency department of the hospital or from another hospital or were referred by primary care clinics.

As a pragmatic RTC, the CODES trial enrolled all patients admitted into the internal medicine wards of the hospital during the course of the trial, without applying any exclusion criteria. The trial protocol is available in Supplement 1 , and a version of the protocol has been published. To generate patient-specific guidance and reminders, including therapeutic suggestions, EBMEDS integrates structured patient data from EHRs using the following controlled vocabularies and code sets: medical history, vital signs, diagnoses International Classification of Diseases , Ninth Revision , symptoms International Classification of Primary Care , Second Edition , medications World Health Organization Anatomical Therapeutic Chemical Classification System , and immunization dates, allergies, and laboratory data Logical Observation Identifiers Names and Codes , as well as imaging reports.

We endeavored to integrate the CDSS into the clinical workflow by implementing alert features to grade different alerting priorities eg, using color codes. However, we preferred noninterruptive reminders, which are locally more accepted, as opposed to interruptive alerts, which usually are associated with statistically significantly higher level of effectiveness.

By the end of the trial, the intervention was active in all internal medicine wards of Vimercate Hospital. Once the CDSS was integrated in a ward, all rules were implemented simultaneously. The format of the rules was broad, encompassing alerts and reminders, recommendations from clinical guidelines, condition-specific order sets, diagnostic support, and contextually relevant reference information. Suggestions were based on international evidence—based point-of-care summaries, including Cochrane systematic reviews.

Examples of specific patient guidance are reported in the Results section. Because combinations of drugs can trigger alerts on thousands of drug-drug interactions and adverse events, alerts were organized and presented based on severity following the taxonomy adopted by the Medbase software program Medbase Ltd. Guidance on EBMEDS, including a reference source for potentially inappropriate care, was available to physicians seeing patients in the intervention group.

Therapeutic-specific and diagnosis-specific links to full-text guidelines were available in the control group, representing modestly enhanced usual care. Patients were randomized through their anonymous patient identification numbers. The computer-generated allocation to each study arm on a basis was stratified by sex and age using permuted blocks of random sizes.

We randomized patients based on the assumption that the EHR operates primarily at the level of the individual patient. However, this choice created a potential for cross-contamination and dilution of the effect size. We reasoned a priori that it is unlikely that physicians might have the opportunity to discuss details of hundreds of reminders among themselves, limiting the risk of contamination.

The project team, including hospital investigators and statisticians H. Physicians were not blinded. The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed or resolved by physicians through a change in practice.

The CDSS tracked all clinical problems that triggered an alert regardless of the treating physician. Alerts were checked for quality and relevance at multiple levels eg, through the EBMEDS Quality Plan, 28 external expert review, feedback on user experience, and proactive monitoring of perceived effectiveness by users.

We also considered the median time to resolution of the reminders, specifically the time between the activation of the warning message and its resolution.

Secondary outcomes explored resolution rates for different types of reminders eg, EBMEDS clinical reminders, class C drug interactions, class D drug interactions, and reminders developed by the project team based on specific hospital needs , as well as clinical outcomes, including the following: 1 in-hospital all-cause mortality measured primarily for safety reasons and mortality at 30 days and 90 days ie, mortality for any reason within 30 days and 90 days after hospital admission , 2 mortality related to venous thromboembolism VTE , 3 in-hospital morbidity for VTE-related causes, and 4 the length of hospital stay measured as a process outcome during the study period.

The outcomes prespecified in the trial registration are the same as those in the published protocol, 10 all of which are reported in this article. However, we also added 2 more time points for mortality at 30 days and 90 days , whereas our protocol prespecified only in-hospital mortality.

Sample sizes were not calculated for secondary outcomes. Mortality data were obtained from the Lombardy region death registry. The research team had no control over the EHRs. The RCT drew on qualitative analysis of data from interviews with clinicians physicians and nurses and hospital senior management carried out before and during the trial to assist in identifying the burden of the intervention and potential barriers eg, irrelevant notifications and alert fatigue to its implementation.

No concurrent patient safety or quality improvement initiatives were conducted at the time of the trial. We estimated the sample size based on the primary outcome the resolution rate. This estimated difference was informed by findings from a systematic review on the association between computer reminders at the point of care and processes and outcomes of care, which reported a mean reduction of 4.

Descriptive statistics are presented as mean SD , median and interquartile range IQR , or percentage, as appropriate. For the primary outcome ie, the resolution rate , we ran conventional and random-effects logistic regression models in which the reminder served as the unit of analysis and the patient was the clustering factor. For all other outcomes, the patient served as the unit of analysis. All analyses in this RCT followed the intent-to-treat principle. Given the potential consequences of frequent alert exposure by clinicians, alert fatigue was also analyzed.

It is a condition in which a health care professional, after having been exposed to too many notifications, develops a defensive attitude against alerts, usually ignoring or overriding them. The interaction between group and time to assess was used if the intervention effect was subject to alert fatigue over time ie, desensitizing response to alerts. When the interim analysis was conducted July 15, , the prespecified final sample of generated reminders had already been reached ie, reminders, with an overall resolution rate of At that point, patients had been randomized to the intervention group and to the control group.

The CDSS triggered 2. We decided not to stop the trial to explore whether the intervention was subject to a decay effect attributable to alert fatigue, thereby increasing power in the analyses of secondary outcomes. The end date was set to December 31, The study population consists of patients The study groups were well balanced with regard to sex, age, and other baseline characteristics Table 1. The mean SD number of medications for chronic diseases per patient was 9.

Most patients were admitted to the hospital for reasons primarily associated with cardiovascular Overall, reminders 8. Table 2 lists examples of the most frequently activated guidance.

The corresponding crude OR was 1. The time to resolution of the reminders was shorter in the intervention group mean [SD], 5. These results are summarized in Table 3. The resolution rate was consistent and statistically significant across the different types of reminders. The rate of in-hospital all-cause mortality was similar between the study groups 5. After discharge, the rates did not differ between groups at 30 days and 90 days Table 4. Of 54 patients with VTE-related in-hospital events 27 in each study group , none died during hospitalization Table 4.

Three patients died in the month after hospital discharge, and 6 patients 3 in the intervention group died within 90 days of hospital discharge. The resolution rate was higher in the intervention group Data for clinical outcomes in-hospital all-cause mortality, in-hospital morbidity for VTE-related causes, and the length of hospital stay were missing for 96 of patients 1. Although the resolution rate increased over time, alert fatigue did not change.

Specifically, the resolution rate increased from In contrast, alert fatigue, as measured by testing for the effects of time and the interaction between group and time, was unchanged during the trial.

An international, vendor-based CDSS that was embedded in the EHRs of a general hospital Vimercate Hospital to provide real-time guidance slightly reduced potentially inappropriate care compared with access to evidence summaries as a part of usual care through manual searching of point-of-care medical information summaries.

The EBMEDS activated a median of 3 reminders per patient per hospital stay, identifying situations at risk for inappropriate medical orders. Although the improvement was small, it was consistent across all types of guidance messages.

Although alert fatigue did not have a major effect on CDSS use, resolution rates increased over time. The small effect on resolution rate might be attributed to the large volume of clinically inconsequential alerts; even if alerts were associated with patient characteristics and integrated with patient-specific variables eg, alerts for nephrology dose reduction warnings were triggered only for high-risk patients , the CDSS was unable to efficiently filter guidance messages.

Overall, the CDSS directed approximately 14 messages to physicians allocated to the intervention group, 13

Contamination of the Surfaces of a Health Care Environment by Multidrug-Resistant (MDR) Bacteria

Wearable technologies can be innovative solutions for healthcare problems. In this study, we conducted a literature review of wearable technology applications in healthcare. Some wearable technology applications are designed for prevention of diseases and maintenance of health, such as weight control and physical activity monitoring. Wearable devices are also used for patient management and disease management. The wearable applications can directly impact clinical decision making.

Shown are hospitalized and randomized patients in the observation period. CDSS indicates clinical decision support system. Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued. If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response. Not all submitted comments are published. Please see our commenting policy for details.

While mHealth has application for industrialized nations , the field has emerged in recent years as largely an application for developing countries , stemming from the rapid rise of mobile phone penetration in low-income nations. The field, then, largely emerges as a means of providing greater access to larger segments of a population in developing countries, as well as improving the capacity of health systems in such countries to provide quality healthcare. The field broadly encompasses the use of mobile telecommunication and multimedia technologies in health care delivery. The term mHealth was coined by Robert Istepanian as use of "emerging mobile communications and network technologies for healthcare". While there are some projects that are considered solely within the field of mHealth, the linkage between mHealth and eHealth is unquestionable. Thus, eHealth projects many times operate as the backbone of mHealth projects. In a similar vein, while not clearly bifurcated by such a definition, eHealth can largely be viewed as technology that supports the functions and delivery of healthcare, while mHealth rests largely on providing healthcare access.

Artificial intelligence in healthcare

Nosocomial infections NIs are known worldwide and remain a major problem despite scientific and technical advances in the field of health. The severity of the infection depends on the characteristics of the microorganisms involved and the high frequency of resistant pathogens in the hospital environment. The aim of this study is to determine the distribution of pathogenic bacteria and their resistance to antibiotics that spread on hospital surfaces, more specifically, on those of various departments in the Provincial Hospital Center PHC of Mohammedia, Morocco. A cross-sectional study was conducted from March to April

Shown are hospitalized and randomized patients in the observation period. CDSS indicates clinical decision support system. Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Wearable Technology Applications in Healthcare: A Literature Review

In health care, the days of business as usual are over. Around the world, every health care system is struggling with rising costs and uneven quality, despite the hard work of well-intentioned, well-trained clinicians.

Mobile Devices and Apps for Health Care Professionals: Uses and Benefits

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Digital technologies are being harnessed to support the public-health response to COVID worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing.

The use of mobile devices by health care professionals HCPs has transformed many aspects of clinical practice. Mobile devices and apps provide many benefits for HCPs, perhaps most significantly increased access to point-of-care tools, which has been shown to support better clinical decision-making and improved patient outcomes. The introduction of mobile computing devices personal digital assistants [PDAs], followed by smartphones and tablet computers has greatly impacted many fields, including medicine. Health care professionals now use smartphone or tablet computers for functions they used to need a pager, cellphone, and PDA to accomplish. The first mobile device that incorporated both communication and computing features was the Blackberry, which was introduced in

The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters. A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

Mobile phone use while driving is common but it is widely considered dangerous due to its potential for causing distracted driving and crashes. Due to the number of crashes that are related to conducting calls on a phone and texting while driving, some jurisdictions have made the use of calling on a phone while driving illegal. Many jurisdictions have enacted laws to ban handheld mobile phone use.

Digital technologies in the public-health response to COVID-19

Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence AI , to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. What distinguishes AI technology from traditional technologies in health care is the ability to gather data, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. These algorithms can recognize patterns in behavior and create their own logic.

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Затем подошла еще одна группа, и жертва окончательно исчезла из поля зрения Халохота. Кипя от злости, тот нырнул в стремительно уплотняющуюся толпу. Он должен настичь Дэвида Беккера.