Background
The opportunity to improve care by delivering decision support to clinicians at the point of care represents one of the main incentives for implementing sophisticated clinical information systems. Previous reviews of computer reminder and decision support systems have reported mixed effects, possibly because they did not distinguish point of care computer reminders from e-mail alerts, computer-generated paper reminders, and other modes of delivering ‘computer reminders’.

Objectives
To evaluate the effects on processes and outcomes of care attributable to on-screen computer reminders delivered to clinicians at the point of care.

Search methods
We searched the Cochrane EPOC Group Trials register, MEDLINE, EMBASE and CINAHL and CENTRAL to July 2008, and scanned bibliographies from key articles.

Selection criteria
Studies of a reminder delivered via a computer system routinely used by clinicians, with a randomised or quasi-randomised design and reporting at least one outcome involving a clinical endpoint or adherence to a recommended process of care.

Data collection and analysis
Two authors independently screened studies for eligibility and abstracted data. For each study, we calculated the median improvement in adherence to target processes of care and also identified the outcome with the largest such improvement. We then calculated the median absolute improvement in process adherence across all studies using both the median outcome from each study and the best outcome.

Main results
Twenty-eight studies (reporting a total of thirty-two comparisons) were included. Computer reminders achieved a median improvement in process adherence of 4.2% (interquartile range (IQR): 0.8% to 18.8%) across all reported process outcomes, 3.3% (IQR: 0.5% to 10.6%) for medication ordering, 3.8% (IQR: 0.5% to 6.6%) for vaccinations, and 3.8% (IQR: 0.4% to 16.3%) for test ordering. In a sensitivity analysis using the best outcome from each study, the median improvement was 5.6% (IQR: 2.0% to 19.2%) across all process measures and 6.2% (IQR: 3.0% to 28.0%) across measures of medication ordering. 

In the eight comparisons that reported dichotomous clinical endpoints, intervention patients experienced a median absolute improvement of 2.5% (IQR: 1.3% to 4.2%). Blood pressure was the most commonly reported clinical endpoint, with intervention patients experiencing a median reduction in their systolic blood pressure of 1.0 mmHg (IQR: 2.3 mmHg reduction to 2.0 mmHg increase).

Authors' conclusions
Point of care computer reminders generally achieve small to modest improvements in provider behaviour. A minority of interventions showed larger effects, but no specific reminder or contextual features were significantly associated with effect magnitude. Further research must identify design features and contextual factors consistently associated with larger improvements in provider behaviour if computer reminders are to succeed on more than a trial and error basis.

The effects of on‐screen, point of care computer reminders on processes and outcomes of care

A system and method for providing continuous expert network critical care services from a remote location 202. A plurality of intensive care units 204 (ICU's) with associated patient monitoring instrumentation 232 is connected over a network to a command center 202 which is manned by intensivists 24 hours a day, 7 days a week. The intensivists are prompted to provide critical care by a standardized series of guideline algorithms for treating a variety of critical care conditions. Intensivists monitor the progress of individual patients at remote intensive care units 204. A smart alarm system provides alarms to the intensivists to alert the intensivists to potential patient problems so that intervention can occur in a timely fashion. A data storage/data warehouse 208 function analyzes individual patient information from a plurality of command centers 202 and provides updated algorithms and critical care support to the command centers 202.

System and method for providing continuous, expert network critical care services from a remote location(s)

Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery

Recently the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence (AI). It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage. Later chromatographic techniques identified an enormous number of volatiles in human clinical specimens that might serve as potential disease markers. "Artificial nose" technology has been employed in several areas of medical diagnosis, including rapid detection of tuberculosis (TB), Helicobacter pylori (HP) and urinary tract infections (UTI). Preliminary results have demonstrated the possibility of identifying and characterising microbial pathogens in clinical specimens. A hybrid intelligent model of four interdependent "tools", odour generation "kits", rapid volatile delivery and recovery systems, consistent low drift sensor performance and a hybrid intelligent system of parallel neural networks (NN) and expert systems, have been applied in gastric, pulmonary and urine diagnosis. Initial clinical tests have shown that it may be possible in the near future to use electronic nose technology not only for the rapid detection of diseases such as peptic ulceration, UTI, and TB but also for the continuous dynamic monitoring of disease stages. Major advances in information and gas sensor technology could enhance the diagnostic power of future bio-electronic noses and facilitate global surveillance models of disease control and management.

Sniffing out the truth : clinical diagnosis using the electronic nose

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed.

/pdf/rf-sensing-technologies-for-assisted-daily-living-in-4ikk015bn2.pdf

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

One of the most important factors that directly affects the quality of health care is patient safety. Minimize the occurrence of adverse events is one of the main challenges for health professionals. This requires continuous tracking of the patient by different areas and services, a process known as traceability and proper patient identification and medication prescribed. This article presents an information system for patient tracking and drugs developed for the Emergency Department of Hospital A Coruna. The systems use RFID technology to perform various tasks: (1) locate patients in different areas; (2) measure patient care times and waiting times; (3) identify unitary doses of medication; and (4) ensure the correct matching between the patient and the medication prescribed by the doctor. The hardware infrastructure as well as the optimal configuration of devices interconnected via a wireless network was determined by conducting a detailed coverage study. To support all the functionality needed, specific tools were designed and integrated with proprietary software applications. The RFID system was evaluated positively by staff from different professional profiles involved in its development or subsequent implementation.

Application of RFID Technology in Patient Tracking and Medication Traceability in Emergency Care

The sleep apnea syndrome (SAS) is a respiratory disorder, which is characterised by the occurrence of five or more apneic events (apnea or hypopnea) per hour of sleep. Diagnosis of the SAS is a process that is markedly heuristic by nature, in that doctors handle information that is both numerical and symbolic, and employ qualitative descriptive terminology. An expert draws up a contextualised clinical interpretation that relates a patient's sleep process and respiratory physiology, involving a detailed analysis of the polysomnograph corresponding to a night's sleep. This task, implying a great deal of work on the part of clinical staff and a high economic cost, can in fact be partially automated. Our paper describes a modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph. The main tasks of our system are the identification and classification of respiratory events, the construction of the patient's hypnogram and the correlation of all the information obtained so as to arrive at a final diagnosis with respect to the existence of the syndrome. Finally our article presents and discusses the results obtained following a preliminary validation of the developed system.

An intelligent system for the detection and interpretation of sleep apneas

An effective diagnosis of the sleep apnea syndrome (SAS) is based on a contextual analysis of the patient's polysomnograph, consisting of simultaneously recording electrophysiological and pneumological signals during a night's sleep. Currently, the prevalence of this disorder has caused an increase in the demand for specialist clinical assistance and sleep units. As in other areas of medicine, the volume of clinical data that has to be processed is enormous, which justifies the construction of computerized decisionmaking tools that partially automate these routine tasks. Our system, SAMOA, belongs to this category of help tools, being an automatic SAS diagnostic system that incorporates both conventional programming and artificial intelligence techniques. This article describes the most important aspects of the temporal data management in the different analysis processes and the final correlation of all the symbolic information generated by the different cooperative modules.

Intelligent diagnosis of sleep apnea syndrome

The authors describe PATRICIA, an intelligent monitoring system designed to advise clinicians in the management of patients dependent on mechanical ventilation. PATRICIA incorporates a patient-oriented symbolic approach for representing knowledge and a symbolic-oriented temporal approach for the intelligent control of the monitoring process. The results and methodology used in the validation of PATRICIA are presented. Preliminary results show that PATRICIA could be a useful tool for the management of patients receiving mechanical ventilatory support. >

http://ieeexplore.ieee.org/iel1/51/6355/00248168.pdf

The PATRICIA project: a semantic-based methodology for intelligent monitoring in the ICU

Efficient patient monitoring requires the integration of bedside monitors, database information and the application of artificial intelligence (AI) techniques, in order to obtain correct interpretations and to prescribe appropriate therapies. In this article, the authors present the new architecture of PATRICIA, an intelligent monitoring system designed to advise clinicians in the management of patients in the intensive care unit (ICU). The system's new architecture is based on current trends in the design of hospital health care systems, and allows integration of bedside monitors to front-end computers, and through the data network to a central monitor that controls and manages all the network operations. We have applied the client–server philosophy that takes advantage from information integration, shared resources and equipment networking. This approach results in an efficient and flexible system, and offers several benefits from the clinical point of view, as it serves as a helping tool for clinical decision-making in an ICU environment.

M. Cabrero-Canosa

Papers

Application of RFID Technology in Patient Tracking and Medication Traceability in Emergency Care

An intelligent system for the detection and interpretation of sleep apneas

Intelligent diagnosis of sleep apnea syndrome

The PATRICIA project: a semantic-based methodology for intelligent monitoring in the ICU

Integration of data, information and knowledge in intelligent patient monitoring