1895090
The effect of telemedicine in critically ill patients: systematic review and meta-analysis
IntroductionTelemedicine extends intensivists' reach to critically ill patients cared for by other physicians. Our objective was to evaluate the impact of telemedicine on patients' outcomes.MethodsWe searched electronic databases through April 2012, bibliographies of included trials, and indexes and conference proceedings in two journals (2001 to 2012). We selected controlled trials or observational studies of critically ill adults or children, examining the effects of telemedicine on mortality. Two authors independently selected studies and extracted data on outcomes (mortality and length of stay in the intensive care unit (ICU) and hospital) and methodologic quality. We used random-effects meta-analytic models unadjusted for case mix or cluster effects and quantified between-study heterogeneity by using I2 (the percentage of total variability across studies attributable to heterogeneity rather than to chance).ResultsOf 865 citations, 11 observational studies met selection criteria. Overall quality was moderate (mean score on Newcastle-Ottawa scale, 5.1/9; range, 3 to 9). Meta-analyses showed that telemedicine, compared with standard care, is associated with lower ICU mortality (risk ratio (RR) 0.79; 95% confidence interval (CI), 0.65 to 0.96; nine studies, n = 23,526; I2 = 70%) and hospital mortality (RR, 0.83; 95% CI, 0.73 to 0.94; nine studies, n = 47,943; I2 = 72%). Interventions with continuous patient-data monitoring, with or without alerts, reduced ICU mortality (RR, 0.78; 95% CI, 0.64 to 0.95; six studies, n = 21,384; I2 = 74%) versus those with remote intensivist consultation only (RR, 0.64; 95% CI, 0.20 to 2.07; three studies, n = 2,142; I2 = 71%), but effects were statistically similar (interaction P = 0.74). Effects were also similar in higher (RR, 0.83; 95% CI, 0.68 to 1.02) versus lower (RR, 0.69; 95% CI, 0.40 to 1.19; interaction, P = 0.53) quality studies. Reductions in ICU and hospital length of stay were statistically significant (weighted mean difference (telemedicine-control), -0.62 days; 95% CI, -1.21 to -0.04 days and -1.26 days; 95% CI, -2.49 to -0.03 days, respectively; I2 > 90% for both).ConclusionsTelemedicine was associated with lower ICU and hospital mortality among critically ill patients, although effects varied among studies and may be overestimated in nonrandomized designs. The optimal telemedicine technology configuration and dose tailored to ICU organization and case mix remain unclear.
30637059
Are Intensivists Safe?
Intensivists have an identity problem. We do not perform unique procedures, such as coronary angioplasty or endoscopy. We used to take pride in our skills placing and interpreting data from pulmonary artery catheters, but this art is dying rapidly as the evidence mounts against its utility (1). We do not have an organ focus, as do neurologists or nephrologists. Many of us are internists, but others are pediatricians, anesthesiologists, neurologists, and surgeons. Despite this identity crisis, critical care medicine has not faded into obscurity but rather is enjoying a period of focused attention and popularity. Among the reasons are recent epidemiologic studies that emphasize the burden of critical illness and prominent, if controversial, clinical trials that are finally creating an evidence base for our practice (25). Of note, we have attracted the attention of payers and quality assurance agencies, primarily because of studies showing that organized staffing by intensivists improves the outcome of critically ill patients (68). In most of the world, critically ill patients are managed in closed intensive care units (ICUs). Therefore, research about different intensivist staffing models is only possible in and primarily of interest to clinicians in the United States (9). The terminology for models of intensive care physician staffing is not standardized and can be confusing (see Glossary). In the most cited systematic review (7), high-intensity staffing referred to ICUs with policies that require transfer of responsibility for care of every critically ill patient to a single intensivist team (closed ICUs) or that mandate consultation by an intensivist. The high-intensity models evaluated in these studies were based on unit-level policies that reflect an environment of care rather than the individual physician (intensivist or nonintensivist) assigned to the patient. We use the term intensivist staffing to specifically refer to high-intensity models of care and not to the specialty of the individual physician assigned to the patient. In this issue, Levy and colleagues (10) explore the effects of ICU physician staffing models on mortality in Project IMPACT, a consortium of ICUs that receive benchmarking data in an effort to improve their care. Their findings not only refute the claim that intensivist-staffed ICUs improve outcomes but raise the possibility that intensivists are actually harmful. It is a complex study made more so by combining 2 separate research questions: one on the effect of intensivist care in choice ICUs and the other on intensivist care in no-choice ICUs. Most of the patients in this cohort received care in choice ICUs, where someone, presumably a physician, decided whether to involve an intensivist. The investigators found that elective management by an intensivist in choice ICUs was associated with increased mortality. Elective management by an intensivist in a choice ICU should not be confused with intensivist staffing. The choice ICUs would all be classified as low-intensity staffed ICUs because intensivist involvement with a patient's care was discretionary. The effect of intensivists in low-intensitystaffed ICUs has not been studied extensively, but Levy and colleagues are not the first to raise concerns about this model of care. Treggiari and colleagues (11) demonstrated that elective consultation by a pulmonologist in open ICUs was associated with no benefit in patients with acute lung injury. These authors attributed the lack of association to indication bias, arguing that physicians would be more likely to seek help in cases that they judged to have worse prognosis. Because physicians' predictions of outcome contribute to prognoses generated by mathematical models, indication bias is difficult to eliminate from observational studies with regression models (12). Estimates of prognosis probably affect the decision to involve an intensivist, making the analysis of the choice ICUs susceptible to the same bias. Despite these limitations, these 2 studies certainly raise concerns that elective intensivist consultation in open ICUs may not substitute for intensivist staffing of the entire unit. In a separate analysis, Levy and colleagues showed that no-choice ICUs, in which at least 95% of patients were cared for during their entire stay by an intensivist, had higher mortality rates than ICUs in which 5% or fewer of the patients received care by an intensivist (10). Presumably, the former would qualify as intensivist-staffed units, and the latter are not only open units but open units with no access to intensivists. This analysis should be comparable to previous studies of intensivist staffing. Moreover, indication bias is unlikely, because no one had a choice about whether to involve an intensivist. Although the overall cohort was subject to multiple sensitivity analyses to account for assumptions about patient mix and statistical models, the analysis of the no-choice ICUs was not. Because the findings of the primary analyses were robust across a wide range of assumptions, a cautious reader must ask, is it possible that intensivist-staffed ICUs are dangerous? In trying to answer this question, readers should consider the strength of the evidence supporting the alternate hypothesis (that intensivist-staffed ICUs reduce mortality) and the evidence supporting possible causal mechanisms for harms or benefits. The evidence that mortality is lower in intensivist-staffed ICUs is extensive and consistent across various study designs and patient populations (8, 9). In addition to studies involving general ICU patients, studies show that patients with acute lung injury, trauma, abdominal aortic surgery, and esophageal resection have better outcomes in intensivist-staffed ICUs (11, 13, 15). Curiously, an analysis of an earlier version of the same Project IMPACT data set that Levy and colleagues used to show that intensivists were harmful was used to demonstrate that intensivists with dedicated neuro-ICUs improved outcomes in patients with intracerebral hemorrhage (16). Are these associations between intensivist care and better outcomes examples of cause and effect? One way to answer this key question is to look for dose effects. If intensivists are dangerous, greater exposure to them should cause even greater harm. In fact, a higher dose of intensivists, even in intensivist-staffed ICUs, appears to confer additional benefit (17, 18). In assessing whether an association is causal, readers should also look for evidence for a causal mechanism. Intensivist-staffed ICUs are more likely to use evidence-based treatments and to initiate earlier end-of-life discussions, which may explain their effect on outcomes (11, 19, 20). Although Levy and colleagues speculate about mechanisms by which intensivists might increase mortality, they do not provide evidence to support a proposed mechanism. Patients cared for by intensivists were sicker and more likely to be mechanically ventilated and, not surprisingly, more likely to receive ICU procedures than patients in the care of nonintensivists. This more aggressive approach, which might be accompanied by nosocomial complications, is a plausible culprit. But patients who did not receive ICU procedures also had worse outcomes when they received care by an intensivist, making the procedures an unlikely explanation. Therefore, until someone replicates Levy and colleagues' results in another cohort and provides evidence for a mechanism by which intensivist-staffed ICUs increase mortality, their study will remain one observation against many. Another explanation for these data could present a unique opportunity to learn how to improve critical care. Although the intensivist-staffed no-choice units appear to be harmful, it is possible that they only appear harmful because they were being compared with 21 high performing units that achieved superior outcomes without intensivists. Zimmerman and colleagues (21) found that a similar group of high-performing ICUs did not have a single approach to intensivist staffing but did consistently use treatment protocols, participated in benchmarking studies like Project IMPACT, used specific criteria for selecting patients for ICU admission, and had extensive staff training. Although multiple lines of compelling evidence indicate that mortality and length of stay are better in intensivist-staffed ICUs, none of it precludes the possibility that some ICUs can, in the absence of an organized intensive care service, evolve alternative strategies to deliver high-quality care (22). With a looming shortfall in intensivists and many other barriers to creating intensivist-staffed ICUs in the United States, studies that explicitly identify these techniques are urgently needed (23). Although intensivist staffing is an evidence-based recommendation, it is probably only a first step toward, and hardly a guarantee of, the care that critically ill patients and their families deserve. Glossary Closed ICU: All patients are cared for by 1 team of intensivists in collaboration with a primary service. Only intensivists have admitting privileges to the ICU. Also called mandatory transfer. Open ICU: Any physician can admit patients to the ICU. Elective consultation Intensivists are available for consultation at the discretion of the responsible physician. Choice ICUs: An ICU in which an intensivist is the responsible physician for some patients but not others; presumably an open ICU with elective consultation. No-choice ICU: An ICU in which there is no choice about intensivist careeither all patients or no patients have an intensivist as their responsible physician. Intensivist: A physician with subspecialty training in critical care medicine High-intensity staffing: Includes both closed and mandatory consult models Low-intensity staffing: Any model other than closed or mandatory consult model
72060911
Physician Staffing Patterns and Clinical Outcomes in Critically Ill Patients: A Systematic Review
Searching MEDLINE was searched from 1 January 1965 to 30 September 2001, using the following MeSH terms: 'intensive care units', 'ICU', 'health resources/utilization', 'hospitalization', 'medical staff', 'hospital organization and administration', 'personnel staffing and scheduling', 'length of stay' and 'LOS'. The textwords used were 'staffing', 'intensivist', 'critical', 'care' and 'specialist'. Controlled clinical trials were retrieved using the search strategy proposed by Robinson and Dickersin (see Other Publications of Related Interest no.1). Observational studies were identified using the MeSH terms 'case-control study' and 'retrospective study'.
28475539
Impact of critical care physician workforce for intensive care unit physician staffing.
The Society for Critical Care Medicine has advocated for intensivist lead multi-disciplinary critical care for our 30 years; growing evidence supports their assertion. It is estimated that if intensive care unit (ICU) physician staffing (IPS) was implemented in non-rural United States hospitals, 53,000 lives and $5.4 billion would be saved annually. Despite the benefits of hiring physicians specialized in the treatment of critically ill patients, many hospitals worry about their ability to hire critical care physicians to staff their ICUs. In this essay, we discuss issues regarding the future supply of and demand for critical care physicians beginning with an overview of how to evaluate physician supply and demand in general. We then discuss supply and demand for critical care physicians considering emerging issues such as the Leapfrog standard that may impact estimates of the supply and demand for critical care physicians.
20015183
Reorganizing adult critical care delivery: the role of regionalization, telemedicine, and community outreach.
Variation in the quality of critical care services across hospitals coupled with an emerging workforce crisis necessitates system-level change in the organization of intensive care. In this review, we evaluate three alternative organizational models that may expand access to high-quality critical care: tiered regionalization, intensive care unit telemedicine, and quality improvement through regional outreach. These models share a potential to increase survival and reduce costs. Yet there are also major barriers to implementation, including the lack of a strong evidence base and the need for significant upfront financial investment. Reorganization of intensive care will also require the support of all involved stakeholders: patients and their families, critical care practitioners, administrative and public health professionals, and policy makers. To varying degrees these models require a central authority to implement and regulate the system, as well as specific legislation, investment in information technology, and financial incentives for providers. The existing evidence does not strongly support exclusive use of a particular model, and creation of a hybrid model that integrates the three complementary approaches is a practical option. A potential framework for implementation involves triage guidelines developed by professional societies leading to demonstration projects and national legislation in support of optimal systems. Additional research is needed to determine the comparative effectiveness and cost implications of these approaches, with a goal of best matching high-quality critical care to patients' needs and professional preferences at the hospital, regional, and national level.
29361702
Effect of a multiple-site intensive care unit telemedicine program on clinical and economic outcomes: An alternative paradigm for intensivist staffing*
ObjectiveTo examine whether a supplemental remote intensive care unit (ICU) care program, implemented by an integrated delivery network using a commercial telemedicine and information technology system, can improve clinical and economic performance across multiple ICUs. DesignBefore-and-after trial to assess the effect of adding the supplemental remote ICU telemedicine program. SettingTwo adult ICUs of a large tertiary care hospital. PatientsA total of 2,140 patients receiving ICU care between 1999 and 2001. InterventionsThe remote care program used intensivists and physician extenders to provide supplemental monitoring and management of ICU patients for 19 hrs/day (noon to 7 am) from a centralized, off-site facility (eICU). Supporting software, including electronic data display, physician note- and order-writing applications, and a computer-based decision-support tool, were available both in the ICU and at the remote site. Clinical and economic performance during 6 months of the remote intensivist program was compared with performance before the intervention. Measurements and Main ResultsHospital mortality for ICU patients was lower during the period of remote ICU care (9.4% vs. 12.9%; relative risk, 0.73; 95% confidence interval [CI], 0.55–0.95), and ICU length of stay was shorter (3.63 days [95% CI, 3.21–4.04] vs. 4.35 days [95% CI, 3.93–4.78]). Lower variable costs per case and higher hospital revenues (from increased case volumes) generated financial benefits in excess of program costs. ConclusionsThe addition of a supplemental, telemedicine-based, remote intensivist program was associated with improved clinical outcomes and hospital financial performance. The magnitude of the improvements was similar to those reported in studies examining the impact of implementing on-site dedicated intensivist staffing models; however, factors other than the introduction of off-site intensivist staffing may have contributed to the observed results, including the introduction of computer-based tools and the increased focus on ICU performance. Although further studies are needed, the apparent success of this on-going multiple-site program, implemented with commercially available equipment, suggests that telemedicine may provide a means for hospitals to achieve quality improvements associated with intensivist care using fewer intensivists.
32070971
Association of telemedicine for remote monitoring of intensive care patients with mortality, complications, and length of stay.
CONTEXT
Telemedicine technology, which can enable intensivists to simultaneously monitor several intensive care units (ICUs) from an off-site location, is increasingly common, but there is little evidence to support its use.


OBJECTIVE
To assess the association of remote monitoring of ICU patients (ICU telemedicine [tele-ICU]) with mortality, complications, and length of stay (LOS).


DESIGN, SETTING, AND PATIENTS
Observational study conducted in 6 ICUs of 5 hospitals in a large US health care system to assess the use of tele-ICU. The study included 2034 patients in the preintervention period (January 2003 to August 2005) and 2108 patients in the postintervention period (July 2004 to July 2006).


MAIN OUTCOME MEASURES
Hospital and ICU mortality, complications, and hospital and ICU survivors' LOS, with outcomes adjusted for severity of illness.


RESULTS
Local physicians delegated full treatment authority to the tele-ICU for 655 patients (31.1%) and authority to intervene only in life-threatening events for the remainder. Observed hospital mortality rates were 12.0% (95% confidence interval [CI], 10.6% to 13.5%) in the preintervention period and 9.9% (95% CI, 8.6% to 11.2%) in the postintervention period (preintervention to postintervention decrease, 2.1%; 95% CI, 0.2% to 4.1%; P = .03); observed ICU mortality rates were 9.2% (95% CI, 8.0% to 10.5%) in the preintervention period and 7.8% (95% CI, 6.7% to 9.0%) in the postintervention period (preintervention to postintervention decrease, 1.4%; 95% CI, -0.3% to 3.2%; P = .12). After adjustment for severity of illness, there were no significant differences associated with the telemedicine intervention for hospital mortality (relative risk, 0.85; 95% CI, 0.71 to 1.03) or for ICU mortality (relative risk, 0.88; 95% CI, 0.71 to 1.08). There was a significant interaction between the tele-ICU intervention and severity of illness (P < .001), in which tele-ICU was associated with improved survival in sicker patients but with no improvement or worse outcomes in less sick patients. There were no significant differences between the preintervention and postintervention periods for hospital or ICU LOS.


CONCLUSION
Remote monitoring of ICU patients was not associated with an overall improvement in mortality or LOS.
1469264
Intensive care unit telemedicine: Alternate paradigm for providing continuous intensivist care
ObjectiveIntensive care units (ICUs) account for an increasing percentage of hospital admissions and resource consumption. Adverse events are common in ICU patients and contribute to high mortality rates and costs. Although evidence demonstrates reduced complications and mortality when intensivists manage ICU patients, a dramatic national shortage of these specialists precludes most hospitals from implementing an around-the-clock, on-site intensivist care model. Alternate strategies are needed to bring expertise and proactive, continuous care to the critically ill. We evaluated the feasibility of using telemedicine as a means of achieving 24-hr intensivist oversight and improved clinical outcomes. DesignObservational time series triple cohort study. SettingA ten-bed surgical ICU in an academic-affiliated community hospital. PatientsAll patients whose entire ICU stay occurred within the study periods. InterventionsA 16-wk program of continuous intensivist oversight was instituted in a surgical ICU, where before the intervention, intensivist consultation was available but there were no on-site intensivists. Intensivists provided management during the intervention using remote monitoring methodologies (video conferencing and computer-based data transmission) to obtain clinical information and to communicate with on-site personnel. To assess the benefit of the remote management program, clinical and economic performance during the intervention were compared with two 16-wk periods within the year before the intervention. Measurements and Main ResultsICU and hospital mortality (observed and Acute Physiology and Chronic Health Evaluation III, severity-adjusted), ICU complications, ICU and hospital length-of-stay, and ICU and hospital costs were measured during the 3 study periods. Severity-adjusted ICU mortality decreased during the intervention period by 68% and 46%, compared with baseline periods one and two, respectively. Severity-adjusted hospital mortality decreased by 33% and 30%, and the incidence of ICU complications was decreased by 44% and 50%. ICU length of stay decreased by 34% and 30%, and ICU costs decreased by 33% and 36%, respectively. The cost savings were associated with a lower incidence of complications. ConclusionsTechnology-enabled remote care can be used to provide continuous ICU patient management and to achieve improved clinical and economic outcomes. This intervention’s success suggests that remote care programs may provide a means of improving quality of care and reducing costs when on-site intensivist coverage is not available.
23971850
Association of health information technology and teleintensivist coverage with decreased mortality and ventilator use in critically ill patients.
BACKGROUND
Little evidence exists to support implementing various health information technologies, such as telemedicine, in intensive care units.


METHODS
A coordinated health information technology bundle (HITB) was implemented along with remote intensivist coverage (RIC) at a 727-bed academic community hospital. Critical care specialists provided bedside coverage during the day and RIC at night to achieve intensivist coverage 24 hours per day, 7 days per week. We evaluated the effect of HITB-RIC on mortality, ventilator and vasopressor use, and the intervention length of stay. We compared our results with those achieved at baseline.


RESULTS
A total of 954 control patients who received care for 16 months before the implementation of HITB-RIC and 959 study patients who received care for 10 months after the implementation were included in the analysis. Mortality for the control and intervention groups were 21.4% and 14.7%, respectively. In addition, the observed mortality for the intervention group was 75.8% (P < .001) of that predicted by the Acute Physiology and Chronic Health Evaluation IV hospital mortality equations, which was 29.5% lower relative to the control group. Regression results confirm that the hospital mortality of the intensive care unit patients was significantly lower after implementation of the intervention, controlling for predicted risk of mortality and do-not-resuscitate status. Overall, intervention patients also had significantly less (P = .001) use of mechanical ventilation, controlling for body-system diagnosis category and severity of illness.


CONCLUSION
The use of HITB-RIC was associated with significantly lower mortality and less ventilator use in critically ill patients.
205538595
Clinical and economic outcomes of the electronic intensive care unit: Results from two community hospitals*
Objective:To determine the impact of a telemedicine system, the electronic intensive care unit (eICU), on ICU, and non-ICU mortality, total mortality, total and ICU-specific length of stay, and total hospital cost at two community hospitals. Design:Observational study with one baseline period and two comparison periods (eICU wave one and eICU wave two). Each time period was 4 months in duration. Setting:Four ICU from two community hospitals in the metropolitan Chicago area. Hospital one is a 610-bed teaching hospital with three adult ICU (ten-bed medical ICU, ten-bed cardiac ICU, and 14-bed surgical ICU). Hospital two is a 185-bed nonteaching hospital with a ten-bed mixed medical/surgical ICU. Patients:All patients 18 yrs or older with an ICU stay of at least 4 hrs during the specified time period were included. Interventions:The eICU was implemented at both hospitals in April 2003. Measurements and Main Results:Mortality, length of stay, and total cost were measured. Age, gender, race/ethnicity, trauma status, Acute Physiology and Chronic Health Evaluation III score, and physician utilization of the eICU were included as covariates.Included in the analysis were 4088 patients (1371 at baseline, 1287 in eICU wave one, and 1430 in eICU wave two). The eICU did not have a significant effect on ICU/non-ICU/total mortality or hospital length of stay. ICU length of stay increased over time and was associated with higher physician utilization of the eICU. Although total hospital costs increased over time, the rate of increase was steeper for those patients whose physicians permitted only a low level of eICU involvement. Conclusions:In our study of >4000 patients representing two community hospitals, we did not find a reduction in mortality, length of stay, or hospital cost attributable to the introduction of the eICU.
20021098
Impact of telemedicine intensive care unit coverage on patient outcomes: a systematic review and meta-analysis.
BACKGROUND
Although remote intensive care unit (ICU) coverage is rapidly being adopted to enhance access to intensivists, its effect on patient outcomes is unclear. We conducted a meta-analysis to examine the impact of telemedicine ICU (tele-ICU) coverage on mortality and length of stay (LOS).


METHODS
We conducted a systematic review of studies published from January 1, 1950, through September 30, 2010, using PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Global Health, Web of Science, the Cochrane Library, and conference abstracts. We included studies that reported data on the primary outcomes of ICU and in-hospital mortality or on the secondary outcomes of ICU and hospital LOS.


RESULTS
We identified 13 eligible studies involving 35 ICUs. All the studies used a before-and-after design. The studies included 41 374 patients (15 667 pre-tele-ICU and 25 707 post-tele-ICU patients). Tele-ICU coverage was associated with a reduction in ICU mortality (pooled odds ratio, 0.80; 95% confidence interval [CI], 0.66-0.97; P = .02) but not in-hospital mortality for patients admitted to an ICU (pooled odds ratio, 0.82; 95% CI, 0.65-1.03; P = .08). Similarly, tele-ICU coverage was associated with a reduction in ICU LOS (mean difference, -1.26 days; 95% CI, -2.21 to -0.30; P = .01) but not hospital LOS (mean difference, -0.64; 95% CI, -1.52 to 0.25; P = .16).


CONCLUSION
Tele-ICU coverage is associated with lower ICU mortality and LOS but not with lower in-hospital mortality or hospital LOS.
22376760
[The clinical application of remote critical care network].
OBJECTIVE
To discuss and evaluate whether a remote critical care program network can improve clinical and economic performance across multiple intensive care units (ICUs).


METHODS
The consultative center composed of intensivists and physician extenders to provide remote consultations for the critical patients. Supporting software, including electronic data display and communication interface were available both in the ICU center and at the remote sites. Clinical and economic performance after 1 year application of the program was compared with the performance before the intervention.


RESULTS
During January 2008 to July 2009, there had been 63 hospitals in Zhejiang Province joined the network. A total of 1 617 patients had received remote critical care consultations. One hundred and seventy-three remote teaching ward rounds and 72 lectures had been conducted on the network during this period. The before-and after-data comparison for 23 hospitals which had joined the network for longer than 1 year showed that, the program had decreased ICUs raw mortality by 11.6% (12.9% vs. 14.6%), and reduced transfer rate by 38.3% (2.9% vs. 4.7%), and ICUs bed occupancy rate increased by 6.1% (83.4% vs. 78.6%).


CONCLUSION
The application of a remote critical care program was associated with improved clinical outcomes of critically ill patients and hospital financial performance.
38976658
Use of telemedicine to provide pediatric critical care inpatient consultations to underserved rural Northern California.
OBJECTIVE
To report a novel application of telemedicine and to assess the resulting quality and satisfaction of care. Study design An existing telemedicine program was evaluated through the use of a nonconcurrent cohort design. Cohorts of patients were compared by means of the Pediatric Risk of Mortality, version III (PRISM III), to adjust for severity of illness and assess risk-adjusted mortality rates. Satisfaction and quality of care surveys administered to the pediatric patient's parents and providers were also analyzed.


RESULTS
Telemedicine consultations (n=70) were conducted on 47 patients during a 2-year period. Patients receiving telemedicine consultations were sicker than the average pediatric patient cared for in the adult intensive care unit (ICU) (n=180) and compared with historic control pediatric patients (n=116) (mean PRISM III score of 9.6 versus 7.7 and 7.5, respectively). PRISM III-standardized mortality ratios were consistent among the same cohorts of patients (0.24, 0.36, and 0.37, respectively). Overall satisfaction and perception of quality of care was high among parents and rural health care providers.


CONCLUSIONS
This study demonstrates that a regional pediatric ICU-based telemedicine program providing live interactive consultations to a rural adult ICU can provide quality care that is considered highly satisfactory to a select group of critically ill pediatric patients.
5930715
Impact of an Intensive Care Unit Telemedicine Program on a Rural Health Care System
Abstract We evaluated the impact of a 15-hospital, rural, multi-state intensive care unit (ICU) telemedicine program. Acute Physiology, Age, and Chronic Health Evaluation (APACHE® III) scores, raw mortality rates, and actual-to-predicted length of stay (LOS) ratios and mortality ratios were used. Surveys evaluated program impact in smaller facilities and satisfaction of the physicians staffing the remote center. Smaller facilities' staff reported improvements in the quality of critical care services and reduced transfers. In regional hospitals, acuity scores increased (retention of sicker patients) while raw mortality was the same or lower. Length of stay ratios were reduced in these hospitals. In the tertiary hospital, actual-to-predicted ICU and hospital mortality and LOS ratios decreased.
79550924
The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses
Nonrandomised studies, including case-control and cohort studies, can be challenging to implement and conduct. Assessment of the quality of such studies is essential for a proper understanding of nonrandomised studies. The Newcastle-Ottawa Scale (NOS) is an ongoing collaboration between the Universities of Newcastle, Australia and Ottawa, Canada. It was developed to assess the quality of nonrandomised studies with its design, content and ease of use directed to the task of incorporating the quality assessments in the interpretation of meta-analytic results. A 'star system' has been developed in which a study is judged on three broad perspectives: the selection of the study groups; the comparability of the groups; and the ascertainment of either the exposure or outcome of interest for case-control or cohort studies respectively. The goal of this project is to develop an instrument providing an easy and convenient tool for quality assessment of nonrandomised studies to be used in a systematic review.
6319826
Quantifying heterogeneity in a meta-analysis.
The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.
18492784
Measuring inconsistency in meta-analyses
Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? 

Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis.

Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4

Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted?

A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …
45709820
The statistical basis of meta-analysis.
Two models for study-to-study variation in a meta-analysis are presented, critiqued and illustrated. One, the fixed effects model, takes the studies being analysed as the universe of interest; the other, the random effects model, takes these studies as representing a sample from a larger population of possible studies. With emphasis on clinical trials, this paper illustrates in some detail the application of both models to three summary measures of the effect of an experimental intervention versus a control: the standardized difference for comparing two means, and the relative risk and odds ratio for comparing two proportions.
38976658
Use of telemedicine to provide pediatric critical care inpatient consultations to underserved rural Northern California.
OBJECTIVE
To report a novel application of telemedicine and to assess the resulting quality and satisfaction of care. Study design An existing telemedicine program was evaluated through the use of a nonconcurrent cohort design. Cohorts of patients were compared by means of the Pediatric Risk of Mortality, version III (PRISM III), to adjust for severity of illness and assess risk-adjusted mortality rates. Satisfaction and quality of care surveys administered to the pediatric patient's parents and providers were also analyzed.


RESULTS
Telemedicine consultations (n=70) were conducted on 47 patients during a 2-year period. Patients receiving telemedicine consultations were sicker than the average pediatric patient cared for in the adult intensive care unit (ICU) (n=180) and compared with historic control pediatric patients (n=116) (mean PRISM III score of 9.6 versus 7.7 and 7.5, respectively). PRISM III-standardized mortality ratios were consistent among the same cohorts of patients (0.24, 0.36, and 0.37, respectively). Overall satisfaction and perception of quality of care was high among parents and rural health care providers.


CONCLUSIONS
This study demonstrates that a regional pediatric ICU-based telemedicine program providing live interactive consultations to a rural adult ICU can provide quality care that is considered highly satisfactory to a select group of critically ill pediatric patients.
15976033
Intensive care unit robotic telepresence facilitates rapid physician response to unstable patients and decreased cost in neurointensive care.
BACKGROUND
The timely assessment and treatment of ICU patients is important for neurosurgeons and neurointensivists. We hypothesized that the use of RTP can improve physician rapid response to unstable ICU patients.


METHODS
This is a prospective study using a before-after, cohort-control design to test the effectiveness of RTP. Physicians used RTP to make rounds in the ICU in response to nursing pages. Data concerning several aspects of the RTP interaction including the latency of the response, the problem being treated, the intervention that was ordered, and the type of information gathered using the RTP were documented. The effect of RTP on ICU length of stay and cost was assessed.


RESULTS
The use of RTP was associated with a reduction in latency of attending physician face-to-face response for routine and urgent pages compared to conventional care (RTP: 9.2 +/- 9.3 minutes vs conventional: 218 +/- 186 minutes). The response latencies to brain ischemia (7.8 +/- 2.8 vs 152 +/- 85 minutes) and elevated ICP (11 +/- 14 vs 108 +/- 55 minutes) were reduced (P < .001), as was the LOS for patients with SAH (2 days) and brain trauma (1 day). There was an increase in ICU occupancy by 11% compared with the prerobot era, and there was an ICU cost savings of $1.1 million attributable to the use of RTP.


CONCLUSION
The use of RTP enabled rapid face-to-face attending physician response to ICU patients and resulted in decreased ICU cost and LOS.
3843614
Clinical outcomes after telemedicine intensive care unit implementation*
Objective: To examine clinical outcomes before and after implementation of a telemedicine program in the intensive care units of a five-hospital healthcare system. Design: Observational study with the baseline period of 1 yr before the start of a telemedicine intensive care unit program implementation at each of 5 hospitals. The post periods are 1, 2, and 3 yrs after telemedicine intensive care unit program implementation at each hospital. Setting: Ten adult intensive care units (114 beds) in five community hospitals in south Florida. A telemedicine intensive care unit program with remote 24/7 intensivist and critical care nurse electronic monitoring was implemented by a phased approach between December 2005 and July 2007. Measurements and Main Results: Records from 24,656 adult intensive care unit patients were analyzed. Hospital length of stay, intensive care unit length of stay, hospital mortality, and Case Mix Index were measured. Severity of illness using All Patient Refined-Diagnosis Related Groups scores was used as a covariate. From the baseline year to year 3 postimplementation, the severity-adjusted hospital length of stay was lowered from 11.86 days (95% confidence interval [CI] 11.55–12.21) to 10.16 days (95% CI 9.80−10.53; p < .001), severity-adjusted intensive care unit length of stay was lowered from 4.35 days (95% CI 4.22–4.49) to 3.80 days (95% CI 3.65–3.94; p < .001), and the relative risk of hospital mortality decreased to 0.77 (95% CI 0.69–0.87; p < .001). Conclusions: After 3 yrs of deployment of a telemedicine intensive care unit program, this retrospective observational study of mortality and length of stay outcomes included all cases admitted to an adult intensive care unit and found statistically significant decreases in severity-adjusted hospital length of stay of 14.2%, intensive care unit length of stay of 12.6%, and relative risk of hospital mortality of 23%, respectively, in a multihospital healthcare system.
12876080
A multifaceted intervention for quality improvement in a network of intensive care units: a cluster randomized trial.
CONTEXT
Evidence-based practices improve intensive care unit (ICU) outcomes, but eligible patients may not receive them. Community hospitals treat most critically ill patients but may have few resources dedicated to quality improvement.


OBJECTIVE
To determine the effectiveness of a multicenter quality improvement program to increase delivery of 6 evidence-based ICU practices.


DESIGN, SETTING, AND PARTICIPANTS
Pragmatic cluster-randomized trial among 15 community hospital ICUs in Ontario, Canada. A total of 9269 admissions occurred during the trial (November 2005 to October 2006) and 7141 admissions during a decay-monitoring period (December 2006 to August 2007).


INTERVENTION
We implemented a videoconference-based forum including audit and feedback, expert-led educational sessions, and dissemination of algorithms to sequentially improve delivery of 6 practices. We randomized ICUs into 2 groups. Each group received this intervention, targeting a new practice every 4 months, while acting as control for the other group, in which a different practice was targeted in the same period. MAIN MEASURE OUTCOMES: The primary outcome was the summary ratio of odds ratios (ORs) for improvement in adoption (determined by daily data collection) of all 6 practices during the trial in intervention vs control ICUs.


RESULTS
Overall, adoption of the targeted practices was greater in intervention ICUs than in controls (summary ratio of ORs, 2.79; 95% confidence interval [CI], 1.00-7.74). Improved delivery in intervention ICUs was greatest for semirecumbent positioning to prevent ventilator-associated pneumonia (90.0% of patient-days in last month vs 50.0% in first month; OR, 6.35; 95% CI, 1.85-21.79) and precautions to prevent catheter-related bloodstream infection (70.0% of patients receiving central lines vs 10.6%; OR, 30.06; 95% CI, 11.00-82.17). Adoption of other practices, many with high baseline adherence, changed little.


CONCLUSION
In a collaborative network of community ICUs, a multifaceted quality improvement intervention improved adoption of care practices.


TRIAL REGISTRATION
clinicaltrials.gov Identifier: NCT00332982.
25461996
Evaluation of the Impact of a tele-ICU Pharmacist on the Management of Sedation in Critically III Mechanically Ventilated Patients
Background: An organized and uniform approach to managing sedation in critically ill patients has been associated with improved outcomes, but the most effective means of optimizing sedative medication use in clinical practice has not been fully determined. Pharmacist interventions directed at improving sedation guideline compliance have been shown to reduce the duration of mechanical ventilation. Objective: To determine the impact that pharmacy staffing configurations that include a tele-ICU pharmacist have on compliance with an intensive care unit (ICU) sedation guideline in critically ill mechanically ventilated patients requiring continuous-infusion sedative medications. Methods: Compliance with an established ICU sedation guideline, the performance of daily sedative interruptions, and the number of sedative medication–related interventions were evaluated before and after expansion of the ICU pharmacist staffing model to include comprehensive off-hours pharmacist coverage supported with established tele-ICU resources. In both groups, sedation was managed by the primary ICU team. In the intervention group, a pharmacist working in the tele-ICU center performed electronic record audits and made sedative medication recommendations to the primary team. Results: The addition of third shift tele-ICU pharmacist support was associated with a significant increase in the percentage of patients who received a daily sedative interruption (45% vs 54%; p < 0.0001). This occurred in the context of significant increases in the total number of ICU pharmacist interventions (36 vs 49.4 per 100 patient days, p < 0.0001), the number of therapeutic interventions (20.4 vs 26.1 per 100 patient days, p < 0.001), and the number of sedative-related interventions (0.9 vs 4.4 per 100 patient days, p < 0.0001). Conclusions: Tele-ICU resources can be utilized to increase compliance with an established ICU sedation guideline and extend the benefits that daytime ICU clinical pharmacy services provide. Increased ICU pharmacist availability may have additional benefits not measured in this study.
19116352
Implementation of a Model of Robotic Tele-Presence (RTP) in the Neuro-ICU: Effect on Critical Care Nursing Team Satisfaction
IntroductionRobotic Tele-Presence (RTP) is a form of mobile telemedicine, which enables a direct face-to-face rapid response by the physician, instead of the traditional telephonic paradigm. We hypothesized that a model of RTP for after-hour ICU rounds and emergencies would be associated with improved ICU nurse satisfaction.MethodsWe implemented a prospective nighttime multidisciplinary ICU round time, using RTP at our Neuro-ICU. To test for critical ICU nurse team satisfaction, a questionnaire was implemented. The primary outcome was nurse satisfaction measured through a questionnaire with answers trichotomized into: agreement, disagreement, and no opinion. The occurrence of outcomes was compared between the groups by χ2 or Fisher exact tests for the difference in proportions (PD) with Bonferroni correction for multiple pairwise comparisons.ResultsIn total, 34 nurses completed the pre-survey and 40 nurses completed the post-survey. Night nurses were more likely to agree that RTP was associated with: ICU physicians being sufficiently available in the ICU (agreement 6–20 %, PD 14 %, p = 0.008), present during acute emergencies (agreement 44–65 %, PD 21 %, p = 0.007), and had enough time to get questions answered from the physician team (agreement 41–53 %, PD 11 %, p = NS).ConclusionsThis data suggest improvement in critical care nursing team satisfaction with a model of RTP in the Neuroscience ICU, particularly during nighttime hours. RTP is a tool that may enhance communication among components of the ICU team.
19498566
The research agenda in ICU telemedicine: a statement from the Critical Care Societies Collaborative.
ICU telemedicine uses audiovisual conferencing technology to provide critical care from a remote location. Research is needed to best define the optimal use of ICU telemedicine, but efforts are hindered by methodological challenges and the lack of an organized delivery approach. We convened an interdisciplinary working group to develop a research agenda in ICU telemedicine, addressing both methodological and knowledge gaps in the field. To best inform clinical decision-making and health policy, future research should be organized around a conceptual framework that enables consistent descriptions of both the study setting and the telemedicine intervention. The framework should include standardized methods for assessing the preimplementation ICU environment and describing the telemedicine program. This framework will facilitate comparisons across studies and improve generalizability by permitting context-specific interpretation. Research based on this framework should consider the multidisciplinary nature of ICU care and describe the specific program goals. Key topic areas to be addressed include the effect of ICU telemedicine on the structure, process, and outcome of critical care delivery. Ideally, future research should attempt to address causation instead of simply associations and elucidate the mechanism of action in order to determine exactly how ICU telemedicine achieves its effects. ICU telemedicine has significant potential to improve critical care delivery, but high-quality research is needed to best inform its use. We propose an agenda to advance the science of ICU telemedicine and generate research with the greatest potential to improve patient care.
22745062
Costs and effectiveness of tele-ICUs in reducing morbidity and mortality in intensive care units
A substantial amount of healthcare is delivered in intensive care units (ICUs); amounting to over 13 million ICU bed-days per year 1 . Intensive care is a costly and growing expense in the US healthcare system, with ICUs comprising between 5–10% of hospital beds, consuming 20–34% of all acute care resources, and a total of 1% of the US gross domestic product 2 . These costs are increasing due to more severely ill patients and new technologies 3 .
5573811
Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care
Key Summary Points Health information technology has been shown to improve quality by increasing adherence to guidelines, enhancing disease surveillance, and decreasing medication errors. Much of the evidence on quality improvement relates to primary and secondary preventive care. The major efficiency benefit has been decreased utilization of care. Effect on time utilization is mixed. Empirically measured cost data are limited and inconclusive. Most of the high-quality literature regarding multifunctional health information technology systems comes from 4 benchmark research institutions. Little evidence is available on the effect of multifunctional commercially developed systems. Little evidence is available on interoperability and consumer health information technology. A major limitation of the literature is its generalizability. Health care experts, policymakers, payers, and consumers consider health information technologies, such as electronic health records and computerized provider order entry, to be critical to transforming the health care industry (1-7). Information management is fundamental to health care delivery (8). Given the fragmented nature of health care, the large volume of transactions in the system, the need to integrate new scientific evidence into practice, and other complex information management activities, the limitations of paper-based information management are intuitively apparent. While the benefits of health information technology are clear in theory, adapting new information systems to health care has proven difficult and rates of use have been limited (9-11). Most information technology applications have centered on administrative and financial transactions rather than on delivering clinical care (12). The Agency for Healthcare Research and Quality asked us to systematically review evidence on the costs and benefits associated with use of health information technology and to identify gaps in the literature in order to provide organizations, policymakers, clinicians, and consumers an understanding of the effect of health information technology on clinical care (see evidence report at www.ahrq.gov). From among the many possible benefits and costs of implementing health information technology, we focus here on 3 important domains: the effects of health information technology on quality, efficiency, and costs. Methods Analytic Frameworks We used expert opinion and literature review to develop analytic frameworks (Table) that describe the components involved with implementing health information technology, types of health information technology systems, and the functional capabilities of a comprehensive health information technology system (13). We modified a framework for clinical benefits from the Institute of Medicine's 6 aims for care (2) and developed a framework for costs using expert consensus that included measures such as initial costs, ongoing operational and maintenance costs, fraction of health information technology penetration, and productivity gains. Financial benefits were divided into monetized benefits (that is, benefits expressed in dollar terms) and nonmonetized benefits (that is, benefits that could not be directly expressed in dollar terms but could be assigned dollar values). Table. Health Information Technology Frameworks Data Sources and Search Strategy We performed 2 searches (in November 2003 and January 2004) of the English-language literature indexed in MEDLINE (1995 to January 2004) using a broad set of terms to maximize sensitivity. (See the full list of search terms and sequence of queries in the full evidence report at www.ahrq.gov.) We also searched the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database; hand-searched personal libraries kept by content experts and project staff; and mined bibliographies of articles and systematic reviews for citations. We asked content experts to identify unpublished literature. Finally, we asked content experts and peer reviewers to identify newly published articles up to April 2005. Study Selection and Classification Two reviewers independently selected for detailed review the following types of articles that addressed the workings or implementation of a health technology system: systematic reviews, including meta-analyses; descriptive qualitative reports that focused on exploration of barriers; and quantitative reports. We classified quantitative reports as hypothesis-testing if the investigators compared data between groups or across time periods and used statistical tests to assess differences. We further categorized hypothesis-testing studies (for example, randomized and nonrandomized, controlled trials, controlled before-and-after studies) according to whether a concurrent comparison group was used. Hypothesis-testing studies without a concurrent comparison group included those using simple prepost, time-series, and historical control designs. Remaining hypothesis-testing studies were classified as cross-sectional designs and other. We classified quantitative reports as a predictive analysis if they used methods such as statistical modeling or expert panel estimates to predict what might happen with implementation of health information technology rather than what has happened. These studies typically used hybrid methodsfrequently mixing primary data collection with secondary data collection plus expert opinion and assumptionsto make quantitative estimates for data that had otherwise not been empirically measured. Cost-effectiveness and cost-benefit studies generally fell into this group. Data Extraction and Synthesis Two reviewers independently appraised and extracted details of selected articles using standardized abstraction forms and resolved discrepancies by consensus. We then used narrative synthesis methods to integrate findings into descriptive summaries. Each institution that accounted for more than 5% of the total sample of 257 papers was designated as a benchmark research leader. We grouped syntheses by institution and by whether the systems were commercially or internally developed. Role of the Funding Sources This work was produced under Agency for Healthcare Research and Quality contract no. 2002. In addition to the Agency for Healthcare Research and Quality, this work was also funded by the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services, and the Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services. The funding sources had no role in the design, analysis, or interpretation of the study or in the decision to submit the manuscript for publication. Data Synthesis Literature Selection Overview Of 867 articles, we rejected 141 during initial screening: 124 for not having health information technology as the subject, 4 for not reporting relevant outcomes, and 13 for miscellaneous reasons (categories not mutually exclusive). Of the remaining 726 articles, we excluded 469 descriptive reports that did not examine barriers (Figure). We recorded details of and summarized each of the 257 articles that we did include in an interactive database (healthit.ahrq.gov/tools/rand) that serves as the evidence table for our report (14). Twenty-four percent of all studies came from the following 4 benchmark institutions: 1) the Regenstrief Institute, 2) Brigham and Women's Hospital/Partners Health Care, 3) the Department of Veterans Affairs, and 4) LDS Hospital/Intermountain Health Care. Figure. Search flow for health information technology ( HIT ) literature. Pediatrics Types and Functions of Technology Systems The reports addressed the following types of primary systems: decision support aimed at providers (63%), electronic health records (37%), and computerized provider order entry (13%). Specific functional capabilities of systems that were described in reports included electronic documentation (31%), order entry (22%), results management (19%), and administrative capabilities (18%). Only 8% of the described systems had specific consumer health capabilities, and only 1% had capabilities that allowed systems from different facilities to connect with each other and share data interoperably. Most studies (n= 125) assessed the effect of the systems in the outpatient setting. Of the 213 hypothesis-testing studies, 84 contained some data on costs. Several studies assessed interventions with limited functionality, such as stand-alone decision support systems (15-17). Such studies provide limited information about issues that today's decision makers face when selecting and implementing health information technology. Thus, we preferentially highlight in the following paragraphs studies that were conducted in the United States, that had empirically measured data on multifunctional systems, and that included health information and data storage in the form of electronic documentation or order-entry capabilities. Predictive analyses were excluded. Seventy-six studies met these criteria: 54 from the 4 benchmark leaders and 22 from other institutions. Data from Benchmark Institutions The health information technology systems evaluated by the benchmark leaders shared many characteristics. All the systems were multifunctional and included decision support, all were internally developed by research experts at the respective academic institutions, and all had capabilities added incrementally over several years. Furthermore, most reported studies of these systems used research designs with high internal validity (for example, randomized, controlled trials). Appendix Table 1 (18-71) provides a structured summary of each study from the 4 benchmark institutions. This table also includes studies that met inclusion criteria not highlighted in this synthesis (26, 27, 30, 39, 40, 53, 62, 65, 70, 71). The data supported 5 primary themes (3 directly r
17194990
Room for improvement? A survey of the methods used in systematic reviews of adverse effects
BackgroundAlthough the methods for conducting systematic reviews of efficacy are well established, there is much less guidance on how systematic reviews of adverse effects should be performed.MethodsIn order to determine where methodological research is most needed to improve systematic reviews of adverse effects of health care interventions, we conducted a descriptive analysis of systematic reviews published between 1994 and 2005. We searched the Database of Abstracts of Reviews of Effects (DARE) and The Cochrane Database of Systematic Reviews (CDSR) to identify systematic reviews in which the primary outcome was an adverse effect or effects. We then extracted data on many of the elements of the systematic review process including: types of interventions studied, adverse effects of interest, resources searched, search strategies, data sources included in reviews, quality assessment of primary data, nature of the data analysis, and source of funding.Results256 reviews were included in our analysis, of which the majority evaluated drug interventions and pre-specified the adverse effect or effects of interest. A median of 3 resources were searched for each review and very few reviews (13/256) provided sufficient information to reproduce their search strategies. Although more than three quarters (185/243) of the reviews sought to include data from sources other than randomised controlled trials, fewer than half (106/256) assessed the quality of the studies that were included. Data were pooled quantitatively in most of the reviews (165/256) but heterogeneity was not always considered. Less than half (123/256) of the reviews reported on the source of funding.ConclusionThere is an obvious need to improve the methodology and reporting of systematic reviews of adverse effects. The methodology around identification and quality assessment of primary data is the main concern.
207385682
Staff acceptance of tele-ICU coverage: a systematic review.
BACKGROUND
Remote coverage of ICUs is increasing, but staff acceptance of this new technology is incompletely characterized. We conducted a systematic review to summarize existing research on acceptance of tele-ICU coverage among ICU staff.


METHODS
We searched for published articles pertaining to critical care telemedicine systems (aka, tele-ICU) between January 1950 and March 2010 using PubMed, Cumulative Index to Nursing and Allied Health Literature, Global Health, Web of Science, and the Cochrane Library and abstracts and presentations delivered at national conferences. Studies were included if they provided original qualitative or quantitative data on staff perceptions of tele-ICU coverage. Studies were imported into content analysis software and coded by tele-ICU configuration, methodology, participants, and findings (eg, positive and negative staff evaluations).


RESULTS
Review of 3,086 citations yielded 23 eligible studies. Findings were grouped into four categories of staff evaluation: overall acceptance level of tele-ICU coverage (measured in 70% of studies), impact on patient care (measured in 96%), impact on staff (measured in 100%), and organizational impact (measured in 48%). Overall acceptance was high, despite initial ambivalence. Favorable impact on patient care was perceived by > 82% of participants. Staff impact referenced enhanced collaboration, autonomy, and training, although scrutiny, malfunctions, and contradictory advice were cited as potential barriers. Staff perceived the organizational impact to vary. An important limitation of available studies was a lack of rigorous methodology and validated survey instruments in many studies.


CONCLUSIONS
Initial reports suggest high levels of staff acceptance of tele-ICU coverage, but more rigorous methodologic study is required.