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Original Research: Critical Care |

A Multicenter Study of ICU Telemedicine Reengineering of Adult Critical CareICU Telemedicine Trial FREE TO VIEW

Craig M. Lilly, MD, FCCP; John M. McLaughlin, PhD, MSPH; Huifang Zhao, PhD; Stephen P. Baker, MScPH; Shawn Cody, RN, MSN, MBA; Richard S. Irwin, MD, Master FCCP; for the UMass Memorial Critical Care Operations Group
Author and Funding Information

From the Departments of Medicine (Drs Lilly and Irwin), Anesthesiology (Dr Lilly), Surgery (Dr Lilly), Quantitative Sciences (Mr Baker), and Cell Biology (Mr Baker), the Clinical and Population Health Research Program (Drs Lilly and Zhao), and Graduate School of Biomedical Sciences (Drs Lilly and Zhao and Mr Baker), University of Massachusetts Medical School, Worcester, MA; M.O.R.E. Data Analytics, LLC (Dr McLaughlin), Columbus, OH; the Graduate School of Nursing Sciences (Mr Cody), UMass Memorial Medical Center (Drs Lilly and Irwin and Mr Cody), Worcester, MA.

Correspondence to: Craig M. Lilly, MD, University of Massachusetts Medical School, UMass Memorial Medical Center, 281 Lincoln St, Worcester, MA 01605; e-mail: craig.lilly@umassmed.edu


* A complete list of group members is located in e-Appendix 1.

Funding/Support: Support of this study was provided by the University of Massachusetts Medical School.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;145(3):500-507. doi:10.1378/chest.13-1973
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Background:  Few studies have evaluated both the overall effect of ICU telemedicine programs and the effect of individual components of the intervention on clinical outcomes.

Methods:  The effects of nonrandomized ICU telemedicine interventions on crude and adjusted mortality and length of stay (LOS) were measured. Additionally, individual intervention components related to process and setting of care were evaluated for their association with mortality and LOS.

Results:  Overall, 118,990 adult patients (11,558 control subjects, 107,432 intervention group patients) from 56 ICUs in 32 hospitals from 19 US health-care systems were included. After statistical adjustment, hospital (hazard ratio [HR] = 0.84; 95% CI, 0.78-0.89; P < .001) and ICU (HR = 0.74; 95% CI, 0.68-0.79; P < .001) mortality in the ICU telemedicine intervention group was significantly better than that of control subjects. Moreover, adjusted hospital LOS was reduced, on average, by 0.5 (95% CI, 0.4-0.5), 1.0 (95% CI, 0.7-1.3), and 3.6 (95% CI, 2.3-4.8) days, and adjusted ICU LOS was reduced by 1.1 (95% CI, 0.8-1.4), 2.5 (95% CI, 1.6-3.4), and 4.5 (95% CI, 1.5-7.2) days among those who stayed in the ICU for ≥ 7, ≥ 14, and ≥ 30 days, respectively. Individual components of the interventions that were associated with lower mortality, reduced LOS, or both included (1) intensivist case review within 1 h of admission, (2) timely use of performance data, (3) adherence to ICU best practices, and (4) quicker alert response times.

Conclusions:  ICU telemedicine interventions, specifically interventions that increase early intensivist case involvement, improve adherence to ICU best practices, reduce response times to alarms, and encourage the use of performance data, were associated with lower mortality and LOS.

Figures in this Article

Economic factors, the patient safety movement, and humanitarian commitment to improve access to care1 have contributed to a growing societal focus on making high-quality care more available.2 The high costs of adult critical care3,4 and concerns about the efficiency and sustainability of current paradigms of critical care delivery5 demand new strategies that leverage technologic advances to improve quality and access and limit costs.6 ICU telemedicine is one promising technologic approach that increases the availability of adult critical care services and has been shown to improve efficiency of care delivery and patient outcomes in some, but not all, studies.715 In the context of critical illness, telemedicine has been defined as the provision of care to critically ill patients by remotely located health-care professionals using audio-visual communication technologies.16 A previous study of a single health-care system demonstrated that implementation of an ICU telemedicine program was associated with lower mortality and length of stay (LOS) and that part of these associations were attributable to higher rates of adherence to ICU best practices, more timely responses to alerts for physiologic instability, and earlier involvement of an intensive care specialist.15 The current study builds upon this previous research by exploring a broader range of process and setting of care metrics that content experts have previously identified to likely be (1) altered by the introduction of an ICU telemedicine program and (2) associated with lower mortality and LOS.17,18 In addition, the substantial size of this study allows insights regarding whether ICU telemedicine programs are associated with lower hospital mortality; prior studies have not had adequate power to exclude type 1 error. This study was designed to test whether the implementation of a multicomponent ICU telemedicine program was associated primarily with lower hospital mortality and secondarily with lower ICU mortality and shorter ICU and hospital LOS. As a secondary aim, we evaluated the relationship between individual process and setting of care factors that varied among ICU telemedicine interventions and the four main outcomes (ICU and hospital mortality and LOS).

Study Design and Patients

This study was a nonrandomized, unblinded, pre/post assessment of ICU telemedicine interventions. Twenty-one health-care systems known to be implementing an ICU telemedicine program were invited to collect patient-level data using standardized instruments. Patients were recruited from 56 participating ICUs located in 15 states representing each of the US census divisions.19 Nineteen participating health systems enrolled patients over an average of 1,340 days (range, 729-2,056). The first system started enrolling patients on May 16, 2003, and the last system enrolled the last patient on December 31, 2008. The study design, timeline, patient selection, and exclusions are presented in Figure 1. Internal and external auditing demonstrated that electronic and manual methods of collection by abstractors, trained as previously described,15 yielded similar datasets and APACHE (Acute Physiology and Chronic Health Evaluation) IV scores.

Figure Jump LinkFigure 1. Study timeline, case selection, and availability of acuity scores. APACHE = Acute Physiology and Chronic Health Evaluation.Grahic Jump Location

Minimal enrollment targets for the control group for each ICU were designed to provide 80% power to detect a 4.5% difference in hospital mortality at a significance level of 0.05 and to capture a minimum of 25 deaths. A 1:10 ratio of consecutive control to intervention cases was selected based on diminishing returns of power at higher ratios. The study was also designed to have sufficient degrees of freedom to evaluate the association between mortality and LOS and 32 individual ICU telemedicine metrics related to intervention-specific changes in ICU personnel and process and setting of care. The study was conducted with prior approval of the University of Massachusetts Human Subjects Committee (H-13346), which waived a requirement for informed consent. Participating entities provided deidentified data after local waiver of the requirement for informed consent.

ICU Telemedicine Interventions

Each ICU implemented similar technical components, including audio and video connections, an ICU-focused medical record, and software for detecting evolving physiologic instability (Koninklijke Philips N.V.). However, changes in process of care delivery, ICU admission procedures, rounding and governance structure, communication among caregivers, how performance information was used, how care was documented, how technical support was provided, and other factors varied among implementations. Data describing characteristics of each ICU and process of care, as well as structural and organizational characteristics before and after the implementation of the ICU telemedicine program were measured for each ICU using the American College of Chest Physicians ICU Telemedicine Survey instrument.17

Measurements

Patient-level factors, including date and time of admission and discharge, vital signs and status, laboratory values, admission diagnoses, clinical disposition, geographic location, and the elements of the APACHE IV acuity score, were abstracted from electronic or hard copy medical records as previously described and validated.15 The 11-domain American College of Chest Physicians ICU Telemedicine Survey instrument was used (with permission) to gather information about 32 factors related to ICU personnel, process, and setting of care before and after the intervention. These measures included information about ICU type, intensivist staffing model, teaching status, ICU governance structure, use of performance information, US census region, and aspects of the ICU telemedicine support center.17 Complete survey data from the ICU medical director, nurse manager, or both was obtained using electronic survey delivery for each of the 56 ICUs that participated in this study.

Statistical Analyses

Hazard ratio (HR) (ICU telemedicine intervention group vs control group) for dying in the hospital was prespecified as the primary study outcome. Secondary outcomes included ICU mortality and hospital and ICU LOS. Descriptive statistics were derived for continuous variables, and univariate comparisons between groups for continuous outcomes were made using the Mann-Whitney U or the Student t test. Comparisons between groups for categorical variables were made using Fisher exact or χ2 tests.

Both crude and adjusted Cox proportional hazards regression models were constructed to evaluate the effects of the ICU telemedicine interventions on hospital and ICU mortality. For Cox regression analyses, likelihood ratio χ2 tests were used to determine improved statistical fit. The proportional hazards assumption was tested for all Cox models. Any meaningful, statistically significant interaction terms or appreciable confounders remained in final parsimonious models. Confirmatory analyses using logistic regression were also performed.20 The statistical modeling, survey domains, and composite scores are described and detailed in e-Appendix 2.

All P values were calculated using two-sided tests, and values ≤ .05 were considered statistically significant. All statistical analyses were conducted using SAS, version 9.2 (SAS Institute Inc) and STATA, version 10 (StataCorp LP).

Of 21 health-care systems that collected data, 19 submitted patient-level deidentified datasets for prespecified analyses. Participating ICUs (N = 56) were geographically dispersed across 15 US states; eight ICUs (14%) were located in the Northeast, 28 (50%) in the Midwest, eight (14%) in the South, and 12 ICUs (21%) in the West US census region. Participating ICUs were from 38 hospitals that ranged in size from 88 to 834 licensed beds that were part of 19 health-care systems. Seven (13%), 17 (30%), and 32 (57%) ICUs served rural, suburban, and urban populations, respectively. Nine ICUs (16%) served populations < 100,000, 36 (64%) served populations of 100,000 to 999,999, and 11 (20%) served populations ≥ 1 million. A broad spectrum of adult ICU types was included: 27 mixed medical-surgical ICUs (48%), nine medical ICUs (16%), eight surgical ICUs (14%), six coronary care units (11%), four neuroscience ICUs (7%), and two (4%) cardiothoracic ICUs (7%). Twenty-one ICUs (38%) were nonteaching, 20 (36%) were teaching hospitals but unaffiliated with a university or academic medical center, and 15 (27%) were affiliated with a major academic medical center or university.

A total of 118,990 adults who had a valid ICU admission event as defined by the APACHE IV methodology were identified from 119,169 records (Fig 1). Comparison of 11,558 control subjects with 107,432 ICU telemedicine group patients revealed that ICU telemedicine group patients had significantly higher APACHE IV acuity scores and predicted mortality, had a larger proportion of medical primary admission diagnoses, were less likely to have been admitted from an operating room, and had a significantly different distribution of primary admission diagnoses (Table 1).

Table Graphic Jump Location
Table 1 —Patient Characteristics

Data are presented as mean ± SD or No. (%). APACHE = Acute Physiology and Chronic Health Evaluation; APS = Acute Physiology Score.

Overall, 11,907 or 10% of the patients died in the hospital. Unadjusted analyses revealed that a significantly higher proportion of control group patients (1,242 of 11,558, 11%) than intervention group patients (10,665 of 107,432, 10% intervention, P < .01) died in the hospital over a median follow-up of 6.2 days (range, 1 h to 880 days). Similarly, 7,134 patients (6%) died in the ICU. A significantly larger proportion of control group patients (901 of 11,558, 8%) died in the ICU than ICU telemedicine group patients (6,233 of 107,432, 6%, P < .01) over a median follow-up of 1.9 days (range, 1 h to 383 days). Survival analyses, which adjusted for relevant covariates, revealed significantly lower hospital and ICU HRs for patients in the ICU telemedicine group compared with the control group (adjusted hospital mortality: HR = 0.84; 95% CI, 0.78-0.89; P < .001; adjusted ICU mortality: HR = 0.74; 95% CI, 0.68-0.79; P < .001) (Fig 2). There was no evidence of violation of the proportional hazards assumption. Confirmatory analyses using logistic regression yielded similar results.20

Figure Jump LinkFigure 2. A, Adjusted ICU-specific (left) and hospital-specific (right) survival estimated by Cox proportional hazards regression. Models adjusted for APACHE IV score, age, hospital or ICU identifier (as a random effect), admission source, primary admission diagnosis, operative status, time from start of study enrollment, heart rate, admission and highest creatinine values, respiratory rate, admission hematocrit value, BUN, WBC count, Glasgow Coma Score, prothrombin time, anion gap, urine output (in the first 24 h), base excess, and total bilirubin and albumin values. B, Adjusted ICU-specific (left) and hospital-specific (right) survival estimated by health-care system. The center of the diamond represents the effect estimate, the bars represent 95% CIs, the symbol size is proportional to the number of observations for the corresponding health-care system, and the overall effects are presented as diamonds in the bottom row. HR = hazard ratio. See Figure 1 legend for expansion of other abbreviation.Grahic Jump Location

Hospital and ICU LOS were significantly shorter for ICU telemedicine intervention group patients. After adjustment, ICU LOS for ICU telemedicine intervention group patients was 20% shorter (95% CI, 19%-22%; P < .001) and hospital LOS was 15% shorter (95% CI, 14%-17%; P < .001) compared with control subjects (Fig 3). In addition, crude and adjusted analyses revealed that the effect of the ICU telemedicine intervention on changes for hospital and ICU LOS depended on how long the patient stayed. Specifically, the effectiveness of the interventions for reducing LOS was clinically meaningful only among patients who remained in the hospital for at least 1 week (P for interaction < .01). Adjusted hospital LOS was reduced, on average, by 0.5 (95% CI, 0.4-0.5), 1.0 (95% CI, 0.7-1.3), and 3.6 (95% CI, 2.3-4.8) days among those who stayed in the hospital for ≥ 7, ≥ 14, and ≥ 30 days, respectively (Fig 3). Similarly, adjusted ICU LOS was reduced, on average, by 1.1 (95% CI, 0.8-1.4), 2.5 (95% CI, 1.6-3.4), and 4.5 (95% CI, 1.5-7.2) days among those who stayed in the ICU for ≥ 7, ≥ 14, and ≥ 30 days, respectively (Fig 3A).

Figure Jump LinkFigure 3. A, Changes in ICU (left) and hospital (right) LOS attributable to the ICU telemedicine interventions by duration of stay. The magnitude of the effects of the ICU telemedicine interventions on LOS increased with duration of stay. Intervention effects were statistically significant for both short- and long-stay patients but clinically important only for the groups with longer stays. *P values < .01 in adjusted models for the LOS of the ICU telemedicine group compared with the control group. B, Percent change in ICU (left) and hospital (right) LOS as a function of health-care system. The center of the diamond represents the effect estimate, with the bars representing 95% CIs. The size of each symbol is proportional to the number of observations for the corresponding health-care system. Overall effects are presented as diamonds in the bottom row. LOS = length of stay.Grahic Jump Location

In addition to identifying the overall effects of implementing an ICU telemedicine program on mortality and LOS, we examined the effect of each of the 11 ICU-telemedicine survey domains.17 Adjusted analyses revealed that changes in the ICU characteristics domain (OR = 0.70; 95% CI, 0.56-0.87; P < .01), physician leadership domain (OR = 0.80; 95% CI, 0.70-0.92; P < .01), and best practices and performance review domain (OR = 0.82; 95% CI, 0.71-0.95; P < .01) were associated with significant reductions in hospital mortality, whereas only changes in the ICU characteristics (OR = 0.71; 95% CI, 0.56-0.91; P < .01) and the physician leadership domains (OR = 0.74; 95% CI, 0.64-0.86; P < .001) were associated with significant reductions of ICU mortality. Changes in the ICU telemedicine experience domain (OR = 0.89; 95% CI, 0.81-0.97; P < .01) were associated with reduced hospital LOS, and changes in the integration and teamwork domain (OR = 0.95; 95% CI, 0.91-0.99; P = .01) were associated with reduced ICU LOS.

Individual survey items that accounted for ≥ 15% change in domain scores that were significantly associated with any of the four outcomes were (1) higher frequency of intensivist case review within 1 h of ICU admission, (2) more frequent review of performance data with hospital leadership, (3) higher levels of adherence to ICU best practices, (4) more rapid responses to alerts and alarms, (5) more frequent interdisciplinary rounds, and (6) more effective ICU committee as judged by ICU clinical leaders. Community characteristics, hospital size, teaching status, region, and intensivist staffing model were not significantly related to ICU or hospital mortality or LOS. We found that composite scores (derived from important survey items) demonstrated significant step-wise relationships with all four outcomes (Fig 4).

Figure Jump LinkFigure 4. Relationship between Composite American College of Chest Physicians ICU Telemedicine Survey Score and ICU (left) and hospital (right) mortality and LOS. Individual survey items that accounted for the changes in each domain score that was significantly associated with each outcome were identified. A three-component composite score was created that included (1) the change (after-before) in all individual survey items contributing to ≥ 15% of the observed change in domain score, (2) a three-point increase for ICUs in the top decile (because they could not improve), and (3) a three-point decrease for ICUs in the bottom decile of item response (because they did not improve). See Figure 3 legend for expansion of abbreviation.Grahic Jump Location

The main finding of this study was that implementation of an ICU telemedicine program was associated with significantly lower mortality and shorter LOS in both the ICU and hospital setting. Significantly reduced hospital and ICU mortality and LOS were found in both crude analyses and analyses that were adjusted for potential confounding factors, including differences in acuity score, operative status, effects of time alone, and primary admission diagnosis. The association of the ICU telemedicine interventions with lower hospital mortality is notable because prior studies have not had adequate power to provide unequivocal evidence of this association. Notably, the reduction in LOS attributed to the ICU telemedicine intervention was most clinically meaningful among patients who stayed in the hospital or ICU for at least 1 week. The large size of the study and its finding that improvements in performance were not limited to a single type of ICU, size of hospital or community served, hospital teaching status, or US region suggests that these findings are broadly, rather than narrowly, applicable. Adult critical care therapeutic interventions that reduce mortality among high-acuity patients are generally associated with increased LOS due to the longer recovery times.21 The combination of lower mortality with decreased LOS suggests that ICU teams that are supported by a telemedicine program more quickly stabilize patients and facilitate recovery to discharge leading to an overall reduction in mortality, facilitate earlier transition to rehabilitative care, or more efficiently transition patients who will die in the hospital to comfort-only care. Incorporating intervention components centered around prevention—including higher levels of adherence to ICU best practices and quicker response times to alerts and alarms—were related to improved outcomes, supporting the notion that instilling a substantial preventive component in ICU-telemedicine programs is key.

In addition to demonstrating that the overall ICU telemedicine intervention was associated with significantly reduced mortality and LOS in both adjusted and unadjusted analyses, we identified individual ICU process and setting of care domains that were significantly associated with an improvement in at least one of the four major outcomes. To provide a more granular view of which changes in processes were associated with improved outcomes, we also identified six individual survey items that drove the differences in domain scores. First, having an intensivist perform a workstation-assisted review of the care plan within 1 h of patient admission was identified as a driving item for the survey domains that were significantly associated with all four study outcomes. Second, having more frequent collaborative review and use of performance data were associated with lower mortality and LOS, consistent with the quality improvement tenet that how performance information is used is more important than the availability of reports.22 Third, implementation-related increases in rates of adherence to ICU best practices were associated with lower mortality and LOS, confirming our previous findings.15 Fourth, shorter response times for laboratory value alerts and alarms for physiologic instability were associated with shorter ICU LOS, consistent with prior patient safety studies.15,23,24 Fifth and sixth, like previous studies,25 interdisciplinary rounds and institutional ICU committee effectiveness were associated with lower adjusted mortality. The identification of these six specific ICU process improvement elements may help ICUs with limited resources identify where best to focus their quality improvement efforts.

Notably, however, the individual impact of these six distinct survey items on study outcomes was small. Instead, individual survey items appeared to have additive effects, as demonstrated by the larger overall effects of the domains and composite scores. One interpretation is that impact of an ICU telemedicine program is related more to the breadth of change of key ICU care processes rather than any one factor and that improvements in key ICU processes are additive with respect to their association with clinically important health outcomes.

Interestingly, ICU physician staffing model was not a significant predictor of any of the outcomes. Taken together with previously inconclusive studies,26,27 findings from this study suggest that it is not the on-site presence of an intensivist that drives better outcomes; rather, it is when and how that individual is engaged in case management. Engagement using telemedicine tools was associated with reduced ICU and hospital mortality and LOS for both high-intensity and low-intensity staffing models.

Identifying both domains and individual components of the broader ICU telemedicine intervention that were associated with improved outcomes helps to resolve previous concerns raised by systematic reviews and meta-analyses that the associations of ICU telemedicine programs with lower mortality and/or LOS are due only to unknown factors or chance alone.28,29 Implementation of ICU telemedicine programs, to date, have improved mortality and LOS by improving the timeliness of access to intensivist case management, encouraging the effective use of performance information, facilitating ICU best practice adherence, increasing interdisciplinary rounding, improving ICU committee effectiveness, and other factors.13

The findings of this study should be interpreted in the context of its limitations. The sample of hospitals and ICUs was not a random sample from the United States; instead, the 19 geographically and demographically diverse sites were self-selected based on willingness to invest in care improvement. Furthermore, although this study controlled for a wealth of clinical, demographic, and process and setting of care factors, the nonrandomized pre/post study design does not provide robust protection against bias introduced by unmeasured confounders or the effects of time alone. We performed extensive analyses to identify time-related changes that would indicate the improvements we observed would have occurred in the absence of the telemedicine intervention. Analyses that included a variety of sequence of enrollment or time factors in our models did not materially alter results, and we included a representative time of enrollment factor in our final models. Time-stratified analyses also identified changes in outcomes that corresponded to the time of intervention. Finally, because the validated instrument that we used to measure process and setting of care variables did not explain all of the variance that we observed in the outcomes, it is possible that some important predictive factors were not included or unaccounted for effects of time may be present.

Despite these limitations, this study demonstrated that implementation of an ICU telemedicine program by 56 ICUs across 19 diverse US health-care systems was associated with meaningfully decreased mortality and LOS in both adjusted and unadjusted analyses. Improved outcomes were primarily attributable to earlier intensivist management, coordinated timely usage of performance information, achievement of higher rates of adherence to best practices, shorter alarm response times, more frequent interdisciplinary rounds, and a more effective ICU committee. Moreover, although each of these components had small, independent effects on mortality and LOS, their effects were additive, suggesting that breadth of change in these key ICU care processes is more important than any single factor.

Author contributions: Dr Lilly had full access to the data and takes responsibility for its integrity and the accuracy of the analyses.

Dr Lilly: contributed to study concept and design, acquisition of data, analysis and interpretation of data, reviewed and approved the design of the study, and the writing of the first and final drafts of the manuscript.

Dr McLaughlin: contributed to study concept and design, analysis and interpretation of data, reviewed and approved the design of the study, and the writing, reviewing, and approval of the final draft of the manuscript.

Dr Zhao: contributed to study concept and design, acquisition of data, analysis and interpretation of data, and reviewed and approved the design of the study and the final draft of the manuscript.

Mr Baker: contributed to study concept and design, analysis and interpretation of data, and reviewed and approved the design of the study and the final draft of the manuscript.

Mr Cody: contributed to acquisition of data, and reviewed and approved the design of the study and the first and final drafts of the manuscript.

Dr Irwin: contributed to study concept and design, analysis and interpretation of data, and reviewed and approved the design of the study and the first and final drafts of the manuscript.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr McLaughlin is a salaried employee and stockholder of Pfizer Inc. Drs Irwin and Lilly and Mr Cody have disclosed that although their institution has purchased their tele-ICU product from VISICU, now owned by Koninklijke Philips N.V., none of the authors has any financial relationship with the company. Dr Irwin discloses that the review of this manuscript and the ultimate decision to publish it was made by others without his knowledge. The UMass authors (Drs Irwin and Lilly and Mr Cody) are in strict compliance with the University of Massachusetts conflict of interest policies and accordingly have not accepted anything of value from any commercial entity. Dr Zhao and Mr Baker have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

Additional information: The e-Appendixes can be found in the “Supplemental Materials” area of the online article.

APACHE

Acute Physiology and Chronic Health Evaluation

HR

hazard ratio

LOS

length of stay

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Figures

Figure Jump LinkFigure 1. Study timeline, case selection, and availability of acuity scores. APACHE = Acute Physiology and Chronic Health Evaluation.Grahic Jump Location
Figure Jump LinkFigure 2. A, Adjusted ICU-specific (left) and hospital-specific (right) survival estimated by Cox proportional hazards regression. Models adjusted for APACHE IV score, age, hospital or ICU identifier (as a random effect), admission source, primary admission diagnosis, operative status, time from start of study enrollment, heart rate, admission and highest creatinine values, respiratory rate, admission hematocrit value, BUN, WBC count, Glasgow Coma Score, prothrombin time, anion gap, urine output (in the first 24 h), base excess, and total bilirubin and albumin values. B, Adjusted ICU-specific (left) and hospital-specific (right) survival estimated by health-care system. The center of the diamond represents the effect estimate, the bars represent 95% CIs, the symbol size is proportional to the number of observations for the corresponding health-care system, and the overall effects are presented as diamonds in the bottom row. HR = hazard ratio. See Figure 1 legend for expansion of other abbreviation.Grahic Jump Location
Figure Jump LinkFigure 3. A, Changes in ICU (left) and hospital (right) LOS attributable to the ICU telemedicine interventions by duration of stay. The magnitude of the effects of the ICU telemedicine interventions on LOS increased with duration of stay. Intervention effects were statistically significant for both short- and long-stay patients but clinically important only for the groups with longer stays. *P values < .01 in adjusted models for the LOS of the ICU telemedicine group compared with the control group. B, Percent change in ICU (left) and hospital (right) LOS as a function of health-care system. The center of the diamond represents the effect estimate, with the bars representing 95% CIs. The size of each symbol is proportional to the number of observations for the corresponding health-care system. Overall effects are presented as diamonds in the bottom row. LOS = length of stay.Grahic Jump Location
Figure Jump LinkFigure 4. Relationship between Composite American College of Chest Physicians ICU Telemedicine Survey Score and ICU (left) and hospital (right) mortality and LOS. Individual survey items that accounted for the changes in each domain score that was significantly associated with each outcome were identified. A three-component composite score was created that included (1) the change (after-before) in all individual survey items contributing to ≥ 15% of the observed change in domain score, (2) a three-point increase for ICUs in the top decile (because they could not improve), and (3) a three-point decrease for ICUs in the bottom decile of item response (because they did not improve). See Figure 3 legend for expansion of abbreviation.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Patient Characteristics

Data are presented as mean ± SD or No. (%). APACHE = Acute Physiology and Chronic Health Evaluation; APS = Acute Physiology Score.

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