The effect of ICU telemedicine on mortality and length of stay

Abstract

We conducted a retrospective, observational study of patient outcomes in two intensive care units in the same hospital. The surgical ICU (SICU) implemented telemedicine and electronic medical records, while the medical ICU (MICU) did not. Medical charts were reviewed for a one-year period before telemedicine and a one-year period afterwards. In the SICU, records were obtained for 246 patients before and 1499 patients after implementation; in the MICU, records were obtained for 220 patients and 285 patients in the same periods. The outcomes of interest were ICU length of stay and mortality, and hospital length of stay and mortality. Outcome variables were severity-adjusted using APACHE scoring. A bootstrap method, with 1000 replicates, was used to assess stability of the findings. The adjusted ICU length of stay, ICU mortality, and hospital mortality for the SICU patients all decreased significantly after the implementation of telemedicine. There was no change in adjusted outcome variables in the MICU patients. Implementation of telemedicine and electronic records in the surgical ICU was associated with a profound reduction in severity-adjusted ICU length of stay, ICU mortality, and hospital mortality. However, it is not possible to conclude definitively that the observed associations seen in the SICU were due to the intervention.

Introduction

Telemedicine has been practised in intensive care units (ICUs) in the US for at least 20 years and there has been an increase in the number of centres using telemedicine in the last 10 years.¹ Unfortunately, studies evaluating the impact of remote ICU care on morbidity and mortality have not been conclusive.^2–5 Most ICU telemedicine studies have employed a single-centre historical control design, comparing outcomes after implementing telemedicine with outcomes from the same ICU before the use of telemedicine.^6–8 Such a design cannot control for potential confounding variables such as changes in the healthcare system. As a result, it is difficult to know whether observed changes are due to random variation, patient selection, changes in staffing structures, new medicines or technology, or the implementation of novel quality/safety initiatives^2,9 While it is difficult, although not impossible, to conduct randomized, double-blind, placebo-controlled studies with a telemedicine intervention, there are a number of techniques which can reduce confounding effects, such as patient matching, stratification and/or propensity analysis.^10,11

In a previous study, a comparison of pre-and post implementation data in our surgical ICU (SICU), suggested that the introduction of telemedicine was associated with reductions in mortality and length of stay, in addition to major cost savings for the health system.^12,13 However, the telemedicine programme was implemented in tandem with other hospital-wide quality improvement initiatives, such as a hand hygiene campaign and the use of care bundles to prevent deep vein thrombosis, ventilator associated pneumonia and central line infections. The aim of the present study was to compare mortality and length of stay changes over time between the SICU (a unit with telemedicine services) and a medical intensive care unit (MICU), a unit without telemedicine services at the same hospital.

Telemedicine

The telemedicine system was installed in the SICU in November 2004. The software included an electronic medical record system and videoconferencing (VISICU eICU remote monitoring system, Phillips Electronics, Amsterdam, The Netherlands). This enabled the provision of critical care services from an offsite central monitoring facility.¹⁴ The system includes two-way audio conferencing, one-way video conferencing (i.e. the telemedicine team can view activity in the patient's room by means of a remotely controlled camera), an electronic medical record available to both the telemedicine and bedside clinicians, and continuous physiological monitoring that can detect trends in vital signs and laboratory values as well as alert the telemedicine staff if these numbers deviate from pre-defined limits. Using this system, a small number of physicians and critical care nurses assist the bedside care team from a remote location, known as the Clinical Operations Room. The remote ICU team consults on critical issues, monitors patients for physiological deterioration and facilitates communication between care providers. In addition, telemedicine physicians have access to all radiology examinations and continuous telemetry data. Details of all ICU admissions are entered into the system by either the telemedicine nurse or a trained data coordinator. At the time of the present study, the telemedicine programme was responsible for covering a total of five intensive care units (69 beds) in three hospitals. The SICU analysed for the present study was the largest (24 beds) unit covered by telemedicine.

Two care providers in the Clinical Operations Room monitor ICU patients 24 h/day, seven days a week. During daytime hours (07:00–19:00) there are two ICU nurses, and in the evening (19:00–07:00) there is one physician (intensivist) and one ICU nurse. The telemedicine nurses perform audits for benchmarking of outcomes, review patient profiles for updates in the plans of care and respond to clinical alarms and enquiries. In addition, they evaluate and intervene on patient safety measures (e.g. redirecting a delirious patient who is attempting to get out of bed) and ensure compliance with best-care practices. Rounds involve the evaluation of all new patient data and videoconferencing into the patient room, and are completed every 1–4 h based on need. Updates are communicated by the day telemedicine nurse to the incoming physician and nurse. The physicians communicate frequently with the bedside team and are able to assist as necessary. A button in each SICU room can be pressed to alert the remote intensivist that there is a request to assist with an emergency and they should activate the camera. In addition, the tele-ICU team frequently initiate contact with the ICU staff if there is a particular concern regarding patient status or if they believe there should be a change in management. Either party may call the other by telephone to discuss any matters of concern more privately. All telemedicine physicians are credentialled, but their non-telemedicine clinical duties are entirely at the Hospital of the University of Pennsylvania.

The staffing paradigms (i.e. standard of care) within the MICU and SICU are similar but not identical. The MICU follows a closed intensivist staffing model that is covered overnight solely by residents. Both the attending intensivist and the pulmonary fellow (training in critical care) are available on-call at home. In-house coverage overnight is provided by residents, in addition to a fellow (training in critical care). The attending SICU intensivist is available on-call at home. Implementation of the telemedicine programme did not affect either staffing paradigm. The MICU does not use an electronic medical record.

Methods

We performed a retrospective, observational study using medical chart review of patients in the SICU and MICU at the Hospital of the University of Pennsylvania. The study was approved by the appropriate ethics committee. Specially trained critical care nurses conducted chart reviews and extracted ICU admission day information. APACHE scoring was performed using data from the first 24 h of ICU admission.¹⁵ The range for APACHE scores is 0–299, higher scores indicating more severe illness. For the pre-implementation phase, a list of all patients admitted to the MICU and SICU between April 2003 and March 2004 was obtained. In general, the MICU patients were older and had a greater number of co-morbidities when compared with SICU patients (thus explaining their greater APACHE scores). A minimum of 65 consecutive charts for each quarter were reviewed for data abstraction to ensure the availability of information needed to calculate the APACHE scores. Pre-implementation records were selected at the same starting point and were chosen as consecutive admissions over a period of one year.

People trained in quality assurance performed audits of abstracted data on 10% of the records to ensure accurate data collection. Records that did not provide the necessary data to permit APACHE calculation were excluded from the study and the next admission was then reviewed. This process resulted in 246 SICU patients and 220 MICU patients for analysis in the pre-implementation phase.

Implementation of telemedicine and electronic medical records occurred in the SICU in November 2004. Post-implementation data were collected for the two units from July 2005 to June 2006 and were collected in the MICU with the same methodology described above, resulting in 285 patients for analysis. Post-implementation data were collected on all SICU patients admitted during the 12-month period via the newly implemented electronic medical record (n = 2100). Readmissions and off-service patients (primary neurosurgical or primary cardiac surgical patients) were excluded from analysis. In addition, records that were either incomplete or otherwise ineligible for APACHE calculation were excluded. This resulted in a total of 1499 SICU patients for the post-implementation phase. Four outcomes were considered in the statistical analysis: hospital and ICU length of stay, and hospital and ICU mortality.

Standard packages were used for the statistical analysis (STATA 11, StataCorp, TX, USA and SAS 9.1, SAS Institute Inc. Cary, NC, USA). Mean APACHE III scores were compared between ICUs by analysis of variance (ANOVA). Analyses of covariance (ANCOVA) were used to compare pre and post tele-ICU implementation with hospital length of stay, using the APACHE III score as a covariate. A similar ANCOVA model was fitted for the ICU length of stay outcome. Pre- to post-implementation changes in hospital and ICU mortality outcomes were analysed with logistic regression. Finally, logistic regression was used to compare the pre- and post-telemedicine implementation with changes in ICU mortality. A bootstrap method, with 1000 replicates, was used to assess stability of the findings.

Results

Severity of illness

On average, MICU patients were more ill than SICU patients (P < 0.0001), as indicated by higher APACHE scores, see Table 1. While the mean MICU APACHE score decreased from pre- to post-implementation (indicating that the severity of illness decreased), the mean SICU APACHE score increased from pre- to post-implementation (indicating that the severity of illness increased). The ANOVA indicated that both of these changes were significant (P < 0.0001 and P = 0.005 for MICU and SICU, respectively), and that the pre- to post-rates of change in the two units were significantly different from each other (ANOVA interaction P < 0.0001).

Table 1

Hospital length of stay and mortality

	Control			Telemedicine
	MICU (Pre)	MICU (Post)	P-value	SICU (Pre)	SICU (Post)	P-value
No of patients	220	285		246	1499
APACHE score mean (SEM)	100.7 (2.5)	80.1 (2.5)	<0.001	46.2 (1.5)	54.1 (0.6)	0.005
Unadjusted hospital length of stay mean d (SEM)	13.2 (1.0)	11.3 (0.7)	NS	15.6 (0.9)	15.1 (0.6)	NS
Severity adjusted hospital length of stay mean d (SEM)	12.5 (1.1)	10.9 (0.8)	NS	19.0 (1.0)	16.7 (0.8)	NS
Unadjusted hospital mortality mean (SEM)	0.88 (0.02)	0.65 (0.03)	<0.001	0.11 (0.02)	0.06 (0.01)	0.003
Severity-adjusted hospital mortality mean (SEM)	0.74 (0.05)	0.56 (0.04)	NS	0.13 (0.03)	0.04 (0.01)	0.023

NS denotes P ≥ 0.05

Hospital length of stay and mortality

The unadjusted and severity-adjusted hospital length of stay and mortality results for both ICUs pre- and post- telemedicine implementation are summarised in Table 1. Unadjusted and severity-adjusted hospital length of stay decreased for both the MICU and SICU, although neither change was significant. Similarly, the MICU and SICU pre- to post-rates of change were not significantly different from each other. Unadjusted hospital mortality in the MICU population decreased significantly. However this difference was not significant after adjusting for severity of illness (P = 0.24). Hospital mortality in the SICU, decreased significantly after telemedicine was implemented, both in the unadjusted analysis as well as when adjusted for severity of illness (0.13 to 0.04, OR 0.30, P = 0.023). The MICU and SICU model-adjusted rates of change were not significantly different from each other.

ICU length of stay and mortality

The unadjusted and severity-adjusted length of stay and mortality results for both ICUs pre- and post- telemedicine implementation are summarised in Table 2. Unadjusted (4.9 to 5.9 d, P = 0.08) and severity adjusted (5.3 to 6.1 d, P = 0.62) ICU length of stay both increased in the MICU after telemedicine; however neither of these findings were significant. In contrast, both unadjusted (5.0 to 3.3d, P < 0.001) and severity adjusted (6.3 to 3.9 d, P < 0.001) ICU length of stay decreased significantly in the SICU after implementation of telemedicine. The rates of change between the two units over time were significantly different from each other (ANCOVA interaction P = 0.005). While unadjusted ICU mortality decreased significantly in the MICU after the telemedicine intervention (0.80 to 0.57, P < 0.001), there was no significant change in the MICU mortality after adjusting for severity of illness. Both unadjusted and severity adjusted ICU mortality for SICU patients decreased significantly after implementation of telemedicine (0.09 to 0.01, OR 0.15, P = 0.003).

Table 2

ICU length of stay and mortality

	Control			Telemedicine
	MICU (Pre)	MICU (Post)	P-value	SICU (Pre)	SICU (Post)	P-value
No of patients	220	285		246	1,499
APACHE score mean (SEM)	100.7 (2.5)	80.1 (2.5)	<0.001	46.2 (1.5)	54.1 (0.63)	0.005
Unadjusted ICU length of stay mean d (SEM)	4.87 (0.42)	5.89 (0.40)	NS	5.04 (0.48)	3.29 (0.13)	<0.001
Severity-adjusted ICU length of stay mean d (SEM)	5.27 (0.52)	6.09 (0.43)	NS	6.25 (0.50)	3.86 (0.17)	<0.001
Unadjusted ICU mortality mean (SEM)	0.80 (0.03)	0.57 (0.03)	<0.001	0.08 (0.02)	0.03 (0.004)	<0.001
Severity-adjusted ICU mortality mean (SEM)	0.54 (0.06)	0.42 (0.04)	NS	0.09 (0.02)	0.01 (0.003)	0.003

NS denotes P ≥ 0.05

Discussion

A randomized, double-blind, placebo-controlled study would provide the best evidence about whether or not telemedicine affects outcomes. However, it would be difficult to organize. As a result many centres, including our own, have chosen to conduct observational studies by comparing historical control data with post-implementation data.^{4,5,6,8,12,16,17,18} Unfortunately, it is not possible to control for all potential confounding variables. To help mitigate some of these confounders, we evaluated data from two ICUs in the same health system. The results indicate an association between the implementation of telemedicine and a decrease in ICU length of stay, ICU mortality and hospital mortality. No such associations were seen in the medical ICU not exposed to telemedicine. Despite an increase in the severity of illness scores (i.e. patients were more ill) after telemedicine implementation, there was a profound decrease in ICU length of stay and ICU mortality in the SICU. In the non-intervention ICU, however, there was a decrease in severity of illness scores (i.e. patients were less ill), with no change in ICU length of stay. These results may be partly explained by the initiation of the telemedicine service and the ability to provide additional oversight with best practices. Compliance with best care processes has been shown to reduce ICU morbidity.^19,20

The present study had certain limitations. The principal drawback of all observational studies is that causal relations cannot be established from observed associations. In addition, the medical chart abstraction was non-randomized and involved selecting consecutive charts within each quarter. This could bias results if the start of each quarter coincided with other confounding influences. During the period of study, working time restrictions were instituted for all house staff (residents and fellows). While it is possible that these regulations may have had a differential effect on mortality in the two ICUs, a comparison in different medical specialties did not support this contention.²¹

One important difference in the overnight staffing, however, was that an advanced trainee (critical care fellow) was present in the SICU and not the MICU. It is therefore possible that the SICU had greater oversight during evening hours, which may have contributed to the improved outcomes. During the study period, there were no significant changes in faculty staffing or in the level of training for the residents who were providing patient care in the two ICUs. Another limitation of the study was that severity of illness in the populations being compared was very different. In particular, the MICU patients were not only significantly more ill (as indicated by their higher APACHE scores) than the SICU patients, but their baseline APACHE scores in the pre-intervention period were significantly greater than most similar MICU populations.^19,22,23,24 Given this finding, however, one would have expected the ICU length of stay to have significantly decreased as the severity of illness scores decreased. This did not occur.

In the present study there was a dramatic disparity in the sizes of the post-implementation populations compared. This was a result of the electronic medical record that was implemented at the same time as telemedicine. Since the software automatically calculates the APACHE score for the first 24 h of ICU admission, we chose to include all patients admitted to the SICU during the study period because manual chart abstraction was no longer needed. Thus, manual chart abstraction for the purposes of APACHE scoring was undertaken for both pre-implementation groups in addition to the post-implementation MICU group. We used a bootstrap calculation to confirm the stability of our findings.

A final limitation arises from the fact that our telemedicine programme, from the beginning, consisted of two key elements: (1) additional oversight by remote nurses and physicians via bi-directional communication links and (2) an electronic medical record. Thus it is not possible to know whether the observed outcomes were due solely to the additional oversight provided and what, if any, was the independent effect of installing the electronic medical record. Indeed, the marriage between most telemedicine technologies and electronic medical records is a confounding effect that must be considered in all such studies.²

In conclusion, implementation of telemedicine in the surgical ICU was associated with significant reductions in severity-adjusted ICU length of stay and mortality, as well as hospital mortality. Over the same period, and within the same hospital, a medical ICU not using telemedicine had no significant change in any of the measured outcomes after adjusting for severity of illness. However, it is not possible to conclude definitively that the observed associations seen in the SICU were due to the intervention. Further work is thus required to quantify the effect of telemedicine on ICU outcomes.

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