Research

Original Investigation

Impact of an Intensive Care Unit Telemedicine Program on Patient Outcomes in an Integrated Health Care System Boulos S. Nassar, MD, MPH; Mary S. Vaughan-Sarrazin, PhD; Lan Jiang, MS; Heather S. Reisinger, PhD; Robert Bonello, MD; Peter Cram, MD, MBA

IMPORTANCE Intensive care unit (ICU) telemedicine (TM) programs have been promoted as

Invited Commentary page 1167

improving access to intensive care specialists and ultimately improving patient outcomes, but data on effectiveness are limited and conflicting.

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OBJECTIVE To examine the impact of ICU TM on mortality rates and length of stay (LOS) in an integrated health care system. DESIGN, SETTING, AND PARTICIPANTS Observational pre-post study of patients treated in 8 “intervention” ICUs (7 hospitals within the US Department of Veterans Affairs health care system) during 2011-2012 that implemented TM monitoring during the post-TM period as well as patients treated in concurrent control ICUs that did not implement an ICU TM program. INTERVENTION Implementation of ICU TM monitoring. MAIN OUTCOMES AND MEASURES Unadjusted and risk-adjusted ICU, in-hospital, and 30-day mortality rates and ICU and hospital LOS for patients who did or did not receive treatment in ICUs equipped with TM monitoring. RESULTS Our study included 3355 patients treated in our intervention ICUs (1708 in the pre-TM period and 1647 in the post-TM period) and 3584 treated in the control ICUs during the same period. Patient demographics and comorbid illnesses were similar in the intervention and control ICUs during the pre-TM and post-TM periods; however, predicted ICU mortality rates were modestly lower for admissions to the intervention ICUs compared with control ICUs in both the pre-TM (3.0% vs 3.6%; P = .02) and post-TM (2.8% vs 3.5%; P < .001) periods. Implementation of ICU TM was not associated with a significant decline in ICU, in-hospital, or 30-day mortality rates or LOS in unadjusted or adjusted analyses. For example, unadjusted ICU mortality in the pre-TM vs post-TM periods were 2.9% vs 2.8% (P = .89) for the intervention ICUs and 4.0% vs 3.4% (P = .31) for the control ICUs. Unadjusted 30-day mortality during the pre-TM vs post-TM periods were 7.7% vs 7.8% (P = .91) for the intervention ICUs and 12.0% vs 10.2% (P = .08) for the control ICUs. Evaluation of interaction terms comparing the magnitude of mortality rate change during the pre-TM and post-TM periods in the intervention and control ICUs failed to demonstrate a significant reduction in mortality rates or LOS. CONCLUSIONS AND RELEVANCE We found no evidence that the implementation of ICU TM significantly reduced mortality rates or LOS.

Author Affiliations: Author affiliations are listed at the end of this article.

JAMA Intern Med. 2014;174(7):1160-1167. doi:10.1001/jamainternmed.2014.1503 Published online May 12, 2014. 1160

Corresponding Author: Boulos S. Nassar, MD, MPH, Division of Pulmonary, Critical Care, and Occupational Medicine, Department of Internal Medicine, University of Iowa Hospitals and Clinics, 200 Hawkins Dr, Iowa City, IA 52242 ([email protected]). jamainternalmedicine.com

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Intensive Care Unit Telemedicine Program

I

ntensive care unit (ICU) telemedicine (TM) programs have been heralded as a potential solution to a number of vexing problems facing critical care, including a finite supply of intensivists, difficulty in attracting and retaining intensivists to smaller rural hospitals, and reductions in the availability of trainees in academic medical centers.1-3 Despite great promise, empirical data evaluating the impact of ICU TM programs are limited.4 As with most multifaceted system-level interventions, evaluating the impact of ICU TM programs is complex. Programs often involve several related interventions, including an electronic health record, standardization of treatment protocols, changes in ICU staffing, and enhanced patient monitoring.1,5 Although ICU TM has the potential to affect numerous intermediate outcomes (eg, nosocomial infections, ventilator days), a primary motivation has always been the potential to reduce mortality rates. Early single-center studies by inventors of the ICU TM technology suggested that these programs significantly reduced mortality rates.1,6 More recent studies have provided a more complex picture.7-9 Limitations of prior studies include a lack of concurrent control groups and the inability to look at longer-term outcomes (eg, 30-day mortality rates). Furthermore, most prior studies were conducted within academic health care systems, and few included smaller rural facilities that might stand to benefit the most from improved access to skilled intensivists. To better understand the impact of ICU TM, we used a quasi-experimental design to evaluate the impact of TM implementation on short-term (ICU and in-hospital) and longerterm (30-day) mortality rates and length of stay (LOS) within a regional network of 7 hospitals in the US Department of Veterans Affairs (VA).

Methods Our study was approved by the institutional review board at the University of Iowa and Iowa City VA Health Care System. No patient consent was deemed necessary by both boards.

Setting Our study was conducted in a network of 7 VA hospitals (8 ICUs; 73 beds) located within a regional VA health care network in the upper Midwest. All hospitals used the same electronic health record; this record includes clinic notes, computerized order entry, and patient laboratory and radiographic test results. In 2011, the network began implementing a state-of-theart ICU TM system. This system included (1) a central telemonitoring center located at the Minneapolis VA hospital, staffed 21 hours per day, 7 days per week with an experienced intensivist plus 2 critical care nurses; (2) a clinical information system that provided the monitoring center with real-time access to patient vital signs, intravenous infusion rates, and ventilator settings; and (3) high-speed video and audio connections between all ICU patient rooms and the monitoring center. The systems provided alerts to the TM staff when laboratory values were abnormal or vital signs exceeded prespeci-

Original Investigation Research

fied parameters. The TM system was implemented in a staggered manner across the 7 hospitals between August 2011 and February 2012. The ICU staffing patterns and use of standardized order sets were not altered by the introduction of the ICU TM program. Our study included 3 academic medical centers, 1 small urban hospital, and 3 rural hospitals. The 8 ICUs included 1 medical ICU, 1 surgical ICU, and 6 mixed ICUs (see eTable 1 in the Supplement for further details regarding the ICUs). In 6 of the ICUs, the TM staff were authorized to monitor patients and make interventions as they deemed appropriate. For the 2 remaining ICUs, the monitoring center staff could intervene only when explicitly requested. Nurses and physicians in the monitoring center evaluated new admissions and patients whose condition was deteriorating and then contacted the bedside provider with recommendations. In emergency situations (eg, cardiac arrest or hemodynamic instability), the monitoring staff were preauthorized to intervene at all sites.10

Data Our analysis included both a pre-post comparison (with each ICU that received the TM system serving as its own control) and comparison with concurrent control groups (ie, VA ICUs that did not receive the ICU TM system). Specifically, we matched each intervention ICU with a single control ICU drawn from the 121 VA ICUs that were not part of the ICU TM program. Intervention and control ICUs were matched according to (1) ICU type (medical, surgical, or mixed); (2) ICU admission volume; and (3) racial mix, measured as percentage of ICU admissions with patients categorized as white. After identifying a list of potential control ICUs for each ICU receiving the TM system, we reviewed geographic location and VA ICU level (ICU levels scored as 1-4, with level 4 providing the most basic ICU services [eg, telemetry monitoring] and level 1 providing comprehensive tertiary care) to select a final control ICU (eTable 2 in the Supplement). After completing the matching process, we identified all admissions to each intervention ICU during the 6 months before TM implementation (pre-TM period) and the 6 months after TM implementation (post-TM period). For each control ICU, admissions were identified using the same pre-TM and post-TM time windows as their respective intervention ICU. We obtained patient-level data from an array of VA administrative files that have been used previously in evaluating ICU outcomes.11,12 These files contained (1) patient demographics; (2) primary diagnosis and comorbid illnesses, as captured in International Classification of Diseases, 9th Revision, Clinical Modification codes; (3) patient location (ie, ICU or floor); (4) laboratory values; and (5) mortality rates in the ICU, in the hospital, or after discharge. After identifying all admissions to our intervention and control ICUs, we applied several exclusion criteria. First, we limited our cohort to the first ICU admission during the same hospital stay for a given patient. Second, we included only the first admission for each patient within a 30-day window to avoid including multiple readmissions that might occur in rapid succession for a single patient. Finally, we excluded patients enrolled in palliative care at the time of ICU admission.

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Research Original Investigation

Intensive Care Unit Telemedicine Program

Figure 1. Flow Diagrams for Generation of Final Analytical Cohorts Pretelemedicine period (6 mo)

Posttelemedicine period (6 mo)

1729 ICU index admissions (intervention)

1649 ICU index admissions (control)

1678 ICU index admissions (intervention)

1932 ICU index admissions (control)

21 Admissions for palliative care patients excluded

2 Admissions for palliative care patients excluded

31 Admissions for palliative care patients excluded

12 Admissions for palliative care patients excluded

1708 ICU admissions included in the analysis (intervention)

1647 ICU admissions included in the analysis (control)

1647 ICU admissions included in the analysis (intervention)

1920 ICU admissions included in the analysis (control)

Flow diagrams outlining the selection process and generation of the final analytical cohorts. ICU indicates intensive care unit.

Statistical Analysis Our primary outcome was mortality rates (ICU, in-hospital, and within 30 days of ICU admission), and our secondary outcomes were ICU and hospital LOS. First, we used bivariate methods (t test and χ2 statistic) to compare patient demographics, comorbid illness, admitting diagnosis, and severity during the pre-TM and post-TM periods in our intervention and control ICUs. We identified comorbid illnesses by applying algorithms developed by Elixhauser et al13 and Quan et al.14 We identified the primary condition associated with each ICU admission using algorithms from the Agency for Healthcare Research and Quality’s Clinical Classifications Software.15 Our principle analyses focused on all patients admitted to the intervention and control ICUs. We calculated predicted mortality rates for patients admitted to our intervention and control ICUs during the pre-TM and post-TM periods using models described in the eAppendix (Supplement). We compared predicted mortality rates across time (pre-TM and post-TM), as well as across intervention and control ICUs as a summary measure of patient complexity. Second, we compared unadjusted ICU, in-hospital, and 30day mortality rates during the pre-TM and post-TM periods in the intervention and control ICUs, as well as ICU and inhospital LOS. Third, we used mixed models to compare risk-adjusted mortality rates and LOS for patients hospitalized in the intervention and control ICUs (SAS software GLIMMIX procedure; SAS Institute). In particular we compared (1) pre-TM and post-TM mortality rates for the ICUs receiving the TM program; (2) pre-TM and post-TM mortality rates for the control ICUs (to evaluate secular trends in mortality rates in the control group); and (3) the magnitude of the change in mortality rates (pre-TM vs post-TM) in the intervention vs control ICUs based on interaction terms from our models. The dependent variable for our models was either mortality rate or LOS. Independent variables included patient demographics, comorbid illnesses, and primary diagnosis at ICU admission. We supplemented administrative data with selected laboratory test values to mirror risk-adjustment models developed for ICU patients (eg, Acute Physiology and Chronic Health Evaluation [APACHE] III scores). The C statistics for our mortality rate models (0.823-0.843) were generally similar to those for other VA ICU risk adjustment models.11,16 1162

Fourth, we conducted an array of sensitivity analyses to examine the robustness of our findings. We examined the impact of the ICU TM in each intervention and control ICU individually, recognizing that our statistical power for these comparisons was markedly reduced. We stratified patients into 3 groups based on the predicted risk of death and examined the impact of TM on mortality rates in low-, intermediate-, and high-risk patients. We examined the impact of the ICU TM program in larger ICUs only (levels 1 and 2) and smaller ICUs only (levels 3 and 4); we also examined the impact of the TM program focusing exclusively on patients who required mechanical ventilation and those with sepsis. Finally, we repeated our analyses using an interrupted time series design as an alternative method for examining the potential impact of the TM program. Further details on our predicted mortality rate calculation and interrupted time series design are provided in the eAppendix (Supplement). All analyses were conducted using SAS software, version 9.3.

Results Patients Our final analytical cohort consisted of 6939 ICU admissions (6654 patients), with 3355 admissions to intervention ICUs and 3584 admissions to control ICUs (Figure 1). Patient demographics, comorbid illness, and severity for the intervention and control ICUs for the pre-TM and post-TM periods are displayed in Table 1. For the intervention ICUs, patient demographics, admission diagnoses, comorbid illnesses, and predicted mortality rates were similar in the pre-TM and post-TM ICU TM periods. Likewise, there was little change in patient characteristics (Table 1) over the pre-TM and post-TM periods in the control ICUs. Viewing Table 1 from a different perspective, patients admitted to the intervention and control ICUs during the pre-TM and post-TM periods were generally similar. That said, predicted mortality was modestly lower in the intervention ICUs than in the control ICUs during the pre-TM period (3.0% vs 3.6%; P = .02) and post-TM period (2.8% vs 3.5%; P < .001), suggesting that patients treated in the control ICUs were modestly sicker.

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Intensive Care Unit Telemedicine Program

Original Investigation Research

Table 1. Characteristics of Patients Admitted to Intervention and Control ICUs Intervention Pre-TM Period (n = 1708)

Post-TM Period (n = 1647)

66.2 (11.5)

66.5 (11.4)

White

1581 (92.6)

Black

68 (4.0)

Other

32 (1.9)

52 (3.2)

Characteristic

Control

P Value

Pre-TM Period (n = 1664)

Post-TM Period (n = 1920)

P Valuea

P Valueb

.42

67.5 (12.2)

67.7 (12.2)

.81

.70

1504 (91.3)

1467 (88.2)

1729 (90.1)

54 (3.3)

99 (5.9)

83 (4.3)

46 (2.8)

54 (2.8)

.15

.03

a

Demographics Age, mean (SD), y Race, No. (%)

Unknown Male sex, No. (%) Invasive mechanical ventilation at admission, No. (%)

.03

27 (1.6)

37 (2.2)

52 (3.1)

54 (2.8)

1658 (97.1)

1607 (97.6)

.37

1611 (96.8)

1857 (96.7)

.87

.44

119 (7.0)

131 (8.0)

.28

111 (6.7)

124 (6.5)

.80

.35

116 (6.8)

109 (6.6)

.84

41 (2.5)

52 (2.7)

.65

.62

ICU admission diagnosis, No. (%)c Coronary atherosclerosis and other heart disease Respiratory failure

91 (5.3)

86 (5.2)

.89

73 (4.4)

92 (4.8)

.56

.61

Sepsis

75 (4.4)

69 (4.2)

.77

57 (3.4)

106 (5.5)

Impact of an intensive care unit telemedicine program on patient outcomes in an integrated health care system.

Intensive care unit (ICU) telemedicine (TM) programs have been promoted as improving access to intensive care specialists and ultimately improving pat...
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