CONTINUOUS MONITORING ON GENERAL FLOORS FOR EARLY RECOGNITION OF PATIENT DETERIORATION Eyal Zimlichman MD, MSc Deputy General Director and Chief Quality Officer Sheba Medical Center December 8, 2014 1
2 Disclosures Received a research grant from Earlysense LTD. Serves as a consultant (Advisory Board) and received consultation fees
Introduction Patients treated in hospitals are becoming more and more complex, still, most are hospitalized in a non-icu setting. 10-20% of hospitalized patients develop complications and 5-8% of all patients die in-hospital. 33-50% of these events may be preventable. 60-84% of patients who developed cardiac arrest had instability within the 8h window preceding the event. 3
4 Opportunities for Intervention Taenzer AH et al. Anesthesiology 2011:115
5 The Solution: Rapid Response Teams
6 Rapid Response Systems: Why aren t we getting the results we expect? Outcome studies looking into the benefits of RRS have generally reported mixed results. MERIT trial 1 (randomized controlled trial of 23 hospitals in Australia) No substantial affect on incidence of cardiac arrest, unplanned ICU admissions, or unexpected death. Implementation of a RRS at Brigham and Women s Hospital A large Meta-analysis 3 concluded that the data supporting RRSs are not "robust." 1 Hillman K et al. Lancet 2005: 365. 2 Rothschild JM et al. Jt Comm J Qual Patient Saf. 2008: 34. 3 Chan PS et al. Arch Intern Med. 2010;170.
7 Rapid Response Systems: Why aren t we getting the results we expect? The patient Efferent limb: Planned response Quality analysis Afferent limb: Event detection and response triggering Staff s skills Adequate therapy at hand? RRT Sufficient staff? Staff s skills Sufficient monitoring?
8 Reasons for Failure to Rescue Addressed by RRS Not addressed by RRS Jones DA et al. N Engl J Med 2011;365:139-146.
Introduction-Cont. More and more evidence is generated that emphasizes the importance of continuous vital signs monitoring outside of ICU in earlier recognition of patient instability. New technology will need to enable continuous monitoring for low-risk patients. This technology will need to be: Easy to use by the staff Places little limitations on the patients Low false positive rate (preventing alert fatigue) Cost-effective 9
10 Technology Solutions Continuous monitoring of vital signs 3-lead ECG SpO2 ETCO2 Wearables (emerging for hospital use) Application of early warning scores to vital signs and clinical data.
11 The EarlySense Monitoring system Piezoelectric based sensor with advanced mathematical modeling based algorithms intended to alert for: 1. Clinical deterioration (respiration rate, heart rate) 2. Pressure ulcers (movement) 3. Patient falls (movement)
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13 Validation Preliminary validation in sleep lab patients. Follow up validation in an ICU setting. Performance testing: Patient s weight Bed position Patient position / activities Feasibility studies Sheba Medical Center, Israel Meir Medical Center, Israel MetroWest Medical Center, Framingham, MA Ben-Ari J, Zimlichman E et al. J Med Eng Technol. 2010 Oct-Nov;35(7-8):393-8.
14 The Sheba-Sapir study: Results Vital signs alerts Out of 113 patients hospitalized with respiratory symptoms, we identified 10 major clinical events (cardiac arrest, death, unplanned ICU transfer). Through retrospective monitoring data analysis we have calculated the accuracy of the EarlySense monitor in identifying these events. Sensitivity Specificity Positive predictive value Negative predictive value 40 HR 115 82% 67% 21% 97% 8 RR 40 64% 81% 26% 95% HR and RR 55% 94% 50% 95% Zimlichman E et al. J Hosp Med. Aug 2012.
15 The Sheba-Sapir study: Results Trend Alerts For trend analysis we grouped together HR and RR readings for running 6 hour periods throughout the day and compared the median of the readings for each period with the corresponding period on the previous day. Sensitivity Specificity Positive predictive value Negative predictive value HR 20 78% 89% 41% 97% RR 5 100% 69% 25% 100% HR and RR 78% 93% 54% 98% Zimlichman E et al. J Hosp Med. Aug 2012.
Research Sites California Hospital Medical Center (Dignity Health) 316-bed community hospital in downtown LA Newton-Wellesley Hospital (Partners Healthcare Systems) 313-bed community teaching medical center affiliated with Tufts University School of Medicine and Harvard Medical School
17 CHMC Study Objectives To evaluate the effect of continuous monitoring in a medical-surgical unit on transfers to the ICU and length of stay in the ICU. Primary outcomes: Transfer to intensive care units Length of stay (overall and ICU) Secondary outcomes: Code Blue events, mortality, pressure ulcers Return on investment ($)
18 Study Results Control Unit Intervention Unit CU-IU post All Units Baseline (pre) Control (post) p value Baseline (pre) Intervention (post) p value p value p value LOS in med/surg unit (days) ICU transfers Transfers / 1000pt Days / 1000pt 32.69 85.36 LOS, mean (median) APACHE II score Code blue events n (/1000pt) 3.80 3.61 0.26 4.00 3.63 0.03 0.91 0.10 18.89 19.06 1.00 26.52 25.93 0.92 0.12 0.21 1.73 (1.32) 4.48 (2.12) 0.01 120.11 63.44 4.53 (2.33) 2.45 (1.85) 0.05 0.02 0.01 4 13.08 14.06 0.59 15.19 13.38 0.25 0.61 0.65 6 (3.9) 5 (2.1) 0.36 9 (6.3) 2 (0.9) <0.0 1 0.45 0.02 Brown H, Terrence J, Vasquez P, Bates DW, Zimlichman E. Am J Med. 2014;127:226-32.
Results: Number of Alerts No. of patients 73 No. of Heart Rate (HR) Alerts average per week No. of Respiration Rate (RR) Alerts average per week No. of Turn Patient Alerts average per week No. of Bed Exit Alerts average per week Total no. of alerts per week (168 hours) Average number of alerts / hour Estimated True Alerts Estimated False Alerts 10 60 60 37 167 1.0 alert / hour 117 50 Average number of alerts per 12 hours shifts (for all nurses) Average number of alerts per 12 hours shift per nurse (assuming 6 nurses on shift) Estimated false alerts per nurse per shift 12 2 0.60 19
20 Alert Burden on Staff Study Current study In-patient setting Medical-surgical units Type of alerts Alerts per 100 recording hours Heart rate (18%) and respiratory rate (82%) Chambrin et al., 1999 27 ICU Ventilators (38%), cardiovascular monitors (37%), pulse oximeters (15%) and capnography (14%) Lawless et al., 1994 28 Pediatric ICU Pulse oximeter (44%), ventilators (31%), cardiovascular monitors (24%), capnography (1%) Görges et al., 2009 29 ICU Ventilators (40%), cardiovascular monitors (21%), pulse oximeters (15%), infusion pumps (12%) Siebig et al., 2010 30 ICU Cardiovascular monitors (66%), pulse oximeters (26%), respiration rate (3%) Wiklund et al., 1994 31 Postanesthesia Malviya et al., 2000 32 care unit Pediatric Postanesthesia Care Unit 2.2 161 230 636 604 Pulse oximeters 730 Pulse oximeters 167 Masimo Signal Extraction Technology pulse oximeters 200
21 Financial Analysis Objectives To estimate the cost savings attributable to the implementation of a continuous monitoring system in a medical-surgical floor. To determine the return on investment associated with its implementation. Slight SP, Franz C, Olugbile M, Brown HV, Bates DW, Zimlichman E. Crit Care Med. 2014 ;42.
22 Benefits Data on costs and outcomes were obtained from our before-and-after controlled study conducted at a 316-bed community hospital in LA. Hospital Length of Stay Ø LOS decreased from 4.0 to 3.6 days (p=0.03). Intensive Care Unit Length of Stay Ø Total ICU days were 47.2% lower (p=0.05) after the intervention Pressure Ulcer Incidence Ø A reduction of stage-two and above pressure ulcers from 6 to 2 per 1000 patients (p=0.04)
23 Results: Financial analysis Implementation costs: $274,000 in capital costs, $15,000 in one-time noncapital costs, and $293,000 in ongoing operational costs to implement the EarlySense TM system. The system saved between $3,268,000 (Conservative Model B) and $9,089,000 (Base Model A), given an 80% prospective reimbursement rate. This resulted in a net benefit of between $2,687,000 ($658,000 annualized) and $8,508,000 ($2,085,000 annualized) respectively.
24 Results: Financial analysis 5-Year Return on Investment for the monitoring system Slight SP, Franz C, Olugbile M, Brown HV, Bates DW, Zimlichman E. Crit Care Med. 2014 ;42.
Results: Sensitivity Analysis 25
Results: Sensitivity Analysis 26
We believe that the coming together of four major trends or innovations promises substantial improvements to patient outcomes by preventing delayed recognition and management of deteriorating patients on general hospital wards. These trends include: 1. The uniform use of electronic health records in hospitals 2. Major advances in physiological sensor development 3. The rapid adoption of mobile technologies 4. The ability to perform analytics in the background to provide decision support at the point of care