Julie Reisetter, MS, RN Chief Nursing Officer

Banner Telehealth

• One of the largest non-profit health systems in the country

•25 Acute Care Hospitals

•39,000 employees

• Medicare Pioneer ACO •Banner Health Network with nearly 300,000 members

•Banner Alzheimer's Institute

•55 bed Simulation Medical Center

•$153 million a year in charity care •Unpaid cost of public programs, Medicaid and other indigent care programs - $227 million

•Total cost of the community benefit and the unpaid cost of Medicare - $520 million

Mesa, AZ Santa Monica, CA Denver, CO Tel Aviv, Israel

Major telehealth service lines include: TeleICU (2006) TeleAcute (2010) Banner iCare™ (2013)

Virtual team partnering to care for patients in over 450 ICU/ED beds, over 150 Med-Surg/PCU beds and over 400 Banner Health Network members with complex, chronic illness in their homes Providing telehealth services in 21 facilities (+3 in progress) in 5 states; ranging from large 600+ bed hospitals to critical access hospitals

Banner Health Critical Care Results

ICU Days Saved Beyond Predicted

Estimated Savings from LOS Reduction

Lives Saved Beyond Predicted

From TeleICU to TeleAcute

• Leverage early learnings from TeleICU • Create environment where all patients are monitored and

have continuous surveillance by a remote team (including 2 way A/V in each room)

• Early identification of troubling data • Identify high acuity alarms to be sent to bedside team • Convert information into a more rapid change in care

delivery • Create a data stream; environment for research

Configuration and management overview

• Telepacks and Pulse Oximetry...on everyone • Alerts and alarms are non-audible at the bedside • Interfaces to Philips eCareManager software • Primary notifications to the Remote Operations Center

(ROC), are validated and then sent to the Bedside: – V-Tach, V-Fib, Asystole, and Apnea.

• Notification which go to both the ROC and bedside: – Bed Exit and Code Blue. (Goes to all bedside staff)

• In-Op Alerts such as Leads Offs and Low battery are system generated directly to the bedside.

Configuration and management overview

• Tachycardia Limit Alerts: trigger for >130 or 2 consecutive 1 min. values of >150.

• Tachycardia Trend Alerts: trigger for HR increases of 15 bpm over a 4 hour period based on 10 min. median values.

• Bradycardia Limit Alert: trigger at <50 or for 1 min, back to back HRs <40. • No trending low HR alert • TeleRNs have the ability to set custom alert parameters for patients with

known Bradycardia or who are paced

• V-Tach: trigger for a run of 7 or more PVCs in a row. • V-Fib • Asystole • Extreme Brady: HR drops to 40 bpm or below • Apnea: Apnea delay is 40 sec. (requires direct connect to MP5 for

continuous RR) • RR: trigger for rate >30 • SpO2: trigger for SpO2 of 80% or less

IEM (Emergin) Command Center • Primary function of the Command Center is to display high priority

information and allow for immediate paging to the beside RN. • TeleRN triages the alert to determine validity; either dismisses the

alert or pages the alert out to the bedside RN. • Alerts that display on the IEM Command Center:

– VT, V-Fib, Asystole, and Apnea – Bed Exit (goes directly to all staff on unit)

• Failure to either dismiss or page out a RED alert results in an expired alert which auto pages to the bedside.

• Red alerts are paged out directly to the bedside RN, with escalation to the Clinical Manager if not acknowledge; will break through any other message currently occurring on the Vocera.

Banner Gateway Medical Center TeleAcute - Year 1

“ Application of the eICU care model to the medical surgical patient population reflects an impact on patient outcomes,

throughput and costs…”

13

Baseline TeleAcute

Average LOS 3.96 days 3.30 days

Cases / month 307 389

Cost / case $6161 $5166

ICU Admissions from general ward

first 24 hours 0.9% 0.8%

after 24 hours 1.1% 0.6%

Discharge status death or hospice 2.7% 2.0%

home 84.1% 86.5%

Falls per 1000 days 3.3 2.1

In the absence of data…“saves”

Pt had dc order written; Telehealth RN noted bradycardia 38-42; Assessment via A/V equipment, bedside RN notified; discharged discontinued; stat EKG. transferred to PCU, STAT EKG

Alarm noted SVT 199-200 (>2 min) checked on Pt via camera; was symptomatic notified RN and Manager. Had family place O2 on Pt (it was laying at bedside and turned on) EKG ordered; consult ordered

Post op pt - Bariatric surg - new onset A-fib. TeleRN reviewed chart and found no mention of this in history. Notified RN and MD - stat EKG, prelim results - A-fib, V rate 67-106, HR 88. Pt transferred to PCU . Patient with V-tach alert, upon review of the rhythm the pt was found to have 10 beats of V-tach followed by A-flutter. Faxed a strip to the floor, notified the RN. EKG and labs ordered; transfer pt to PCU. Noted O2 sats sustained at 80-81% w/ good waveform, no alarm heard via camera although alarm was not shut off. Notified RN who promptly adjusted alarm volume and applied NC O2, sats now in the 90's. Pt had alert for Asystole; pulled up on monitor and rhythm confirmed with HR of 0; via camera pt was observed to be lying in bed with head against the bed rail and was without respirations. Bedside RN notified; Code Blue called; CPR started; Pulse and BP re-established , intubated and transferred to ICU

So what’s been happening since 2011?

• Conducting research in partnership with Philips – Accelerometer study and continuous respiratory rate – Evaluation of LOC and EWS – Patient deterioration detection

• Research Data Export (3 year recording period) includes 176,000 hours of data from over 8500 patients; continuous waveforms of EKG and Pleth along with periodic blood pressure and temperature

• And waiting….. – Lower price two-way audio/video – Release of new population management software that is

customized to non-ICU population – New patient monitoring devices

Accelerometer data collection

Spot check RR vs. Accelerometer RR

Spot check RR of the population vs. Accelerometer RR of the population

RR

RR = 15-16

RR = 17-18

RR = 19-20

Results: For 87 patients with both spot check and accelerometer respiratory rate data, there were 6,274 spot check determined respiratory rates and 122,007 accelerometer determined respiratory rates. For the accelerometer, a moving five minute median was used, and then the results were rounded to the nearest integer. The spot check distribution was centered at a respiratory rate of 16 and the accelerometer distribution at 15. However, the distributions were very different. The accelerometer values had a bell shaped distribution, whereas the spot check values were skewed to the right: 34% of the accelerometer values were less than 15, whereas only 8% of the spot check values were. Conclusions: We were able to see a statistically significant difference in respiratory rate as captured manually versus via accelerometers. This difference would lead to a clinical difference in an EWS that assigns 1 point for respiratory rates between 15-20. However, in EWS systems based on newer research where 0 points are assigned for rates between 12-20, there would be minimal clinical difference between the two methods, but continuous monitoring would still have the advantage of earlier identification of deterioration, automatic recording, and simplified workflow

Data-driven continuous risk profiling for early deterioration detection

International Conference on Complexity in Acute Illness Objectives: • Unexpected deterioration in the general ward is strongly associated with adverse

outcomes. The Early Warning Score (EWS) has been deployed in many institutions as part of a "Track-and-Trigger" system, where the EWS is recorded and used to engage specified care team responses with the goal of preventing these deterioration events. The EWS, Modified Early Warning Score (MEWS), and other related Aggregate Weighted Track and Trigger Systems (AWTTS) are based on the variation of a patient's vital signs from arbitrarily agreed "normal" ranges, and the arbitrary nature of these ranges lead to high false positive rates in the use of the scores. We investigate the feasibility of eliminating this dependence by deriving risk scores directly from patient data, and of creating a system that can be used in place of the EWS to engage existing Track-and-Trigger action plans.

Methods: • The database used for this analysis contains the vital signs measurements and

outcomes of patients who were treated in a 176 bed community hospital from July, 2011 - January, 2013. General ward stays were the focus of the current research and deterioration events were defined to be transfers from the general ward to the Progressive Care Unit (PCU) or Intensive Care Unit (ICU), or patient death. A modified Naïve Bayes approach was applied to derive value-risk profiles for each feature: lookup tables relating the value of the feature with the associated risk of deterioration. These value-risk profiles were used in the manner of AWTT systems: the value of each feature in a set of vital signs measurements was used to determine the risk associated with that value, and the sum of these risks was taken to be the patient's Early Deterioration Indicator (EDI).

Results: A total of 10,733 patients were used in this study, with a combined 13,944 encounters and 14534 general ward episodes. 1102 general ward episodes (7.6%) ended in a deterioration event (death or transfer to ICU or PCU), consistent with other reports of 3% - 17% in this care setting. Value-risk profiles were created for each of the features commonly used in AWTT systems, and heart rate, respiratory rate, systolic blood pressure, temperature, SpO2, and age were determined to be most important for performance and were included in the EDI. MEWS, one of the widely used AWTT systems, was determined to have an AUROC of 0.729+-0.017. EDI was evaluated and found to have an AUROC of 0.820+-0.016. A mapping was created to convert EDI scores to MEWS scores, and the improvement in specificity was 9.6% at the first defined action threshold. Conclusions: We have developed a data-driven framework to detect deterioration in the general ward, the Early Deterioration Indicator (EDI), and a technique for mapping EDI scores to existing Track-and-Trigger systems. We demonstrated the application of this technique to the action plan at one hospital, but this is a general technique that may be applied to adapt the EDI for use as an AWTTS replacement at any facility.

Technology Development for Monitoring Recommendation and Deterioration Detection across the Care Continuum

• Study duration: 2 years • Estimated sample size = 2,052,216 (26 facilities) • Observational epidemiologic study to support development of algorithms for

efficient usage of monitoring resources and better detection of patient deterioration across all the care levels, from ED to discharge. Four major work streams: – Develop a research dataset including adult patients from all the care

levels of Banner Health hospitals. Data will be collected from Banner’s Electronic Medical Record (EMR) through Banner’s electronic data warehouse and also Philips physiologic monitoring data. This research dataset will serve as a data mining source to uncover critical parameters for algorithm development.

– Develop a pediatric research dataset based on EMR and Philips physiologic monitoring. This dataset will serve as a data mining source to develop critical deterioration detection algorithms for pediatric patients.

– Develop CDS algorithms for monitoring recommendation and deterioration detection in adult patients. Test the effectiveness of the tools.

– Develop CDS algorithms for deterioration detection in pediatric patients. Test the effectiveness of the tools.

Upcoming research projects

• Acoustic classification to measure alarm load; recording all alarm occurrences (time, device, alarm type etc.) along with the associated noise levels to allow simple, efficient and vendor independent assessment of alarm load; collected data (Belgium, Netherlands and Banner site) will be used to validate alarm classification algorithms.

• Patient deterioration detection– ED population

• Analysis of inpatient data and in-home data (during first

30 days post discharge)

25

Banner iCare™ Program

• Extension of the TeleICU and TeleAcute care model –Tele-health team manages highest risk, highest cost outpatients

Design concept

• ‘Perpetual’ management of high risk patients with chronic health conditions ‒ Targeted population (top 5%) with high intensity, home-

focused care ‒ Dedicated IAC care team (physicians, nurses,

pharmacists, coaches, MSW, quarterbacks) provides coordinated, proactive care

Care model

• Advanced data tools and in home devices enable daily patient assessment and centralized patient management from Telehealth center

• High touch services for patient education and support

Structure

ACO Patient Population

5% of population represents ~50% of cost

Lower acuity patients managed by existing

programs

95%

5%

Education

Acute and chronic Care Plans

Risk Assessment

Lifestyle Coaching Compliance Tracking

Physical Status Monitoring

Care Coordination / transitions

Utilization Management

Banner iCare™ Patient Selection Guidelines

• High level of organic disease supported by high level of claims and identified with multiple chronic diseases

• Life expectancy >12 months • Banner Health Network member

26

Home Devices

• Philips Patient Telehealth Station • Scale • Blood Pressure Cuff • Pulse Ox • Glucometer • Rhythm Strip Recorder • Thermometer • Philips Lifeline (fall alert)

Mobile Team Home Health Nurse Health Coach

Health Center Primary Care Medical Assistant

Banner iCare™ Telehealth Team

iPhysician iMSW

iRN iPharmacist Care Quarterback

Home Patient & Caregiver

Banner iCare™

• Identify adverse trends and intervene before an adverse outcome occurs using Philips software

• Responsible for active problem and chronic condition management • Coordination of team activities and active tracking of all active

issues • Daily team meetings to address new / escalated issues • Education oversight • Update & create plan of action • Prevent unnecessary hospital/ED visits • Documentation of all services

62 members; 3-month follow-up

Early Financial Data

Months -12/0 -11/1 -10/2 -9/3 -8/4 -7/5 -6/6 -5/7 -4/8 -3/9 -2/10 -1/11

Pre 2276 2755 4182 2281 1851 2477 2384 2101 2016 2447 2747 1482

Post 2151 747 863 578 450 265 0 0 0 0 0 0

iCare period

Initial results and next steps

• Cost reduction approximately 50% (uncorrected) – Estimates range from 40-60% (using different

definitions of enrollment) • To be investigated further:

– Seasonality – Regression towards the mean – Cost breakdown in IP, OP, Meds – Drop-outs (in next iterations with longer follow-up) – Death bias adjustment