Next-Generation Remote Patient Monitoring for Heart Failure March 3, 2016 Raj Khandwalla, MD Cedars-Sinai Medical Center Martin S. Kohn, MD, Sentrian
Conflict of Interest Raj Khandwalla, M.D. M.A. FACC Attending Cardiologist, Director of Cardiovascular Education Cedars-Sinai Heart Institute Salaried employee of Cedars-Sinai Consultant for Stanson Health Martin S. Kohn, MD, MS, FACEP, FACPE Chief Medical Scientist, Sentrian Salaried employee of and stockholder in Sentrian
Agenda Population Health Cardiology Implications for chronic disease management First Generation Remote Monitoring Remote Monitoring + Predictive Analytics the future of population health
Learning Objectives Contrast the benefits of current remote patient monitoring technology with next-generation remote patient intelligence technology (predictive analytics and machine learning). Describe how to prevent hospital admissions for patients with complex chronic disease through the application of caregivertuned analytics for remote patient monitoring. Explain methods for using technology to improve the efficiency and effectiveness of nurse practitioners, extensivists, case managers and other healthcare professionals involved in the remote monitoring of patients
Cardiology circa 2008
High cost, poor outcomes
5% Consume 50% of Health Spending A Cure for Health Care Costs, Business Report MIT Technology Review
Heart Failure: Defining the Problem The inability of the heart to supply blood to the tissues commensurate with the metabolic needs of the body Incidence and Prevalence > 5 million Americans > 650,000 new cases annually Estimated that 75% of hospital readmissions are avoidable Morbidity and Mortality 5 year mortality rate 50%!!! > 1 million hospital admissions annually 30 day readmission rate -- 25% 6 month readmission rate -- 50% Economics $30.7 billion total cost of care (50% hospitalizations)
Evidence Based Medicine Works
Admissions MEAN UTILIZATION Hospital Admissions 6 Month Pre and Post HF Program 70 60 50 40 30 20 10 0 CHF DM (N = 119) Control (N = 319) 63 Pre* Post* Difference 41 % Change Pre* Post* Difference 6 Months Pre-Enrollment 6 Months Post-Enrollment * Both Pre- and Post-Enrollment periods were 6 months 18 % Change Difference- in- Difference p=0.001 8 All-Cause Heart Failure Utilization Avoided Inpatient Stays 0.57 0.29-0.29-50% 0.53 0.34-0.19-36% -0.10-11.6 Hospital Days 3.45 1.83-1.62-47% 3.44 2.11-1.33-39% -0.29 p=0.0008 Observation Stays 0.08 0.07-0.01-11% 0.03 33 0.04 0.00 9% -0.01-1.4 ED Encounters 0.21 0.18-0.03-12% 0.10 0.12 0.03 26% -0.05-6.0 Total Encounters 0.86 0.54-0.32-37% 0.66 0.50-0.16-24% -0.16-19.0-34.5 days
Major Problem: Most Days of Heart Failure Management Are Not Clinic Days HF Clinic 14
Pathophysiology of Congestion Adamson PB. Curr Heart Fail Reports. 2009; 6: 287-292. 15
0.50 0.60 0.70 0.80 0.90 1.00 0.50 0.60 0.70 0.80 0.90 1.00 Better Effectiveness After Transition Heart Failure (BEAT-HF) study Telemonitoring: weight, blood pressure, heart rate, symptoms Daily use of Bluetooth-enabled weight scale and a blood pressure/heart rate monitor Data transferred via cellular bandwidth, daily review by RN 1,437 individuals enrolled and randomized between October 2011 and September 2013 715 intervention, 722 usual care No significant participant characteristics differences between groups 0 10 20 30 analysis time randgrp = 0. control 30 day readmission 180 day readmission randgrp = 1. intervention 0 30 60 90 120 150 180 analysis time randgrp = 0. control randgrp = 1. intervention
1 st Generation RPM vs. Remote Patient Intelligence Limitations: Poor patient selection Assumes one condition Single measurement Binary rules Results: Missed deterioration High false alarms Staff inefficiency Can t scale or deliver ROI Improvements: Targeted patient selection Monitors multiple conditions Multiple measurement Machine learning algorithms Results: Detects deterioration early Low false alarms Improved staff efficiency Proven ROI
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Remote Monitoring End Stage Heart Failure Patients Click to Resize 19
Study Design Prospective, non-randomized, observational pilot trial Six week study period 20-25 patients Standard of care labs Weight Data streams Thoracic Fluid Impedence Cardiac Output Admissions Endpoints Emergency Room Visits Blood pressure Mean arterial pressure Pulse pressure Oxygen Saturation Steps per day Stroke Volume Heart Rate Variability Skin Temperature Posture Symptoms (KCCQ12) Urgent Care Visits IV lasix administration Thoracentesis CV events (CVA, MI, revasc) Mortality 20
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Patient Survey I feel better connected to my Care Team 11/11 agree or agree strongly The time spent taking measurements is worthwhile 10/10 agree or agree strongly
Cumulative Results Data Stream #TP #FP Sensitivity Specificity Pulse Pressure 7 13 38.9% 99.2% Heart Rate 11 44 61.1% 97.3% Thoracic Fluid 13 67 72.2% 95.9% Weight 14 98 77.8% 94.0% Total Steps 15 132 83.3% 91.9%
Prediction Accuracy 100 80 60 40 20 0 Physician rules 5 data streams Machine learning rules 3 data streams Machine learning rules 5 data streams
In summary Changes in policy environment incentives for population health Integrated care decreases costs and improves care Remote monitoring can transform healthcare from a snapshot model to a continuous model Second generation monitoring allows for machine learning add and predictive analytics to remote monitoring Patient Engagement is enhanced
Questions Raj Khandwalla Raj.Khandwalla@cshs.org Martin Kohn marty@sentrian.com