mHealth Israel_Professor Retsef Levi_Healthcare Innovation and Hospital Optimization
-
Upload
levi-shapiro -
Category
Healthcare
-
view
201 -
download
0
Transcript of mHealth Israel_Professor Retsef Levi_Healthcare Innovation and Hospital Optimization
Sloan Health System Innovation
Retsef LeviJ. Spencer Standish (1945) Professor
of Operations Management,Sloan School of Management, MIT
mHealth, Tel-Aviv, Israel, June 2015
Bio
• Israeli Defense Forces, intelligence officer (1990-2001)
• B.A. in Mathematics with trend in Operations Research, Tel-Aviv University, Israel
• PhD (2005) in Operations Research, Cornell University
• Spent a year at IBM
• Research: Inventory, supply-chain, healthcare and revenue management optimization, risk management
• Experience in industry: Healthcare (MGH, Children, Beth-Israel, AAMC), FDA, Oil Industry (Sunoco, BP), Air Force logistics, Hi-Tech, Print industry
U.S. HC System Solution Approaches
• Market Approach (Economists and Policy Makers) =
Change the incentives and market design and let the players adjust
• Front-Line Approach =
Change the system design and operations of healthcare delivery systems (create new ones) and make the change from the inside
How do we change the healthcare cost-effectiveness equation?
Multidisciplinary approach to rethink the organizational capabilities of health systems (organization design, analytics, HR policies, IT,…)
PCAST Report on Health SystemsRecommendation 1: Accelerate the alignment of payment incentives and reported information with better outcomes for individuals and populations.
Recommendation 2: Accelerate efforts to develop the Nation’s health-data infrastructure.
Recommendation 3: Provide national leadership in systems engineering by increasing the supply of data available to benchmark performance, understand a community's health, and examine broader regional or national trends.
Recommendation 4: Increase technical assistance (for a defined period—3-5 years) to health-care professionals and communities in applying systems approaches.
Recommendation 5: Support efforts to engage communities in systematic health- care improvement.
Recommendation 6: Establish awards, challenges, and prizes to promote the use of systems methods and tools in health care.
Recommendation 7: Build competencies and workforce for redesigning health care.
Health System Innovation at Sloan
• Over 30 faculty doing health related research recently formed into the Initiative for Health System Innovation
• Fits within the mission of Sloan:
- Make an impact on the world (research & outreach)- Calls for business innovation- Employment opportunities- Educational mission (certificate)
http://mitsloan.mit.edu/mba/program-components/healthcare-certificate.
• Massachusetts is pioneering in healthcare delivery (the national reform builds upon the MA reform), and a hub of health innovation
Healthcare Network
Health Systems: New Challenges
6
Academic Medical Center
Physician Organizations
Community Hospitals and Clinics
…Network1 Network2 Networkn
Manage Population Health
B2B Market Interactions
System Re-Design and Resource Deployment to meet Network’s
Objectives
Fee-for-service
Risk ContractsCapitation +
Quality
Under Risk Contracts
Pay/Design for Performance
Operations Research Applied to
Academic Medical Centers
MGH-MIT Collaboration
The Team
MGH • Perioperative Administration in collaboration with other areas:
• 950 beds, 48,500+ admissions annually
• 37,000+ surgical operations in CY14
MIT
• Sloan School of Management & Operations Research Center
• Broad set of disciplines:
Operations Research
Operations Management
Economics
Organizations
Finance
Peter Dunn, Bethany Daily
Cecilia Zenteno
Clinicians, administrators,
data analysts, project specialists
Retsef Levi
Postdoctoral Fellows
LGO Fellows (Master’s students)
Data Analytics
(decision support
tools )
Simulation-Optimization
Models (predictions)
System/Process Re-
design
(business practices)
Collaboration
Admitting
Primary Care
Neurosciences
Cancer Center
Other Specialty Outpatient clinics
Work to date
Implemented & Results
• OR scheduling of elective and non-elective cases
• Inventory management of surgical supplies - Part I
• ICU patient flow (awaiting results)
Ready to start implementation
• Intra-day surgical scheduling
• Post-surgery Recovery Area patient flow
• Cancer Center
• Primary Care Redesign
New projects
• Hospital bed capacity management (predictions & decision support)
• Primary Care Redesign – Provider Scheduling
• Critical Care
• EP – Cath Lab
• Non-Oncology Infusion
9
In Implementation
New projects
ICU Patient Flow
Objective: Study hospital-wide ICU bed availability and
throughput as part of optimizing patient flow and eliminate
patient delaysImpact:i. Increased subsequent LOS
1 day of delay 1.18 additional days on the floor
ii.~$12 million in additional costsICU cost per day ~$3.5k
iii.Longer wait times from ED/OR to ICU
Cumulative # ICU delayed bed-days2
B. Christensen, P. Cobb, B. Daily, S. Dolcetti , A. Doney, P. Dunn, J. Lee, R. Levi, D. Scheinker, T. Tehan
ICU Patient Flow
B. Christensen, P. Cobb, B. Daily, S. Dolcetti , A. Doney, P. Dunn, J. Lee, R. Levi, D. Scheinker, T. Tehan
Potential reasons for result
1. Clinical factorsStaffing (PT, OT, SLP), equipment (invasive monitoring), care elements
2. Non-clinical factorsDesign of medical plan, hand-off process between ICU and floor
Results for different Patient Segments
Segment* Delay impact (coeff., 95% CI)
a. All ICUs 1.18 [1.00,1.36]
b. Neurosurgery ICU 1.02, [0.80,1.23]
c. Neurology ICU 1.72, [1.49,1.95]
Cancer Infusion CenterObjective: Improve Infusion Center throughput by smoothing chair utilization throughout the day (MGH: Mara Bloom, Inga Lennes, Debra Burke; MIT: Wendi Reib, R. Levi)
• Midday congestion causes
perception of insufficient
capacity.
• Root-cause: (separate)
scheduling practices in Infusion
Unit and Practice.
• Developed scheduling algorithm
that takes into account relevant
constraints.
.
Avg (std. dev.) peak reduction: 57 (±7.6) to 40 (±4.9)
0
10
20
30
40
50
60
Infusion Center Average Uti-lization
Retrospective Orig
Time of Day
Chair
s
Current State
0102030405060
Infusion Center Average Uti-lization
Retrospective OrigProspective
Time of Day
Chair
s
CurrentState
New Approach to Safety and Risk Management
Joint work with Fernanda Agnes Hu, Yiyin-Ma and Adam Traina from MIT
and Pat Folcare, Danny Talmor and others from Beth Israel Deaconess Medical Center
Quality Efforts in the U.S.
• To Err is Human (IOM, 2000)
• 5 Million (100K) Lives Campaign (IHSI, 2006)
• Check List (Peter Pronovost, J. Hopkins, 2008)
- Millions of central lines are put every year in ICUs- 4% of patients develop infection- 5-25% mortality rate and extensive LOS- Reduced infection rate by 66% in 18 months
• Measure safety through frequency of (a few) harms and develop (many) check lists
New Approach
• Develop a notion of aggregated ‘burden of harm’ (allows statistical power)
• Identify ‘environmental’ and system risk (states) drivers (unit and shift level) that lead to harm
• Apply statistical models to find correlation that identifies risky states (higher likelihood of harm)
• Intervene to monitor, control and avoid risky states!
The “Burden of Harm”Consortium Defined HarmsCLABSIVAC/IVAC/PossVAP/ProbVAPHigh Tidal VolumeVTE-PEICU-Acquired DeliriumDecrease in Function Mobility Scale
BIDMC Incident Reporting SystemICU-Acquired Pressure UlcerFallsMedication / Fluid ErrorLab Specimen IdentificationCommunication / Handoff IssueCode Purple / SecurityNutrition…
IHI Global Trigger ToolPositive C. Diff and Blood CultureBleedingOversedatoinReadmission to ICUReintubation and Unplanned ExtubationGlucose < 50 while on insulin BUN or Creatinine Doubled BaselinePTT > 100 while on HeparinINR > 6 while on WarfarinAdministering NaloxoneAdministering Vitamin K
Other Source (BIDMC)Catheter-associated Urinary Tract InfectionIatrogenic PneumothoraxCode Blue
Risk Drivers
AcuitySOFA ScoreNursing IntensityLength of Unit StayLength of Hospital StayPts in first 24 hrs
ExperienceFloat NurseNew Nurse <1 yearRare Unit ProcedureBoarding Patient
Other EventsReadmission"ED Critical"UnitNight shift vs Day shiftWeekend vs Weekday
UtilizationHours of CareAdmissionsDischargesNursing Intensity Score (Workload)
Risk Drivers:Conditions (states) of the ICU environment, system and people in the system that increase the likelihood of harms
Statistical Analysis
• Assume common risk drivers to harms
• Employ statistical analysis to identify risky (safe) states of increased (decreased) likelihood of harm
• Many technical challenges!
- Descriptive identification of risky states- How to aggregate harm?- How to validate?
Risky State (I)
Driver Low High
TISS 24.59
New Nurse
24%
Staff Ratio
2:1
Float Nurse
21%
Boarders 76%
SOFA 11.8
The chance of harm is 7%
Number of Shifts:124
P-Value 1.54e-05
Success Drivers and Challenges
• System level innovation is essential for creating required organizational capabilities
• Multi-lens approach and skills are key! (analytics, organizational change management, behaviors)
• New formats of collaboration needed (learn the respective characteristic of the organization)
• System innovation: Models to predict impact? Infrastructures to test ideas? Methodologies to measure post implementation effectiveness?