The H-SIMM: Clinically Integrated Supply Chain Maturity Tool for Health Systems

The H-SIMM: Clinically Integrated Supply Chain Maturity Tool for Health Systems Join the conversation!

@SCAN_Health & @WIN_Health Dr. Anne Snowdon, BScN, MSc, PhD, FAAN Director of Clinical Research, HIMSS Analytics Professor, Strategy and Entrepreneurship Odette School of Business

Agenda

• Key Drivers of Supply Chain Infrastructure in Healthcare • What is a maturity matrix?

• Progress to date

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THE GLOBAL CHALLENGE OF SUSTAINABILITY 10.7% 17.4%

Rising Global Healthcare

Costs: 5.3% per year (2014-2017)

6.4% 8%

6.6%

Highest global spending as a percentage of GDP is in North America

Deloitte, 2014

Trends in Health Systems: Personalization and Precision • “Omics” technologies – make personalization possible personalized to the unique genetic make-up of the individual • Information Technologies driving new expectations and demands among consumers

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Pressure of PRECISION MEDICINE Drive towards precision medicine vs. the cost of these therapies for health systems - the 10 highest grossing drugs in the USA, number of people that improve (blue) vs. number that fail to improve (red) Significant pressure to find value at the system level to achieve sustainability Personalized medicine: Time for oneperson trials Nicholas J. Schork Nature 2015. Volume 520, Issue 7549

Evidence of Impact and Outcomes for Personalized Medicines o f the Future Clinical trials will no longer be “enough” evidence to adopt new medicines and therapies.

System level tracking and traceability will be foundational to quality and safety for the future of health systems

50% of new drugs on the market today demonstrate no value for patients. Clinical trials will no longer be “enou gh”

COMPLEXITY Meet Jim: COPD, stroke, 24 medications, 8 providers visiting weekly, 8 admissions to hospital in last 14 weeks, multiple ED visits & wife struggling to cope

Complexity means that "One Size Fits All" (Clinical Pathways) is not effective, this population requires “One size fits one�

Medical Error is now the 3rd leading cause of death in North America.

…251,454 deaths in the U.S., 633 people/day (Makary, 2016) International Consortium of Investigative Journalists analysis of U.S. data reveals: • 1.7 million injuries (2008-2017) • 83,000 deaths linked to Medical Devices

Baker, 2004

Product Traceability: Global Traceability of Product Failure

In the case of the metal-on-metal joint implants it took 4–5 years before evidence was accumulated and reported. We are left with more than 500 000 patients with metal-onmetal prostheses in the U.S. and more than 40 000 in the UK who are at elevated risk of device failure, which will inevitably result in the burden of further surgical treatment as well as billions of dollars in costs to taxpayers (Lancet, 2015) 9

Traceability of Care Outcomes in Communities

13.5% Adverse Events, nearly double the rate in hospitals (7.5%) 10

• Slow to understand the importance of supply chain in health, way behind other industries like grocery and retail pharmacy • Resource constraints in healthcare – don’t see supply chain as an asset • Medical error is the third leading cause of death in North America, the ability to track and trace products is key to solving error Ebel, T. George, K. Larsen, E. Neal, E. Shah, K. Shi, D. Strength in unity: the promise of global standards in healthcare. 2012. McKinsey and Company.

Makary, MA. Daniel, M. Medical error-the third leading cause of death in the US. BMK. 2016 May3;352:i2139.

Supply Chain in the Health Sector 11

Supply Chain Transformation Globally Case study research examined supply chain transformation as a strategy to strengthen health system performance in three global health systems: • Canada – Alberta Health Services • UK – National Health Service • U.S. - Mercy Health System

Case studies released February 15, 2018

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Clinically Integrated Supply Chain Strategy Implementation

Alberta

• Province wide supply chain infrastructure: inventory, tracked relative to demands for province wide optimization -> significant waste reduction • Online adverse event reporting province wide, automated traceability of every product and every piece of equipment.

NHS – Scan4Safety • Digital tracking of every patient, product, care process, clinician and location of care in six hospital Trusts, using point of care scanning • Transparency has reduced variation and waste, accurate case costing, released provider time to care for patients.

Mercy • Scaling supply chain infrastructure across 45 hospitals, traceability of every product linked to patient outcomes – Cardiology, Perioperative • Revenues $1 billion since 2002 from supply chain • 70% reduction in “never events”

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Emerging Findings Globally Integration of Supply Chain in Clinical Programs Creates System Transparency • Transparency of what care patients receive, by who, using what products linked to outcomes – cost, safety, quality in “real-time” • Offers accurate case costing for every case linked to clinician-surgeon and product use • Clinician leadership is the single most critical factor in success • Transparency of variation: reduces variation in cost, care processes informed by patient outcomes, decision led by clinical teams • Integration of supply chain teams into clinician teams – “coming together of two separate worlds”

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Return on Investment: Three Countries Country

Canada

England

United States

Health System

ROI

• 7:1 to date from inventory savings only Alberta • $301,438,786 in savings over 7 years Return onHealth Investment Summary: Services • Savings are from inventory only to date

National Health Service

• 4:1 expected by year 3 from inventory savings, £1,034,000,000 savings projected by year 7 (£30M/mon. all Trusts) • 16 FTE’s in labour savings/ Trust

Mercy Health System

• $1 billion savings as a direct outcome of optimizing and transforming supply chain processes across Mercy • 29.5% decline in labour costs and 33% decline in supply costs

Example: Impact of Supply Chain on Perioperative Program Total Knee Arthroplasty (Mercy Health System)

Total CPI Adjusted Intraoperative Supply Cost Per Case, May 2012 Jan 2016 (n=11,834)

Vance Moore (2017). SCAN Health Annual Networking Event, St. Louis, Missouri

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Surgeon Performance Indicators Block Utilization Dr. “Mercy” Supply Cost/Case

Use of Pathways

Patient Satisfaction

Readmission Rate 17

Impact and Value of Automated Supply Chain Management (based on supply chain data from 10 North American Hospitals)

Typical control span of supply chain team (35% of spend | med/surg)

Nursing units

Clinician driven Typically managed by clinicians (65% of spend | high-items)

Cath labs

Operating rooms

Performance metrics

Improvements

Clinical engagement in decisions

-55% to -80%

Orders, Replenishment, Stock Taking

-36% to -78%

Stock-outs

-90% to -98%

Inventory value

-20% to -53%

Expiries

-37% to -75%

Expense write-offs

-50% to -63%

Pharmacy

Supply Chain Implementation: Automated capture of product use with point of care scanning across highest cost units and highest revenue/risks

Alberta Health Services, NHS England, Mercy focused on Operating rooms as a 1st step

Payback within 12 months (annualized) Supply Chain team works with Clinicians on product standardization, demand forecasting = Value

Our approach to development of H-SIMM: • We used the Instrument Development Process (Chen and Paulraj) • Three stages: examination of literature, identifying constructs and conducting expert examination • Continuous cycle of consultation with key experts • This method assesses the construct validity and dimensions of the maturity matrix

Literature Review

Expert consultation

Delphi Survey

Ahire, S.L., Golhar, D.Y., Waller, M.A., 1996. Development and validation of TQM implementation constructs. Decision Sciences 27 (1), 25–56.

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Literature Review • Conducted a literature review of 423 articles from a variety of scientific databases • 80 articles were relevant, analyzed for key dimensions of supply chain • Industry research, 35 key informant interviews with experts in the industry, stakeholders suggested relevant supply chain models to be examined • Key concepts across all published literature assembled, interviews validated concepts with experts for relevance, key indicators for each concept developed from interview data analysis 20

Literature Review revealed supply chain measurements but not healthcare specific • Supply chain measures vary in underlying purpose of the measures: • the decision-making process • costing • quantitative and qualitative (customer satisfaction/relationships)

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What is the Delphi Approach?

• Developed by RAND in the 1950’s • Entails the use of experts to reply to questionnaires and receive feedback of the “group response” • Repeat the process until reaching close to “expert consensus” https://www.rand.org/topics/delphimethod.html

Survey

Discussion

• Obtain opinions from experts • Assessment of group response

• Review of results • Group discussion and interview

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Survey Participation

Ideal participation is between 10 to 30 experts

Participants have differing knowledge/ experience

Different perspectives are collected to bring a range of perspectives

We used a variety of supply chain experts from different fields to bring perspective to our maturity tool

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Methodology Grey literature, studies, expert suggestion

Article search

423 articles reviewed

Results Models

Models in supply chain

5 Frameworks

Country Level

Roadmap

Empirical Research

Consulting

Matrix 1 and Indicators

Matrix

Feedback

Expert Opinion

Matrix 2 and Indicators

Matrix

Dephi Survey

Expert Opinion

Matrix 3 and Indicators

Dimensions

Levels Indictors

Consulting

Matrix

* Adapted from: Chen, I. Paulraj, A. Towards a theory of supply chain management the constructs and measurements. Journal of Operations Management (204). 119-150

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4 Key Dimensions: Automation: automation of data capture for products, care processes, clinician teams, procurement, traceability of products and supplies Integration: clinical integration of supply chain expertise in clinical programs, automated data captured at point of care Data>Information>Knowledge: data captured at point of care, uploaded and translated into knowledge of forecast purchasing, productivity, to identify value, safety at point of care Leadership: C-suite leaders view supply chain infrastructure as a strategic asset for the organization, mobilize teams to advance supply chain strategy

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PAT I E N T ENGAGEMENT AI TOOL S

MERCY NHS

ALBERTA

PHYSICIAN LEADERSHIIP

AU TO M AT I O N

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Clinically Integrated Supply Chain Enables Transparency of Real-World Data and Evidence of Value and Performance Procurement Best Practice

Real-World Evidence

informed by real-world evidence emerging from supply chain data, identifies solutions needed, products which work best for patients

informs solutions and innovation priorities, for specific patient segments, to enable procurement teams to procure priority solutions that offer best outcomes for patients

Data Flow

Data Flow

Procurement Research

Supply Chain Research

models to procure innovation, procure solutions to achieve value

real-world evidence of value to inform health system performance

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Timeline by project phase Date

March 2019

April 2019

May 2019

June 2019

July 2019

August 2019

Sept 2019

Oct. 2019

Plan Survey Build Testing (Internal) Pre-Pilot Pilot Operational Readiness H-SIMM Model Go-Live

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Deliverables • Pilot Group: healthcare providers/hospitals (global), vendors • Product: target market, key features, benefits, problem definition, pricing strategy • Marketing: sales sheet, criteria sheet, website updates, lead generation tactics, updates to marketing collateral, Certification Program assets • Operations: onboarding process, Salesforce updates, contract, survey, FAQ, operationalize Certification organizations • Enablement documentation: stage 6 & 7 prep guides • Support: support processes documented • Training: internal (globally) and external (globally) • PR • Post go-live activities

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Pilot group secured 11 health system/hospitals Alberta Health System (pre-pilot) Geisinger OUH (Odense, Denmark) Mercy Leeds (NHS) UCLA Intermountain Health System Harris Health Brazil - 1) Beneficencia Portuguesa de SĂŁo Paulo Brazil - 2) Hospital Santa Izabel from Santa Casa da Bahia in Salvador city Ochsner

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Digital Health Ecosystem Model for Health Systems: Opportunity for Global Leadership Join the conversation!

@WIN_Health & @SCAN_Health Dr. Anne Snowdon, BScN, MSc, PhD, FAAN Director of Clinical Research, HIMSS Analytics Professor, Strategy and Entrepreneurship Odette School of Business

Every industry TODAY is a tech industry‌ except healthcare Advertising Music Retail Media Taxi/Transportation Hospitality/Travel Entertainment Executive Search Food & Agriculture

> > > > > > > > >

Google Apple, Spotify Amazon Facebook Uber, Lyft AirBnB Netflix LinkedIn (Microsoft) Monsanto, John Deere

All of these businesses are IP and data-driven businesses Cantech Conference, Jan 2019 Jim Balsillie

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Growth in gross domestic product for the digital economy and the total economy, Canada, 2011 to 2017 (Statistics Canada, 2019)

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Support services and telecommunications produce twice the GDP growth compared to products, hardware, e-commerce. (Statistics Canada, 2019) 36

Digital Economy in the U.S.

According to the U.S. Chamber of Commerce, IP and data intensive industries currently contribute over $7 trillion annually to the US economy.

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Patent Cooperation Treaty – Patent Filings

The 8 most valuable companies in the world are data and IP driven, fueled by big data (IOT)

PCT Applications Filed

Dramatic rise in patents fueled by the knowledge based economy and data driven economy

USPTO – Patent Filings

Source: http://www.wipo.int/pct/en/3million/index.html

“Patents are the most concrete and comparable measure of innovative output over countries and time.”

The IT Revolution and the Globalization of R&D (http://www.nber.org/papers/w24707)

Cantech Conference Jan 2019 Jim Balsillie

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Change in Patent Cooperation Treaty Filings from 2014 to 2017 740%

143% 131% 91% 45% 41%

28% 26%

20% 20% 19% 16% 15%

14% 13% 12% 10% 10% 9% 7% 6% 6% 5% 5% 2% 2% 1% -1% -3% -4% -5% -5% -7% -8% -8% -12% -18%-18%-21%-22%

Canada

New Zealand

Greece

Spain

Finland

USA

Singapore

Hungary

Mexico

Poland

South Africa

France

Saudi Arabia

Austria

Sweden

Brazil

Italy

Netherlands

Germany

United Kingdom

Australia

Ireland

Switzerland

Denmark

India

Belgium

Japan

Israel

Russia

Chile

Norway

South Korea

Portugal

Luxembourg

Colombia

Turkey

China

Thailand

Malaysia

Ukraine

Top 5 Countries (Increased Filings) • China +23,334 • Ukraine +1,088 • Turkey +382 • Thailand +89 • Malaysia +83

Bottom 5 Countries (Decreased Filings) • Canada -687 (22% decline) • Spain -307 • Finland -216 Cantech Conference Jan. 2019 • New Zealand -74 Jim Balsillie • Greece -23 39

Tangible Economy Objective: manufacture and sell products, goal is to move inventory Revenue: producing and selling physical goods

“What is an Intangible Economy?�

Intangible Economy Objective: amass and protect valuable data, transformed into IP Revenue: customers pay for access to services, knowledge emerging from IP and data tools

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What could the digital economy look like in the Health Sector?

Today’s Health Economy: “Tangible” Products • Objective = sell products (drugs, supplies, devices, capital equipment)

Tomorrow’s Health Economy: ”Intangible” Services • Objective: mobilize health data, transformed into rich IP resources, that become the solutions for global health systems

• Revenue: sales of physical goods • Performance: in North America is poor

• Revenue: IP rich data tools, health services enabled by digital tools, create real-world evidence strategy • Performance: economic growth, safety and quality outcomes for citizens 41

What Citizens Wa n t and Need – Digital Health To o l s and Services

Experience: every clinician encounter has all of my data, understands my health goals, partners with me to achieve my goals Ease of Access: (virtual) specialist care when and where needed Choices: care delivery options (online, in person, virtual teams) Price Transparency: cost up front, not on the invoice at discharge Guidance and Wayfinding: information that is relevant on where to access care and what care is needed and when Safety-Quality: confidence I am getting the best possible care, based on real-world evidence of people like me Literacy Tools: tools that translate health data into meaningful knowledge so people can make decisions (ex. Mint, Schwab) Traceability and Transparency: automated error reporting, risk alerts to support and manage health; tracking every care process linked to outcomes creates data >> real-world evidence >> economic value

L E A D G LO B A L H E A LT H S Y S T E M T R A N S F O R M AT I O N TO A DVA N C E D I G I TA L H E A LT H E C O S Y S T E M S F O C U S E D O N H E A LT H A N D W E L L N E S S

Opportunity for HIMSS Analytics

G LO B A L C O N V E N E R O F D I G I TA L H E A LT H S O L U T I O N S , T E C H N O LO G I E S BUILD ON KEY ASSETS OF HIMSS M AT U R I T Y TO O L S A N D G LO B A L CONNECTIVITY D I S S E M I N AT E A N D S C A L E D I G I TA L TO O L S A N D S E R V I C E S TO A DVA N C E A N D AC C E L E R AT E D I G I TA L H E A LT H S Y S T E M T R A N S F O R M AT I O N G LO B A L LY 43

Population Health: Proactive, predictive care delivery informed by analytics (AMAM)(AI), traceability of outcomes by patient population segments (H-SIMM) Patient Engagement Portal (O-EMRAM; H-SIMM; CCMM)

Maturity Tool Concept Map - Maturity models assembled into a connected framework

- Mobile apps to engage in lifestyle behavior management; pt. reported tracking progress and outcomes and progress towards personal health goals; social network engagement - Journey of care (H-SIMM-AMAM); Outcomes- Case Costing = Value Transparency

Data Visualization >>Evidence in Real Time – progress towards goals, risk analysis (AMAM;HSIMM), traceability

Virtual Care Connectivity for Specialist Expertise

Clinician Teams -partner with individual pts. to support goal attainment - track progress AMAM)

-enables and supports primary care, access to expert specialist care to patient virtually

INFRASTRUCTURE - ENABLER OF DATA MOBILIZATION - SECURITY, PRIVACY, DATA FLOW (INFRAM) -

Accessible, wireless enterprise mobile management, security, ID management, modular, scalable

KEY ATTRIBUTES OF THE DIGITAL HEALTH ECOSYSTEM

“ H E A LT H I T E N A B L E S T H E P O W E R O F T H E I N D I V I D UA L TO B E D E V E LO P E D A N D U N L E A S H E D T O A C T I V E LY M A N A G E T H E I R H E A LT H , PA R T N E R I N G W I T H THEIR CARE PROVIDER TEAMS, SOCIAL NETWORKS, ENABLED BY C O N N E C T I V I T Y A N D I N F O R M AT I O N T E C H N O L O G Y .”

OF F I C E OF T H E N AT I ON A L C OOR D I N ATOR ’S 2 0 2 0 V I S I O N F O R H E A LT H I N F O R M AT I O N T E C H N O L O G Y

Key Assumptions 1. Predictive analytics focuses on health and wellness outcomes for populations > proactively manage risks, personalized to the unique circumstances of the individual 2. Data mobilization creates real time analysis of outcomes across the journey of care, informs and drives system learning – what works best for who and under what conditions 3. Patient-centred healthcare: system is digitally enabled, accessible and supports dynamic engagement/activation with patients - power and voice in their personal health goals 4. Data tracked at the point of care across the journey of care: transparency across the system, real time data access to inform decisions, traceability of outcomes to the individual, analytics identify priority population segments, track outcomes to achieve system learning 5. Linked information infrastructure and liberation of data across the system is translated into knowledge is foundational to healthcare quality, sustainability and value, achieved by supporting integrated care delivery systems and data networks

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DIGITAL HEALTH ECOSYSTEM FRAMEWORK Interpret

Best outcomes for population segments

Predictive, Proactive

Continuous Feedback

Analyze

clinician teams patient reported outcomes

Outcomes, predict risk, cue pts. clinicians, proactive care & self management

Provider teams

Mobilize Data across data sources

Provider teams

Analytic Tools algorithms

Flow of Data in Real Time

Provider teams

Informed Decisions clinician, patient population system learning outcomes

LINKED DATA NETWORK ACROSS THE JOURNEY OF CARE; DATA REPOSITORY ACROSS THE JOURNEY OF CARE: SECURE, PRIVACY, VIRTUAL, Integrated Workforce: clinicians, data science, supply MOBILE chain, AI, analytics, population health

System learns and predicts outcomes, cues patients and clinicians to risk, enables proactive intervention and prevention

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Quality Outcomes of Digital Health Ecosystem Framework ( Quadruple Aim Criteria) • • •

• • •

Safe: avoiding harm to patients through automated adverse event reporting; tracking and traceability; proactive cues to clinicians of risk to patients (H-SIMM tool) Effective: providing evidence-based analytics in real time to clinician teams to ensure only care services and products that offer the best outcomes are offered to each individual patient, redundant or harmful services are eliminated Patient-centred: patient’s goals for health and wellness are at the centre of digital infrastructure framework whereby data and progress reports flow directly to patients who input feedback on progress, satisfaction, experience. Patient is acknowledged as a partner in care delivery with clinician teams across the journey of care. Thereby, care is respectful and responsive to individual patient preferences (e.g., mobile, virtual, in-person care), needs, values and care is not only patientvalue-centred but is patient-led and aligned with personalized goals Timely: wait times and harmful delays are reduced as the digital health system environment is automated and integrated to increase productivity and efficiency for team. Information is received in near real time to inform care in a more timely manner.

Patient feedback is automated in real time to track progress and satisfaction Efficient: clinically integrated supply chain process and data offers health systems waste reduction (e.g., 30% reduction in labour costs, 33% reduction in wasted products) to further strengthen efficiency (H-SIMM infrastructure) Equitable: providing care that standardized and informed by real-world evidence; aligned with unique circumstances of each person, geographic location and access to care. The digital health system framework enables and supports options for care delivery such as automated monitoring using sensors and wearables, virtual care visits online and telehealth tools to ensure every patient has access to the right care when and where it is needed

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Thank You! Dr. Anne Snowdon, Director of Clinical Research, HIMSS Analytics Professor, Strategy and Entrepreneurship Odette School of Business Anne.Snowdon@himssanalytics.org

Find us online at: www.scanhealth.ca OR www.worldhealthinnovationnetwork.com Follow us on Twitter: @SCAN_Health OR @WIN_Health Like us on Facebook: Supply Chain Advancement Network in Health OR World Health Innovation Network Follow us on LinkedIn: Supply Chain Advancement Network in Health OR World Health Innovation Network Subscribe to us on YouTube: WIN Health

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