Team DEFUSE - SHDC20 Full Solution

Intelligent Automation Inc. Response to SCAN Health Virtual Design Competition 2020

Cover Page Submission in response to: SCAN Health Design Competition 2020

Project Title: DEFUSE: Deep Learning Empowered Supply Chain Risk Management for Healthcare System

Submitted To:

Team:

Judging Panel for the 2020 SCAN Health Design Competition

Intelligent Automation, Inc. (IAI) 15400 Calhoun Drive, Suite 190, Rockville, MD 20855 TPOC: Yi Shi, Lead Research Scientist University of Tennessee, Knoxville (UTK) 304 Stokely Management Center, Knoxville, Tennessee 37996 TPOC: Dr. Randy V. Bradley, Associate Professor of Information Systems and Supply Chain Management Southeast Health (SEH) 1108 Ross Clark Circle, Dothan, AL 36301 TPOC: Jeremy Johnson, Director Supply Chain Management

Submission Date:

Nov. 9. 2020

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Contents Executive Summary ........................................................................................................................ 1 1

2

3

4

Problem Statement and Solution Overview ............................................................................ 3 1.1

Problem Statement .......................................................................................................... 3

1.2

Solution Overview .......................................................................................................... 5

DEFUSE System ..................................................................................................................... 8 2.1

DEFUSE System Components ....................................................................................... 8

2.2

DEFUSE Key Features ................................................................................................. 10

2.3

DEFUSE Data Inputs .................................................................................................... 11

2.3.1

Hospital Consumption Data ...................................................................................... 12

2.3.2

Device Approval Documents .................................................................................... 12

2.3.3

Supplier Marketing and Sales Data........................................................................... 12

2.3.4

Formal Registries ...................................................................................................... 12

2.3.5

Manufacturer Information ......................................................................................... 12

2.3.6

Outcomes and Surveillance....................................................................................... 13

2.3.7

Healthcare Benchmarks ............................................................................................ 13

2.3.8

Healthcare Categorization ......................................................................................... 13

2.4

Data Source List ............................................................................................................ 13

2.5

DEFUSE Data Accuracy............................................................................................... 16

2.5.1

Handling data element discrepancies across external data sources .......................... 16

2.5.2

Data Reconciliation ................................................................................................... 17

DEFUSE Application Workflows......................................................................................... 18 3.1

Primary Objective of the Implementation..................................................................... 18

3.2

Health System Resource Needs .................................................................................... 19

3.3

Current DEFUSE Workflows ....................................................................................... 20

3.4

Equivalent substitute of medical devices/supplies ........................................................ 21

3.5

Case Study .................................................................................................................... 21

DEFUSE ROI........................................................................................................................ 23 4.1

Use Case Impact for Eastern Health and SEH: ............................................................. 23

5

DEFUSE Team Members ..................................................................................................... 24

6

Eastern Health Vision & Closing Remarks .......................................................................... 25

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Executive Summary The COVID-19 pandemic highlighted the criticality for health systems to easily identify primary personal protection equipment (PPE) and equivalent substitute products. However, the consolidation of generic manufacturers and a move towards global supply chains raised great challenges to current Supply Chain Management (SCM) practices due to the information fragmentation and invisibility, and human-centric decision making, and no or less consideration of risks. Therefore, health systems are urgently seeking more advanced supply chain management solutions to improve existing SCM practice.

To address this critical need, the team, including Intelligent Automation, Inc. (IAI, solution provider), Southeast Health (SEH, health system partner), and the University of Tennessee (UTK, academic partner), propose the DEFUSE -- Deep Learning Empowered Supply Chain Risk Management for Healthcare System. DEFUSE targets at the fundamental issue of information invisibility and provides a scalable big data solution to exploit both internal (local hospital) and external public data sources (e.g. device approval documents, manufacture information, device registries, etc.) and provide a list of analysis to improve SCM process. Specifically, DEFUSE will provide the following capabilities: (1) Equivalent product identification: DEFUSE always checks external data sources to compare related products, and identifies comparable or equivalent products with confidence level.

(2) Product and supplier risk assessment: DEFUSE uses global data sources to identify and quantify potential risks with multiple risk factors to support smart inventory planning. It analyzes factors that contribute to risks, recommend alternative sources of supply that are 1

not as susceptible to the risk factors, or identifiable equivalent products to ensure they have a lower risk profile.

(3) Demand forecasting: DEFUSE generates accurate and actionable demands forecasts with advanced forecasting algorithms; (4) Smart inventory planning: DEFUSE monitors and matches the supply and predicated demands, generate alerts to potential shortage or excess situation and risks.

(5) Inventory optimization: Dynamically adjust stocking policies and strategies to deliver target service levels with low investment.

(6) Scalable ingestion, storage, and processing to accommodate big data: DEFUSE automates data collection and intelligent information aggregation using state-of-the-art techniques.

In short, DEFUSE offers supply chain intelligence, helps you better understand your supply chain situation, identify potential risks, and make better decisions. DEFUSE will leverage existing capability of supply chain risk prediction and mitigation developed in another industry and the team’s extensive experiences on healthcare SCM, big data processing, software development.

Supported by our health system partner--Southeast Health (SEH), DEFUSE will first apply on Southeast health data and be tested on SEH facility. Our target users include SCM professionals, clinicians (e.g. physicians, nurses, pharmacists), value analysis team, regulatory and compliance, quality and risk management. DEFUSE is designed on an open system and service-oriented architecture, leveraging the state-of-the-art technologies on big data ingestion, storage, and processing. Service-based architecture provides an easy integration capability with third-party systems. DEFUSE is technology agnostic; this promotes product data standardization and harmonization due to its ability to relay data with existing systems.

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1 Problem Statement and Solution Overview 1.1 Problem Statement The COVID-19 pandemic highlighted the criticality for health systems to easily identify primary personal protection equipment (PPE) and equivalent substitute products. There remains a critical need for some PPEs, vital testing supplies (including kits), chemicals, reagents, and the physical materials needed for collecting and transporting samples. Health systems, such as Eastern Health, OEMs, pharmaceutical companies, distributors, and other major stakeholders in healthcare supply chains are subject to many risks from unreliable sources and the excipients used to synthesize PPEs and medicines. The consolidation of generic manufacturers and a move towards global supply chains have decreased the number of facilities capable of manufacturing quality PPEs. Moreover, countries have complex political and contentious trade relationships, which could threaten healthcare supply chains in the future.

The vulnerabilities and often manual processes that health systems go through to identify equivalent products are a concern for the safety and security of patients globally. There are a number of areas in which health systems’ current supply chain management (SCM) practices are inadequate to promote agility and resiliency, including (1) Inefficient and fragmented SCM process: Similar to the supply chains in manufacturing and other industries, the healthcare supply chain system becomes so large and complex to understand and manage. It consists of multiple independent agents, such as insurance companies, hospitals, doctors, employers, and regulatory agencies, whose economic structures, and hence objectives, differ and in many cases conflict with each other.

However, due to vertical internal structures, supplies and supply data historically have been

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siloed and firewalled so that information important for efficient business operations is fragmented, which causes significant overspending of inventory within these various silos.

(2) Supply and demand mismatching: Again, due to the informant invisibility, both supply and demand for services are uncertain in different ways, making it very difficult to match supply to demand. This task is complicated because demand for services is determined by both available technology (i.e., available treatments) and financial considerations, such as whether or not certain treatments are covered by insurance. In addition, decisions made by one party often affect the options available to other parties, as well as the costs of these options.

(3) Heavily relying on human knowledge for decision making: There is primary reliance on human knowledge and rudimentary metrics for decision making, e.g. identifying equivalent products, product re-stocking, due to a lack of effective decision support tools and standardized processes.

(4) Decision making on incomplete data and less attention to vulnerability and risk (V&R): Existing methods do not support end-to-end visibility and primarily consider first tier suppliers, without addressing issues regarding second tier suppliers and beyond.

Existing SCM tools focus more on supplier selection and pay less attention to V&R prediction and SC disruption mitigation.

Error! Reference source not found. describes the improvement to be done in healthcare SCM, including better inventory planning, advanced forecasting for supply and demand equilibrium prediction, reducing supply costs, making risks visible, and incorporating data from multiple sources to support decisions. Fundamentally, the information visibility and transparency have to be enabled and ensured.

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CHALLENGES

Enable Information Visibility and Transparency

Inventory Planning and Management

Proactive Supply and Demand Equilibrium

SC Risk Visibility and Mitigation

Harnessing Multiple Sources of Truth

Reduce Supply Costs

Figure 1. The Top Challenges to be Addressed in Healthcare SCM

1.2 Solution Overview Therefore, health systems are urgently seeking more advanced supply chain management capabilities to improve existing SCM practice and usher in practices consistent with a NextGen supply chain. To address this critical need, the team, including Intelligent Automation, Inc. (IAI, solution provider), Southeast Health (SEH, health system partner), and the University of Tennessee, Knoxville (UTK, our academic partner), propose DEFUSE -- Deep Learning Empowered Supply Chain Risk Management for Healthcare System.

DEFUSE offers supply chain intelligence, helps you better understand your supply network, provides warnings of potential supply and supplier risks, and improves organizational decision making. DEFUSE leverages the existing capability of our supply chain risk prediction and mitigation solution developed as a proof of concept outside of healthcare, and the team’s extensive experiences on healthcare SCM, big data processing, software development. It is worth noting that, as evidence of engaging key stakeholders, we have tested a preliminary version of DEFUSE with 5

a large government entity and received positive feedback from a U.S. health system about the potential of DEFUSE to positively impact procurement activities and service delivery, improve supply chain efficiency, and offer actionable insights.

Error! Reference source not found. shows the DEFUSE strategies.

Figure 2. DEFUSE Strategies •

Enhance Information Visibility o DEFUSE exploits multiple external data sources, in addition to user provided local data, to collect richer and broader data for analysis. The global data (from external data sources) is used to find equivalent products and to improve the prediction on product availability. o DEFUSE provides a GUI (graphical user interface) to receive user input and to present analysis results. The results page will include product and supplier data along with analyzed risks associated each product and supplier.

•

Match Supply and Demand in Near Real-time

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o DEFUSE uses both external and internal (health system provided) data to understand supply and service demands, and to reduce uncertainty. o DEFUSE analyzes supply (inventory) and demand (consumption) data in real-time and dynamically alert the user to make necessary inventory management adjustments. •

Improve Decision Making. o DEFUSE enriches existing product data by adding additional contextual information o DEFUSE leverages smart algorithms, including deep learning algorithms (i.e., feedforward neural network, convolutional neural network, and recurrent neural network) for equivalency matching, risk prediction, context enrichment, and inventory optimization.

The DEFUSE system offers advanced actionable intelligence and manages supply chain risks timely and accurately compared to standard tools. It can identify alternative providers of primary PPE, as well as equivalent products (from the same and different suppliers), based on adherence to supply chain (not just healthcare) leading practices that help mitigate potential supply network risks as identified by our solution. It can effectively assess and predict the V&Rs of PPE supply through advanced analytics (such as machine learning algorithms) using both internal and external data sources.

As demonstrated by our health system partner, Southeast Health (SEH), DEFUSE is not burdensome to health system personnel. In fact, the nimbleness of its design allows it to make use of health system data in its native form while also taking advantage of existing distributor, clinical system, and operational system APIs to extract relevant data pertaining to PPE lists, products, 7

suppliers, product-based specifications for determining product equivalency, and processes for identifying potentially equivalent products. Given its ease of use, target users for DEFUSE include SCM professionals, clinicians (e.g. physicians, nurses, pharmacists), value analysis teams, regulatory and compliance and quality and risk management personnel. DEFUSE’s value proposition for Eastern Health and other health systems is tied to the easy access to information about recommended equivalent products and alternative sources of supply. Key aspects of that the value proposition include: 1) Reduced clinical and supply chain staff burden; 2) Elimination of manual approach to identifying equivalent products; 3) Improved visibility of supply chain risks and contributing factors; 4) Procurement activity cost reductions (i.e., improved labor efficiency); 5) Augmented clinical and supply chain personnel; and 6) High-fidelity and timely decisions.

2 DEFUSE System Although DEFUSE can be used for any type of supplies, for the purpose of this competition, we restrict the focus of DEFUSE to handle medical supplies (in particular PPE) shortage during the COVID-19 pandemic.

2.1 DEFUSE System Components DEFUSE provides a unified automated framework for inventory optimization, supply and demand analysis, risk assessment, prediction, and mitigation with the following key modules: (1). Data management module 8

a. Imports user data; b. Automatically collects raw data from multiple data sources; c. Cleans and pre-processes data to improve data quality; and d. Merges and processes raw data to ascertain ground truth to facilitate further analysis.

(2). Inventory optimization module a. Analyzes supply chain data using advanced methods and techniques for supply shortage predication and demand forecasting.

(3). Risk management module a. Identifies V&Rs based on health systems’ requirements of acceptable fragility and criticality; b. Discovers/predicts new V&Rs from data analysis; c. Recommends alternative sourcing approaches to mitigate V&Rs to support improved efficiency and assurance of supply; and d. Incorporates what-if analysis to help novice and experienced users understand the potential impact of each decision.

(4). Global and local search module a. Provides relevant information for particular products/devices from all data sources externally and internally for the product name entered. It can be a product/device from local inventory list, or general product/device not from local list.

DEFUSE is designed on an open system and service-oriented architecture, leveraging the state-ofthe-art technologies on big data ingestion, storage, and processing. Service-based architecture provides an easy integration capability with third-party systems. DEFUSE is technology agnostic; 9

this promotes product data standardization and harmonization due to its ability to relay data with existing systems regardless of the data standard used.

2.2 DEFUSE Key Features As depicted in Figure 3, DEFUSE provides the following key capabilities: (1). Demand forecasting: DEFUSE generates accurate and actionable demands forecasts with advanced forecasting algorithms.

(2). Smart inventory planning: DEFUSE monitors and matches the supply and predicated demands, generate alerts to potential shortage or excess situation and risks.

(3). Equivalent product identification: DEFUSE always check external data sources to compare related products and identify comparable or equivalent products with confidence level.

(4). Inventory optimization: Dynamically adjust stocking policies and strategies to deliver target service levels with low investment.

(5). Product and supplier risk assessment: DEFUSE uses global data sources to identify and quantify potential risks with multiple risk factors to support smart inventory planning. It determines the country of origin of PPE components, and final products used by health systems in Canada and the USA, analyze factors that contribute to SC V&R, recommend alternative sources of supply that are not as susceptible to the risk factors, or identifiable equivalent products to ensure they have a lower risk profile.

(6). Scalable ingestion, storage, and processing to accommodate big data: DEFUSE automates data collection, intelligent information aggregation using state-of-the-art big data techniques.

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Figure 3. Improve Inventory Through Enhanced Visibility

2.3 DEFUSE Data Inputs DEFUSE is a data-driven solution. It is heavily dependent on timely data. Figure 4 lists the nine categories of data DEFUSE will initially utilize to assist with equivalent product identification, matching confidence, and risk predictions. We classify data as global data and local data. Global data is publicly available and universal for any health system, whereas local data is related to a specific health system. Two examples of global data are the Global Unique Device Identification Database (GUDID) and Global Data Synchronization Network (GDSN). An example of local data is a health system’s consumption rate of a product or supply.

Local Hospital Data Approval Documents Authorized Medical Devices for COVID-19 Use Supplier Marketing and Sales Data Formal Registries Manufacturer Information Outcomes and Surveillance Healthcare Benchmarks Healthcare Categorizations

• Consumption rate, supply status • Details about specific products and the original sponsors.

• Details about products authorized for COVID-19 use • Device identifiers with varying additional attributes (i.e., description, unit of measure, other catalog numbers).

• Device identification, specifications, categories, licenses, etc.

• Location, financial, activities, operations, funding and investments • recalls, adverse events, results of clinical trials, post approval studies. Majorly for risk assessment • References for sales prices, active drugs, cost pricing benchmarks, etc.

• Standard for the classification and unambiguous description of products and services

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Figure 4. Data Inputs and Knowledge Extraction

2.3.1 Hospital Consumption Data Past hospital consumption data can be collected and provided as user input to DEFUSE. This is local data, including local product list, consumption rate, supply status. We can use this data to predict future consumption and then predict the number of available products.

2.3.2 Device Approval Documents There are device approval documents at FDA that list the approved medical devices and the product level. This data is global data and is publicly available. The product level for PPE describes different level of protection and the applicable scenarios. The products at the same level are considered as equivalent products.

2.3.3 Supplier Marketing and Sales Data This is mainly local data that the user may provide based on previous record. Suppliers may also provide their data at company websites, which can be considered as global data. This data can be used to analyze the cost of product by different suppliers

2.3.4 Formal Registries There are efforts to register medical devices and provide ID for registered devices. Product information will be available to public. Examples include GS1’s Global Data Synchronization Network (GDSN) and the FDA’s Global Unique Device Identification Database (GUDID.

2.3.5 Manufacturer Information The manufacturer activity is global data that can be obtained by tracking company websites, social network accounts, and other public records. We collect information on production increases or 12

challenges, product retirements, merger and acquisition activity, and product-line divestiture. This information is used to predict issues with product availability in future. We also collect additional information on location, financial, activities, operations, funding and investments, and key personnel changes to assess the risk of suppliers and the products they produce.

2.3.6 Outcomes and Surveillance The post-market survey (PMS) is used to track product quality. This global data is provided by Health Canada's Marketed Health Products Directorate (MHPD). MHPD collects and analyzes reports of adverse health product reactions through its network of regional reporting centers and disseminates new health product safety information, which DEFUSE will use for risk assessments.

2.3.7 Healthcare Benchmarks Provides references for sales prices, active drugs, cost pricing benchmarks, etc. which will be used for inventory optimization.

2.3.8 Healthcare Categorization Standard for the classification and unambiguous description of products and services to avoid duplicates, conflicts.

2.4 Data Source List Tables 1 and 2 contain a summary of the initial sources of data DEFUSE will use.

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Table 1: Product and Supplier Data Sources Data Source

Focal Data Elements

Local Data/Health System Data

•

Approval Documents

•

• •

• • • • Authorized Medical Devices for Uses Related to COVID19

• • • • • • •

Supplier Marketing and Sales Data

•

Formal Registries

• •

•

• • • •

Knowledge/Insight Extracted

by DEFUSE

Update Frequency

Health System PPE and Other Product Lists Item/Product Usage Profile Inventory Status

• • •

Biologics License Applications (BLA), FDA Medical Device 510 (k) & PDF Documentation, FDA Medical Device HDE Approvals & Documentation, FDA Medical Device Premarket Approval (PMA) Documentation, FDA Vaccine, Blood, and Biologics Establishments, FDA

Details about specific products and sponsors, including clinical evidence of reasonable assurance of safety and effectiveness.

Daily

Canadian List of Authorized Medical Devices Canadian List of Authorized Testing Devices Canadian List of Designated Medical Devices for Exceptional Importation and Sale Canadian List of Medical Devices for Expanded Use Canadian List of Products No Longer Authorized Under Interim Order NIOSH Certified Equipment List, CDC Personal Protective Equipment Emergency Use Authorizations, FDA

• • • • •

Manufacturer Name Product Name Product Model Number Product Description List Date

Daily

Manufacturer’s Publicly Posted Catalogs and Crosswalks Manufacturer Websites

Device identifiers with varying additional attributes (i.e., description, unit of measure, other catalog numbers).

Daily

Australian UDI Database Dictionary of Medicines and Devices, UK NHS Global Data Synchronization Network (GDSN), GS1 Global Unique Device Identification Database (GUDID), FDA Medical Device Active License Listing (MDALL), Health Canada National Drug Code Directory, FDA

Device identification, specifications, categories, licenses, etc.

Monthly/Daily per source update frequency

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Product Consumption Burn/Utilization Rate Supply Availability

• • •

Daily Hourly Hourly

Manufacturer Information

•

Disadvantaged Business Enterprise (DBE) and Minority Business Enterprise (MBE) Vendor List, Various State Website Sources.

EDGAR Company Filings, Securities and Exchange Commission (SEC).

Form 5500, U.S. Department of Labor Form 990, IRS Medical Device Registrations & Listings, FDA. Service-Disabled Veteran-Owned Small Business (SDVOSB) List, VA Veteran- Owned Small Business (VOSB) List, VA.

• • • • • •

Elements for supply chain risk profile, including location, financial, activities, operations, legal, funding, investments, M&A

Daily

Table 2: Healthcare Categorization, Outcomes, and Benchmark Data Sources Data Source

Healthcare Categorizations

Focal Data Elements

• • • • • • • • • • •

Outcomes and Surveillance

• • • • • • • • • • •

Knowledge/Insight Extracted by DEFUSE

Update Frequency

Approved Drug Products with Therapeutic Equivalence Evaluations, Orange Book, FDA Current Procedural Terminology (CPT), AMA Diagnosis Related Groups (DRG), CMS ECLASS GMDN GMDN Explorer Terms Healthcare Common Procedure Coding System (HCPCS), CMS ICD-10-Clinical Modification (ICD-10-CM), NCHS Medical Device Classification, FDA SNOMED © International 2019 United Nations Standard Products and Services Code® (UNSPSC®)

Standard for the classification and unambiguous description of products and services

Daily

Medical Device Recalls, FDA Medical Device Enforcement Reports, FDA Medical Device Adverse Events, FDA Drug & Therapeutic Biologic Products Adverse Event Reporting System, FDA Drug & Therapeutic Biologic Products Recall Enterprise System, FDA. Clinical Trials.gov Results Database, NIH Observational Medical Outcomes Partnership (OMOP) Common Data Model Post Approval Studies (PAS), FDA. Society for Vascular Surgery Vascular Quality Initiative (SVS VQI National Cardiovascular Data Registry (NCDR®), American College of Cardiology National Radiology Data Registry (NRDR®), American College of Cardiology

Recalls, adverse events, results of clinical trials, post approval studies.

Majorly for risk assessment

Daily

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Health Benchmarks

• • • • • • •

Affordable Care Act Federal Upper Limits, CMS Centers for Medicare and Medicaid Services (CMS) Medicare Part B Drug Average Sales Price, CMS Drug Products in the Medicaid Drug Rebate Program, CMS Medicare Provider Utilization and Payment Data for Inpatient Claims Medicare Provider Utilization and Payment Data for Outpatient Claims National Average Drug Acquisition Cost, CMS Open Payments, CMS

Average sales prices, Active drugs that have been reported by participating drug manufacturers under the Medicaid Drug Rebate program, surveys of retail community pharmacy prices, inpatient discharges for Medicare fee-forservice beneficiaries

Daily

2.5 DEFUSE Data Accuracy In this section, we will discuss how DEFUSE will ensure accuracy and integrity of data.

2.5.1 Handling data element discrepancies across external data sources It is possible that DEFUSE can collect multiple copies of the same data from multiple external data sources. For example, recall events information can be obtained from post-market survey.

However, multiple survey companies may provide different lists of events. The discrepancies across external data sources can be resolved by different approaches based on the particular type of data.

1)

If there is time information on when data was updated, we will use the most recent data.

2)

If some data are inconsistent, we will use the common data from multiple data sources (i.e., we accept one record only if all sources provide the same record) or from most sources (i.e., we accept one record if most sources agree).

3)

If all data elements are accurate, but the record is incomplete, we will perform a union across the accurate data sources to put forth and a single complete and accurate data record.

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2.5.2 Data Reconciliation In addition to handling discrepancy and inaccuracy, DEFUSE will also reconcile all the data. One important aspect is handling outdated data. DEFUSE will build prediction models based on deep learning to predict the current (or future) data from outdated data. For example, we can collect recall events for a supplier’s products to train a model and then use this model on a relatively new product of the same supplier with limited recall events, to predict potential recalls in future.

We will consider different deep learning model for different data. In general, we can use feedforward neural networks (FFN) to predict a result đ?‘Œ from input đ?‘‹. Figure 5 shows such a network, where data is received at the input layer and result is obtained at the output layer. There can be multiple hidden layers between the input and output layers. Each neuron in a layer calculates its value as a function of the neuron values in the previous layer. If we know that the problem is time related (e.g., the above example on predicting recalls), we can use a more efficient network structure, recurrent neural network (RNN) (see Figure 6) or its special case long short-term memory (LSTM). Comparing with FNN, RNN has some backwards arcs, i.e., some neuron updates its value based on neurons in future layers. RNN can achieve similar accuracy as FNN with a much smaller network. Thus, RNN has smaller memory consumption and less computational complexity than FNN for temporal data. If we know that the problem is space related (e.g., analyzing an image), we can use convolutional neural network (CNN), which has the same network structure as FNN but the function to update neuron value is a particular function called convolution. Again, CNN can also reduce memory consumption and computational complexity than FNN for spatial data. In DEFUSE, the suitable deep neural network is chosen based on the analyzed data to obtain accurate results efficiently.

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Figure 5. FFN structure.

Figure 6. RNN structure.

3 DEFUSE Application Workflows 3.1 Primary Objective of the Implementation We want to have a highly efficient implementation to manage and analyze data for health system, to predict supply chain risks, and to provide risk mitigation suggestions. Although each type of user (e.g., clinicians value analysis team, regulatory and compliance, quality and risk management, etc.) could have a different use case and varying levels of system comfort, DEFUSE’s GUI makes

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it simple for any user to navigate the system with ease. DEFUSE will have the following software components: 1. Frontend GUI. This graphical user interface is the main interface for users to interact with DEFUSE. All users will be required to authenticate (based on health system policies) to perform any operation provided by DEFUSE. DEFUSE will use the user details to determine the types of information the user can access.

2. Local database. It will maintain local and global data such that there is no need to ask users to provide data for every analysis or search global data from external databases. DEFUSE will also have data input capability such that users can load local data and or DEFUSE can retrieve data from external databases via APIs. 3. Analytics engine. DEFUSE will apply deep learning algorithms to analyze supply chain risks and to provide risk mitigation suggestions. The system will minimize the input required from users and generate results in graphs and tables such that users can easily find the most important information. DEFUSE can also generate reports on its data and results.

3.2 Health System Resource Needs We envision two deployment options for DEFUSE. Option 1, which requires the fewest health system resources, is the cloud deployment model. IAI will make DEFUSE available via a public cloud service provider (e.g., AWS) as a software as a service (SaaS) offering. Option 2 is a local deployment, in which the health system will run DEFUSE on its own IT infrastructure. As it pertains to data needs for identifying equivalent products there is no need for a health system to devote a whole FTE once the deployment is complete. Regardless of the deployment option chosen by the health system, it is possible to deploy DEFUSE within a 12-month window.

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3.3 Current DEFUSE Workflows The workflow and reports of DEFUSE is value-driving. It will simplify the current manually workflows and make automatic analysis with minimum requirement of user actions. Users do not need to create an Excel file to save data and to calculate numbers. Instead, DEFUSE will collect most of data from external databases without the need of manual input. DEFUSE will also analyze data for users to identify any potential risks and to find risk mitigation approaches (e.g., equivalent products). The managed data and analyzed results can be viewed in DEFUSE, as well as exported as Excel or PDF files.

The DEFUSE system has the following steps: 1. Data management. DEFUSE collects data from user input (local data), from multiple external data sources (global data). These data will be processed to remove discrepancy and inaccuracy, to improve data quality, and to reconcile raw data. The processed data will be saved in a local database in the JSON format to ascertain ground truth and to facilitate further analysis. This data can also be saved as Excel files so that a user may view it outside the DEFUSE system.

2. Risk analytics module analyzes supply chain data using advanced machine learning techniques, with a primary focus on deep learning, to predict future risks based on health systems’ requirements of acceptable fragility and criticality. We will analyze the expected annual cost of a product, the predict storage over time, the product risks such as recall, out of stock, and the supplier risks such as out of business, backorder, operation risk. One particular challenge we aim to handle is due to COVID-19, PPE may not be sufficient at many health systems. Thus, DEFUSE will analyze PPE and identify any risks.

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3. Inventory optimization module recommends various approaches to optimize supply chain performance. For example, DEFUSE can identify alternative suppliers or equivalent products such that the expected annual cost can be minimized.

4. Risk mitigation module recommends various approaches to mitigate risks to support improved efficiency and assurance of supply. For example, alternative sourcing can be exploited whenever the predicted storage may reach zero, in particular it is very likely for PPE due to COVID-19. DEFUSE will recommend equivalent products that can provide the same level of personal protection if PPE devices will not be sufficient.

3.4 Equivalent substitute of medical devices/supplies A user can use the system (via built-in algorithms) to generate lists for equivalent substitute of medical devices/supplies. DEFUSE will search products with the same quality (e.g., protection level for PPE) to ensure that substitute product can have the same quality (the same level of protection) using product certification and production standards. DEFUSE will also examine the new product and its supply chain to identify any risk potential associated with each substitute item.

User can review these risks and then take action to accept these substitute products or not.

3.5 Case Study Figure 7 depicts the use case where a user is looking for a list of appropriate face coverings. Using the product name, code or description DEFUSE returns a list of products, along with key pieces of information such product name, GUDID, catalog number, manufacturer name, FDA code, package DI, UNSPSC, category, sub-category, availability of substitute products, backorder or not, rejection or not, and risk level. Of note is the potential risk level for that product not being able to be supplied. However, DEFUSE doesn’t stop there. DEFUSE goes farther by showing the

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user the reason why that particular product is at a higher risk for not being supplied (see Figure 7).

Figure 8 shows analyzed results for a particular product 3M N95 Mask. The top left portion shows device information in the database, which is the information listed in the Table in previous figure.

The bottom left is device specifications. The top right portion shows substitute products for 3M N95 Mask. We can also click the compare button to compare specification of these products. The middle right portion shows the analyzed result on storage, where we can see whether this product will be sufficient in future. The bottom right portion shows risk analysis results for this product and its suppliers.

Figure 7. Searching result page.

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Figure 8. Analysis result page.

In addition to that, DEFUSE will allow the user to conduct what-if analysis (by changing certain sourcing decisions) to see what impact changes could potentially have on product availability.

4 DEFUSE ROI 4.1 Use Case Impact for Eastern Health and SEH: The current challenge faced by both Eastern Health and SEH is the inability to identify global certainty of product equivalency from non-visible to near real-time data on demand. Due to acknowledged reset or refocus (due to COVID-19), health systems must leverage advanced data tools that give them the ability and trust knowing that we can identify SC V&R with 100% true product equivalency. DEFUSE offers the ability to analyze data anytime versus the hinderance of the platform pushing data at designated times. Access for Quality/Nursing/Operational Effectiveness/Regulatory/Medical Staff Services/SCM prevents delays, produces labor savings 23

and drives consistency for all users. In turn, this value-driven data enhances the decision-making process at the clinician level without the worry of risks and flexibility of direction. This enables a clear picture of SCM not pushing the decision and being able to focus on the risk analytics scale for visibility.

Engagement and relationship building are crucial for tracking key milestones and organizational cohesiveness. Because DEFUSE is relatively easy to use many health system stakeholders can be purveyors of information that can help stimulate cross-departmental conversations for product rationalization and standardization as a result of the visibility DEFUSE provides.

5 DEFUSE Team Members DEFUSE team shown in Figure 9 will include Intelligent Automation Inc. (IAI), Prof. Randy Bradley from University of Tennessee, Knoxville, and Jeremy Johnson from Southeast Health.

Figure 9. DEFUSE team.

IAI has Headquartered in Rockville, MD with 195+ professional staff (>65% with advanced degrees including 45% with Ph.Ds.). IAI has diverse set of research and development efforts, including the recently completed MDA SBIR Phase II project “Deep Machine learning for risk Analysis and Prediction (D-MAP) in supply chains�.

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Randy Bradley is a Professor at University of Tennessee, Knoxville. He has worked many years on marketing and supply chain management, including IT in the supply chain, strategic value of enterprise architecture, healthcare IT, and IT governance.

Jeremy Johnson is the Director of Supply Chain Management Southeast Health (SEH) in Dothan, AL. He has extensive experience of supply chain management in health systems.

6 Eastern Health Vision & Closing Remarks As Eastern Health and other health systems across Canada and throughout the world continue to grapple with supply disruptions, increasing patient volumes and demands related to the COVID19 pandemic, a solution like DEFUSE addresses the concern and eases the burden of identifying equivalent products. Furthermore, it does something that the Eastern Health did not articulate, but is sorely needed by most, if not all health systems, provide clarity on the disruption risk levels for products and suppliers. This added capability offers tremendous insight that is valuable when identifying equivalent products and choosing the source of supply.

DEFUSE uses state of the art characteristics and techniques, based on leading industry and academic practices, to identify the most important components of risk and then put forth an easily and single solution that can be used by any particular professional, whether they’re a novice or extremely experienced or whether they're in supply chain or a practicing clinician. DEFUSE is transformative and the right solution for Eastern Health’s PPE and equivalent product woes, as well as the vehicle for other health systems to become pandemic resilient.

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