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    OS02 - Emerging Technologies - Block Chain / Genomics (ID 3)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 4
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      OS02.01 - Blockchain in Healthcare - Separating the Hype from Reality (ID 20)

      G. Lanteigne, Tectonic Advisory Services Inc.; Burlington/CA

      • Abstract

      Purpose/Objectives: Blockchain technology has shown its considerable adaptability in recent years as a variety of market sectors sought ways of incorporating its abilities into their operations. While so far most of the focus has been on the financial services industry, several projects in other service related areas such as healthcare show this is beginning to change. Numerous starting points for Blockchain technology in the healthcare industry are the focus of this presentation. With examples for public healthcare management, user-oriented medical research and drug counterfeiting in the pharmaceutical sector, this report aims to illustrate possible influences, goals and potentials connected to this disruptive technology.

      Methodology/Approach: 1. Primary Research - Interviews from 50 organizations both public and private sector across Canada. 2. Secondary Research - Material from across Canada and internationally. 3. Results from Think Tanks and Focus Groups

      Finding/Results: The five blockchain-based healthcare realities for healthcare 1. Clinical Health Data Exchange and Interoperability 2. Claims Adjudication and Billing Management 3. Pharma Clinical Trials and Population Health Research 4. Cyber Security and Healthcare IoT 5. Drug Supply Chain Integrity

      Conclusion/Implications/Recommendations: Blockchain technology creates unique opportunities to reduce complexity, enable trustless collaboration, and create secure and immutable information. Healthcare needs to be a part of this rapidly evolving field to identify trends and leverage areas where government and/or Industry support is needed for the technology to realize its full potential in health care. To shape blockchain’s future, the industry in Canada should consider mapping and convening the blockchain ecosystem with the key stakeholders, establishing a blockchain framework to coordinate early-adopters, and supporting a consortium for dialogue and discovery in Canada.

      140 Character Summary: WHERE DOES BLOCKCHAIN FIT INTO HEALTHCARE AND VICE VERSA

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      OS02.02 - Patient Control: How Blockchain Can Transform Health Care and Society (ID 536)

      D. Wiljer, University Health Network; Toronto/CA
      S. Brudnicki, UHN Digital, University Health Network; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: Health data is one of our most valuable assets that helps determine diagnosis and course of treatment, and expedites medical discovery. However, information silos, duplication of data, inaccurate data, and lack of interoperability remain a barrier to obtaining complete and accurate information. In addition, the growing trend of decentralized data to include personal health data as part of evolving consumer technologies contributes to the complexity of multiple and accurate data sources. Imagine a world where patients are able to take ownership and control of their own medical and personal health data; and decide what, where, and with whom they share their own health information. This session will examine blockchain technology and how it can transform health care and society, as patients gain control to improve their own health and wellness.

      Methodology/Approach: Blockchain is described as “the second era of the internet” and has potential to transform health care and society as we know it. Our organization is in the early stages of examining the potential of blockchain in health care, re-inventing patient/citizen empowerment and engagement through patient control of data. Patient consent and control of primary and secondary use of data can help increase collaboration among public and private sector, and advance medical breakthroughs in a joint effort to benefit all participants, including patients, citizens, and care providers.

      Finding/Results: Strategy considerations and planning range from examining and addressing identity management and authentication, trust, data governance, and legal and security implications to managing large-scale culture change as part of the transition to patient/citizen ownership and control of their own data. Considerations can be profound to impact not only each organization, but the nation. Real-life examples of blockchain in other industries will also be illustrated.

      Conclusion/Implications/Recommendations: A collaborative public-private partnership between health care organizations, patients, government, and strategic partners can help advance strategic planning efforts to transform health care and society through the use of blockchain technology. By connecting the health ecosystem to a universal data infrastructure, blockchain becomes a connector to machine learning, predictive analytics and population health management. It can strengthen opportunities for collaboration between patients, providers and organizations; advance health ecosystem partnerships; accelerate innovation in medical research; and transition toward new standards for future large?scale implementation.

      140 Character Summary: This session will examine blockchain technology and how it can transform health care and society, as patients gain control to improve their own health and wellness.

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      OS02.03 - Genomics Data and Cancer: Risk Prevention to Diagnosis and Treatment (ID 42)

      B. May, CGI; Victoria/CA

      • Abstract
      • Slides

      Purpose/Objectives: The purpose of this presentation is to provide an overview of the current state of genomics data use and to highlight the associated successes and limitations. The applications addressed will encompass the pursuit to provide effective and personalized methods of cancer prevention, diagnosis, and treatment. Furthermore, technical considerations that would allow improved data sharing and analysis will be presented.

      Methodology/Approach: Current literature and clinical standards have been assessed to illustrate initiatives taken to catalogue the gene-level data pertaining to: the DNA-related factors correlated with increased rates of cancer development, the genetic fingerprints of observed cancer types, and the expected efficacy of different chemotherapy regimens. Furthermore, a comparative analysis on the efficacy of these initiatives when implemented are compared with standard treatment protocols. Lastly, the barriers inhibiting the large-scale implementation of individualized genomic data in personalized medicine will be addressed.

      Finding/Results: Personalized medicine has become an appealing facet of cancer management strategies for its perceived suitability in handling a disease with a unique genetic fingerprint between individuals. However, its implementation has been piecewise and in need of improved coordination because of the vast array of technological factors and related clinical directions. Genetic markers that indicate an individual’s predisposition to cancer have been identified in many cell types but large-scale intervention by means of changes to health care standards have been slow to act on such data. Major barriers to this include the cost of genome sequencing and the immense amount of information that needs to be maintained and analyzed. In spite of this, the long-term relative savings in health care could be significant. Many oncologists currently use xenografts to gather patient-specific genomic data of cancerous cells; however, this does not always involve full genome sequencing. Although this provides a more personalized approach in designing treatment protocols, it fails to encapsulate fully many important factors at the genomic level. Full genome sequencing could provide more detailed evidence regarding prognosis and which drugs would be most effective for individual patients. Lastly, given the sheer number of different genetic presentations of cancer, a large collaborative network of researchers and clinicians would be required to provide an information library that adequately spans the disease’s genetic manifestations. Reliable data storage, access, and analysis at this scale presents concerns over the near-future feasibility of personalized genomic medicine.

      Conclusion/Implications/Recommendations: While the benefits of personalized medicine have been addressed in many healthcare contexts, full-scale data at the genomic level has yet to be included from institutions outside of academic contexts. Technological concerns regarding data access and analysis across practitioners and researchers highlight the need to develop specialized data sharing methods and techniques.

      140 Character Summary: There is a wealth of knowledge related to cancer available at the genomic level but means of data sharing and analysis are needed to allow the benefits to be realized.

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      OS02.04 - Multi-Omic Analysis Revolutionizing Personalized Health (ID 35)

      R. Fraser, Molecular You Corp; Vancouver/CA

      • Abstract
      • Slides

      Purpose/Objectives: Healthcare is undergoing a revolution as omics analyses have become increasingly affordable and accessible to evaluate risk of disease, diagnose disease, match treatment to disease and to enable preventive medicine. These analyses include genomics to detect genetic diseases and ascertain disease risk, and proteomic, metabolomic and exposomic to determine the environmental impact in disease development and to monitor response to treatment. Further, microbiomic analysis can give insight into the origins of immune disorders and other diseases. What is required is technology that brings together a full suite of omic tests, bioinformatics and systems biology expertise and big data analytics to deliver personalized health insights and action plans in an all-in-one online platform.

      Methodology/Approach: With the use of big data analytics and artificial intelligence tools, individual data clouds of genomic, proteomic, metabolomic, microbiomic, exposomic and phenomic profiles are generated that can precisely characterize the presence and potential of disease, match treatments and monitor outcomes. The unique datasets of each individual can be formulated and referenced against curated literature to produce personalized health insights and custom action plans. Machine-learning tools can be utilized to suggest a customized action plan for individuals, assembled together with their health practitioners, that target actionable results from the molecular analysis. To provide valuable longitudinal data, tracking tools can be used to track an individual’s performance over time, capturing valuable live data insights and motivating individuals to re-test to evaluate their progress. With the live reporting of actions connected to molecular measures in the action plan, the machine-learning data centre will capture behaviors and the effects of treatment interventions.

      Finding/Results: A pharmacogenomics study indicated that 97% of the population studied has a variant in their genetic code that would change the dose or medication for the most common health conditions. The value of multi-omic analyses has been demonstrated in a 108 person study applied to “healthy” individuals. The results indicated that every person in the study had an actionable possibility for better health. The power of integrating multi-omic analyses is evident when looking at complex diseases where unique bio-signatures emerge and the co-morbidities are presented even as they are emerging. Generally our health status determinants are 30% genetics, 60% behaviour and environmental and 10% health care.

      Conclusion/Implications/Recommendations: Any single omic analysis is limited in its scope, making it necessary for an integration of a maximal number of omic analyses to provide information concerning diseases you have or are trending towards. Multi-omic analysis supports personalized healthcare and allows individuals to understand their body’s unique characteristics, its sensitivities and how it best responds. With this foundation of knowledge, an individual can make informed health decisions. Data analytics with artificial intelligence systems will provide novel insights not previously imagined. Benefits could include identification of novel biomarkers associated with disease and the precursors of disease. The use of multi-omic analyses will shift our current delivery of the reactive ‘sick-care’ model to a truly preventive, personalized “health care” model.

      140 Character Summary: Implement advances in multi-omic methods, computational analysis, data visualization and design for personalized health solutions.

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    OS08 - Patients as True Partners in Care (ID 12)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Executive
    • Presentations: 4
      • Abstract
      • Slides

      Purpose/Objectives: *Objectives*: The province’s objective is to use technology to provide anonymous access to naloxone and to improve prescribing/monitoring practices by establishing morphine-to-opioid equivalency.

      Methodology/Approach: *Approaches*: The provincial strategy calls for widespread access to naloxone to prevent overdoses. Free naloxone kits will be available through pharmacies and to support this distribution an electronic process is being implemented to submit claims to reimburse pharmacies. The electronic billing process requires dispenses of naloxone kits to be recorded in pharmacy software and submitted to the DIS, like other device dispenses. The challenge is that naloxone kits cannot be associated with an identifiable patient; to address this requirement, a patient profile only for free naloxone kit dispenses has been setup in the Client Registry. To ensure this profile is used exclusively for naloxone dispenses, a new business rule that will be sufficiently flexible to account for future changes will be implemented in the DIS to restrict dispenses on that patient profile to naloxone products only. Morphine equivalency addresses the lack of a standard potency measurement across all opioids making it difficult to identify high risk opioid doses leading to overdose. A tool that allows for comparison of opioid doses has been developed to equate the different opioids into one standard value based on morphine, referred to as morphine milligram equivalents (MME) or morphine equivalent doses (MED). MME is a value assigned to opioids representing their relative strength in terms of morphine. MME is determined by using an equivalency factor to calculate a morphine dose equivalent to the ordered opioid. Daily MED is the sum of the MME of all opioids a patient is likely to take within 24 hours; that total is used to determine if the patient is nearing a dangerous threshold. The province is working with its DIS vendor to determine if its product could support calculating and displaying daily MED for prescriptions and dispenses. This equivalency would provide clinicians with Morphine Equivalent information for opioids so they can make educated decisions on the appropriate therapy of opioid drugs for patients. The implementation of Morphine Equivalence will be a multi-phased approach based on the timing and availability of reliable ME data from FDB and determining messaging requirements. The implementation’s first phase will address two capabilities with respect to Morphine Equivalence (ME): capturing and storing ME using a simple file structure and communicating ME information for a specific drug to clinicians accessing the DIS. A second phase will include support for ME data once it is available from FDB and the third phase will build on the available FDB data and provide maintenance of ME thresholds, business rule development, and reporting on patient opioid use.

      Finding/Results: *Results*: At the time of writing, both these projects were in the initiation stage; however, it is expected that by May 2018, they will be sufficiently advanced to report on successes and obstacles.

      Conclusion/Implications/Recommendations: *Recommendations*: The province recommends seeking innovative ways of utilizing technology to further serve the public and, especially as in this case, to save lives.

      140 Character Summary: A province’s use of digital methods – enabling naloxone access and establishing morphine equivalency – to address opioid misuse.

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      OS08.02 - The Critical Yet "Not So Glamourous" Trusted Citizen Identity (ID 580)

      A. Carter-Langford, Canada Health Infoway; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: Digital health solutions have the ability to improve the health of Canadians, transform the quality of care and reduce health system costs. This can be realized in large part by empowering Canadians to play a more active role in managing their own health. Canadians want to access their personal health information (PHI) and they expect more digital health services. While the private and public sectors are keen to help make this a reality, there are foundational enablers that are necessary to make this improved digital health services a possibility for all.

      Methodology/Approach: A foundational enabler to achieving citizen access to PHI is deploying identity and access management solutions. The availability and use of common trusted digital identities and credentials to enable access to PHI and digital health services and solutions is lacking. By contrast, credentials issued by certain financial institutions are being used today by the Federal government to enable some of its online services. This panel will consist of Government, not-for-profit organizations, and industry stakeholders who will share their perspectives and insights on the opportunity for private/public co-operation in the development and use of trusted digital identities. Panelists will engage in a provocative dialogue on the barriers to use federated, trusted digital identities for healthcare applications and share key learning for initiatives undertaken to date. An outcome of this panel discussion will be a greater shared understanding of the roles and contributions from these groups of stakeholders to achieve the future Canadian expect and deserve.

      Finding/Results: Trusted digital identities for the purpose of PHI access and its enabling of digital health services is complex because of the interdependence of stakeholders, lack of robust governance models, financial incentives, limited deployment and lack of demonstrable success in Canada. These complexities make this a “not so glamorous” undertaking that many groups simply do not want to “dabble” in. However, there is now strong growing awareness and consensus that trusted digital identities are critical to harness the full potential of PHI access and to enable digital health solutions. The time is now to develop trusted digital identities in healthcare.

      Conclusion/Implications/Recommendations: The trusted digital identity in healthcare is: - Acknowledged as foundational yet lacking presenting a challenge for PHI access and development of digital health services - An opportunity for governments, industry and not-for-profit groups to collaborate to empower citizens to take control of their own health and benefit from online services - Key to enable innovations in digital health

      140 Character Summary: Trusted digital identity; critical for citizen access to Personal Health Information and the gateway to improving digital health services.

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      OS08.03 - Mustimuhw Citizen Health Portal – National Expansion Project (ID 242)

      K. Mallory, Mallory Consulting Ltd.; Victoria/CA

      • Abstract
      • Slides

      Purpose/Objectives: The successful completion of an Infoway-supported consumer health demonstration project and strong benefits evaluation results have led to the expansion of the Mustimuhw Citizen Health Portal. Cowichan Tribes has engaged with First Nations in multiple regions across Canada to support the adoption and use of nationally consistent digital health tools. These tools, designed by First Nations for First Nations, support citizen access to health records and citizen integration into the holistic clinical and wellness workflows typically delivered in on-reserve health centres. Our presentation will describe how the successful Closing the Circle of Care Demonstration Project has inspired a targeted implementation and adoption model that is now being extended to others. The Mustimuhw Citizen Health Portal National Expansion Project is a significant opportunity for First Nations to advance their digital health capacity and enable their community members with electronic access to their health records. The National Expansion Project has the potential to support First Nations providers and citizens to benefit from consumer health technologies and take a leadership role in enabling health information continuity across the circle of care.

      Methodology/Approach: The Citizen Health Portal uses an Infoway-Certified PHR platform that enables patients to submit and access personal health information. Leveraging the successful implementation processes developed during the demonstration project, the project team is now engaging with other First Nations across Canada to extend the use of the Citizen Health Portal digital health solution and support the establishment of a practical and sustainable national model. The combined use of the proven Mustimuhw community EMR (cEMR) and the Citizen Health Portal establishes a digital health presence in on-reserve health centres, enables patient access and creates an interoperability platform to support information sharing and workflow integration with local, regional and provincial healthcare partners.

      Finding/Results: To date, response and willingness to adopt both the cEMR and the Citizen Health Portal is strong – by both providers and citizens. However, and not surprisingly, engagement and change management activities require both creativity and effort, particularly in light of the capacity challenges many on-reserve health centres deal with. As adoption proceeds, refinements are constantly being applied to the implementation model based on our many lessons learned. These lessons will be of interest to others seeking to enable consumer health models for their target patient and provider groups.

      Conclusion/Implications/Recommendations: Early results indicate that a PHR solution is a viable and beneficial consumer health tool within First Nations communities. Use of a PHR can address longstanding issues and challenges that previously have impeded patient access to health services and provider access to important patient data – particular challenges in First Nations communities. Given that there are many similar health care requirements across First Nations communities in Canada, and many similarities in the challenges posed to effective information sharing between First Nations and their health care partners, a recommendation can be made to continue to extend the cEMR and the Citizen Health Portal to other First Nations within Canada wishing to adopt and use these tools.

      140 Character Summary: The Mustimuhw Citizen Health Portal provides an opportunity for First Nations to benefit from digital health and consumer health tools.

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      OS08.04 - Innovation Through Predictive Analytics in the Non-Profit Sector (ID 55)

      D. McIntosh, Sierra Systems Group Ltd; Calgary/CA

      • Abstract
      • Slides

      Purpose/Objectives: The Calgary Drop-In and Rehab Centre (The DI) serves the homeless population in Calgary and much more: provides a ‘voice’ for the marginalized, provides access to affordable housing, counselling services and employment transition. The DI is undertaking a transformation of its information technology ecosystem. Fingerprint scanning serves as the current check-in function to identify active clients. This process is time consuming and can result in significant queues, particularly in cold weather. The DI may feed 300 – 400 clients over a lunch hour. Currently, it may take up to 30 seconds to check in a client, resulting in long line ups, potential bypassed check-in for those clients known to The DI staff and duplication of client profiles. Further, if The DI staff is unaware of the client’s presence, they cannot be helped. The DI was seeking a more efficient means of check-in and confirmation of clients on site. Integration of facial recognition technology with CRM allows many advantages – elimination of human error prone manual check-in, instant notification of client check-in triggering delivery of scheduled services such as counsellor appointments, health clinic appointments, medication review or employment services follow-up. In short, leveraging technology to support The DI as a resource hub for homelessness and poverty. This presentation will demonstrate aspects of emotional-IT bringing true value to the clients of The DI.

      Methodology/Approach: Validation and / testing of the following: - - Development of test algorithms to enhance accuracy of recognition - Ambient lighting, skin colour, presence of hats/baseball caps or sunglasses - System recognition of previously captured images with profile merging in CRM - Camera position - Wall colour in check in area - CRM configuration and image integration (e.g., impact of facial hair) - Lighting / hue of background objects - Still images vs video

      Finding/Results: - Facial recognition accuracy > 95% - Decrease in manual entries at Security - Integration with CRM facilitating services, tasking and staff workflow - Real time notification of client presence - Increased efficiency and effectiveness of the The DI’s intake and check in processes - Enhanced case management capability for counsellors and health resources

      Conclusion/Implications/Recommendations: - Reduced human error and manpower requirements at security check in - Elimination of duplicate client profiles - Ambient lighting and wall/environment colours impact resolution - Iterative test algorithms are required (video preferred) Office 365 helped The DI realize a modern IT workspace, increased security and staff collaboration and decreased manual workload centre staff. The use of state of the art biometrics using Azure to perform facial recognition linked to a Dynamics 365 client database was fundamental to achieving these outcomes. This translates in to not only significant cost savings, but also greatly enhances the level of service received by their clients primarily through speeding up the process to render assistance to individuals often in extremely stressful situations.

      140 Character Summary: Integrating facial recognition software at a community drop-in centre key in expediting security check-in and streamlining client processes

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    OS14 - Leveraging Successes and Lessons Learned (ID 21)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 4
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      OS14.01 - Ontario’s Digital Health Immunization Repository (ID 526)

      K. Hay, Ministry of Health and Long Term Care; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: To enable the vision of a provincial immunization system where individuals, healthcare providers, and public health all have real-time access to the same immunization information, Ontario is taking an innovative, coordinated approach to facilitate easier collection of and access to complete, accurate, and timely immunization information. Our goal is to improve health outcomes by making comprehensive immunization information accessible in real-time to support healthcare providers in clinical practice and to engage the public as active partners in the management of their own health.

      Methodology/Approach: Employing HL7 FHIR® messaging and SNOMED-CT® terminology standards, the Ontario Ministry of Health & Long-Term Care has created a technology and policy infrastructure around the provincial Digital Health Immunization Repository (DHIR) that forms the foundation of a broader interoperable ecosystem for immunization data in Ontario.

      Finding/Results: Ontario’s Digital Health Immunization Repository (DHIR) is a clinical data repository containing more than 96 million immunization records for more than 6.5 million Ontarians. The DHIR is currently accessed by Ontario’s 36 public health units through a web-based tool (Panorama), in support of population and public health programs. The ministry has developed a number of integrated extensions to support public health units’ immunization activities. We are working towards enabling the public to update and view their immunization records in the provincial DHIR through a web-based service called Immunization Connect Ontario (ICON) or the Digital Yellow Card. The ministry is trialling a number of approaches to public authentication through ICON, starting with public health unit public identity verification and credentialing, moving to use of the Health Number, and piloting the use of banking credentials. Lessons learned will inform future approaches to public authentication for access to health data online. The DHIR’s corresponding secure web services will enable broader provincial Electronic Health Record (EHR) integration. The ministry is now provisioning for health care providers to submit and look up immunization records in the DHIR through a clinical version of ICON, and through direct DHIR integration with electronic medical records (EMRs) and regional clinical viewers. Expanded consumer access to immunization records beyond ICON will be accomplished through integration of the DHIR with consumer mobile applications and patient portals.

      Conclusion/Implications/Recommendations: The DHIR advances Ontario’s digital health goals and objectives, helping to ensure a population that is optimally protected against vaccine-preventable diseases.

      140 Character Summary: An HL7 FHIR® based technology ecosystem that supports clinician, public health, and consumer access to immunization information in Ontario.

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      OS14.02 - Measuring the Impact of Quorum: A Healthcare Quality Improvement E-Community (ID 480)

      R. Desjardins, Health Quality Ontario; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: In 2017, Health Quality Ontario launched Quorum, Ontario’s new open online health care quality improvement community. The mission of this community of practice is to collaborate, share experiences, and support innovation from idea inception through to meaningful improvement. To monitor progress towards these aims, we developed a performance measurement framework that incorporates four dimensions of knowledge generation with the concept of a community life cycle (Figure 1). a performance measurement framework for communities of practice.jpg

      Methodology/Approach: We selected performance measures across the four dimensions of knowledge generation (connection, content, collaboration, and value) and conducted an analysis to determine how to collect the data. To facilitate on-going monitoring, we used an enterprise intelligence tool (Microsoft Power BI) to create the Quorum Analytics Report (Figure 2).quorum analytics report.png

      Finding/Results: Since launching eight months ago, the Quorum community has grown to over 1,000 members. To assess collaboration, we monitor the number of questions, answers, and comments. To assess connection, we monitor total contributions, number of direct messages, and the number of friendships formed through the Friends feature. To assess content, we monitor the number of Lessons Learned, Projects and Posts. Finally, to assess value, we monitor weekly sessions, weekly new members, registration conversion rate, and active users. Results are summarized in Table 1. Summary of results from Quorum's measurement plan as of October 20, 2017 Dimension Measure Result Collaboration # of questions 24 Collaboration # of questions answered 21 Collaboration # of comments 108 Connection Aggregated content (Posts, Porjects, Lessons Learned, Questions, Answers, Comments, Feedback posts, and Group Messages 297 Connection # of messages 95 Connection # of friendships 801 Content # of lessons learned 7 Content # of projects 54 Content # of posts 49 Value Average weekly sessions 550 Value Average new members per week 43 Value Registration conversion rate 8.2% Value # of active users 187

      Conclusion/Implications/Recommendations: Communities of practice can support quality improvement by facilitating knowledge transfer and generation. To realize these benefits for Quorum, we implemented an ongoing performance measurement plan. This approach is being used to identify opportunities for improvement and to optimize knowledge-building capacity. For other planned communities of practice, opportunities exist to implement a more robust, holistic measurement plan from launch to help achieve their potential for sustained knowledge generation.

      140 Character Summary: We implemented performance measurement framework to monitor the impact of Quorum, an online community dedicated to health care quality improvement.

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      OS14.03 - Pandemic Preparedness in Canada: Who Has Been Vaccinated? (ID 363)

      V. Gupta, InfoClin Analytics; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: Physicians need to identify patients within their practice who have been vaccinated against influenza and those who have not been vaccinated in case of a potential influenza pandemic. In most EMR systems in Canada, a physician would initiate this search through a simple text search. The effectiveness of the search would depend on the skill of the physician and whether the EMR would allow the physician to easily separate patients who have had their vaccinations from those who have not. We have developed a Smart Algorithm that can rapidly and reliably identify records of true vaccinations.

      Methodology/Approach: Our Smart Algorithm identifies those patients who received a vaccination, those who declined a vaccination and those who were not vaccinated in the clinic but received their vaccination elsewhere. We compared our Smart Algorithm, to a search that might be done by a physician in a traditional EMR system using a simple text search of “flu”. In the vaccination record with the ability to use the ‘not given’ flag to identify those who did not receive the flu shot. The ‘Reason for Not Given’ is a text field and we assume that the doctors would not be able to query it for information about why it was not given (patient refused, patient is allergic, vaccination received elsewhere).

      Finding/Results: The Smart Algorithm accurately identifies a greater number of patient records which have either have or have not been administered vaccinations for influenza from a file with a total number of 20,878 vaccine records. A Simple Search misses as many as 1/3 of patients who did get an immunization and mis-identifies about 2.5% of patients as having had one when in fact they had not. Algorithm TOTAL + - Simple Search + 4,184 112 4,296 - 2,091 14,491 16,582 TOTAL 6,275 14,603 20,878 Sensitivity (Recall): 66.7% Specificity: 99.2% PPV (Precision): 97.4% NPV: 87.4% F-score: 79.1

      Conclusion/Implications/Recommendations: When the simple search in the EMR finds a patient that has been vaccinated, the chances are high that the patient was indeed vaccinated (PPV=97.4%). However, a simple search performed by a physician would miss approximately 33.3% of vaccinated patients (Sensitivity of 66.7%), requiring them to spend time reviewing each record manually or contacting patients directly to confirm the patient’s vaccination status. When compared to a simple search in the EMR, the Smart Algorithm performs better and allows for better time management for physicians and their staff.

      140 Character Summary: Pandemic Preparedness: We developed a Smart Algorithm that rapidly and reliably identifies records of vaccinations for influenza compared to a simple EMR search.

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      OS14.04 - Clinical Information System Integration: A Consolidated View of the Patient (ID 229)

      G. Atwal, Fraser Health; Surrey/CA

      • Abstract
      • Slides

      Purpose/Objectives: Fraser Health has launched a key initiative to link and empower clinicians - Unifying Clinical Information (UCI). Through the creation of an interoperable connectivity solution, sharing of the most relevant and up to date information is provided to clinicians.The vision is to establish a connected health environment that enables the integration of clinical and business workflows and exchange of information to support better health across Fraser Health.

      Methodology/Approach: The Unifying Clinical Information initiative provides a holistic integrated view of the patient across Fraser Health. This solution has been expanded to now include Provincial Lab, Provincial DI and information from the Vancouver Coastal/ Providence Health/PHSA (VPP) Careconnect solution. This supports optimized patient care and safety and a reduction in the provision of duplicate tests. Fraser Health has chosen AllScripts as its business partner to develop a solution that will achieve the goal of tying together information from various clinical systems. The solution provides an integrated and consolidated summary view of patient information across the Lower Mainland. The solution leverages the Provincial EMPI which provides a source for cross referencing person identity information within Fraser Health and with provincial sources.

      Finding/Results: The UCI project has been expanded in a phased manner by integrating Provincial Diagnostic Imaging, VPP Encounters and Clinical Documents. Clinical information available from other Health Authorities in Careconnect will also be made available in UCI.

      Conclusion/Implications/Recommendations: As a result of this key initiative, FH will have achieved the following objectives: • Deployment of an Integration Service Application that aggregates patient information from multiple FH core clinical systems and information from Lower Mainland and Provincial Repositories. This allows patient data to be viewed from a number of access points without changing core applications. • Providing Fraser Health with a foundation for the future development of wider electronic health information exchange, both internally and externally to FH. • Improve data integrity and quality of person information to facilitate linkage of electronic health records within Fraser Health and with Provincial eHealth.

      140 Character Summary: The Unifying Clinical Information solution integrates person-centered health information, across the continuum of care, in support of optimizing health care.

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    OS19 - Expanding EMR Use in Communities (ID 29)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Technical/Interoperability
    • Presentations: 4
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      OS19.01 - Increasing Access to Care with Patient Preferred Secure Communication (ID 312)

      S. Wilson, Neurology and EMG; Calgary/CA

      • Abstract
      • Slides

      Purpose/Objectives: Coordinating patient care using phone, fax, and mail requires a large amount of staff time and resources and fails to centre care around the patient. Clinicians, patients, and staff express frustration over conducting office visits for simple issues when that time could be saved for more critical cases. Crowfoot Village Family Practice (CVFP) and Brightsquid conducted a trial to augment clinic-to-patient communications using Secure Health Exchange, a compliant email service. The goal was to increase access to care while also creating clinic efficiency using secure asynchronous communication.

      Methodology/Approach: Secure Health Exchange was used by the clinic to: Follow up with treatment plan reminders or notify patients of changes Check in on the effectiveness of treatment Send patients test results Respond to questions as appropriate Deliver educational materials Secure Health Exchange was used by patients to: Ask follow-up questions Request non-urgent medical advice Consolidate your healthcare records Request prescription refills

      Finding/Results: Following the trial, patients revealed a preference for secure-email over phone communication and office visits in 3 out of 6 common scenarios for an aggregate first choice ranking. patient preference chart.jpg When describing their experience with Secure Health Exchange: 84% of patients said it was ‘convenient’; 83% said it was ‘easy’; 71% said it was ‘time saving’; 8% labeled the service ‘impersonal’; and 6% said it was ‘tedious’. (Survey respondents: 104, 50% 50-69 yrs) With 25% of patients registered on Secure Health Exchange, the number of phone calls handled by clinic staff dropped by 17%. Average call length reduced by 45 seconds, equivalent to saving 5 hours of phone time each week. Now patients that do need to call the clinic get through more easily because phone lines are less congested and often issues can be resolved with a single communication instead of trying to coordinate over voicemail. Staff are more focused and experience fewer interruptions. Physicians can manage 10 appropriate patient concerns remotely in the time it takes to conduct three office visits, opening 7 appointments for patients that require a clinic visit sooner. By receiving visit notes and treatment plans to patients through Secure Health Exchange patients are better informed, more compliant to treatment plans, and can be more active in their own care.

      Conclusion/Implications/Recommendations: The clinic has increased capacity to care. The inclusion of secure messaging in clinic-to-patient communications improves patient access while boosting attachment and satisfaction. Simple issues are handled via secure email, creating more clinic time for complex or urgent issues. Clinic staff and physicians are more productive because they can deliver all required information in a single secure message and work with fewer interruptions.

      140 Character Summary: Clinics using patient preferred secure electronic communication can increase access to care even for patients not using the service.

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      OS19.02 - Electronic Medical Records: Information Aggregators or Gateway Systems? (ID 39)

      M. Leduc, Product Strategy and Delivery, OntarioMD; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: Is an Electronic Medical Record (EMR) a machine for aggregating information? Is it a launchpad for accessing broader digital health solutions and national repositories? Is it both? Early EMRs were effectively electronic versions of paper charts – isolated silos of manually entered information. The advent of integrating systems such report manager solutions and laboratory information systems helped the EMR become less isolated and more interconnected with broader sources of information; however, the EMR continued to rely on its own database for supporting patient care. Now, integration efforts with systems such as eConsult and digital health immunization repositories are changing the EMR paradigm. No longer are EMRs standalone systems wherein community-based physicians work, they have become one piece of an interconnected web of independent databases, comprehensive decision-support logic, and tools for legislative compliance. This panel presentation will explore the effect of this evolving role of EMRs on: 1. Physician practices, including their ability to deliver patient care and meet legislative requirements; 2. EMR product vendors, that need to adapt to the changing needs and opportunities for physicians; and 3. The ability for the system to deliver high-quality patient care.

      Methodology/Approach: With a growing number of EMR users and interconnecting systems, it is increasingly difficult for digital health vendors and stakeholders, as well as their physician customers, to continue building tightly-integrated solutions in the EMR – forms and pages developed within the EMR that integrate with external solutions. Increasingly complex decision-support tools and greater quantities of stored data further stress EMR infrastructure as these systems are expected to deliver mass scope on minimal scale. This challenging trend is encouraging loosely-integrated solutions in the EMR – where the EMR passes user and patient context to a partner system, and presents that other system’s pages within the frame of the EMR. Loosely-integrated solutions reduce the burden on the EMR to adapt to ever-changing requirements while still delivering needed functionality to physicians.

      Finding/Results: Loosely-integrated EMR solutions are easier for the product vendors and the evolving system; however, they introduce new challenges for practising physicians: 1. EMR users are faced with an inconsistent and unfamiliar look and feel to their EMRs, as each loosely-integrated solution introduces system-specific layout and features; and 2. Information in support of clinical decision-making that used to be stored within the EMR exists across an array of digital health solutions, complicating a physician’s ability to reflect the best information available at a given time. On the other hand, tightly-integrated EMR models may lead to higher EMR prices and system costs as solution vendors accommodate multiple specifications and system-specific needs. Further, EMR users face the burden of more frequent upgrades and updates to deliver changing functionality to their systems.

      Conclusion/Implications/Recommendations: This panel presentation will bring together voices representing community-based physicians, digital health product vendors, and digital health system stakeholders to consider the best path forward for EMRs, the community-based physicians who use them, and the patients who benefit from their care.

      140 Character Summary: Listen to a panel of users and system stakeholders discuss the future of EMRs as either tightly-integrated systems or loosely integrated with external solutions.

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      OS19.03 - Standardizing Free Text in EMRs: Automating Data Coding in Primarycare (ID 361)

      V. Gupta, InfoClin Analytics; Toronto/CA

      • Abstract
      • Slides

      Purpose/Objectives: To automate the standardization of clinical data in electronic medical record systems (EMRs). Data collected in EMRs do not always accurately and consistently map to medical concepts that physicians need to identify individuals at high risk of specific health conditions and their complications. Asking physicians to enter structured data puts too great a burden on already busy clinicians. Addressing data quality problems through new advanced artificial intelligence and machine learning techniques holds the promise of saving doctors hundreds of hours of coding time while increasing data quality in their EMRs.

      Methodology/Approach: We developed several advanced text mining Smart Algorithms to identify patients with a range of chronic diseases that can lead to severe complications such as blindness, cancer, stroke, kidney failure, heart attacks and death. We compared our Smart algorithms (SA) to a simple search (SS) that a reasonable physician might conduct in their EMR for the same disease.

      Finding/Results: Disease: GERD Breast Ca Prostate Ca Lung Ca Schizophrenia Depression Diabetes Polycystic Ovarian Disease % Identified by SS 67% 63% 58% 68% 88% 81% 90% 46% % Missed by SS 33% 37% 42% 32% 12% 19% 10% 54% % False Positive by SS 3% 13% 13% 24% 7% 3% 5% 7% % Accuracy of SS 80% 73% 68% 72% 90% 88% 92% 62% % Identified = patients identified by SS compared to SA.Table. Comparison of Physician Search in EMR against a Smart Algorithm % Missed = percent patients with disease but not identified by SS. % False positive = percent of patients that SS detects who actually don’t have the disease. % Accuracy = how well SS compares to SA in terms of not making any errors in detection. Simple searches in EMRs can find as many as 90% of patients with a disease and as few as 46% (Table). Although the false positive rate is quite low (3-13% for the most part), it can be as high as 24% in some cases (Lung ca). The accuracy of SS in EMR can be as high as 92% (e.g., diabetes), but is usually lower than that and can get as low as 62%.

      Conclusion/Implications/Recommendations: Smart data cleaning approaches are required to overcome problems raised by heath data inconsistency and to help physicians accurately identify high risk and targeted groups. These data standardization algorithms are also important for more advanced uses such as predictive analytics, patient engagement, health system management reports and machine learning applications.

      140 Character Summary: New advanced Smart data cleaning Algorithms standardize data in EMRs more consistently than doctors.. Smart Algorithms help doctors provide better patient care.

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      OS19.04 - Optimizing your EMR: Tools and Processes for Understanding Patient Panels (ID 430)

      D. Fawcett, Community Practice and Quality, Doctors of BC; Vancouver/CA

      • Abstract
      • Slides

      Purpose/Objectives: Within the context of the patient medical home model of primary care, having accurate patient registries within EMRs was highlighted by the GPSC as an essential component for proactive and quality patient care. In order to facilitate this process, the Practice Support Program (PSP) partnered with BC EMR vendors to build EMR based tools to provide more intuitive reporting functions for understanding your patient panel. PSP regional support teams (RSTs) were trained and deployed to assist physician in optimizing their EMR functionality.

      Methodology/Approach: The initial tools were built with three of the major EMRs in BC (Telus MedAccess/Wolf, and Intrahealth Profile), allowing both RSTs and physicians to easily view panel counts, using either a simple dashboard or reporting interface. This reduced the need to run multiple reports within the EMR. The EMR reporting tools were designed to query standardized disease codes pulled from patient charts to identify prevalence of patients with active status, polypharmacy use, and diagnostic codes for 15 disease indicators. RSTs were trained to provide in-practice support to physicians and their teams in using the tools and improve patient coding methods. We hypothesized that active patient status and polypharmacy prevalence would decrease, and disease registry prevalence’s would increase. These changes would indicate improved patient data quality and allow for comprehensive problem summaries. Physicians were asked to choose 3-5 indicators to manage and provide their baseline and post clean-up scores to the provincial office, along with an evaluation to assess the functionality of the tools and skill set of the RSTs. Additionally, focus groups were conducted at the end of the initial pilot with RSTs and physicians to evaluate the success of the pilot phase, and to inform improvement opportunities. Questions were designed to assess motivators, barriers, and facilitators of action.

      Finding/Results: Over 400 general practitioners (GPs) have now enrolled to use the EMR based tools across BC. The average physician rated somewhat satisfied with the functionality of the EMR panel clean-up tools, and very satisfied with the coaching support. The findings were in line with our hypothesis; based on the subset of physicians who reported their scores, the average active patient status went down by 22% and each of the disease prevalence averages increased. Polypharmacy also showed an average decrease of 54% of patients on 5+ medications and 52% on 10+ medications. Focus groups revealed that participation was influenced by perceived benefits, normative beliefs, and perceived control. Perceived benefits included themes like preparing for retirement, normative beliefs included themes around peer support and community initiatives, and perceived control themes included EMR skill level, tool usability, and availability of resources. Specific barriers included lack of time, lack of strong RST relationships within some clinics, and technical problems with the tools.

      Conclusion/Implications/Recommendations: Overall, the initiative continues to be successful in meeting its objectives. The EMR tools facilitated the process of panel management, are a helpful for RST coordinators to engage the physicians in good data entry practices, and act as a stepping off point for other PSP support services.

      140 Character Summary: The Practice Support Program has helped over 400 General Practitioners to better understand their patient panels with innovative EMR tools, and coaching support.

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    OS27 - Disrupting Technology into the Next Decade (ID 43)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Technical/Interoperability
    • Presentations: 6
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      OS27.01 - Distributed Consent Management by Blockchain (ID 477)

      E. Brown, Computer Science, Memorial University; St. John's/CA

      • Abstract
      • Slides

      Purpose/Objectives: Different medical software applications are adopted proliferate for different purposes and organizations, including clinics, hospitals and pharmacies. The difficulty of enforcing consistent privacy and consent rules in multiple systems is one factor encouraging centralized data management. Centralized data repositories may then attempt to leverage technologies such as portals to mimic interoperability instead of actually supporting data exchange protocols. This paper presents an alternative approach which separates consent management from data records. Consent directives are maintained in a blockchain’s ledger, which is distributed and publicly accessible. As a consequence of blockchain technology, this consent ledger can be copied and re-distributed, is inherently consistent and reliable across different applications, is immutably secure with respect to consent history, and can be referenced by any records system, including paper records. Since the consent ledger is public, the resulting infrastructure allows any application or user to verify consent without requiring special authorization to access the ledger. Future designs, technology or records systems will also have access to the ledger without requireing re-engineering or revision to the blockchain. Additional implementation to enforce privacy and consent rules for different systems is not necessary since all systems can access the same distributed consent ledger. Access is unconstrained since the consent ledger is public and replicable. Since health record data is not stored with the consent ledger, public accessibility of the ledger does not increase the risk of privacy breach.

      Methodology/Approach: A demonstration implementation is provided using Solidity e-contract language under the Ethereum blockchain technology.

      Finding/Results: We illustrate a consent directive model which supports authorization, delegation and revocation of consent. It also supports configurable data specification within consent directives, so any data storage technology or data type can be referenced. The semantics for describing record data can be revised, without modifying the model, so changes in regulations or health policy can be reflected without requiring software revision.

      Conclusion/Implications/Recommendations: We argue that the flexibility of an open consent model and reference implementation for any record and data technology can encompass existing privacy enforcement mechanisms, such as role based access control, since such mechanisms can be mimicked by determining or redefining consent directive semantics. In addition, there is deceased risk of technology or regulation lock-in, as technology, legislation and social policy of today are less prone to become part of the the out-of-date and too-expensive-to-replace legacy systems of tomorrow.

      140 Character Summary: A blockchain health consent ledger increases flexibility in privacy protection, avoids technology lock-in and allows health information policy to evolve.

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      OS27.02 - Deep Learning Techniques to Improve Patient Care with Neural Networks (ID 572)

      D. Connors, Allscripts Analytics; Denver/US

      • Abstract
      • Slides

      Purpose/Objectives: Neural Networks are biologically inspired learning algorithms. Artificial networks have come a long way and are now considered some of the most powerful and robust learning algorithms deployed in numerous emerging software-based innovations. The goal behind neural networks (deep learning) is to construct a large hypothesis space of functions that contains a good approximation to the underlying function that represents the deterministic behavior of a process that generates the data. In the case of healthcare, the are definitive use cases of applying advanced predictive models using similar patient data to train a neural network. With improved care opportunities that are guided by predictive systems, comes the added potential of better care management including case management, disease mangagement, and high-risk case identification. An example of a neural network is shown below. deepnetwork.png The talk will demonstrate the results of applying deep learning training techniques to patient electronic health data.

      Methodology/Approach: TensorFlowTMis an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated be- tween them. The flexible architecture allows you to deploy computation to one or more computer systems with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains. In the context of this presentation, TensorFlow was applied to patient records that were formulated by the Johns Hopkins Adjusted Clinical Groups (ACG) model. The ACG system measures health status by grouping diagnoses into clinically cogent groups. The goal of the ACG system is to assign each individual a single, mutually exclusive ACG value, which is a relative measure of the individual's expected or actual consumption of health services. ACGs are closely related to many health trends.

      Finding/Results: nn_vs_acg.png Results show nearly 50% reduction of error in cost prediction.

      Conclusion/Implications/Recommendations: Results show promise of neural networks to reduce the mean cost prediction error of patients by over 50%.

      140 Character Summary: In healthcare, there are definitive use cases of applying advanced predictive models using similar patient data to train neural networks.

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      OS27.03 - The Power of Patient/Provider Messaging: From Human to AI (ID 232)

      A. Weiler, Wellpepper, Inc; Seattle/US

      • Abstract
      • Slides

      Purpose/Objectives: Patients love being able to message with their providers, and messaging between in-clinic visits can improve adherence and outcomes. However, many providers are concerned about the extra time that may be associated with patient messaging. This session explores best practices for managing remote patients and driving adherence based two clinical studies of interactive care plans, with people with Parkinson's disease and for seniors at risk of falls. In both studies, patients were accessed in person, and then assigned a personalized mobile care plan, that included the ability to message remotely with a care provider. Providers used this communication and also analysis of patient-reported outcomes to advance the care plans without in-person visits, and improve patient adherence remotely. We will also explore insights from machine learning classification applied to over 80,000 messages between patients and providers, and discuss how artificial intelligence when combined with caregiver interactions can be used to scale care. The session will debunk common myths about patient messaging, and show an opportunity that combines both technology and a human touch.

      Methodology/Approach: Study protocol for Falls Study: https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-017-0618-x Study Protocol for Parkinson's Study: screen shot 2017-10-19 at 7.19.31 pm.png Study goals -Provide safe and challenging exercise intervention via mobile -Enable patient and provider messaging -Decrease in-person patient visits without impacting outcomes Methodology for ML message classifer: -Trained on 80,000 patient provider messages and keywords that may indicate adverse event -Initial input, manual classification of messages into categories of "informational, questions, urgent"

      Finding/Results: Findings are still being collected for REACH study and will be presented at ACRM at end of October 2017. Initial indicators are positive. Patients in the Parkinson's study were 81% adherent to their care plan and reported 9/10 patient satisfaction with the program. Lower activated patients saw 2 times greater increase in activity with mobile intervention than those in control group. Analysis of 80,000 patient messages, 70% informational, 28% questions, and 2% urgents. Benefits of patient messaging can outweigh the perceived costs. Note that we expect to have further analysis of messaging, and the outcomes from the REACH study available to present by the conference in May 2018.

      Conclusion/Implications/Recommendations: This session explores both qualitative feedback from remote messaging, and compare messaging styles to the theoretical framework for behavior change, and identify how messaging and adherence to interactive care plans fulfill the targets of self-efficacy, outcome expectations, motivation, knowledge, and social persuasion. Digital interventions that connect patients with providers outside the clinic can have positive impact on patient outcomes, without increasing the costs of care. Patients can benefit from remote interactions with providers, and these can either replace other forms of communication.

      140 Character Summary: Patient and provider communication backed by machine learning can improve patient outcomes and satisfaction, without increasing costs of care.

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      OS27.04 - Iris Scanners as an Identification Tool for Individuals Experiencing Homelessness (ID 248)

      C. Forchuk, Parkwood Institute; London/CA

      • Abstract
      • Slides

      Purpose/Objectives: The rationale for this research is the disadvantage that is inherent to the frequent loss of ID cards and the subsequent limited access to non-emergency healthcare services. The overall objectives of this study were to assess the functionality of iris scanning technology in a community setting and to evaluate the acceptability of iris scanning for client identification among participants.

      Methodology/Approach: Participants were recruited from a Salvation Army homeless shelter who were either staying there for the month or accessing its services such as the food bank. As participants checked-in to the shelter, they were asked to have a scan of their iris which generated a unique identifying number for each participant. In addition, 50 participants were then asked to perform a second iris scan to verify that the technology could accurately identify them and match their identifying number with their previous scan. Participants answered a short questionnaire focusing on client acceptability and feasibility of the iris scanning identification system. Iris scanning was performed with iris recognition equipment called Seek Avenger, developed by Crossmatch Technologies. Quantitative and qualitative analyses were conducted to generate descriptive statistics and to determine a thematic grouping of responses, respectively.

      Finding/Results: The research team recruited 200 participants over the course of three visits. A total of 191 participants agreed to an iris scan. Qualitative findings revealed three themes for agreeing to a scan; safe and fast identification, incentive for participation ($2 coffee card), and to help a good cause. Of the 167 participants who answered the question on identification preference, 146 (87%) stated they preferred an iris scan over a health card. Reasons for this preference included simplicity, and losing ID cards/not required to carry an ID card. The 21 participants (13%) that preferred an ID card to iris scanning was either for fear of being tracked by government agencies, information falling into criminal hands for misuse, or the lack of system capacity to manage the data. The iris scan was successful in scanning the eye(s) of 182 participants and unsuccessful for six participants, resulting in a success rate of 97%. Furthermore, 50 participants agreed to a second iris scan of which 49 were accurately matched with their previous scan, resulting in an accuracy rate of 98%. The remaining 2% was due to the participant not being able to stay still long enough for the scanner to focus on the eye.

      Conclusion/Implications/Recommendations: Based on the current qualitative and descriptive statistics, iris scanning is a safe and reliable form of identification that can withstand fraudulent activity. If implemented, it can be cost-effective in this population given the frequency of ID card replacement, and the lack of access to non-emergency health care services, the latter of which leads to an increase in emergency department usage and therefore funding. This study recommends the establishment of a quality-assured iris recognition program for identification of individuals experiencing homelessness in order to gain access to services. Concerns and perceptions of surveillance, inabilities of the system, and safety of information must be addressed before implementation.

      140 Character Summary: Iris recognition was found to be an acceptable, reliable and feasible method of identification among individuals experiencing homelessness.

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      OS27.05 - Using Blockchain to Enable Informed Patient Consent for Research (ID 215)

      D. Mukherjee, Deloitte Inc; Toronto/CA
      B. McManus, PROOF Centre of Excellence; Vancouver/CA

      • Abstract
      • Slides

      Purpose/Objectives: The PROOF Center (Vancouver), St. Paul’s Hospital Vancouver, University of Nebraska Medical Centre and Deloitte are collaborating to reimagine how clinical and genomics data is shared in a secure and transparent manner, and setting up the foundations for the next evolution of research analytics. Currently, the process of enrolling a new patient in a research program is time intensive and paper based. The enrolled patient has little control over their own data and the process for providing their consent to share and receive their data. The objective is to prove that a blockchain-powered solution will enable the process to identify the right patient and enroll them in the research program. The solution will make it easy for the patient to own and provide consent to share their data, enabling easy access for researchers.

      Methodology/Approach: Developed a solution with consent workflows that are powered by blockchain. For the PoC we went with a web-based UI, but we envision building a mobile app in future. The process leveraged a hybrid agile approach that provides for an up-front framing and stakeholder alignment phase followed by iterating prototype sprints. The key stakeholder for this POC were: PROOF (Prevention of Organ Failure) Center, St. Paul’s Hospital Vancouver, Nebraska Medical Centre, and Deloitte.

      Finding/Results: Through this POC, we were able to collect patient consent and store it in a transparent, secure and a verifiable manner. Timestamps corresponding to consent statuses provided an audit trail for audit purposes. The key benefit for the health / research community is that blockchain technology can be leveraged for a more efficient, secure, and reliable process of accessing recorded patient consent before releasing the patient’s data. Blockchain technology also allows for secure audit trail of the shared patient data.

      Conclusion/Implications/Recommendations: We have seen in this proof-of-concept study that all consent-related data can leave an unfalsifiable and verifiable fingerprint on the Blockchain. This is important both on the stakeholder’s side, letting them prove the existence and the consistency of the data, and on the patient’s side, giving them more visibility, transparency, and hence control over their consent. Tracking the complex data flow with numerous diverse stakeholders, and documenting it in real-time through a timestamping workflow, is a key step towards proving data transparency and inviolability, and could improve clinical trial process. The application of Blockchain technologies in the context of clinical research is broad and promising. The decentralized nature of blockchain technology helps introduce communities to contemporary clinical research, thus allowing researchers to enroll patients using a more targeted approach.

      140 Character Summary: Blockchain based solution captures and tracks patient consent in secure and verifiable manner while enabling targeted enrolling of patients in research programs

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      OS27.06 - Improving Management of Long Stay Patients with Machine Learning Prediction (ID 41)

      P. Tan, Fraser Health Authority; Surrey/CA

      • Abstract
      • Slides

      Purpose/Objectives: A grand challenge that hospitals face is managing facilities and manpower under the uncertainty of how long an admitted patient will stay. This uncertainty can restrict efficient utilization of hospital resources by prohibiting effective scheduling and coordination of early discharge preparations. Long stay patients (?30 days) are particularly impacted, where prolonged hospitalization is frequently associated with significant social, economic, physical and psychological burden. At Fraser Health, the year-to-date average number of long stay patients is 17% higher than the fiscal year-end target. Early identification of long stay patients and effective care planning to reduce long stay can lead to better health outcomes, greater patient and provider satisfaction, reduced risk of adverse incidents and complications, and contribute to overall healthcare system sustainability.

      Methodology/Approach: Health and Business Analytics (HBA) has designed a predictive model called long stay predictor (LSP) that can predict whether an admitted patient will be a long stay patient or a short stay patient with 75% accuracy. Through artificial intelligence trained on over 10,000 historical long stay cases, the model is capable of identifying new long stay patients within 48 hours of admission. Patients flagged by the predictor are notified through daily email, upon which an early escalation of discharge planning is triggered to identify discharge barriers and establish a monitoring process on the patient. Staff can follow-up on individual patients real-time through an in-house web tool. Figure 1. Model’s inputs & outputs Figure 2. Workflow

      Finding/Results: At present, the tool has been deployed across White Rock/South-Surrey, Delta, Burnaby and Chilliwack. A 5-month post-implementation analysis at Peace Arch Hospital reveals a 16% reduction in long stay patients, and 25% reduction in bed days. Operational lessons learned from this project, along with novel insights that reveal why some patients are long stay, will be shared at the conference.

      Conclusion/Implications/Recommendations: Fraser Health has created a tool to predict patients likely to become long stay, and have operationalized this information to help clinicians engage in early care and discharge planning, leading to a significant reduction in bed days. Next steps include expanding adoption across all Fraser Health sites and adapting model to predict readmissions.

      140 Character Summary: Predictive modelling provides timely alerts to improve care planning for long stay patients at Fraser Health, contributing to a 25% reduction in bed days.

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