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Profisee - HIMSS workshop - Mar 2025 - final.pptx
Workshop presentation given at the HIMSS 2025 conference, featuring Martin Boyd from Profisee, Anna Taylor from Multicare, Brigitte Tebow from Azulity, and Camille Whicker from Microsoft.
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Profisee - HIMSS workshop - Mar 2025 - final.pptx
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- 2. 2 Any Why YouShould Care CMS-0057-F OVERVIEW
- 3. HIMSS25 | March3-6 | Las Vegas Brigitte Tebow VP Data Management Services, Azulity Brigitte Tebow is a seasoned healthcare technology executive with over 20 years of experience in data management and analytics. As the VP of Data Management Services at Azulity, she leads strategic initiatives to improve healthcare outcomes through innovative data solutions. Brigitte's expertise spans across data governance, interoperability, and advanced analytics, making her a trusted advisor in the healthcare technology sector. Her passion – Teaching Healthcare Master Data Services to improve patient care outcomes with the goal of making the lives of providers easier! 3
- 4. HIMSS25 | March3-6 | Las Vegas Master Data Management by design … CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Content of Provider MDM LLC, Not for copy or distribution 4
- 5. HIMSS25 | March3-6 | Las Vegas Compliance Countdown: Are You Ready for These Critical Dates? 5 CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange 2026 2027 1. Upgrade Patient Access API: • Add prior authorization data (requests, decisions, expirations) while maintaining HL7® FHIR® R4 standards and secure access. 2. Automate Payer-to-Payer Data Exchange: • Enable secure transfer of patient data between payers using HL7® FHIR® R4 and encrypted transmission. 3. Ensure API Compliance: • Test for HL7® FHIR® R4 compliance, validate security, and document implementation for CMS audits and stakeholders. 1. Initiate Patient Access API Metric Reporting: • Track API metrics (requests, errors, uptime) and establish secure data pipelines for CMS-compliant reporting. 2. Develop and Deploy Provider Access API: • Implement an HL7® FHIR® R4-based API for real-time data exchange with in-network providers, secured with OAuth 2.0. 3. Integrate Prior Authorization API: • Automate workflows with an HL7® FHIR® R4 API, enabling real-time status updates and seamless EHR integration. Immediate Action Required Prepare for Advanced Technical Updates
- 6. HIMSS25 | March3-6 | Las Vegas Interoperability Standards Ensuring your data follows a defined standard allows for the secure exchange of electronic health information, which can lead to better patient care, lower costs, and increased efficiency CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Ensure compliance with industry standards like HL7, FHIR, and ICD-10 to facilitate data exchange across systems. Resource Profile: USCDI CareTeam Profile The Payer Data Exchange (PDex) Implementation Guide (IG) Content of Provider MDM LLC, Not for copy or distribution 6
- 7. HIMSS25 | March3-6 | Las Vegas CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Impacted payers must implement an HL7® FHIR® Patient Access API to provide patients with access to their health data, including prior authorization information (excluding drugs). This API must be implemented by January 1, 2027. Impacted payers must implement a Payer-to-Payer API to facilitate data exchange between payers. This API must be implemented by January 1, 2027. Impacted payers must implement and maintain a Provider Access API to share patient data with in-network providers with whom the patient has a treatment relationship. This API must be implemented by January 1, 2027. Impacted payers must implement a Prior Authorization API to streamline the prior authorization process and reduce the burden on providers and patients. Payer-to-Payer API Prior Authorization API Patient Access API Provider Access API 1 2 3 4 The CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F) Content of Provider MDM LLC, Not for copy or distribution 7
- 8. HIMSS25 | March3-6 | Las Vegas CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Patient Identity This includes managing patient demographics, medical records, and prior authorization information. Provider Identity Accurate identification of healthcare providers is essential for coordination of care. This includes managing provider credentials, affiliations, and roles within healthcare organizations. Payer Identity Managing the identities of payers, such as insurance companies and government programs, is necessary for efficient data exchange and prior authorization processes. This includes managing payer- specific information and ensuring secure access to patient data. CMS requirements will necessitate MDM solutions that allow for custom models, multiple domains, and high volume record counts CMS 0057 – Where Identities Matters Most Content of Provider MDM LLC, Not for copy or distribution 8
- 9. HIMSS25 | March3-6 | Las Vegas CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange As WEDI stated, "lean into what you know and partner for support on what you don’t” o Complex and Inconsistent: FHIR IGs can vary widely, making it challenging to achieve seamless system integration. o Slow and Uneven Adoption: Healthcare organizations adopt FHIR IGs at different rates, which can create gaps in interoperability. o Customization Challenges: Many organizations tailor IGs to their needs, but these customizations can sometimes lead to compatibility issues. o Resource Considerations: Implementing FHIR IGs requires time, expertise, and financial investment, which can be more challenging for organizations with limited resources. Content of Provider MDM LLC, Not for copy or distribution 9 FHIR Roadblocks You will Have to Break Through
- 10. 10 Your Common DataPlatform MICROSOFT & PROFISEE
- 11. Safeguard your people, health data,and infrastructure Help protect and govern your health data across clouds, apps and devices Empower your healthcare workforce Unlock value from clinical and operational data Accelerate research, discovery, and development Stay focused with AI tools that enhance productivity and automate workflows Realize savings and drive better outcomes through unified data insights. Innovate with data and AI at scale to transform healthcare Enhance patient and member experiences Deliver personalized and connected experiences to improve care management Microsoft Cloud for Healthcare
- 12. Microsoft Cloud for Healthcare Global Partner Ecosystem Healthcare-relevant IP Clinical applications Modern work Business applications Digitaland app innovation Infrastructure Data and AI Security Our unique approach empowers healthcare customers and partners to innovate with AI responsibly at every layer of the cloud.
- 13. An end-to-end analyticsplatform that brings together the data and analytics tools that healthcare organizations need to go from the data lake to the business user All your data in one location Data Factory Synapse Power BI Data Activator Synapse Synapse Data Integration Data Engineering Data Warehouse Synapse Data Science Real Time Analytics Business Intelligence Observability Unified data foundation Onelake UNIFIED SaaS product experience Security and governance Compute and storage Business model Microsoft Fabric Healthcare data solutions Healthcare data models Healthcare clinical data pipelines Healthcare analytical solutions Healthcare data solutions
- 14. Healthcare Data Solutionsin Microsoft Fabric Healthcare Data Model – Harmonize healthcare data into a comprehensive model A data model that is both comprehensive and familiar. It can handle the many business domains found in healthcare, such as clinical, administrative, financial, and social. The healthcare data model supports analyzing FHIR clinical data leveraging traditional SQL based tooling, by providing FHIR data in a relational form. Clinical Records Data (Relational FHIR) Imaging Data (FHIR Imaging Study) Social Determinants of Health Data DAX Copilot Data Claims Data Public Preview GA Patient engagement
- 15. Some Data ProductsCore to the HDM Healthcare Data Model – Harmonize healthcare data into a comprehensive model A data model that is both comprehensive and familiar. It can handle the many business domains found in healthcare, such as clinical, administrative, financial, and social. The healthcare data model supports analyzing FHIR clinical data leveraging traditional SQL based tooling, by providing FHIR data in a relational form. Clinical Records Data (Relational FHIR) Imaging Data (FHIR Imaging Study) Social Determinants of Health Data DAX Copilot Data Claims Data Public Preview GA Patient engagement Patient 360 Provider 360 Reference Data Payer 360
- 16. 16 04_13_21 16 BUILDING THE HEALTHCAREDATA MODEL Patient 360 Provider 360 Payer 360 Reference Data Consumable Data Products Consolidated, certified, documented Trusted and ready to use Healthcare Data Model Microsoft Fabric Databricks Snowflake
- 17. 17 04_13_21 17 BUILDING THE HEALTHCAREDATA MODEL Patient 360 Provider 360 Payer 360 Reference Data Consumable Data Products Consolidated, certified, documented Trusted and ready to use Healthcare Data Model o ERPs o CRMs o Legacy Apps Data from many siloed sources – inconsistent and incomplete o Regional systems o Data Feeds o Etc. ? Many Data Sources Dynamics 365 Kyruus Health Symplr EPIC Systems Legacy App Cloud App Custom App Microsoft Fabric Databricks Snowflake
- 18. 18 04_13_21 18 BUILDING THE HEALTHCAREDATA MODEL o ERPs o CRMs o Legacy Apps Data from many siloed sources – inconsistent and incomplete o Regional systems o Data Feeds o Etc. ? Many Data Sources Dynamics 365 Kyruus Health Symplr EPIC Systems Legacy App Cloud App Custom App Patient 360 Provider 360 Payer 360 Reference Data Consumable Data Products Consolidated, certified, documented Trusted and ready to use Healthcare Data Model Microsoft Fabric Databricks Snowflake
- 19. 19 04_13_21 19 MODERN DATA ESTATE– MDM BEFORE ANALYTICS PLATFORM Microsoft Purview • Scan and classify sources • Define data standards Profisee MDM • Enforce data standards • Match/merge • Standardize • Validate • Remediate o ERPs o CRMs o Legacy Apps Data from many siloed sources – inconsistent and incomplete o Regional systems o Data Feeds o Etc. Many Data Sources Dynamics 365 Kyruus Health Symplr EPIC Systems Legacy App Cloud App Custom App Patient 360 Provider 360 Payer 360 Reference Data Consumable Data Products Consolidated, certified, documented Trusted and ready to use Healthcare Data Model Microsoft Fabric Databricks Snowflake
- 20. 20 04_13_21 20 BUILDING THE HEALTHCAREDATA MODEL Data Stewardship Master Data Match/ Merge Workflow Profisee MDM Microsoft Purview Data Catalog Master Data Assets and Processes Governance Standards and Policies Data Quality o ERPs o CRMs o Legacy Apps Data from many siloed sources – inconsistent and incomplete o Regional systems o Data Feeds o Etc. Many Data Sources Dynamics 365 Kyruus Health Symplr EPIC Systems Legacy App Cloud App Custom App Patient 360 Provider 360 Payer 360 Reference Data Consumable Data Products Consolidated, certified, documented Trusted and ready to use Healthcare Data Model Microsoft Fabric Databricks Snowflake
- 21. 21 04_13_21 21 BUILDING THE HEALTHCAREDATA MODEL Patient 360 Provider 360 Payer 360 Reference Data Consumable Data Products Consolidated, certified, documented Trusted and ready to use Healthcare Data Model Microsoft Fabric Databricks Snowflake Regulatory compliance • CMS-0057-F • Cures Act Trusted data to share • With other organizations • With your internal customers Analytic and Operational improvements • Prior authorization process • Patient engagement • Population health foundation • Claims analysis • Outcome analysis • Provider Directory • Provider scheduling • Asset utilization • …
- 22. 22 Benefits of aCommon Data Platform MULTICARE
- 23. Purchaser Value Criteria Rangeof Services: Adequacy to serve members/ beneficiaries Affordability Outcomes of Care Effectiveness of Care Proactive quality & equity of health Addressing Health Equity and Social Determinants of Health Utilization Management Digitally Interoperability Coordinated Care Team of Teams
- 24. Strategic Alignment Operational Engine Enterprise Business Case (Requirements) Drivers Federal Rulings & Compliance Economic Access to Technology Diminishing Reimbursement Rates Internaltipping points driving the transition from historical methods of interoperability to an advanced standardized solution Signals for Semantic Interoperability
- 25. Sources Workflows & Operations Internal(on Prem) Systems Acquisition & Aggregation Advanced Analytics & Business Intelligence Delimited file, xlsx, txt, json – all unique formats and layouts except for json payloads, these follow standards API, sFTP, secure email Payer Payer Payer Payer Payer Payer Payer Payer Payloads are picked up based on Payer portal: API, sFTP, Webportal, Automated ELT package, secure email. Internal File Share Internal Aggregation processes Aggregate and/or Raw file distribution Outbound Clinical Data Master Member Reference Mart Hospital at Home Health Information Exchange Population Health Engine Financial Data Warehouse External (off Prem) Systems Provider EMR CCDA Provider EMR CCDA Provider EMR CCDA Provider EMR CCDA Provider EMR CCDA Provider EMR CCDA Actuarial Database Customer Relationship Management System Master Provider Reference Mart Provider Provider Provider Provider Provider Network Performance Management CDC DOH Enterprise Data Warehouse Population Health Engine Community ADT Notifies Sources Off - Prem Digital Services ON - Prem Digital Services Interoperability Services (DMZ) Clinical Decision support Financial Data Warehouse Copy Internal Quality & Risk Reporting Payor Reporting Electronic Medical Record
- 26. • Let’s solvefor the problem together: Regence • Let’s try something new: reporting quality measures using open standard APIs • Let drive results:
- 27. Prior Authorization TimeStudy • December 2023 Time Study completed by MultiCare Pre-Service centralized Auth Team • Completed for all real-time authorization tools as well as payer portals (manual) • Grouped by specialty Bot Technology EMR Solution FHIR App Payer Portals Lower is better Types of Auths Average Minutes to Complete Prior Auth Went from 3-5 Auth per hour to 10-12 per hour
- 28. What we learned: Identityas a foundation of APIs • Grouping for CMS 0057 – Linking Plans • No Universal Plan Codes • No Universal Payor Codes • How FHIR APIs are presented (Semantics, our EHR utilizes all active coverages in the coverage API • Creating internal linking keys and identities between Payor and Provider
- 29. Decrease in Patient MatchingError Rates Burden reduction from processing matching errors 5-10 min Per Error 60% Decrease in Error Rate Measuring the Value – Risked Based Membership (ATR) 29 67.5% Time saved per FTE Efficiency gains to be redirected to other activities
- 30. HL7 Da VinciProject Community Roundtable – June 2024 30 Have a Voice - Join Us in the Work • HL7® Da Vinci FHIR Accelerator: • https://www.hl7.org/about/davinci/ • CMS Claims-Based FHIR APIs: BCDA, Blue Button 2.0, AB2D • CMS Office of Healthcare Experience and Interoperability: • https://www.cms.gov/priorities/key-initiatives/burden-reduction/interoperability • NCQA • Digital Implementors Community: https://www.ncqa.org/digital-quality- implementers-community/ • Bulk FHIR Quality Coalition: https://www.ncqa.org/bulk-fhir-api-quality-coalition/ 30
- 31. HL7 Da VinciProject Community Roundtable – June 2024 31 DA VINCI PROJECT: PROJECT CHALLENGE To ensure the success of the industry’s shift to Value Based Care Transform out of Controlled Chaos Develop rapid multi-stakeholder process to identify, exercise and implement initial use cases Collaboration Minimize the development and deployment of unique solutions. Promote industry wide standards and adoption Success Measures Use of FHIR® , implementation guides and pilot projects
- 32. HL7 Da VinciProject Community Roundtable – June 2024 32 USE CASE & IG READINESS Implementation Guide Dashboard: https://confluence.hl7.org/display/DVP/Da+Vinci+Implementation+Guide+Dashboard * Referenced in or supports Federal Regulation Aligned with expected Federal Regulation + Guide Paused and Core Functionality moved to PDex Dial denotes progress in current STU Phase Overall Maturity: Most Mature Active Growth Least Mature Coverage, Transparency & Burden Reduction Clinical Data Exchange Clinical Data Exchange (CDex)t Payer Data Exchange (PDex) * Notifications * Coverage Requirements Discovery (CRD) * Documentation Templates and Rules (DTR) * Prior-Authorization Support (PAS) * Formulary * Plan Net/Directory * Patient Cost Transparency (PCT) * Foundational Assets Member Attribution Listt Health Record Exchange (HRex) Payer Coverage Decision Exchange+ Quality & Risk Data Exchange for Quality Measures/Gaps In Care (DEQM/GIC)t Risk Adjustment (RA) Value Based Performance Reporting (VBPR) Postable Remittance
- 33.
- 34. HIMSS25 | March3-6 | Las Vegas Readily available via MDM • Accepting new patients • PCP and specialty classification types (as defined by ACOs, payors services) • Referral Frequency and Classifications • Attribution Analytics (Admitting, Attending, Consulting) • Ages of Patients Seen • Population Seen • Specific Privileges Performed (not purchased reference data for common scope-of-practice) • Degree Groupings • Specialty Groupings Data Elements not typically managed through a centralized channel: CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Content of Provider MDM LLC, Not for copy or distribution 34
- 35. HIMSS25 | March3-6 | Las Vegas Strategic Roadmap & Milestones 35 Milestones CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange 1 2 3 Reference Architecture and Data Flow Tactical Planning – 2025, 2026, 2027 Before the FHIR - Disaster Preparedness Planning
- 36. HIMSS25 | March3-6 | Las Vegas Before the FHIR 36 • Understand Implementation Guide(s) (IGs) Per Requirement • Define Governance Process to approve IG use • Code Sets vs. Value Sets vs. Extensions • When to Customize the IGs • Internally defined Reference Data • (Groupers, Hierarchies, Code Sets, Ontologies) • Additional Data Elements for local, regional, federal, etc. • Identify Expertise Required – Internally vs. Externally • Define areas of responsibility and critical milestones Milestone 1- Disaster Preparedness Planning CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange 1
- 37. HIMSS25 | March3-6 | Las Vegas Interoperability - Governance FHIR Insurance CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Provider Patient Member Coverage Organizations Facilities Locations Sites Departments Geospatial Models Taxonomies Hierarchies Ontologies Value Sets (Code Sets) Cross-walks Identifiers Historical Data Laboratory Pharma Recruitment Employment International Classification of Diseases Current Procedural Terminologies Diagnostic Related Groups Relative Value Unit Codes National Uniform Claims Codes Phone Types Email Types Communication Preference Types Degree Codes Specialty Codes Fee Schedule Patient Status Code Gender Codes Coverage Code Types Claim Transaction Status Postal Address Type Mapping Cost Share Type Mapping Employment Status Type Zip to County Content of Provider MDM LLC, Not for copy or distribution 37
- 38. HIMSS25 | March3-6 | Las Vegas Before the FHIR 38 • Understand Required Capabilities before tool selection process • Known existing Internal tools/capabilities (Application Inventory) • Collaborate on Infrastructure Needs within the Industry • Internal Peers • Networking Across Industry • Advisory Services • Understand the Data Flow – End-to-End Architecture Milestone 2- Reference Architecture and Data Flow CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange 2
- 39. HIMSS25 | March3-6 | Las Vegas Before the FHIR 39 • Identify Critical Domains and Development Order • Obtain backing/support from Clinical Stakeholders and Application/Source Owners – to clear roadblocks • Build the Bench • Program Resources • Project Resources • Define the Work breakdown Structure and Critical Dates Milestone 3- Tactical Planning CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange 3
- 40. HIMSS25 | March3-6 | Las Vegas Consistent Provider Data CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Degree Status Specialties (Privileges) ARNP Active with privileges Anesthesia A.R.N.P Member with privileges Anes ANP-R Hospital based EP2 AP-RN Office based Anesthesiology APRN Allied health independent AN Promotes consistency Creates common terms for use throughout the organization Allows subject matter experts to understand information more readily Data Sources Collect Consolidate Cleanse Distribute Users M D M P L A T F O R M Centralize and standardize provider information (NPI, credentials, specialties) across different systems to ensure uniform attribution. Content of Provider MDM LLC, Not for copy or distribution 40
- 41. HIMSS25 | March3-6 | Las Vegas • Master Data is used to provide a single point of reference for critical data utilized across the enterprise. • The key objective is to provide the organization an authoritative Source-of- Truth for a given information area (e.g. patient, provider, member, location, reference) • The basic process consists of ingesting data from multiple sources into an MDM engine which then matches and/or merges records and applies logic to provide consumers with data that is standardized into a single “master” or “golden” record that has the agreed upon best (highest trusted source information) and finally, providing it to consumers in the manner they prefer. CMS Interoperability and Prior Authorization Final Rule: Transforming Healthcare Data Exchange Client Use Case Master Data Management Data Flow Content of Provider MDM LLC, Not for copy or distribution 41
- 42. 42 04_13_21 42 RESOURCES LISTEN & SUBSCRIBE https://profisee.com/podcast Hearfrom Data Leaders CATCH THE REPLAY https://profisee.com/d ata-hero-summit CMS-0057 and Related QR Code linking to resources landing page
- 43. Lexmark Confidential 43 THANKS FORYOUR TIME!
Editor's Notes
- #12 Microsoft Cloud for Healthcare is our unique approach empowers healthcare customers and partners to innovate with AI responsibly at every layer of the cloud. From cloud infrastructure, to data and AI, security surrounds everything we do. With the Microsoft Cloud for Healthcare, we are helping our customers innovate responsibly at every layer of the cloud. Let’s dive in a little bit more to each layer of the MC4H. <Click>
- #13 Microsoft Fabric provides a one-stop-shop for data integration, data engineering, real-time analytics, data science, and business intelligence needs without compromising the privacy and security of your data. With this unified SaaS solution, you’ll have a single source of truth for all your data and analytics, enabling secure, democratized insights. By leveraging this powerful open and scalable solution, you’ll accelerate time to value through cost management, spend optimization––making the most of your data investment. Microsoft Fabric leverages the mature Azure Security stack and Azure Purview to discover, understand, govern, safeguard, and improve the risk and compliance posture of data. The solutions enable healthcare organizations to: Connect all the disperate healthcare data sources (electronic health records, images,…) regardless of where they are Harmonize the diverse data records with healthcare data models and transformation pipelines to create a multi modal warehouse. Leverage industry standards, such as FHIR (Fast Healthcare Interoperability Resources) and DICOM (Digital Imaging and Communications in Medicine) to remove complexity and cost of mapping, transforming and synchronizing data between systems. Accelerate time to insight and ease administrative burdens with out-of-the box healthcare specific solutions or develop your own custom advanced analytics Improve collaboration and data governance in the organization by providing real-time view of the data to the right users All in a platform that enables HIPAA (Health Insurance Portability and Accountability Act) compliance <click> <CLICK>
- #14 Talking points A key feature of the healthcare data solutions is our Healthcare Data Model This data model aims to encompass all the major healthcare data domains It is a composite model that leverages standards where they exist, and offers a comprehensive schema for those domains where no standards exist We support the breadth of FHIR data, but go beyond this by supporting DICOM, Patient Engagement Data, Genomics Data, SDOH, We have started our investments on enabling the persistence of FHIR data in a relational form leveraging delta parquet Transition to the next slide to introduce Relational FHIR (move to the next slide)
- #15 Talking points A key feature of the healthcare data solutions is our Healthcare Data Model This data model aims to encompass all the major healthcare data domains It is a composite model that leverages standards where they exist, and offers a comprehensive schema for those domains where no standards exist We support the breadth of FHIR data, but go beyond this by supporting DICOM, Patient Engagement Data, Genomics Data, SDOH, We have started our investments on enabling the persistence of FHIR data in a relational form leveraging delta parquet Transition to the next slide to introduce Relational FHIR (move to the next slide)
- #16 All organizations have multiple data sources, and the larger the organization the greater the number of data sources. Typically, there will be ERPs, CRMs, Legacy applications, regional versions of each of these, external data feeds and so on Inevitably, data that was created in different ‘silos’ with different governance standards to meet the needs of different applications will always have issues. When you look at the data drawn form each of these applications you will see that it is inconsistent in terms of both the standardization of data values and often in terms of the values themselves, and most often individual records are incomplete. And it should be pointed out that this is normal and 100% to be expected. In fact, it would be surprising if this were not the case – but it does give us a problem, as what we really need is data that is complete, and consistent, and accurate. The ability to consolidate data from multiple disparate systems is key if we want to use the data to drive business insights and operational efficiencies – or any form of ‘digital transformation’. What we need in that case is high quality, trusted data that is ready to use, whether it is being consumed in basic enterprise metrics or advanced AI algorithms. So we have a gap between the data as we have it and how we need it. Filling that gap is the job of Data Governance, such as Microsoft Purview, and Master Data Management, such as Profisee MDM! While governance systems can *define* data standards, MDM is where they are *enforced*. Data from different systems can be matched and merged, validated against data quality and governance standards, and remediated where required. Then the new corrected and validated ‘master’ data can be shared to downstream analytics and well and back to source systems to drive operational improvements. By properly creating and maintaining enterprise master data, we transform data into an asset of the business. Now, lets look at how this all fits into an enterprise computing platform such as Azure.
- #17 All organizations have multiple data sources, and the larger the organization the greater the number of data sources. Typically, there will be ERPs, CRMs, Legacy applications, regional versions of each of these, external data feeds and so on Inevitably, data that was created in different ‘silos’ with different governance standards to meet the needs of different applications will always have issues. When you look at the data drawn form each of these applications you will see that it is inconsistent in terms of both the standardization of data values and often in terms of the values themselves, and most often individual records are incomplete. And it should be pointed out that this is normal and 100% to be expected. In fact, it would be surprising if this were not the case – but it does give us a problem, as what we really need is data that is complete, and consistent, and accurate. The ability to consolidate data from multiple disparate systems is key if we want to use the data to drive business insights and operational efficiencies – or any form of ‘digital transformation’. What we need in that case is high quality, trusted data that is ready to use, whether it is being consumed in basic enterprise metrics or advanced AI algorithms. So we have a gap between the data as we have it and how we need it. Filling that gap is the job of Data Governance, such as Microsoft Purview, and Master Data Management, such as Profisee MDM! While governance systems can *define* data standards, MDM is where they are *enforced*. Data from different systems can be matched and merged, validated against data quality and governance standards, and remediated where required. Then the new corrected and validated ‘master’ data can be shared to downstream analytics and well and back to source systems to drive operational improvements. By properly creating and maintaining enterprise master data, we transform data into an asset of the business. Now, lets look at how this all fits into an enterprise computing platform such as Azure.
- #18 All organizations have multiple data sources, and the larger the organization the greater the number of data sources. Typically, there will be ERPs, CRMs, Legacy applications, regional versions of each of these, external data feeds and so on Inevitably, data that was created in different ‘silos’ with different governance standards to meet the needs of different applications will always have issues. When you look at the data drawn form each of these applications you will see that it is inconsistent in terms of both the standardization of data values and often in terms of the values themselves, and most often individual records are incomplete. And it should be pointed out that this is normal and 100% to be expected. In fact, it would be surprising if this were not the case – but it does give us a problem, as what we really need is data that is complete, and consistent, and accurate. The ability to consolidate data from multiple disparate systems is key if we want to use the data to drive business insights and operational efficiencies – or any form of ‘digital transformation’. What we need in that case is high quality, trusted data that is ready to use, whether it is being consumed in basic enterprise metrics or advanced AI algorithms. So we have a gap between the data as we have it and how we need it. Filling that gap is the job of Data Governance, such as Microsoft Purview, and Master Data Management, such as Profisee MDM! While governance systems can *define* data standards, MDM is where they are *enforced*. Data from different systems can be matched and merged, validated against data quality and governance standards, and remediated where required. Then the new corrected and validated ‘master’ data can be shared to downstream analytics and well and back to source systems to drive operational improvements. By properly creating and maintaining enterprise master data, we transform data into an asset of the business. Now, lets look at how this all fits into an enterprise computing platform such as Azure.
- #19 All organizations have multiple data sources, and the larger the organization the greater the number of data sources. Typically, there will be ERPs, CRMs, Legacy applications, regional versions of each of these, external data feeds and so on Inevitably, data that was created in different ‘silos’ with different governance standards to meet the needs of different applications will always have issues. When you look at the data drawn form each of these applications you will see that it is inconsistent in terms of both the standardization of data values and often in terms of the values themselves, and most often individual records are incomplete. And it should be pointed out that this is normal and 100% to be expected. In fact, it would be surprising if this were not the case – but it does give us a problem, as what we really need is data that is complete, and consistent, and accurate. The ability to consolidate data from multiple disparate systems is key if we want to use the data to drive business insights and operational efficiencies – or any form of ‘digital transformation’. What we need in that case is high quality, trusted data that is ready to use, whether it is being consumed in basic enterprise metrics or advanced AI algorithms. So we have a gap between the data as we have it and how we need it. Filling that gap is the job of Data Governance, such as Microsoft Purview, and Master Data Management, such as Profisee MDM! While governance systems can *define* data standards, MDM is where they are *enforced*. Data from different systems can be matched and merged, validated against data quality and governance standards, and remediated where required. Then the new corrected and validated ‘master’ data can be shared to downstream analytics and well and back to source systems to drive operational improvements. By properly creating and maintaining enterprise master data, we transform data into an asset of the business. Now, lets look at how this all fits into an enterprise computing platform such as Azure.
- #20 All organizations have multiple data sources, and the larger the organization the greater the number of data sources. Typically, there will be ERPs, CRMs, Legacy applications, regional versions of each of these, external data feeds and so on Inevitably, data that was created in different ‘silos’ with different governance standards to meet the needs of different applications will always have issues. When you look at the data drawn form each of these applications you will see that it is inconsistent in terms of both the standardization of data values and often in terms of the values themselves, and most often individual records are incomplete. And it should be pointed out that this is normal and 100% to be expected. In fact, it would be surprising if this were not the case – but it does give us a problem, as what we really need is data that is complete, and consistent, and accurate. The ability to consolidate data from multiple disparate systems is key if we want to use the data to drive business insights and operational efficiencies – or any form of ‘digital transformation’. What we need in that case is high quality, trusted data that is ready to use, whether it is being consumed in basic enterprise metrics or advanced AI algorithms. So we have a gap between the data as we have it and how we need it. Filling that gap is the job of Data Governance, such as Microsoft Purview, and Master Data Management, such as Profisee MDM! While governance systems can *define* data standards, MDM is where they are *enforced*. Data from different systems can be matched and merged, validated against data quality and governance standards, and remediated where required. Then the new corrected and validated ‘master’ data can be shared to downstream analytics and well and back to source systems to drive operational improvements. By properly creating and maintaining enterprise master data, we transform data into an asset of the business. Now, lets look at how this all fits into an enterprise computing platform such as Azure.
- #21 All organizations have multiple data sources, and the larger the organization the greater the number of data sources. Typically, there will be ERPs, CRMs, Legacy applications, regional versions of each of these, external data feeds and so on Inevitably, data that was created in different ‘silos’ with different governance standards to meet the needs of different applications will always have issues. When you look at the data drawn form each of these applications you will see that it is inconsistent in terms of both the standardization of data values and often in terms of the values themselves, and most often individual records are incomplete. And it should be pointed out that this is normal and 100% to be expected. In fact, it would be surprising if this were not the case – but it does give us a problem, as what we really need is data that is complete, and consistent, and accurate. The ability to consolidate data from multiple disparate systems is key if we want to use the data to drive business insights and operational efficiencies – or any form of ‘digital transformation’. What we need in that case is high quality, trusted data that is ready to use, whether it is being consumed in basic enterprise metrics or advanced AI algorithms. So we have a gap between the data as we have it and how we need it. Filling that gap is the job of Data Governance, such as Microsoft Purview, and Master Data Management, such as Profisee MDM! While governance systems can *define* data standards, MDM is where they are *enforced*. Data from different systems can be matched and merged, validated against data quality and governance standards, and remediated where required. Then the new corrected and validated ‘master’ data can be shared to downstream analytics and well and back to source systems to drive operational improvements. By properly creating and maintaining enterprise master data, we transform data into an asset of the business. Now, lets look at how this all fits into an enterprise computing platform such as Azure.
- #23 Range of services – Adequacy to serve beneficiaries Price of services Effectiveness of Care Outcomes of Care – (traditional hospital quality measures) Utilization management Proactive quality of health Coordinated Care Addressing Health Equity and SDOH (Closed loop referral – ex. Referring to oncologist vs. housing specialist, system built on medical) – benefits redesign How sophisticated we are in digital interoperability Self funded – market is to employers as well (direct to employer strategies)
- #24 Anna Strategic Alignment: Strategies backed by incentives that are driving performance in population-based care Operational Engine: to move the change forward, operational owners Use Cases: Enterprise culture, changing capabilities in the workforce Rulings Economic Access: Affordability of compute Diminishing Reimbursement Rates: Not an economist, but we have to be able to afford bread on everyone’s table Building a Population Health Engine with in an Integrated Health System Use-cases to drive the capability and capacity - DaVinci This Photo by Unknown Author is licensed under CC BY
- #26 2018 – Joined DaVinci with sponsorship through Cambia 2019 – Proof of concept for quality measures reporting – MHS internal development, returns development investment in year 1. President’s award 2020 – Proof of concept for eligibility 2021 – Prior Authorization Trading Agreements 2022 April: Formal approval from CMS for Waiver Exception to utilize FHIR for Prior Authorization Oct: Go Live for Smart Authorization and proof of concept for scalable FHIR services DaVinci Steering Committee representation Dec: scalable FHIR ecosystem 2023 FHIR Services Eligibility scaled to multiple payers, creating 97% or higher match rates Scaling Data Exchange for Quality Measures to multiple payers Full scale API Management ecosystem Acquisition of Grant Dollars for WA state implementation of Prior Auth (including MCOs and FQHCs) 2024 Developer Portal (Provider Directory Services for Payor connections) Acquisition of MultiCare Connected Care Network provider g(10) APIs for Quality Reporting purposes Acquisition of Provider Access APIs from Payers to Providers (claims data) DaVinci Steering Chair
- #27 233% increase in productivity
- #31 Evolve commentary started on prior slide to pivot to Da Vinci simply is an group of industry payers, providers and HIT partners that understand how critical it is to develop common, ideally eventual standard ways for providers and payers to exchange the critical data required for value base case to work. Scalability at national level. Critical to define standards based solutions that have input, credibility and usability and will be picked up and used by many without SPECIAL EFFORT. Briefly touch upon the HL7 interplay and Da Vinci project with emphasizing membership is about garnering value from investment of dollars and resources to produce and implement standard approaches to solve business pain points. Value will be seen in the improvements achieved through implementation of a use case. In other words, deploying solutions to real world problems and avoiding rework to solutions to achieve interoperability & integration with trading partners. A goal of Da Vinci is to develop repeatable production focused projects that lead to implementations because the use cases represent real world pain points and the solutions are standards based. Additional content if warranted Interplay with HL7 processes: When a Da Vinci project plans to produce an artifact, such as an Implementation Guide, that shall be balloted by HL7, an HL7 Project Scope Statement (PSS) will be created and taken to the appropriate HL7 Work Group(s) for discussion, ownership to manage the PSS, and submission to HL7’s approval process Da Vinci participants will work with the appropriate HL7 Work Group(s) to prepare materials necessary to take the artifact to Notice for Intent to Ballot (NIB), balloting, resolution of ballot comments, and submission for publication A Da Vinci artifact will be subject to all relevant HL7 policies and procedure when submitted using a PSS and in the subsequent standards effort related to the specific artifacts defined in the PSS Artifacts submitted for HL7 balloting (via the PSS process) will become intellectual property owned and distributed under HL7 published guidelines Project Deliverables Define requirements (technical, business, communications, integration) Create Implementation Guide (technical implementation requirements) Create and test Reference Implementation (prove the guide works) Initial Implementation (pilot) Deploy the solution (go-live) Implementation Guide (IG) - A specification that defines how the capabilities defined by the FHIR specification are used in particular data exchanges, or to solve particular problems (adapted from the HL7 FHIR Foundation Implementation Guide Registry (http://www.fhir.org/guides/registry). Reference Implementation (RI) - A reference implementation is, in general, an implementation of a specification to be used as a definitive interpretation for that specification. During the development of the ... conformance test suite, at least one relatively trusted implementation of each interface is necessary to (1) discover errors or ambiguities in the specification, and (2) validate the correct functioning of the test suite.
- #42 Formatted for cut-and-paste into Teams – best to cut-and-paste 1 line at a time (for link preview to be generated) Recap videos: Drive AI-Enablement with Profisee MDM and Microsoft Fabric – 4 mins Profisee MDM the Obvious Choice for Microsoft Customers? – 3 mins Profisee is the Best MDM for Azure – Live demo with MS Fabric – 18 mins Microsoft-related: Microsoft Resource Hub Profisee Reference Architectures