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The Data Operating System: Changing the Digital Trajectory of Healthcare

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In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.

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The Data Operating System: Changing the Digital Trajectory of Healthcare

  1. 1. The Data Operating System Changing the Digital Trajectory of Healthcare Dale Sanders Health Catalyst May 2017
  2. 2. Selling vs. Not In these webinars, I never sell Health Catalyst. • I offer advice from past experience • Advocate change In this webinar, I’ll “sell” Health Catalyst, but only as evidence that we practice what we preach, in this case, development of the Data Operating System There is also advice buried in the “selling”… if we’re building a Data Operating System, maybe other folks and vendors should, too
  3. 3. The Story of Today’s Meeting • What’s a Data Operating System? • Why do we need one now in healthcare? • How can it be implemented? • Is it real or just another buzz phrase?
  4. 4. First, Thanks… • Our entire product development team for their incredible performance • I’ve never been associated with as much change and productivity in 18 months • For the brainstorming, engineering & implementation of the Data Operating System… • Bryan Hinton • Imran Qureshi • Sean Stohl • Rus Tabet, one of our UI and graphics experts • For his illustrations and cartoons in this slide deck. You’ll be able to tell the difference between his and mine.  • Many other better artists than me whose work inspired many of the doodles in these slides
  5. 5. We’re not satisfied with the current trajectory of digital health But, at Health Catalyst, we’re not satisfied with ourselves, either. We are far from perfect.
  6. 6. Fair Warning to the Executives in the Audience • Get ready to dive into topics that you need to understand • The most expensive capital purchase in the history of your healthcare system wasn’t a new hospital… it was your EHR • Software runs your company, for better or worse • Case in point: The ransomware impact on the UK National Health System last week • The healthcare CEOs who thrive going forward, will understand their software technology and data. They will rise to the top.
  7. 7. Sanders Version 1.0 Definition of a Data Operating System (DOS) A data operating system combines real-time, granular data; and domain-specific (e.g. healthcare), reusable analytic and computational logic about that data, into a single computing ecosystem for application development. A data operating system can support the real-time processing and movement of data from point-to-point, as well as batch-oriented loading and computational analytic processing on that data.
  8. 8. Health Catalyst Data Operating System Data Platform Data Ingest Real-time Streaming Source Connectors Catalyst Analytics Platform Core Data Services Real time Processing Fabric Registries Terminology & Groupers Apps FHIR Data Quality Data Governance Pattern Recognition Hadoop/ Spark Data Export 3rd Party Applications Registry Builder Leading Wisely Care Management SAMD & SMD Atlas Hospital IT Applications EHR Integration Machine Learning Models Patient & Provider Matching Real time Data Services NLP Lambda Architecture CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … HL7 Data Pipelines ML Pipelines Security, Identity & Compliance Metadata Data Lake
  9. 9. Apps and Fabric Run on Any Data Platform Fabric & Machine Learning Apps Data to FHIR mapping Various Data Platforms HadoopHealth Catalyst Open APIs (FHIR etc) Epic CernerTeradata Home grownIBM 3rd Party Applications Registry Builder Leading Wisely Care Management Hospital IT Applications CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … Registries Terminology & GroupersFHIR SAMD & SMD EHR Integration Models Patient & Provider Matching ML Pipelines Security, Identity & Compliance Oracle
  10. 10. Seven Attributes of the Healthcare Data Operating System 1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw data and can be accessed, reused, and updated through open APIs, enabling 3rd party application development. 2. Streaming data: Near or real-time data streaming from the source all the way to the expression of that data through the DOS, that can support transaction-level exchange of data or analytic processing. 3. Integrates structured and unstructured data: Integrates text and structured data in the same environment. Eventually, incorporates images, too. 4. Closed loop capability: The methods for expressing the knowledge in the DOS include the ability to deliver that knowledge at the point of decision making, for example back into the workflow of source systems, such as an EHR. 5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS operations such as authorization, identity management, data pipeline management, and DevOps telemetry. These microservices also enable third party applications to be built on the DOS. 6. Machine Learning: The DOS natively runs machine learning models and enables rapid development and utilization of ML models, embedded in all applications. 7. Agnostic data lake: Some or all of the DOS can be deployed over the top of any healthcare data lake. The reusable forms of logic must support different computation engines; e.g. SQL, Spark SQL, SQL on Hadoop, et al.
  11. 11. Why is the DOS important now in healthcare?
  12. 12. Content is King, the Network is Kong • If you look at modern businesses, data content is becoming the driving force behind their business strategy, e.g., GE, Tesla, Google, Facebook, Delta Airlines, UnitedHealth, Amazon, etc. • The network of people around this data content creates value– think of Metcalfe’s Law– and sticky relationships
  13. 13. “Healthcare CEO, what is your Digitization Index?” Data Assets x Data Usage x Data Skilled Labor Healthcare is one of the least digital sectors, and it shows in profit-margin growth. Source: McKinsey Corporate Performance Analysis Tool; BEA; McKinsey Global Institute analysis
  14. 14. C-level Advice for a Digital Healthcare Future 1. Population health, value based care, and precision medicine are all about DATA • You need a strategic data acquisition strategy– What data do you need for population health, risk contracting, and precision medicine? How do you acquire it? • You need a Chief Analytics or Chief Data Officer– is that your CIO or not? 2. Your physicians and nurses are over-measured and under-valued, in large part because they are slaves to data entry and poor software • You need to push all vendors to follow modern, open software APIs, including but not limited to FHIR 3. You need a Data Operating System-- leverage and expand the capability of your Enterprise Data Warehouse
  15. 15. DOS Need #1: The ”Shark Tank” Story 20+ Healthcare IT startups Pitching great software applications and creative ideas No solution or appreciation for the underlying healthcare data that they needed In my head: “We must give these great ideas and applications the data they need. They cannot possibly afford to build the data infrastructure and skills that we have in Health Catalyst. The industry can’t afford it.”
  16. 16. We Haven’t Modernized the Data Content Layer
  17. 17. DOS Need #2: Mergers & Acquisitions • The new company is not integrated until the data is integrated • HIE’s are not sufficient for data integration… not even close • Rip and replacing EHRs with a single, common vendor is not an affordable strategy • Besides, hybrid vigor is a good thing… you should not put all of your digital eggs in one basket
  18. 18. Rip and Replace is Not the Answer for M&A Hundreds of millions of $$ in additional costs and lost time Keep the disparate, existing source systems– Finance, supply chain, registration, scheduling, A/R, EHRs, etc. Virtually Integrated with the Data Operating System Share transaction-level data. Integrate data for common metrics around finance, clinical quality, utilization, etc.
  19. 19. DOS Need #3: Enable a Personal Health Record Updated, integrated, shareable, downloadable, transportable Healthcare data is currently locked in the cage of the health system and the technology of the EHR DOS
  20. 20. DOS Need #4: Scaling Existing, Home Grown Data Warehouses • Home grown data warehouses are easy to start and build, but expensive to evolve and maintain • There are many of these in healthcare • But they are also hard to retire… what do you do? • Rip and replace with a vendor solution? Not attractive. • That was the only answer Health Catalyst had to these scenarios, and that answer does not sell • Not good for Health Catalyst, not good for the industry. We both need better options.
  21. 21. Selfishly Speaking, Health Catalyst Had to Solve This But the industry will benefit, too. That’s the beauty of capitalism.  The DOS Fabric and our new applications addresses this need
  22. 22. DOS Need #5: The Human Health Data Ecosystem And, by the way, we don’t have much of any data on healthy patients Precision medicine & population health need more data than we currently collect in the ecosystem… WAY more data Only 8% of the data we need for precision medicine and population health resides in today’s EHRs
  23. 23. Healthcare Data • Ingesting healthcare data into a data lake or data warehouse is now essentially a commodity, thanks to open source technology and a late binding, schema-on- read approach to data models • What’s not a commodity? • Understanding the data content, data models, and insanely complicated nuances of healthcare data • The analytic logic or “data bindings” to apply to that data • The technology and skills to deliver this data to the right person, at the right time, in the right modality • Keeping up with the changes in the source system data, aka, change data capture • Data quality management and governance • Scaling all of this for a single healthcare system
  24. 24. For dramatic impact, let me share with you the data content sources in the Health Catalyst library…
  25. 25. EMR Data Sources 26 1. Affinity - ADT/Registration 2. Allscripts - Ambulatory EMR Clinicals 3. Allscripts Enterprise/Touchworks - Ambulatory EMR 4. Allscripts Sunrise - Acute EMR Clinicals 5. Aprima ERM 6. Cerner - Acute EMR Clinicals 7. Cerner - PowerWorks Ambulatory EMR 8. Cerner HomeWorks - Other 9. CPSI - Acute EMR Clinicals 10.eClinicalWorks - Ambulatory EMR Clinicals 11.Epic - Acute EMR Clinicals 12.Epic - Ambulatory EMR Clinicals 13.GE (IDX) Centricity - Ambulatory EMR Clinicals 14.McKesson Horizon - Acute EMR Clinicals 15.McKesson Horizon Enterprise Visibility 16.Meditech 5.66 EHR w/DR 17.NextGen - Ambulatory Practice Management 18.Quality Systems (Next Gen) - Ambulatory EMR Clinicals 19.Siemens Sorian Clinicals - Inpatient EMR
  26. 26. Finance/Costing Data Sources 27 1. Affinity - Costing 2. Allscripts (EPSi) - Budget 3. Allscripts (EPSi) - Costing 4. Allscripts (TSI) - Costing 5. BOXI - GL 6. Cost Flex - Costing 7. Digimax Materials Management - Inventory Management 8. IOS ENVI - Costing 9. Kaufman Hall Budget Advisor - Other 10.Lawson - Accounts Payable 11.Lawson - Accounts Receivable 12.Lawson - GL 13.Lawson - Supply Chain 14.McKesson - Accounts Payable 15.McKesson Enterprise Materials Management 16.McKesson HPM - Costing 17.McKesson HPM - GL 18.McKesson PFM - Accounts Payable 19.McKesson PFM - GL 20.McKesson Series - Accounts Receivable 21.Meditech - GL 22.Microsoft Great Plains - GL 23.Oracle (Hyperion) - Costing 24.Oracle (PeopleSoft) - GL 25.Oracle (PeopleSoft) - Supply Chain 26.PARExpress 27.PPM - Costing 28.Smartstream - GL 29.StrataJazz - Costing
  27. 27. Billing Data Sources 28 1. Affinity - Hospital Billing 2. CHMB 360+ RCM - Hospital Billing 3. CPSI - Hospital Billing 4. Epic - Hospital Billing 5. GE (IDX) Centricity - Hospital Billing 6. GE (IDX) Centricity - Professional Billing 7. HealthQuest - Patient Accounting 8. Keane - Hospital Billing 9. McKesson Series - Patient Billing 10.McKesson STAR - Hospital Billing 11.MD Associates - Professional Billing 12.Siemens Sorian Financials - Inpatient Registration and Billing
  28. 28. HR/ERP Data Sources 29 1. API Healthcare - Time and Attendance 2. iCIMS 3. Kronos - HR 4. Kronos - Time and Attendance 5. Lawson - HR 6. Lawson - Payroll 7. Lawson - Time and Attendance 8. Maestro 9. MD People 10.Now Solutions Empath - HR 11.Oracle (PeopleSoft) - HR 12.PeopleStrategy/Genesys - HR 13.PeopleStrategy/Genesys - Payroll 14.Ultimate Software Ultipro - HR 15.WorkDay
  29. 29. Claims Data Sources 30 1. 835 – Denials 2. Adirondack ACO Medicare 3. Aetna - Claims 4. Anthem - Claims 5. Aon Hewitt - Claims 6. BCBS Illinois 7. BCBS Vermont 8. Children's Community Health Plan (CCHP) - Payer 9. Cigna - Claims 10.CIT Custom - Claims 11.Cone Health Employee Plan (United Medicare) - Claims 12.Discharge Abstract Data (DAD) 13.Hawaii Medical Service Association (HMSA) - Claims 14.HealthNet - Claims 15.Healthscope 16.Humana (PPO) - Claims 17.Humana MA - Claims 18.Kentucky Hospital Association (KHA) - Claims 19.Medicaid - Claims 20.Medicaid - Claims - CCO 21.Merit Cigna - Claims 22.Merit SelectHealth - Claims 23.MSSP (CMS) - Claims 24.NextGen (CMS) - Claims 25.Ohio Hospital Association (OHA) - Claims 26.ProHealth - Claims 27.PWHP Custom - Claims 28.QNXT - Claims 29.UMR Claims Source 30.Wisconsin Health Information Organization (WHIO) - Claims
  30. 30. Clinical Specialty Data Sources 31 1. Allscripts - Case Management 2. Apollo - Lumed X Surgical System 3. Aspire - Cardiovascular Registry 4. Carestream - Other 5. Cerner - Laboratory 6. eClinicalWorks - Mountain Kidney Data Extracts 7. GE (IDX) Centricity Muse - Cardiology 8. HST Pathways - Other 9. ImageTrend 10.ImmTrac 11.Lancet Trauma Registry 12.MacLab (CathLab) 13.MIDAS - Infection Surveillance 14.MIDAS - Other 15.MIDAS - Risk Management 16.Navitus - Pharmacy 17.NHSN 18.NSQIPFlatFile 19.OBIX - Perinatal 20.OnCore CTMS 21.Orchard Software Harvest - Pathology 22.PACSHealth - Radiology 23.Pharmacy Benefits Manager 24.PICIS (OPTUM) Perioperative Suite 25.Provation 26.Quadramed Patient Acuity Classification System - Other 27.QNXT/Vital - Member 28.RLSolutions 29.SafeTrace 30.Siemens RIS - Radiology 31.SIS Surgical Services 32.StatusScope - Clinical Decisions 33.Sunquest - Laboratory 34.Sunrise Clinical Manager 35.Surgical Information System 36.TheraDoc 37.TransChart - Other 38.Varian Aria - Oncology 39.Vigilanz - Infection Control
  31. 31. Health Information Exchange (HIE) Data 32 1. Adirondack ACO Clinical Data from HIXNY (HIE) 2. ADT HIE Patient Programs 3. Vermont HIE
  32. 32. Patient Satisfaction Data Sources 33 1. Fazzi - Patient Satisfaction 2. HealthStream - Patient Satisfaction 3. NRC Picker - Patient Satisfaction 4. PRC - Patient Satisfaction 5. Press Ganey - Patient Satisfaction 6. Sullivan Luallin - Patient Satisfaction
  33. 33. Other Sources of Healthcare-Related Data 34 1. 2010 US Census Detail for State of Colorado 2. Affiliate Provider Database 3. All Payer All Claims (certain States) ---In process UT, CO, MA 4. Alliance Decision Support 5. Allscripts - Ambulatory Practice Management 6. Allscripts - Patient Flow 7. Allscripts EHRQIS - Quality 8. Avaya 9. Axis (MDX) 10.Bed Ready - Other 11.Cerner Signature 12.CMS Standard Analytical Files 13.Daptiv 14.Echo Credentialing - Provider Management 15.ePIMS 16.First Click-Wellness 17.FlightLink 18.GE (IDX) Centricity - Practice Management 19.HCUP (NRD, NIS, NED Sample sets) 20.Health Trac 21.HealtheIntent 22.Hyperion 23.InitiateEMPI 24.Innotas 25.IVR Outreach Detail 26.MIDAS - Credentialing Module 27.Morrisey Medical Staff Office for Web (MSOW) 28.National Ambulatory Care Reporting System (NACRS) 29.Nextgate EMPI 30.Onbase 31.PHC Legacy EDW 32.QNXT/Cactus - Provider 33.SMS Legacy - Other 34.Truven Quality 35.University HealthSystem Consortium - Clinical and Operational Resource Database 36.University HealthSystem Consortium - Regulatory
  34. 34. Master Reference & Terminology Data Content 35 1. AHRQ Clinical Classification Software (CCS) 2. Charlson Deyo and Elixhauser Comorbidity 3. Clinical Improvement Grouper (Care Process Hierarchy) 4. CMS Hierarchical Condition Category 5. CMS Place Of Service 6. LOINC 7. National Drug Codes (NDC) 8. NPI Registry 9. Provider Taxonomy 10.Rx Norm 11.CMS/NQF Value Set Authority Center
  35. 35. That’s the data we have in the US healthcare ecosystem, today; but we are barely getting started on the digitization of the industry, so imagine what the future data ecosystem looks like.
  36. 36. DOS Need #6: Providers becoming payers • The insurance industry is the tail wagging the healthcare dog • Does anyone, other than those in the insurance industry, seriously believe that the current payer/insurance economic model is working? • Critical to the improvement of this situation is the ability for providers to model and assume financial risk, and compete with, or completely disintermediate, insurance companies. • With a Data Operating System, providers have more and better data to model and manage risk than the insurers.
  37. 37. DOS Need #7: Extend the life and value of current EHR investments
  38. 38. Good News, Bad News Healthcare is using “information technology from the last century.” • Dr. Robert Pearl, CEO, Permanente Medical Group; CNBC Interview, 16 May 2017 • 9,000 physicians, 34,000 staffers • Given that we’ve invested $30B in tax money, plus billions more out-of-pocket, on that information technology, what do we do now? • Replace? Not a good idea to spend tens or hundreds of millions of dollars on incrementally better products, at best • We can make what we have, better, while new products emerge We are more digitized in healthcare than ever before, but…
  39. 39. The inevitable curve for technology products is stretched or compressed by market demand and the pace of technological commoditization associated with the product The demand for EHRs was stretched by federal incentives. That’s over. The underlying software and database technology of EHRs was commoditized a long time ago. We can stretch the lifecycle of EHRs with DOS and open APIs, e.g. FHIR.
  40. 40. Role Model Vendors in Silicon Valley • Google, Facebook, Amazon, Microsoft, Twitter • Not Apple, by the way • Apache, W3C, Internet Engineering Task Force, Open Compute Project, et al • How do healthcare vendors stack up? Terribly. The evidence is clear. • Even some of the vendor “app stores” that appear to support open APIs, like FHIR, are contractually worded to take your IP and profit from it, if you contribute to the app store Collaborate on standardization, compete on innovation
  41. 41. Moving so Fast, Already Outdated…
  42. 42. These are the tools available for modern software development. We are at the beginning of a software technology renaissance. Most of these tools are, in one form or another, open source.
  43. 43. With Open, Standard Software APIs… “EHRs would become commodity components in a larger platform that would include other transactional systems and data warehouses running myriad apps, and apps could have access to diverse sources of shared data beyond a single health system’s records.” “A 21st-Century Health IT System — Creating a Real-World Information Economy”, Kenneth D. Mandl, MD, MPH; Isaac S. Kohane, MD, MPH; NEJM, 18 May 2017.
  44. 44. Why we can do this, technically, like never before
  45. 45. A Partial History of my Experience with Open Systems Standards At the risk of jinxing myself, I think I know the major patterns of success and failure
  46. 46. At Northwestern Memorial Healthcare, 2005-2009 We didn’t call it a DOS, but we had what amounts to an early version of it, over 10 years ago. Supported analytics and near-real time exchange of single records, before HIEs. Technology options are much better now.
  47. 47. Hybrid Big Data-SQL Architectures Gartner: Hybrid Transactional/Analytical Processing (HTAP) “Because traditional data warehouse practices will be outdated by the end of 2018, data warehouse solution architects must evolve toward a broader data management solution for analytics.”
  48. 48. The Hadoop, Big Data ecosystem gives us all sorts of options that we never had before, technically and financially Note of thanks to Ben Stopford at Confluent New Technology, New Data Capabilities, at a Fraction of Past Cost
  49. 49. Lambda Architecture: Two Streams of Data One stream for batch computations, one for real time transactions and computations Two different code sets
  50. 50. Kappa Architecture: One Stream of Data One stream for batch and real-time computations in the serving layer One code set Both architectures can be implemented with a combination of open source tools like Apache Kafka, Apache HBase, Apache Hadoop (HDFS, MapReduce), Apache Spark, Apache Drill, Spark Streaming, Apache Storm, and Apache Samza. Note of thanks to Julian Forgeat of Google
  51. 51. Health Catalyst Data Operating System Data Platform Data Ingest Real-time Streaming Source Connectors Catalyst Analytics Platform Core Data Services Real time Processing Fabric Registries Terminology & Groupers Apps FHIR Data Quality Data Governance Pattern Recognition Hadoop/ Spark Data Export 3rd Party Applications Registry Builder Leading Wisely Care Management SAMD & SMD Atlas Hospital IT Applications EHR Integration Machine Learning Models Patient & Provider Matching Real time Data Services NLP Lambda Architecture CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … HL7 Data Pipelines ML Pipelines Security, Identity & Compliance Metadata Data Lake
  52. 52. Apps and Fabric Run on any Data Platform Fabric & Machine Learning Apps Data to FHIR mapping Various Data Platforms HadoopHealth Catalyst Open APIs (FHIR etc) Epic CernerTeradata Home grownIBM 3rd Party Applications Registry Builder Leading Wisely Care Management Hospital IT Applications CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … Registries Terminology & GroupersFHIR SAMD & SMD EHR Integration Models Patient & Provider Matching ML Pipelines Security, Identity & Compliance Oracle
  53. 53. Seven Attributes of the Healthcare Data Operating System 1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw data and can be accessed, reused, and updated through open APIs, enabling 3rd party application development. 2. Streaming data: Near or real-time data streaming from the source all the way to the expression of that data through the DOS, that can support transaction-level exchange of data or analytic processing. 3. Integrates structured and unstructured data: Integrates text and structured data in the same environment. Eventually, incorporates images, too. 4. Closed loop capability: The methods for expressing the knowledge in the DOS include the ability to deliver that knowledge at the point of decision making, including back into the workflow of source systems, such as an EHR. 5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS operations such as authorization, identity management, data pipeline management, and DevOps telemetry. These microservices also enable third party applications to be built on the DOS. 6. Machine Learning: The DOS natively runs machine learning models and enables rapid development and utilization of ML models, embedded in all applications. 7. Agnostic data lake: Some or all of the DOS can be deployed over the top of any healthcare data lake. The reusable forms of logic must support different computation engines; e.g. SQL, Spark SQL, SQL on Hadoop, et al.
  54. 54. Health Catalyst Initial Fabric Services Fabric.Identity & Fabric.Authorization microservices • Fabric.Identity provides authentication i.e., verifying the user is who he/she is claiming to be. Fabric.Authorization stores permissions for various user groups and then given a user returns the effective permissions for that user. Fabric.MachineLearning microservice • A micro-service that plugs into a data pipeline (like ours) and runs machine learning models written in R, Python and TensorFlow. It encapsulates all the ML tools inside so all you need to do is supply a ML model. Fabric.EHR set of microservices • Enables SQL bindings, ML models and application code to show data and insights inside the EHR workspace using SMART on FHIR. Fabric.PHR set of microservices • Provides the ability to download, share, and update a Personal Health Record. Integrates data from all available EMRs in a patient’s health ecosystem. Fabric.Terminology set of microservices • Provides the ability for application developers to leverage local and national terminology mapping and update services. Fabric.FHIR microservice • A data service that sits on top of any data platform (HC EDW, Data Lake, Hadoop etc). Applications using this data service become portable to any other data platform. It uses data to FHIR mappings (written in Sql, HiveSql etc) to map data and implements an Analytics on FHIR API using a cache based on Elastic Search. Fabric.Telemetry • Provides infrastructure to web and mobile applications to send telemetry data to our Azure cloud and provides tools to analyze it using ElasticSearch. Default: Build in the FHIR framework, unless it’s not possible
  55. 55. FHIR Mappings (SQL version) <DataSource><Sql> SELECT PatientID AS EDWPatientID, CASE GenderCD WHEN 'Female' THEN 'female' WHEN 'Male' THEN 'male' ELSE 'unknown' END AS gender,BirthDTS as birthDate FROM [Person].[SourcePatientBASE] </Sql></DataSource> <DataSource Path="condition.code" type="object"><Sql> SELECT PatientID AS EDWPatientID, CONCAT(DiagnosisSourceID,'-',RowSourceDSC,'- ',DiagnosisTypeDSC) as KeyLevel1, CONCAT(DiagnosisSourceID,'-',RowSourceDSC,'-',DiagnosisTypeDSC) as KeyLevel2, CASE CodeTypeCD WHEN 'ICD9DX' THEN 'http://hl7.org/fhir/sid/icd-9-cm' WHEN 'ICD10DX' THEN 'http://hl7.org/fhir/sid/icd-10-cm' ELSE NULL END AS system, DiagnosisCD as code, DiagnosisDSC as text FROM [Clinical].[DiagnosisBASE] </Sql></DataSource> 56 This is a real world example of how we are converting our relational data models into FHIR information models
  56. 56. { "EDWPatientID": "Z100069", "gender": "male", "birthDate": "1958-01-05T00:00:00", "condition": [ { "clinicalStatus": "active", "verificationStatus": "confirmed", "category": [ { "coding": "problem-list-item", "text": "ICD Problem List Code" } ], "code": [ { "system": "http://hl7.org/fhir/sid/icd-9- cm", "code": "185", "text": "Malignant neoplasm of prostate (HCC)" } ] } ] } FHIR Output From the Previous Slide 57
  57. 57. Sampling of the 200+ Health Catalyst Reusable Value Sets These, along with the CMS/NQF/MACRA values sets are being ported to the Measures Builder Library (MBL) content management system, for reuse in Health Catalyst and 3rd party applications. Acute Coronary Syndrome (ACS) Blood Utilization Dashboard Breast Milk Feeding Catheter Associated Urinary Tract Infection (CAUTI) Prevention Central Line Associated Blood Stream Infections (CLABSI) Prevention Colorectal Surgery Early Mobility in the ICU Glycemic Control in the Hospital Heart Failure Joint Replacement - Hip & Knee Labor and Delivery Patient Flight Path - Diabetes Patient Safety Explorer Pediatric Appendectomy Pediatric Asthma Pediatric Explorer Pediatric Sepsis Pneumonia Population Explorer Readmission Explorer Sepsis Prevention Spine Surgery Stroke (Acute Ischemic & TIA) Surgical Site Infection Prevention Venous Thrombo-Embolism (VTE) Prevention Coronary Artery Bypass Graft Surgery Diabetes - Adult Chronic Obstructive Pulmonary Disease (COPD)
  58. 58. Central line-associated bloodstream infection (CLABSI) Risk – Clinical Analytics and Decision Support Congestive Heart Failure, Readmissions Risk – Clinical Analytics and Decision Support COPD, Readmissions Risk – Clinical Analytics and Decision Support Respiratory (COPD, Asthma, Pneumonia, & Resp. Failure), Readmission Risk – Clinical Analytics and Decision Support Forecast IBNR claims/year-end expenditures – Financial Decision Support Predictive appointment no shows – Operations and Performance Management Pre-surgical risk (Bowel) – Clinical Analytics and Decision Support and client request Propensity to pay – Financial Decision Support Patient Flight Path, Diabetes Future Risk – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Future Cost– Clinical Analytics and Decision Support Patient Flight Path, Diabetes Top Treatments – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Next Likely Complications (Glaucoma) – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Next Likely Complications (Retinopathy) – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Next Likely Complications (ESRD) – Clinical Analytics and Decision Support Plus several more… (Nephropathy, Cataracts, CHF, CAD, Ketoacidosis, Erectile Dysfunction, Foot Ulcers) Machine Learning Models in DOS In Development Built Planned Patients Like This – Clinical Analytics and Decision Support Sepsis Risk – Clinical Analytics and Decision Support Readmission Risk – Clinical Analytics and Decision Support Post-surgical risk (Hips and Knees) – Clinical Analytics and Decision Support INSIGHT socio-economic based risk – Clinical Analytics and Decision Support and client request Native SQL/R predictive framework and standard package - Platform Feature selection, Parallel Models, Rank and Impact of Input Variables – Platform Predictive ETL batch load times – Platform Composite Health Risk – Clinical Analytics and Decision Support Composite All Cause Harm Risk – Clinical Analytics and Decision Support Early detection of CLABSI, CAUTI, Clostridium difficile (c. diff) hospital infections – Clinical Analytics and Decision Support Early detection of Sepsis/Septicemia (Blood Infection) – Clinical Analytics and Decision Support Hospital Census Prediction - Operations and Performance Management Hospital Length of Stay Prediction – Operations and Performance Management Public data sets, benchmarks, “Catalyst Risk”, expected mortality, length of stay – CAFÉ collaboration Clusters of population risk (near term risk/cost) – Population Health and Accountable Care
  59. 59. Managing and Reusing the Explosion of Measures and Value Sets Measures Builder Library (MBL) is a content management system and set of APIs that allows registries, value sets, and other measures to be consistently managed, verified, governed, and reused for application development
  60. 60. Role Model Software Development for the Fabric 1. Open Source & Collaborative Development: All code is available on github.com/HealthCatalyst. External developers can submit enhancements. 2. Open & Modular: All APIs will be publicly published. Customers can pick and choose from the Health Catalyst components or replace any component with their own or from a third party 3. Secure by Design: Security services make it easy to build security into any application 4. Microservices architecture: REST-based services that can be called from web, mobile or BI tools 5. Big Data: Leverages Big Data technologies to provide high-speed and reliable platform 6. Easy Install & Updates: All services install via Docker 7. Scalable: All services are designed to run in multiple nodes and cluster themselves automatically Why can’t healthcare be the role model, instead of Silicon Valley? Should we aspire to something less? Is that acceptable?
  61. 61. How Will We Know if We are a Role Model? 1. Successfully implementing the Data Operating System 2. Fast, simple releases every 2 weeks. Constant improvement of our apps. 3. Analytics driven UI and applications—intelligent user interfaces, driven by situational awareness of the physician, nurse, patient, etc. 4. Constantly consuming and expanding the ecosystem of data as the enabler to great apps, not apps as the enabler of data 5. Machine learning and pattern recognition that clearly amazes all of us with its value to humanity 6. Economic scalability-- we're so efficient with our products, which work across multiple OS and data topologies, that it's economically efficient to constantly deploy 7. Auto-fill analytics—a play on words, but how do we, through pattern recognition and machine learning, anticipate next steps in our clients’ decision making? 8. When Google, Facebook, Amazon, and Microsoft come to us for advice about software success and value These are Health Catalyst’s software development vital signs
  62. 62. For Health Catalyst Clients 63 Join and explore the Health Catalyst Community to learn more and engage with our team community.healthcatalyst.com
  63. 63. Health Catalyst Platform Community 64 Ask Questions about DOS Request Features Review Roadmaps and Release Notes Contact our Community Manager, Kate Weaver, to request access kate.weaver@healthcatalyst.com
  64. 64. Summary Thoughts There will be people who hope we fail. There will be people who expect us to fail. There are many more people who hope we don’t. That’s who we’re working for.
  65. 65. Healthcare Analytics Summit 17 ERIC J. TOPOL Author, The Patient Will See You Now and The Creative Destruction of Medicine. Director, Scripps Translational Science Institute DAVID B. NASH, MD. MBA Dean, Jefferson School of Population Health JOHN MOORE Founder and Managing Partner, Chilmark Research ROBERT A. DEMICHIEI Executive Vice President and Chief Financial Officer, University of Pittsburgh Medical Center THOMAS D. BURTON Co-Founder, Chief Improvement Officer, and Chief Fun Officer, Health Catalyst DALE SANDERS Executive Vice President, Product Development, Health Catalyst THOMAS DAVENPORT Author , Consultant Competing on Analytics*, , Analyitcs at Work, Big Data at Work, Only Humans Need Apply:Winners and Losers in the Age of Smart Machines. *Recognized by Harvard Business Review editors as one the most important management ideas of the past decade, one of HBR’s ten must-read articles in that magazine’s 90-year history. Summit highlights Industry Leading Keynote Speakers We’ll hear from well-known healthcare visionaries. We’ll also hear from two C-level executives leading large healthcare organizations. CME Accreditation For Clinicians HAS 17 will again qualify as a continuing medical education (CME) activity. 30 Educational, Case Study, and Technical Sessions We have the most comprehensive set of breakout sessions of any analytics summit. Our primary breakout session focus is giving you detailed, practical “how to” learning examples combined with question and opportunities. The Analytics Walkabout Back by popular demand, the Analytics Walkabout will feature 24 new projects highlighting a variety of additional clinical, financial, operational, and workflow analytics and outcomes improvement successes. Analytics-driven, Hands-on Engagement for Teams and Individuals Analytics will continue to flow through the three-day summit touching every aspect of the agenda. Networking and Fun We’ll provide some new innovative analytics-driven opportunities to network while keeping our popular fun run and walk opportunities and dinner on the down. Early Bird PricingSINGLE ENTRY 1 Pass - $595 Save $300 BEST VALUE 3 PACK 3 Passes - $545/each Save $1,000+5 PACK 5 Passes - $495/each Save $2,000+ Sept. 12-14, 2017 Grand America Hotel Salt Lake City, UT

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