A Health Catalyst Overview: Learn How a Data First Strategy Can Drive Increased Outcomes Improvement
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This document discusses how adopting a data-first strategy can drive outcome improvement. It describes building institutional analytic skills through consolidating expertise, mentorship and education, and outsourcing. It also discusses using data to improve clinical practice, citing an example where a hospital reduced complication rates and lengths of stay for hip and knee replacements through a data-driven transformation, saving over $800,000. The document promotes analyzing multiple data sources using descriptive, predictive, and prescriptive analytics across different skill levels to continually improve outcomes.
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A Health Catalyst Overview: Learn How a Data First Strategy Can Drive Increased Outcomes Improvement
- 1. How a Data First Strategy Drives Outcome Improvement March 27, 2018
- 4. © 2018 Health Catalyst Carbolic acid spray apparatus circa 1880
- 5. © 2018 Health Catalyst 5 King Edward VII coronation portrait, 1901
- 7. © 2018 Health Catalyst Harald zur Hausen 7 1976 Published hypothesis that human papillomavirus (HPV) caused cervical cancer 1983 Identified HPV16 and HPV18 in cervical cancers 2006 Vaccine available 2017 60% of adolescent children have been vaccinated 42 years
- 8. © 2018 Health Catalyst Poll a. Less than 1 year b. 2-3 years c. 4-5 years d. 6-8 years e. 9 or more years How long does it take your institution to turn high quality medical evidence into common practice? 8
- 9. © 2018 Health Catalyst Alzheimer's 9 5.7 million AmericansDeaths up 123% since 2000 $277 billion in 2018 No known way to prevent, cure, or slow progression Source: Alzheimer’s Association, 2018 Alzheimer’s Disease Facts and Figures, March 20, 2018
- 10. © 2018 Health Catalyst The use of data to advance clinical practice will have a bigger impact on health care than the discovery of antibiotics. 10
- 11. A Data First Strategy Build Institutional Analytic Skills Use Data to Improve Clinical Practice Key Takeaways
- 12. A Data First Strategy Build Institutional Analytic Skills Use Data to Improve Clinical Practice Key Takeaways
- 13. © 2018 Health Catalyst The Human Data Ecosystem
- 14. © 2018 Health Catalyst 6 Rights of a Data First Strategy 15
- 15. © 2018 Health Catalyst 6 Rights of a Data First Strategy • EHR data is necessary but not sufficient • Utilize all available data, i.e. socio- economic, environmental, genetic • Generate better data, i.e. activity based costing, patient reported outcomes 16 Data
- 16. © 2018 Health Catalyst 6 Rights of a Data First Strategy • Consistent, sharable definitions for: – Metrics – Populations – Identity – Vocabulary • Decentralized stewardship • Culture of data driven decision making and prioritization 17 Data Governance
- 17. © 2018 Health Catalyst 6 Rights of a Data First Strategy • From event to insight quickly • Framework to evaluate the cost of slow • Examine the entire process: – Generation or Entry – Movement – Processing – Analysis 18 Data Time Governance
- 18. © 2018 Health Catalyst 6 Rights of a Data First Strategy 7 Core Skills Domain Knowledge, Query, Movement, Modeling, Analysis, Visualization, Process Improvement 3 Orders of Complexity • Descriptive: What happened, what’s happening • Predictive: What’s likely to happen • Prescriptive: What interventions will have the biggest impact on the desired outcome 19 Data Time Governance Skills
- 19. © 2018 Health Catalyst 6 Rights of a Data First Strategy • At point of care/decision • Variety of form factors and modalities • Everyone in the organization 20 Data Time Place Governance Skills
- 20. © 2018 Health Catalyst 6 Rights of a Data First Strategy • Decouple the data from the apps and algorithms • Innovate with new tools and workflows • Apps both consume existing data and produce new data or insights • Enable data-first apps, not just process-first apps 21 Data Time Place Governance Skills Applications
- 21. © 2018 Health Catalyst The Data Operating System Data Ingest Real-time Streaming Source Connectors Catalyst Analytics Platform Fabric Data Services Real-time Processing Health Catalyst Applications Data Quality Data Governance Pattern Recognition Hadoop/ Spark Data Export Population & Registry Builder Leading Wisely Care Management Atlas Client-built Applications NLP Touchstone Benchmarks CORUS Cost Accounting Patient Safety Measures Manager ACO Financials Patient Engagement HL7 Data Pipelines Metadata Data Lake Reusable Content AI Models Third-party Apps Artificial Intelligence Pipelines Marketplace SAMD & SMD Fabric Application Services Terminology & Groupers EMR Integration Security, Identity & Compliance Patient & Provider Matching Value Sets & Measures Standard, Extensible Data Models RegistriesFHIR HL7 Analytic Accelerators
- 22. © 2018 Health Catalyst 23 Ford or Chevrolet Why the Bias? Huge variation in product performance and customer experience!
- 23. © 2018 Health Catalyst 2018 Chevrolet Silverado: “When nothing less than the most dependable will do” Common Structures Reduce Variation 24 What creates dependability? Customize on common platform for specific needs
- 24. © 2018 Health Catalyst Common Data Structures Reduce Variation 25 Claims Data: “When nothing less than the most dependable will do” What creates dependability? Customize on common platform for specific needs
- 25. © 2018 Health Catalyst ½ Ton Silverado Multiple Common Structures Available… 26 Mid-Size Colorado ¾ Ton Silverado 1 Ton Silverado
- 26. © 2018 Health Catalyst Claims Multiple Common Data Structures Available… 27 Populations and Registries Admissions, Orders, Labs, Rx Cost Accounting
- 27. © 2018 Health Catalyst Creating Customization in Common Data Structure 28 Measures Builder • Standardize and maintain analytic capabilities around measures within a central repository for all measure. Help inform governance and increase efficiency. Two Health Catalyst Examples Population Builder • Standardize and maintain custom populations for analytic use cases. Quickly define precise population, save, replicate on demand, and publish to downstream applications and tools.
- 31. © 2018 Health Catalyst Population Builder Demonstration… 32
- 32. A Data First Strategy Build Institutional Analytic Skills Use Data to Improve Clinical Practice Key Takeaways
- 33. © 2018 Health Catalyst Institutional Analytic Skills Typical 34 Data Scientist Sr. Analytics Engineer Analytics Engineer Report Writer Ideal
- 34. © 2018 Health Catalyst Poll a. Large report queue supplemented with occasional ad-hoc analysis b. Broad access to modern, self-service visualizations c. IT thinks they are doing a good job, but clinicians and the finance team don’t feel their needs are being met d. Analysts collaborate with clinicians and provide real-time insights; data scientists regularly create and update machine learning models e. Data scientists and analytic engineers spend lots of time on Snapchat because there isn’t enough work for them to do How would you describe your institution’s analytic situation? 35
- 35. © 2018 Health Catalyst 8 Core Analytic Skills 36 Movement Modeling Query Visualization Domain Expertise Analysis Machine Learning Process Improvement
- 36. © 2018 Health Catalyst Descriptive 37 What happened in the past, and what is happening now?
- 37. © 2018 Health Catalyst Predictive 38 What is likely to happen in the future?
- 38. © 2018 Health Catalyst Prescriptive 39 What interventions will have the biggest impact on the desired outcome?
- 39. © 2018 Health Catalyst 0 1 2 3 4 5 Reactive Descriptive Prescriptive Analytic Work-Stream Skill Continuum Health Care Data* Data Query Data Movement Data Modeling Data Analysis Data Vizualization Process Improvement Technical Assessment: Analysts (n=41) Skill Capacity Skill Gap Descriptive Predictive Prescriptive
- 40. © 2018 Health Catalyst Consolidate analytic expertise Mentorship and education Outsourcing Strategies to Close the Skills Gap 41 Current Ideal
- 41. © 2018 Health Catalyst Mentoring and Education 42 Outsourcing Costs Time Investment Costs Time Value Delivered
- 42. A Data First Strategy Build Institutional Analytic Skills Use Data to Improve Clinical Practice Key Takeaways
- 43. © 2018 Health Catalyst A Recipe for Sustainable Data Driven Improvement 44
- 44. © 2018 Health Catalyst Total Hip (THA) and Total Knee (TKA) Arthroplasty are the most prevalent surgeries for Medicare patients, numbering over 400,000 cases in 2014, costing more than seven billion dollars annually for the hospitalization alone. Today, more than seven million Americans have hip or knee implants, and the number is rising. Furthermore, substantial variation in the cost per case has raised questions about the quality of care. At Thibodaux Regional Medical Center, total joint replacement for hips and knees emerged as one of the top two cost-driving clinical areas with variation in care processes. To address this, Thibodaux Regional maintained its focus on the IHI Triple Aim while developing organizational and clinical strategies to transform the care of patients undergoing THA and TKA. Thibodaux Regional successfully transformed the care processes and outcomes for patients undergoing hip and/or knee joint replacement. Results include: 76.5% relative reduction in complication rate for total hip and total knee replacement. 38.5% relative reduction in LOS for patients with total hip replacements. 23.3% relative reduction in LOS for patients with total knee replacement. $815,103 cost savings, achieved in less than two years. Using Data to Spotlight Variation and Transform Total Joint Care $
- 45. © 2018 Health Catalyst Total-Joint: Kip and Knee Demonstration… 46
- 46. 6 Rights of a Data First Strategy Data Time Place Governance Skills Applications
- 47. Consolidate analytic expertise Mentorship and education Outsourcing Build Institutional Analytic Skills
- 48. Use Data To Improve Clinical Practice 76.5% relative reduction in complication rate for total hip and total knee replacement. 38.5% relative reduction in LOS for patients with total hip replacements. 23.3% relative reduction in LOS for patients with total knee replacement. $815,103 cost savings, achieved in less than two years.$
- 49. Questions Jared Crapo <jared.crapo@healthcatalyst.com> Sam Turman <sam.turman@healthcatalyst.com>
- 50. Thank You
Editor's Notes
- Thanks for joining us today. In the next hour, Sam and I will share with you some of our ideas about how we can use data first strategy can advance clinical practice and outcomes improvement. We’ll talk about some big ideas, and some practical recommendations we hope you can use right away. I’d like to begin by telling you two stories:
- Appointed Chief Resident at First Obstetrical Clinic of Vienna Hospital in 1846. The First Clinic trained physicians, the Second Clinic trained midwives. The mortality rate in the second clinic was about 3% Maternal mortality rate in the First Clinic averaged 10% but often spiked as high as 30% Most of the women died of a disease they called ”childbed fever”, which we would now call sepsis His friend Jakob was cut with a student’s scalpel while performing a post-mortem examination, and a few days later Jakob died with a disease very similar to childbed fever. He instituted a handwashing policy using chlorinated lime, because he found this solution worked best to remove the putrid smell of infected autopsy tissue
- After instituting the handwashing policy, the mortality rate dropped by 90% No scientific basis for chlorine handwash, but the data clearly showed this intervention worked. Everyone thought he was crazy. He got fired, and was committed to an asylum, where he died.
- -In the late 1850’s Louis Pasteur developed and proved his germ theory, which provided a scientific explanation for Ignaz Semmelweis observations. - Building on Louis Pasteur’s germ theory, Joseph Lister discovered that carbolic acid reduced infection rates for surgical patients. - First antiseptic surgical procedure in the US was performed in 1876 at Presbyterian Hospital in NYC This is a picture of the device they used to blanket the air around the patient with an aerosol of carbolic acid. - As late as 1882 at the annual meeting of the American Surgical Association, anti-Listerism was still the posture of a majority of members Listerine was originally developed in 1879 by Joseph Lawrence, a chemist in St. Louis, Missouri. It’s named after Joseph Lister. It originally was marketed as a general purpose antiseptic, but got famous after a big marketing campaign in the 1920’s as a solution for chronic halitosis.
- - Two days before King Edward VII’s scheduled coronation, he was diagnosed with appendicitis. - The King asked for lister’s advice, and an antesceptic procedure was performed per Lister’s recommendations. - This surgical procedure on the King was about the time antisceptic surgical procedures were widely and commonly utilized
- The second story is about Harald zur Hausen. 2008 Won Nobel Prize in Medicine Half of the women diagnosed with cervical cancer are between 35 and 55 years of age. The earliest cohorts of vaccinated adolescents are still only 25 years old.
- - Lister and zur Hausen had to make their discoveries and validate them. - Today 36,000 randomized controlled trials are published each year, there is plenty of high quality medical evidence out there [read poll question] - The NEJM says it takes 17 years for validated findings to reach broad clinical practice. - https://catalyst.nejm.org/implementing-evidence-based-practices-quickly/ - At Kaiser Permanente, they launch about 1 implementation of a new evidence based practice per month. - Mean time from publication of evidence to launch of implementation is 14 months. - Unfortunately they didn’t publish how long it took from implementation to widespread adoption.
- In 2014 12,578 women were diagnosed with cervical cancer. 480,000 people were diagnosed with Alzheimers. - 5.7 million Americans have the disease - Deaths from other major causes continue to decline; alzheimers deaths have risen 123 percent from 2000 to 2015 - It’s the only top 10 cause of death that can’t be prevented, cured, or slowed (it’s the 6th leading cause of death) - In 2018 estimated direct costs of caring for those with Alzheimer’s will total $277 billion including $186 billion in Medicare and Medicaid payments. That’s more than we spend on all cancer care. - Estimated 18.4 billion hours of unpaid care annually, valued at $232 billion Click to reveal no prevention, cure, or way to slow progression. Say we can shave 12 years off the timeline for HPV. If it takes us 30 years to get an Alzheimers treatment to widespread clinical practice, it will almost be 2048, when the annual cost of Alzheimer’s care is projected to be more than $1.1 trillion.
- Data is the fuel for future advancements in health
- You don’t have to find an effective Alzheimers treatment in order to be a little better today than we were yesterday. I hope that something we discuss together today triggers an Aha moment for you: something you can make use of
- Semmelweis kept his data in his personal notebook. The types and quantities of data available to us today are much greater. Research done by the University of Alberta determined that 8% of the data needed for population health in in the EHR.
- The Health Catalyst Data Operating System is a platform designed to support a Data First strategy. It combines modern software engineering practices with hard lessons learned
- Ford or Chevy Apple or Android Costco or Sam’s Club Think of your brand in your mind… Why the BIAS? My Father had a horrible ford Truck in the early 70’s and swore he would never drive another one. My Favorite Uncle had a horrible Chevy in the early 70’s and swore he would never drive another one. FAST FORWARD to today… I truly don’t have a preference for one brand over the other ALTHOUGH I am definitely in the market for a new pickup truck WHAT HAS CHANGED? Huge variation in product performance and customer experience has changed. Huge variation in the 70s and not so much today in the auto industry
- What creates dependability? Limited to no variation in basic platform (chassis, drive train, body panels, overall design are COMMON amongst all models) Common core structures promotes safety, low variable cost, easy maintenance, dependability for consumer Customization bolted on to common platform for specific needs Accommodate customizations purchasing upgraded trim packages available from manufacturer (LS, LT, LT Z71, LTZ, LTZ Z71) Further customization available through after market products to create the TOOL that fits your specific needs (rims, tires, lift kits, tool boxes, roll cages, bumper guards, performance parts, obnoxious off road light bars, etc.) Assemble the best and right tools to fit your need
- Common base models and structures for auto industry…. Common data structures for health care. Let’s take claims for example. ‘Claims, when nothing but less than the most dependable will do’… which is funny because claims is notoriously a pain to work with… how do we make working with claims palatable? We create dependability What creates dependability? Limited to no variation in basic platform (data structure, mappings, coding, governance, common data pipelines) Common core structures promotes safety, low variable cost, easy maintenance, dependability Customization bolted on to common platform? Accommodate customizations purchasing upgraded trim packages available from manufacturer. Applications from Health Catalyst (Measure Builder, Population Builder, activity based costing with our CORUS product, etc.) Additional capabilities available through after market products as Catalyst supports an open API (VisionWare = EMPI patient matching with MultiVue; Regenstrief Institute = Natural Language Processing with nDepth; Clinical Architecture for Technology Services) Assemble the best tools to fit your need
- Multiple common bassline tools available out of the box…. Not just Truck, but Car, SUV, Van lines, etc.
- These are a few examples of the base line tools provided by Health Catalyst that create constancy and dependability in data delivery for improvement work. The beauty is taking this basic well functionating dependable data asset and making it your own with trim packages and customizations.
- A few examples of these customization on to of common data structures are the Catalyst Measure Builder and Population Explorer Measures Builder All of us in healthcare have some burden of reporting and adhering to certain measures… be it CMS, Private Party Contracts for specified populations, etc… Heaven forbid they have the same definition across measures… Wouldn't it paradisiacal if our disparage reporting agencies did have the same definition? While we don’t have a tool to help fix the disparate definitions we do have the ability to take those endless variations in measures and pull them off of the spreadsheets in numerous office mangers and analysts offices desk and desktop where they live and put them into a format that can help… By allowing a central repertory for measures to be stored electronically (instead of spreadsheets) Help standardize and maintain analytic capabilities around measures Compare measures to find overlap, inform data governance, and increase efficiency Use measures quickly and easily inform downstream application (gaps in care) * Huge value is helping to take overhead manual process away from your analyst and managers and allow them to spend more time working towards meaningful improvement like curing Alzheimer's Population Builder Helps standardize and maintain custom populations for analytic use cases Drag and drop interface allowing quick exploration and building of custom cohorts and populations. Filter by details of Encounter, Diagnosis, Demographic, Lab, Registries, etc. Extremely detailed filtering ability including inclusion and exclusion logic Quickly define precise population, save, replicate on demand, and publish to downstream applications and tools like the Catalyst Total-Joint: Hip and Knee
- Population Builder Helps standardize and maintain custom populations for analytic use cases Drag and drop interface allowing quick exploration and building of custom cohorts and populations. Filter by details of Encounter, Diagnosis, Demographic, Lab, Registries, etc. Extremely detailed filtering ability including inclusion and exclusion logic Quickly define precise population, save, replicate on demand, and publish to downstream applications and tools like the Catalyst Total-Joint: Hip and Knee So we have used the Measures Builder to consolidate, compare, and organize our measures for analytic purposes. NOW how do we go about building populations a cohorts to reconcile metrics across population to accommodate the different populations to satisfy specific measure cohort definitions. We use Pop builder to FIND, SAVE, and MANAGE populations. The ability to create these definition made available to any user instead of being buried in SQL code. Quick and easy drag and drop interface allows us to apply populations filters. In this use case we are interested in exploring our total joint replacement populations as it has been identified as an areas of high variation. Under encounter filters lets search for MS DRG code 469 and 470 Lets look for our most at risk demographics with 65 to 110 Lets look for patients who may have not had proper lab work up prior to joint replacement (CBC, URINALYSIS, ARTERIAL BLOOD GAS) Lets now look for some post operative opportunity by looking for patients who may have had a previous diagnosis of SUBSTANCE ABUSE
- Here's a list of four jobs in an Analyst job family. [briefly describe] <click> A typical healthcare institution is heavily weighted for report writers. <click> An ideal mix of resources is much more balanced.
- Let’s dig in to the various dimensions of analytic skills
- In addition to the 8 core analytic skills, there are 3 orders of complexity in analytics. The first is descriptive analytics, which helps us answer the question, []. We’ll compare these three orders of complexity to mathematics. Descriptive analytics are like this algebra equation. <click> Quadratic formula, which helps you solve quadratic equations.
- The second order of complexity is predictive analytics, which helps us answer the question []. <click> We can compare that level of complexity to this statistical equation for the correlation co-efficient of two data sets
- The highest order of complexity is prescriptive analytics. <click> Surface integral of a scalar field
- Data is a strategic asset. A data first platform and institutional analytic capabilities help us maximize the value of that asset. How do we apply these assets to repeatedly and sustainably improve clinical and financial outcomes?
- Small community hospital in the Mississippi Delta. Though they are small have won numerous JD Power and Associate awards for quality. Why: Thibodaux Regional Medical Center has long been focused on achieving the IHI Triple Aim (Better Health for population, Better value by reducing cost, Better care in patient satisfaction) and is committed to providing high-quality, comprehensive orthopedic services. Grabber: Today, more than seven million Americans are living with an artificial hip or knee. These total joint replacements are the most common inpatient surgeries for Medicare beneficiaries, costing more than $7 billion annually for the hospitalization alone. Big Picture: Total joint replacements are the most prevalent surgeries for Medicare patients and numbered over 400,000 cases in 2014. There is substantial variation in the cost of total joint replacement, with the average Medicare expenditure ranging from $16,500 to $33,000. At Thibodaux Regional, total joint replacement for hips and knees emerged as one of top two cost-driving clinical areas with variation in care processes. Turning Point: Thibodaux Regional also identified sizeable variation in LOS, cost of care, and complication rates. It developed organizational and clinical strategies to transform joint replacement care. It commissioned a Care Transformation Orthopedic Team that set multiple outcome goals. Among its many efforts, the team established standard care processes, created an educational program, redesigned order sets and workflows, and deployed the Health Catalyst Joint Replacement – Hip and Knee Improvement application to monitor compliance with the identified interventions and their impact on outcomes. Thibodaux leveraged the analytics teams and tools, best practices, and adoption methodologies offered by Health Catalyst. RECALLING the earlier example of Population Builder Thibodaux was able to identify their THA and TKA patients populations, filter by patients in the proper demographic, proper pre operative workup, and apply them to the Total Joint application. Resolution: Thibodaux Regional successfully transformed the care processes and outcomes for patients undergoing hip and/or knee joint replacement. Results include: 76.5 percent relative reduction in complication rate for total hip and total knee replacement. 38.5 percent relative reduction in LOS for patients with total hip replacements. 23.3 percent relative reduction in LOS for patients with total knee replacement. Improved patient education, early mobilization, and decreased use of opioids have contributed to a shortened LOS. $815,103 cost savings, achieved in less than two years. All changes accomplished while maintaining high levels of patient satisfaction.