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Finally…Healthcare 2.0 Data and The Age of Analytics Dale Sanders, June 2012.

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Presentation on theme: "Finally…Healthcare 2.0 Data and The Age of Analytics Dale Sanders, June 2012."— Presentation transcript:

1 Finally…Healthcare 2.0 Data and The Age of Analytics Dale Sanders, June 2012

2 My Background Analytics and data warehousing, the constant theme A CIO by various titles for 70% of my 29 yr career – 15 years: US Air Force, national intelligence, consulting – 14 years: Healthcare Integrated Delivery System: Intermountain Academic Medical Center: Northwestern National Health System: Cayman Islands Founder, Healthcare Data Warehousing Association Current affiliations – Mentor CIO, Cayman Islands NHS – SVP, Healthcare Quality Catalyst – Senior Research Fellow, The Advisory Board Company

3 Of Course We Have to Talk About It… The Supreme Court Decision Individual Mandate in the Affordable Care Act – The only valid constitutional debate. Everything else is just politics. Congress has the clear right to levy taxes – If you dont buy insurance, you have to pay the IRS an additional penalty… specifically avoided using the word tax Insurance exempted from Congressional oversight as an Interstate Commerce (McCarran- Ferguson Act 1945) – Individual Mandate would be unconstitutional as a form of Congressional regulation of interstate commerce The Justices opinions pound this its a tax but not a tax back and forth like a tennis ball But… – A tax cannot be legally challenged until it is levied (Anti Injunction Act, 1793) – Individual Mandate does not take effect until 2014 – I wouldnt be surprised if it gets challenged again, as a tax In any case, the Act is going to increase the cost of care to taxpayers so get ready for healthcare costs to reach >20% of GDP if not amended

4 Agenda New thinking in Healthcare 2.0 A Roadmap for Healthcare Analytic Adoption Analytics: The Technical Options Prioritizing Analytics & Process Improvement Predictive Analytics vs Suggestive Analytics Watson and Big Data

5 Healthcare Billing at a Restaurant You wait 45 minutes for a table, even though you had a reservation. You tell the waiter that youre hungry– but theres no menu. The waiter returns with a meal that he thinks is appropriate for you…but he doesnt know how much it costs. You have no idea what the food is or what it costs, but you agree to eat it. You leave without knowing your bill. The restaurant sends the bill to your bank, not you. Your bank tells the restaurant, Your waiter ordered the wrong thing for you. Were not paying for it. 90 days later, the restaurant calls to tell your account is being turned over to collections. 5

6 Employers Are Taking Over We know the healthcare systems that provide the best care and control their costs. We know which ones can show us their data that proves they manage quality and cost. And we know where those healthcare systems operate, geographically. Guess what criteria we consider most important, now, when making a decision to open a new plant or office? – Paul Grundy, MD, Global Director of IBM Healthcare Transformation

7 The Healthcare 2.0 Analytics Equation The Value of Your Healthcare Product = Quality of Health Cost of Production + Margin $$ dividends to employers and patients Cost of Production = Cost of Operations The CFO in Healthcare 2.0 must be able to answer: How much does it cost for us to produce the best health?

8 Healthcare Data Maturity Stages Stage 1: Data Collection – Characterized by the expanded adoption of EMRs Stage 2: Data Sharing – Characterized by the expanded adoption of HIEs Stage 3: Data Analysis – Characterized by the adoption of data warehouses Healthcare 2.0 Healthcare 1.0

9 Analytic Technology Is the Easy Part There are a lot of companies who think they are using data…but historically that sort of data has been used to confirm and support decisions that had already been made by management, rather than learn new things and discover what the right answer is. The cultural change is for managers to be willing to say, Thats an interesting problem, thats an interesting question. Lets set up an analysis to understand it; lets set up an experiment; Show some vulnerability and say, Look we are open to the data. Erik Brynjolfsson Schussel Family Professor of Management MIT Sloan School of Management Analytic Value = Analytic Culture x Analytic Technology

10 Health 2.0 Analytics: Beyond Utilization Metrics 1 2 3 4 5 6 7

11 Key Message: Rapidly Adaptable Analytics As a C-level executive in healthcare, everything I want to improve is constrained by software that cant adapt fast enough Healthcare 2.0 analytics will require a rapidly adaptable infrastructure of data and visualization tools – Data collection must be adaptable– EMRs and other source systems – Data extraction and loading must be adaptable – Analytic data models must be adaptable – Visualization tools must fit a variety of needs The visualization tools and the underlying data models must be de-coupled

12 Assessing Analytic Capability The EMR Adoption Model from HIMSS – Imperfect, but invaluable to the industry What about the same concept applied to analytics? 12

13 13

14 Vocabulary Infrastructure established: Searchable metadata repository, core data elements linked with standardized naming and data types Level 1 Automated internal reporting: Key performance indicators and dashboards for hospital and clinic management, for executives, front line managers, and physicians Level 2 Automated external reporting: For financial incentives such as P4P, PQRS and MU; and accreditation/regulatory bodies such as JCAHO, ACC, STS, NRMI, HEDIS Level 3 Broad analytic deployment: A permanent integrated technical and clinical improvement teams for top 10 conditions; self-service data visualization for at least 60% of employees Level 5 Waste identification and elimination: Integrated patient specific costing and claims data used for identification and elimination of non-value add/non-evidence based activities Level 6 Personalized patient analytics: Integration of genomic, familial, text, and patient self-reported data used for predicative modeling, preventative care and wellness management Level 7 Evidenced-based analytics: Patient registries for top ten conditions within the organization; Chronic condition management reports; measurement of clinical guideline usage (e.g. orders sets) Level 4 Cumulative Capability Level Healthcare Analytic Adoption Model © Level 0 Major data sources in a single repository: Minimum EMR Level 3 data, Revenue Cycle, Financial, Costing, Supply Chain, Patient Experience integrated into a data warehouse

15 How Are You Going To Get There? How long will it take to reach Level 6? – With the right combination of technology, cultural alignment and process improvement framework… – You can get there in 9 months, in one Clinical Work Process area How much will it cost? – At Northwestern, we spent about $1M per year for three years to reach a robust, sustainable capability

16 Technical Options Available Today EMR vendors – No track record of success – Analytics are limited to the data collected in their products Build your own from scratch – Costly, risky… would you build your own EMR? Point solutions – One for JCAHO, one for physician performance, one for supply chain, one for hospital operations, et al… – Redundant patch work of data; costly; not extensible; enterprise wide analytics are not possible – Scarce analyst skills are spread across multiple products

17 Analytic Options Available Today (cont) Outsource and build from scratch – Consulting firms generally use this approach – Costly, risky Outsource and build with generic, reusable enterprise healthcare data model – IBM, Oracle, I2B2 – Generic models = One size fits all = Poor adaptability – Are there any success stories? Outsource and build with adaptable, reusable data models, ETL, and data marts – Very few vendors in this space

18 WHATS NEXT? So… you have the Enterprise Data Warehouse technology

19 Quality Improvement: Focus on Process One of the fundamental concepts of quality improvement theory is to identify key work processes, then organize around them. – A limited number of these processes make up the vast majority of services you provide to patients (80/20 rule). We want to prioritize this subset of key processes in our quality improvement efforts. 19

20 Key Process Analysis (KPA) 2002: Dr. David Burton, Tom Burton – Develop the KPA model while they were at Intermountain – Used the Enterprise Data Warehouse (EDW) as the enabler How do we identify the care processes that offer the greatest opportunity for quality and cost improvement? – Normalized for apples-to-apples comparison across clinical process families – Adjusted for severity

21 EDW Case Mix Billing Data Cost Data APR/DRG Groupings KPA Data Flow Care Process Data Mart EDW KPA Algorithm Highest Opportunity Care Processes KPA Visualization Clinical Leadership Detailed analysis of variation and outcomes Analysts

22 Organizing Around Processes For example… Clinical Program: Women & Newborns – Care Process Family: Deliveries Clinical Work Process: Vaginal & C-Section – APR/DRG Grouper Codes (subset example here…) » 540 Cesarean » 542 Vaginal with complications » 560 Vaginal, normal delivery

23 Process the Data through the KPA Algorithm 1.Organize the codes into Work Processes 2.Calculate and rank by frequency (case count) and cost dollars for each clinical work process 3.Determine percentages of total cost dollars each clinical work process represents 23

24 Pareto Analysis In-patient Resources 24 Cumulative % % of Total Resources Consumed for each sub- service line Key Findings: Number of Sub-Service Lines (e.g., Delivery, Medical Cardiology, Gastroenterology) 80% of all in-patient resources are represented by 23 Sub-Service Lines 23 CPMs 80% 9 CPMs 50% 50% of all in-patient resources are represented by 9 Sub-Service Lines

25 Prioritize Opportunity = Volume x Variation – Removing variability in processes is the first step in process improvement and measurement – As a general rule, standardization leads to lower cost and better outcomes Because we will not be able to work on all clinical work processes at once, we must have some way of prioritizing and planning our work to pursue the greatest opportunities for improvement first. 25

26 26 Internal Variation versus Variable Direct Cost Y- Axis = Internal Variation in Variable Direct Cost Bubble Color = Clinical Process Bubble Size = Case Count X Axis = 2009-2011 Variable Direct Cost 1 2 3 4

27 Coefficient of Variation The Coefficient of Variation allows variation to be evaluated between data sets with different scales Coefficient of Variation = Standard Deviation Mean 27

28 Sample Data Admit dates 2009 – 2011 – 5.3M records/encounters Inpatient – 776,895 records/encounters APRDRG, AdmitDate, and DischargeDate are not null – 242,675 records/encounters 28

29 Case Count Pareto by Sub-Service Line 29

30 The Data By Clinical Area RAN KService LineSub-Service Line % of Total Running %Case Count Work Process Count 1 Women & Children'sDelivery12.2% 29,4322 2 Women & Children'sNormal Newborn11.9%24.1% 28,6241 3General MedicineGastroenterology7.2%31.3% 17,30213 4Cardiac ServicesMedical Cardiology6.9%38.2% 16,73913 5General MedicinePulmonology6.2%44.4% 14,9049 6Behavioral HealthPsychiatry5.1%49.5% 12,30410 7OrthopedicsJoint Replacement3.8%53.3% 9,0773 8General SurgeryColorectal/Lower GI3.2%56.5% 7,8061 9Behavioral HealthSubstance Abuse2.8%59.3% 6,8453 10General MedicineNephrology2.7%62.1% 6,5905

31 Sub-Service Line Options Data Driven Criteria Review Service LineSub-Service Line Case Count Rank Payments Rank LOS Hours Rank Variable Direct Cost Rank Variable Direct Cost Opportunity Rank Data Driven Criteria Results Behavioral HealthPsychiatry613291 ? Women & Children'sDelivery11112 ? Cardiac ServicesMedical Cardiology45633 ? General MedicinePulmonology57564 ? OrthopedicsJoint Replacement721125 ? General MedicineGastroenterology36346 ? General MedicineInfectious Disease118987 ? Oncology/Hematol ogy (Medical)Oncology (Medical)141115148 ? General Surgery Colorectal/Lower GI83779 ? SpineFusion229231110 ? 31

32 32 Women & Childrens: C-Section Average Variable Direct Cost per Case by Provider by Severity Score Bubble Color = APR DRG Severity Score Bubble Size = Case Count for provider X Axis = Average Variable Direct Cost per Case for provider Y Axis = Grouped by APR DRG - Severity Score

33 33 Women & Childrens: Vaginal Delivery Average Variable Direct Cost per Case by Provider by Severity Score Bubble Color = APR DRG Severity Score Bubble Size = Case Count for provider X Axis = Average Variable Direct Cost per Case for provider Y Axis = Grouped by APR DRG - Severity Score

34 34 Women & Childrens: Normal Newborn Average Variable Direct Cost per Case by Provider by Severity Score Y Axis = Grouped by APR DRG - Severity Score Bubble Color = APR DRG Severity Score Bubble Size = Case Count for provider X Axis = Average Variable Direct Cost per Case for provider

35 35 Women & Childrens: Normal Newborn Average LOS Hours per Case by Provider by Severity Score Y Axis = Grouped by APR DRG - Severity Score Bubble Color = APR DRG Severity Score Bubble Size = Case Count for provider X Axis = Average Variable Direct Cost per Case for provider

36 The Data Might Be Ready, But… When choosing a clinical process improvement area to address, consider… – Clinical leadership readiness – Can the vision for the clinical program be articulated? – External pressure and agendas Community, State, or Federal Employers Payer incentives Donors – Research agendas

37 Clinical Impact What about… There are dozens, but we are running out of time, so just a few around appendectomy…

38 Understanding Appendectomy LOS

39 Waste In Healthcare Don Berwick, JAMA, April 2012 Annual waste (2011 figures) – Failures of care delivery: $102B-$154B – Failures of coordinated care: $25B-$45B – Overtreatment: $158B-$226B – Administrative complexity: $107B-$389B – Pricing failures: $84B-$178B – Fraud and abuse: $82B-$272B

40 % of Appendectomy Patients Receiving Evidence Based Tests

41 Evidence-Based Antibiotic Use

42 Predictive Analytics, Watson, Big Data What about…

43 The Problem with Predictive Analytics Predicting healthcare comes in two flavors – Too easy BMI 29, smoker, sedentary = multiple chronic diseases 65 yrs, living alone, post-CABG = re-admission – Too hard: BMI 21, active, 39 yrs, non-smoker = stroke BRCA1 and BRCA2 mutations = 60% chance of breast cancer – Very difficult personally to take action…what would you do? – Over 150 known genetic markers for risk that we largely ignore

44 Predicting vs. Acting Even if we can predict, we have major obstacles against proactive mitigation & intervention – Culturally – Operationally – Behaviorally We knew the [9/11] scenario was a risk, and some airlines had already put storm doors on their cockpits to mitigate hijackers, but we just didnt push it [the mitigator] hard enough.

45 Suggestive Analytics © Surround the decision making environment with suggestions, based on analytic data – Much easier than predicting Worth reading – Nudge: Improving Decisions About Health, Wealth, and Happiness

46 Suggestive Analytics

47 The Antibiotic Assistant Antibiotic Protocol DosageRouteIntervalPredicted Efficacy Average Cost/Patient Option 1500mgIVQ1298%$7,256 Option 2300mgIVQ2496%$1,236 Option 340mgIVQ690%$1,759 47 An EMR clinical decision support tool developed at Intermountain Healthcare

48 The Antibiotic Assistant Impact Complications declined 50% Avg # doses declined from 19 -> 5.3 The replicable and bigger story – Antibiotic cost per treated patient: $123 -> $52 – By simply displaying the cost to physicians 48

49 Watson First, a little background – National Security Agency – Natural Language Processing and Text Mining Watson is revolutionary. – Its the first thing in my IT career that really excited me… everything else has been incremental or variations of the same flavor

50 Watsons Technology Apache – Unstructured Information Management Architecture (UIMA) – Hadoop – Java, C++ Lexicals and ontologies – DBPedia, WordNet, and Yago IBM Content Analytics with Enterprise Search 90 IBM Power 750 servers enclosed in 10 racks 16 Terabytes of memory A 2,880 processor core Linux based Estimated to have cost $1B - $2B

51 What is Watson? Near-word associations coupled with semantic mapping and zillions of sources of knowledge… digitized books, encyclopedias, news feeds, magazines, blogs, Wikipedia, etc. – Equivalent to approximately 240 million pages, in memory Jeopardy answer – A famous red coiffed clown or just any incompetent fool Watsons correct answer – Who is Bozo? Watson searched its indexes for near-word associations, recognized that Bozo was the most common word in the indexes that was missing from the question

52 Watsons Problem With Healthcare Watsons training set for Jeopardy was a HUGE collection of human wisdom, academic and otherwise, stretching back 1,000 years – Wikipedia; digitized books, magazines, newspapers, journal Whats the training set for healthcare wisdom? – A few decades of clinical trials and journals? – Claims processing data from a dysfunctional healthcare system? – No outcomes data to speak of… – Progress notes? Radiology reports? Pathology reports? Watson is not going to impact healthcare in the near term like many hope it will…but its still very cool

53 Big Data Technology Big Data technology was built to process web log, semi- and non-structured tagged text on a massive basis for Google, Amazon. eBay, Facebook, etc. – Our text data in healthcare is not massive and is not tagged – We (healthcare) are small fry data compared to Silicon Valley … we can solve our analytic needs with less complex technology Completely different business processes and information context – Googles and Facebooks of the world are collecting and analyzing data about their business processes that are completely different in content and structure than anything in our current healthcare environment The skills required for big data are equally big and more rare than platinum No major impact on healthcare other than gee whiz for at least 4-6 years.

54 Questions, Thoughts, or Challenges? Cell/Text: 970-403-6090 LinkedIn:

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