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Charles G. Macias M.D., M.P.H. The Imperative of Linking Clinical and Financial Data to Improve Outcomes Chief Clinical Systems Integration Officer, Texas.

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Presentation on theme: "Charles G. Macias M.D., M.P.H. The Imperative of Linking Clinical and Financial Data to Improve Outcomes Chief Clinical Systems Integration Officer, Texas."— Presentation transcript:

1 Charles G. Macias M.D., M.P.H. The Imperative of Linking Clinical and Financial Data to Improve Outcomes Chief Clinical Systems Integration Officer, Texas Children’s Hospital

2 Learning objectives Assess the effectiveness of an organization’s quality gaps to ensure organizational readiness, drive efficiency and leverage opportunities to improve quality. Illustrate how a blend of clinical and financial data informed by analytics from an enterprise data warehouse can improve outcomes. Describe how an EDW and care process implementation can encourage a culture of quality and safety, providing physicians with the necessary tools to integrate financial relevance into the practice of delivering high-quality healthcare. Discuss how strategy for integration of science, data and predictive analytics and operational improvement through improvement science can transform a system towards the triple aim.

3 Jenny Jones and the Challenges of a Fragmented System Within six months, Jenny had visited: One PCP Two Hospitals Three ERs Leading to: Six different Asthma Action Plans with conflicting discharge instructions

4 Quality Defined Institute of Medicine domains:  Safe  Effective  Efficient  Timely  Patient centered  Equitable The degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge. – Lohr, K.N., & Schroeder, S.A. (1990). A strategy for quality assurance in Medicare. New England Journal of Medicine, 322 (10): Importance of minimizing unintended variation in health care delivery 12 3

5 The Healthcare Value Equation In an environment where cost is marginally increasing, healthcare must markedly improve quality. Adoption of EMRs and clinical systems should help push the quality agenda but alone may not be enough to deliver data intelligence. 4 Value = Quality Cost

6 I n Second Look, Few Savings from Digital Health Records New York Times: January 10, 2013  2005 RAND report forecasts $81 billion annual U.S. savings. “Seven years later the empirical data on the technology’s impact on health care efficiency and safety are mixed, and annual health care expenditures in the United States have grown by $800 billion.”  Disappointing performance of health IT to date largely attributed to:  Sluggish adoption of health IT systems, coupled with the choice of systems that are neither interoperable nor easy to use;  The failure of health care providers and institutions to reengineer care processes to reap the full benefits of health IT.  EHRs, Red Tape Eroding Physician Job Satisfaction  Most physicians express frustration with the failure to provide efficiency.  20% want to return to paper 5

7 Variation in Care 6  Describing variation in care in three pediatric diseases: gastroenteritis, asthma, simple febrile seizure  Pediatric Health Information System database (for data from 21 member hospitals)  Two quality-of-care metrics measured for each disease process  Wide variations in practice  Increased costs were NOT associated with lower admission rates or 3-day ED revisit rates  Implications?  Optimal care may be delivered at a lower cost than today’s care! Kharbanda AB, Hall M, Shah SS, Freedman SB, Mistry RD, Macias CG, Bonsu B, Dayan PS, Alessandrini EA, Neuman MI. Variation in resource utilization across a national sample of pediatric emergency departments. J Pediatr. 2013

8 Consumer Care/Cost Uncertainty Consumers:  Trust their physicians  Hope for the best  Struggle to understand cost and care  Don’t often know what they are getting  Don’t always get great outcomes Value is what they want 7

9 Challenge of Healthcare Physicians are:  Driven by science and key values  Overwhelmed with medical literature  Not well trained to turn that experience into high quality patient outcomes Transparency of local data is part of the solution! 8

10 Poll Question #1 In your organization, what percentage of patient visits are your physicians talking about cost and care tradeoffs at the bedside? 9 a)0-19% b)20-39% c)40-59% d)60-79% e)80-100% f)Unsure or not applicable

11 Source: SAEM. Evidence Based Medicine Online Course 2005 Physicians and Care Cost Clinical Expertise Evidence Patient values and preferences Physician preferences Resource issues

12 Once taboo, physicians should take cost into consideration: 11 No MoneyNo MissionNo ExpansionNo Innovation And so providers must…..  Understand what creates improvements  Understand the story that their data tells.

13 About Texas Children’s Hospital Statistics Number of Beds469 Annual Inpatient Admissions 21,744 Annual Outpatient Visits 1.44 million Emergency Room Visits 82,049 Inpatient Surgeries8,655 Outpatient Surgeries 14,439

14 A data management strategy to improve outcomes IMPROVED OUTCOMES from high quality of care SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) ANALYTIC SYSTEM Data analytics and collaborative data CLINICAL CONTENT SYSTEM Science and evidence DEPLOYMENT SYSTEM Operations Informatics, Electronic Data Warehousing Advanced Quality Improvement course, QI curriculum, Care process teams Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials Patient centric outcomes and institutional outcomes achieved

15 Creating a foundation for EB practice IMPROVED OUTCOMES from high quality of care ANALYTIC SYSTEM Data analytics and collaborative data CLINICAL CONTENT SYSTEM Science and evidence DEPLOYMENT SYSTEM Operations Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)

16 Evidence-Based Guidelines: EBOC Deep Vein Thrombosis Diabetic Ketoacidosis Fever and Neutropenia in Children with Cancer Fever Without Localizing Signs (FWLS) 0-60 Days Fever Without Localizaing Signs (FWLS) 2-36 Months Housewide Procedural Sedation Hyperbilirubinemia Neonatal Thrombosis Nutrition/Feeding in the Post-Cardiac Neonate Rapid Sequence Intubation Skin and Soft Tissue Infection Status Epilepticus Tracheostomy Management Urinary Tract Infection Acute Chest Syndrome Acute Gastroenteritis Acute Heart Failure Acute Hematogenous Osteomyelitis Acute Ischemic Stroke Acute Otitis Media Appendicitis Arterial Thrombosis Asthma Bronchiolitis Cancer Center Procedural Management Cardiac Thrombosis Central Line-Associated Bloodstream Infections Closed Head Injury Community-Acquired Pneumonia Cystic Fibrosis – Nutrition/GI >12 y/o Autism Assessment and Diagnosis C-spine Assessment Intraosseus Line Placement IV Lock Therapy Postpartum Hemorrhage

17 Poll Question #2 In ambulatory settings, what is the best estimate for the percentage of questions for which evidence exists to answer clinical questions that affect the decision to treat? 16 a)5% b)10% c)15% d)25% e)50% f)Unsure or not applicable

18 Creating a foundation for data use IMPROVED OUTCOMES from high quality of care ANALYTIC SYSTEM Data analytics and collaborative data CLINICAL CONTENT SYSTEM Science and evidence DEPLOYMENT SYSTEM Operations Informatics, Electronic Data Warehousing SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)

19 Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary; Late binding Financial Source Marts Administrative Source Marts Departmental Source Marts Patient Source Marts EMR Source Marts HR Source Mart FINANCIAL SOURCES (e.g. EPSi,) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Epic) EMR SOURCE (e.g. Epic) DEPARTMENTAL SOURCES (e.g. Sunquest Labs) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Human Resources (e.g. PeopleSoft) Human Resources (e.g. PeopleSoft) TCH’s EDW Architecture Operations Labor productivity Radiology Practice Mgmt Financials Patient Satisfaction + others Clinical Asthma Appendectomy Deliveries Pneumonia Diabetes Surgery Neonatal dz Transplant

20 Creating a foundation for QI deployment IMPROVED OUTCOMES from high quality of care ANALYTIC SYSTEM Data analytics and collaborative data CLINICAL CONTENT SYSTEM Science and evidence DEPLOYMENT SYSTEM Operations Advanced Quality Improvement course, QI curriculum, Care process teams

21 Avenues for Dissemination QUALITY LEADERS National Programs and Partnerships ADVANCED Classroom (e.g. AQI Program, Six Sigma Green Belt) Project Required INTERMEDIATE Online and Classroom (IHI Educational Resources, PEDI 101, EQIPP, Fellows College) Project Required BEGINNER Online and Classroom (e.g. Nursing IMPACT (QI Basic). OJO Educational Resources, Lean Awareness Training) NEW Classroom and Department (e.g. New Employee Orientation, e-Learning, Unit/Department-based training)

22 Changes that result in process improvement Ideas Adapted from: The Improvement Guide: A Practical Approach to Enhancing Organizational Performance, 2nd Ed. Gerald J. Langley, Ronald D. Moen, Kevin M. Nolan, Thomas W. Nolan, Clifford L. Norman, and Lloyd P. Provost; Jossey-Bass 2009 Act Plan Study Do Act Plan Study Do Act Plan Study Do Improvement

23 Pareto 80/20 Principle in Healthcare

24 TCH’s Care Process Analysis Asthma Amount of Variation Size of Clinical Process Bubble Size = Case Count Improvement Opportunity: Large processes with significant variation

25 Driving clinical care improvement: linking science, data management, operations Permanent, integrated teams composed of clinicians, technologists, analysts and quality improvement personnel drive adoption of evidence-based medicine and achieve and sustain superior outcomes. Clinical Program Guidelines centered on evidence-based care #5 Care Process #4 Care Process #3 Care Process #2 Care Process #1 Care Process MD Lead Data Manager Outcomes Analyst BI Developer Data Architect Application Service Owner Clinical Director Domain MD Lead Operation s Lead

26 Balanced scorecard-expanded visualizations 1.Care Process Defined 2.Current Literature Research 3.Individual Ratings 4.Aggregate Ratings5.Group Creates Final Scorecard

27 Severity Adjusted Variation

28 Option 1: Focus on Outliers – the prescriptive approach Strategy eliminate the unfavorable tail of the curve (“quality assurance”) Result Ithe impact is minimal # of Cases Excellent Outcomes Poor Outcomes 1.96 std # of Cases Mean Excellent OutcomesPoor Outcomes 1 box = 100 cases in a year Data Drives Waste Reduction: Alternative Approaches 27

29 Excellent OutcomesPoor Outcomes # of Cases Mean 1 box = 100 cases in a year Excellent Outcomes # of Cases Poor Outcomes Option 2: Focus On Inliers – improving quality outcomes across the majority Strategy Evidence and analytics applied through EBP clinical standards targets inlier variation Result Shifting more cases towards excellent outcomes has much more significant impact Alternative Approaches to Waste Reduction 28

30 Improving Cost Structure Through Waste Reduction 29 Ordering WasteWorkflow WasteDefect Waste Ordering of tests that are neither diagnostic nor contributory Variation in Emergency Care wait time ADEs, transfusion reactions, pressure ulcers, HAIs, VTE, falls, wrong surgery

31 Care Redesign Methodology 30 Evidence against CXR utilization in patients with known asthma, steroids in bronchiolitis Evidence equivocal Hypertonic saline and bronchodilators in select patients with bronchiolitis Evidence Supports Quicker steroid delivery for status asthmaticus, goal directed therapy for septic shock

32 31

33 Improving Cost Structure Through Waste Reduction 32 Ordering WasteWorkflow WasteDefect Waste Ordering of tests that are neither diagnostic nor contributory Variation in Emergency Care wait time ADEs, transfusion reactions, pressure ulcers, HAIs, VTE, falls, wrong surgery

34 Process map before EBG

35 Process map after EBG

36 35

37 Improving Cost Structure Through Waste Reduction 36 Ordering WasteWorkflow WasteDefect Waste Ordering of tests that are neither diagnostic nor contributory Variation in Emergency Care wait time ADEs, transfusion reactions, pressure ulcers, HAIs, VTE, falls, wrong surgery

38 37 Clinical Decision Support to Minimize Errors Streamlining and Improving Processes and Operations to Minimize Errors

39 Value =

40 EC: Early administration of Dexamethasone Expanding evidence based practice -Provider and staff inservicing -Clinical decision support -Bridging a continuum for home care: second dose 10% decrease in TID

41 Inpatient: prolonged LOS Evidence based approach to early medication weaning 35% reduction in LOS No change in 7 or 30 day readmission rate No change in days of school/days of work missed Direct variable cost ($60/hr)

42 The continuum: improved patient experience and outcomes Improved time to first beta agonist (ED or inpatient arrival) Increase chronic severity assessment Improve accuracy Increase appropriate controller prescriptions Clinical decision support Increase influenza vaccination rate Increase number of culturally sensitive education encounters Increase number of social work/ legal support encounters AAP use went from 20% to 44% in first cycle to 52% in second ACT use went from 0% to 30% in first cycle to 41% in second Severity classification went from 10% to 35% in first cycle to 54% in second

43 Registry Financial Score Card 42

44

45

46 Asthma Care Outcomes Dashboard

47 Financial conversations

48 47

49 Examples Demonstrating ROI  Improved clinical care  Decreases in LOS  Decrease in readmission rates  Decreased unnecessary test utilization  Millions in savings across several disease processes  Reducing waste by systematizing reporting  EDW reports cost 70% less to build  Clinical operations tools allow global views for increased operational efficiency 48

50 Organizational direction for data Data reporting Data analytics Decision support Predictive analytics Organizational evolution over time -EMR clinical reports -Financial reports -Shortening event to reporting time -Transforming data and translating to action -Integrating best evidence into delivery system infrastructures -EMR based recommendations and alerts -Integrated plans of care across continuums --Linking likelihood of outcomes to care decisions driven with realtime data -Predicting financial outcomes and linking to clinical decisions for populations of patients -Linking outcomes across infrastructures Improved outcomes for our patients and our enterprise

51 Predictive analytics: High risk asthma SHORT ACTING BETA AGONISTS 6 to 9 SABA = 1 point ≥ 10 SABA = 2 points EC UTILIZATION 1-2 ER = 1 point > 2 ER = 2 points HOSPITALIZATION 1 hospitalization = 1 point >= 2 hospitalizations = 4 points NUMBER PRESCRIBING PROVIDERS >= 3 different prescribing providers in 12 months one of above criteria met, add 1 point PRIMARY CARE VISITS Last PCP visit > 6 months + one of above criteria met = add 1 point INHALED CORTICOSTERIOD >= 6 ICS low dose canister equivalent refills, subtract 1 point Age 1-5, 4 of 5 below Government insurance (Medicaid or CHIP): Q2 under health insurance information Financial barrier to meds :Answered Yes to Q4 under health insurance information Previous asthma hospitalization: Yes to Q2 under past history of asthma care Chronic Severity= Mild persistent Acute Severity= Mild Age 6+ All 3 of the following Government insurance (Medicaid or CHIP): Q2 under health insurance information Chronic Severity= Mild persistent Acute Severity= Mild Or All 3 of the following Government insurance (Medicaid or CHIP): Q2 under health insurance information Exercise induced asthma: Answered yes to exercise page 3 of TEDAS. Acute Severity= Mild Targets: reduce ED visits, hospitalization, albuterol overuse, ICS non adherence Critical data source: TCHP, TDSHS data Lieu TA et al Am J Respir Crit Care Med Apr;157(4 Pt 1): Farber HJ, et al. Ann Allergy Asthma Immunol Mar;92(3): Farber HJ. J Asthma. 1998;35(1):95-9 Spitzer WO, et al. N Engl J Med 1992 Feb 20;326(8):501-6 Suissa S, et al. Thorax Oct;57(10): Targets: reduce ED visits/ unscheduled PCP visits Critical data source: TCH ED, PCP

52 DiabetesPregnancyAsthma Transplant Pneumonia AppendicitisNewborn Hospital Acquired Conditions Sepsis and septic shock Obesity Transitions of care Survey explorer Care Process Teams Additionally, completed a gap strategy for 38 “registries”

53 Assuring an excellent patient experience QI education and culture change Data/predictive analytics: measuring through meaningful metrics Content System Measurement System Deployment System Improved Population Health Deployment strategy— Care Process Teams Evidence Integrated practice via guidelines, order sets and measures Using and innovating best practices Knowledge management for population health

54 #HASummit14 Analytic Insights A Questions & Answers

55 #HASummit14 54 Session Feedback Survey 54 1.On a scale of 1-5, how satisfied were you overall with the Penny Wheeler, MD / Allina session? 2.What feedback or suggestions do you have for Penny Wheeler, MD / Allina session? 3.On a scale of 1-5, how satisfied were you overall with the Charles Macias / TCH session? 4.What feedback or suggestions do you have for the Charles Macias / TCH session?


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