Managing Population Health with Science, Analytics, and Quality Improvement

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1 Managing Population Health with Science, Analytics, and Quality Improvement Charles G Macias MD, MPH Chief Clinical Systems Integration Officer Texas Children s DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Charles G Macias MD, MPH has no real or apparent conflicts of interest to report. HIMSS

3 Learning Objectives Identify gaps in the care delivery process Define a strategy for managing populations with evidence-based practices and data management Discuss the development of evidence-based shared baselines of care Describe the use of near-time and real-time data to improve outcomes of care Value of Health IT Satisfaction: patient experience Treatment/clinical standards and care process delivery for populations Electronic data/ data and predictive analytics Patient education and self-empowerment Savings: reducing the per capita cost of care 2

Respiratory Challenge: Case Illustration 14 month old girl with a viral prodrome but no history of asthma/prior episode of breathing difficulty, cough for one

4 Respiratory Challenge: Case Illustration 14 month old girl with a viral prodrome but no history of asthma/prior episode of breathing difficulty, cough for one day; eczematous rash Coarse wheezing heard (R) but crying loudly; RR of 66 What do we really know to inform action that is highly meaningful for this family? 3

5 Value = Quality Cost Families, providers, and payers want value. Thus, quality is key driver. 4

6 Illustrating the Problem RCT of treatment of hypertension on the jobsite (a steel mill) versus referral to the PCP No difference in compliance between the groups Exploration of factors relating to therapy revealed specific determinants of the clinical decision to treat some, but not other, hypertensive patients: 1. The level of diastolic blood pressure. 2. The patient s age. 3.???? 4. The amount of target-organ damage. 5

7 Illustrating the Problem RCT of treatment of hypertension on the jobsite (a steel mill) versus referral to the PCP No difference in compliance between the groups Exploration of factors relating to therapy revealed specific determinants of the clinical decision to treat some, but not other, hypertensive patients: 1. The level of diastolic blood pressure. 2. The patient s age. 3. The year the physician graduated from medical school. 4. The amount of target-organ damage. 6

8 Minimizing Variation Wide variations in practice are often not related to differences among patients Minimizing variations in practice can improve quality of health care delivery: Variation in beliefs Variation in interpretation of evidence Variation in response when evidence is lacking 7

9 What About the Children? Are they seeing variable outcomes? 8

10 Is indicated care being delivered for children in outpatient settings? Acute medical problems 67.6% Chronic medical conditions 53.4% Preventative care 40.7% Preventative services for adolescents 34.5% <50% are children receiving high quality care 9

11 Variability in pediatrics CBC Chest Radiograph Among 16 hospitals treating children Indicators of quality (e.g. chest radiograph, laboratory blood work, antibiotics, breathing treatments, IV placement) Large variations in practices were NOT explained by severity of illness site Systemic Bronchodilators Corticosteroids Antibiotics IV placement site Source: Variability in inpatient management of children hospitalized with bronchiolitis, Macias et al. Academic Pediatrics

12 IHI Triple Aim Measuring outcomes is critical to knowing a system has achieved the triple aim Value = Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement;

13 Improving care with a population health approach 12

14 Creating a Foundation for Improving Outcomes ANALYTIC SYSTEM Data analytics and collaborative data SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 13

15 Creating a Foundation for Improving Outcomes Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care CLINICAL SCIENCE Evidence and best practice SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 14

16 Creating a Foundation for Improving Outcomes Informatics, Electronic Data Warehousing ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care ANALYTICAL SYSTEM Data analytics and collaborative data SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 15

17 Creating a Foundation for Improving Outcomes Advanced Quality Improvement course, QI curriculum, Care process teams ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care OPERATIONAL IMPROVEMENT TEAMS SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 16

18 Creating a Foundation for Improving Outcomes 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 ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care ANALYTICAL SYSTEM Data analytics and collaborative data OPERATIONAL IMPROVEMENT TEAMS CLINICAL SCIENCE Evidence and best practice SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 17

19 Creating a Foundation for Improving Outcomes Patient centric outcomes and institutional outcomes achieved 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 ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care ANALYTICAL SYSTEM Data analytics and collaborative data OPERATIONAL IMPROVEMENT TEAMS CLINICAL SCIENCE Evidence and best practice SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 18

20 Improving care with a population health approach 19

21 Improving care with a population health approach 20

22 Improving care with a population health approach 21

23 Creating a Foundation for Improving Outcomes Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care CLINICAL SCIENCE Evidence and best practice SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 22

24 Clinical Effectiveness Key Driver Diagram Aim: To Improve the outcomes of children treated by Texas Children s through evidence based practice Goal: To increase evidence based care delivery by (a relative) 40% for the most common diseases treated at TCH within 2 years KEY DRIVERS CHANGE STRATEGIES Evidence based guideline/summary development and culture Providers Create a system for Clinical Decision Support Operations leaders Quality leaders Evidence Based Outcome Competency Center Providers Deploy a metrics/ scorecard system Create a data analytics and translation platform (EDW) Integrate financial systems to EBGs Operations Leaders Operations Leaders Providers Evidence Based Outcome Competency Center Operations Leaders 23

25 We utilize a systematic development for Clinical Standards 1. Identify the quality problem/gaps: mortality, resource consumption, variability, prevalence 2. Search for existing guidelines and assess their applicability 3. Assemble a group of stakeholders (bottom up, never top down) 4. Identify the Patient Intervention Comparison Outcomes (PICO) questions 5. Search the evidence 6. Evaluate the evidence using an evidence rating AND recommendation rating tool 7. Vet with stakeholders 8. Once approved, build into Epic with consider for clinical decision support 24

26 Shared baselines: Evidence Based Outcomes Center Approved summaries, 47 # of products Full guidelines, 34 We are nimble with multiple clinical standards products Evidence summaries, 18 25

27 Creating a Foundation for Improving Outcomes Informatics, Electronic Data Warehousing ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care ANALYTICAL SYSTEM Data analytics and collaborative data SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 26

TCH s EDW Architecture Metadata: EDW Atlas Security and Auditing

EPSi,) Common, Linkable Vocabulary; Late binding

Sunquest Labs) Financial Source Marts Departmental Source Marts ADMINISTRATIVE SOURCES

Deliveries Pneumonia Diabetes Surgery Neonatal dz Transplant Operations Labor

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

29 Creating a Foundation for Improving Outcomes Advanced Quality Improvement course, QI curriculum, Care process teams ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care OPERATIONAL IMPROVEMENT TEAMS SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 28

g. Nursing IMPACT (QI Basics), IHI Educational Resources, Lean Awareness Training) Classroom and Department (e.g. New Employee Orientation, e-learning, Unit/Department-based training) 29

30 Avenues for Dissemination QUALITY LEADERS ADVANCED INTER- MEDIATE BEGINNER NEW National Programs and Partnerships Classroom (e.g. AQI Program, Six Sigma Green Belt) Project Required Online and Classroom (e.g. IHI Educational Resources, PEDI 101, EQIPP, Fellows College) Project Required Online and Classroom (e.g. Nursing IMPACT (QI Basics), IHI Educational Resources, Lean Awareness Training) Classroom and Department (e.g. New Employee Orientation, e-learning, Unit/Department-based training) 29

Linking Science, Data Management, Operations Clinical Program Guidelines centered on evidence-based care MD Lead MD Lead MD Lead MD Lead MD Lead Operations Lead #5 Care Process #4 Care Process #3

31 Linking Science, Data Management, Operations Clinical Program Guidelines centered on evidence-based care MD Lead MD Lead MD Lead MD Lead MD Lead Operations Lead #5 Care Process #4 Care Process #3 Care Process #2 Care Process #1 Care Process Domain MD Lead Evidence based outcomes center Clinical Director Data Manager Outcomes Analyst BI Developer Data Architect Application Service Owner Permanent, integrated teams composed of clinicians, technologists, analysts and quality improvement personnel drive adoption of evidencebased medicine and achieve and sustain superior outcomes. Source: Health Catalyst

32 Population Health approaches: permanent teams drive PDSA cycles Population health approaches: the permanent care process teams Diabetes Pregnancy Asthma Transplant Pneumonia Appendicitis Newborn Hospital Acquired Conditions Sepsis and septic shock Obesity Transitions of care Survey explorer 31

33 Quality? Only Through Measurement Will We Know Population health Health outcomes Disease burden Behavioral and psychological factors Experience of care Patient/family satisfaction IOM domains of quality Per capita cost of care Total cost per person per disease Hospital and ED utilization rate (cost) Adapted from: Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement;

34 Appendicitis across the continuum: a population health model Process mapping a patient through the health care infrastructure Recognizing clinically risk adjusted targets: simple versus complex appendicitis Increased postoperative simple order set adoption rates by 36% and postoperative complex order set adoption rates by 9% 33

35 Clinical standards: driving antibiotic monotherapy 34

36 Decreasing length of stay Weekly Mean LOS Before Intervention Transition After Intervention 35

37 Reduced cost of care 36

38 Diabetes across the continuum: EC and Inpatient: Order Set Utilization 37

39 Insulin timeliness improved Interventions: - Beta-hydroxybutyrate point of care testing in EC % of patients receiving IV insulin within 2 hours of triage % of patients receiving IV insulin within 1 hour following insulin order Interventions: - Verbal Stat order option in EC 38

40 Decreasing Length of Stay 39

41 Diabetes Education: Certified Diabetes Nurses and a New Diabetes Care Unit 80% of patient care experiences will be provided by the CDNs DCU opened on 14WT March 17th 40

Identified serial Diabetic Ketoacidosis risk factors and patient geography Red pins = TC Health Plan patients with 2+ DKA visits within 12 months of

42 Identified serial Diabetic Ketoacidosis risk factors and patient geography Red pins = TC Health Plan patients with 2+ DKA visits within 12 months of each other Red targets = Zip codes with 5+ TC Health Plan encounters since 2010 Black targets = = Zip codes with 1+ TC Health Plan encounters since

43 Organizational Direction for Data Improved outcomes for our patients and our enterprise Data reporting -EMR clinical reports -Financial reports Organizational evolution over time Data analytics -Shortening event to reporting time -Transforming data and translating to action Predictive analytics --Linking likelihood of outcomes to care decisions for populations -Predicting financial outcomes -Linking strategies across former silos in infrastructures Prescriptive analytics --Integrating best evidence into delivery system infrastructures -EMR based recommendations and alerts -Integrated plans of care across continuums -Utilizing big data bi-directionally Predictive analytics and prescriptive analytics have contextual properties 42

44 Asthma: EC: Early Administration of steroids Expanding evidence based practice Provider and staff inservicing Clinical decision support Bridging a continuum for home dose 43

45 Inpatient: Prolonged LOS Evidence based approach to early medication weaning through a pathway 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) Moving Range Individual Value Baseline Improv ement Improv 1 ement I-MR Chart of CT - 1st q3h to d/c by Phase Observation Observation UC L=70.9 _ X=27.4 LC L=-16.1 Baseline Improv ement Improv 1 ement UC L=53.4 MR=16.4 LC L=0 44

46 Order Set Utilization Across the Enterprise Percent of Asthma Hospital Accounts with CXR at anytime. Based on Current selections Enhancing strategies to decrease waste: Decrease unnecessary test utilization through improved EBP order set compliance Focus on EBP tool utilization: Increase AAP utilization Discharge per Quarter 45

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

47 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 from 20% to 52% ACT use from 0% to 41% Severity classification from 10% to 54% 46

48 Transparency: Creating Dashboards Asthma EBP/CSI Dashboard 47

49 Financial Conversations 48

50 49 4 $5,000 $4,500 $4,000 $3,500 $3,000 $2,500 $2,000 $1,500 $1,000 $500 $0 -$500 -$1,000 -$1,500 -$2,000 -$2,500 -$3,000 -$3,500 -$4,000 -$4,500 -$5,000 Margin y = x Q Q Q Q Q Q Q Q Q Q Q Q Q Q2 y = x

51 Predictive analytics for diabetes, appendicitis, epilepsy aligned Predictive with clinical Analytics: care goals and High payment Risk reform Asthma Targets: reduce ED visits, hospitalization, albuterol overuse, ICS non adherence Critical data source: TCHP, TDSHS data 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 Targets: reduce ED visits/ unscheduled PCP visits Critical data source: TCH ED, PCP 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 50

Population Health Approaches: The Permanent Care Process Teams Population health approaches: the permanent care process teams Diabetes Pregnancy Asthma Transplant Pneumonia

52 Population Health Approaches: The Permanent Care Process Teams Population health approaches: the permanent care process teams Diabetes Pregnancy Asthma Transplant Pneumonia Appendicitis Newborn Hospital Acquired Conditions Sepsis and septic shock Obesity Transitions of care Survey explorer A gap strategy for 38 registries with Evidence Based Practice alignment 51

Respiratory Challenge, a Case illustration 14 month old girl with a viral prodrome but no history of asthma/prior episode of breathing difficulty with

53 Respiratory Challenge, a Case illustration 14 month old girl with a viral prodrome but no history of asthma/prior episode of breathing difficulty with a one day history of cough; eczematous rash Coarse wheezing heard (R) but crying loudly; RR of 66 Uncertainty of evidence Best treatment, best advice? 52

54 Respiratory Challenge, a Case Illustration Uncertainty of evidence Best treatment, best advice? Continuums of care? Asthma? 53

55 Accountability and a Shared Vision 54

56 Using knowledge management within and across populations ANALYTIC SYSTEM Data analytics and collaborative data Improved Outcomes from high quality of care ANALYTICAL SYSTEM Data analytics and collaborative data OPERATIONAL IMPROVEMENT TEAMS CLINICAL SCIENCE Evidence and best practice SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 55

57 Improving outcomes for population health 56

58 Questions & Thank You Speaker contact information Charles G Macias MD, MPH Chief Clinical Systems Integration Officer Texas Children s - Houston, Texas cgmacias@texaschildrens.org 57

59 ns.org 58

Building Evidence-based Clinical Standards into Care Delivery March 2, 2016

Building Evidence-based Clinical Standards into Care Delivery March 2, 2016 Charles G. Macias MD, MPH Chief Clinical Systems Integration Officer, Texas Children's Associate Professor of Pediatrics, Section