Analytics Maturity Model: A Starting Point for Big Data Efforts. March 17, 2015

Transcription

1 Analytics Maturity Model: A Starting Point for Big Data Efforts March 17, 2015

2 INTRODUCTION: HIMSS Analytics HIMSS WORLDWIDE HIMSS HIMSS Analytics HIMSS International HIMSS Media

3 Percent of Hospitals EMRAM Distribution: All US Hospitals Q % 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 37.2% 25.1% 19.3% 14.0% 2.2% 1.4% 0.8% 0.0% EMRAM Stage

4 Percent of Hospitals EMRAM Distribution: All US Hospitals Q % 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 32.7% 21.0% 17.9% 14.0% 3.8% 5.1% 3.6% 2.0% EMRAM Stage

5 ANALYTICS MATURATION MODEL Rationale Readiness Direction Competitiveness

6 Introduction Based on work of Thomas Davenport Analytics at Work, 2010 The DELTA model Co-developed in cooperation with the International Institute for Analytics (IIA)

7 Components Data breadth, integration and quality Enterprise approach to managing analytics Leadership passion and commitment Targets first deep then broad Analytics professionals and amateurs

8 Data Data capture Data quality Data integration Use of external data Data consistency Data trustworthiness Analytical tools Big Data utilization Importance Performance IMPORTANCE: 0 = Not Important ; 5 = Extremely Important PERFORMANCE: 0 = Highly Inefficiency ; 5 = Highly Effective

9 Enterprise Enterprise tech management Organization of talent Funding adequacy Non-management utilization Data scalability Clinical tools External Reporting Government mandates Importance Performance IMPORTANCE: 0 = Not Important ; 5 = Extremely Important PERFORMANCE: 0 = Highly Inefficiency ; 5 = Highly Effective

10 Leadership Strategic input Executive advocacy Executive utilization Management utilization Enterprise collaboration Medical staff practices Importance Performance IMPORTANCE: 0 = Not Important ; 5 = Extremely Important PERFORMANCE: 0 = Highly Inefficiency ; 5 = Highly Effective

11 Targets Predictive modelling Goal setting Prioritization Iterative approach Opportunity identification Experimentation Importance Performance IMPORTANCE: 0 = Not Important ; 5 = Extremely Important PERFORMANCE: 0 = Highly Inefficiency ; 5 = Highly Effective

12 Analysts Staffing level Consultative approach Business skills Data science skills Career paths Importance Performance IMPORTANCE: 0 = Not Important ; 5 = Extremely Important PERFORMANCE: 0 = Highly Inefficiency ; 5 = Highly Effective

13 Maturation Levels Beginner Localized Aspiring Capable Leader Data Data Poor Data Limited Data Consolidated Data Connected and Integrated Data Innovative Enterprise Enterprise Unknowledgeable Enterprise Segregated Enterprise Inconsistent and Learning Enterprise Consistent Enterprise Integrated Leadership Leadership None/Local Leadership Aware Leadership Supportive Leadership Knowledgeable Leadership Passionate Targets Targets Irrelevant Targets Random Targets Selective Targets Aligned Targets Strategic Analysts Analysts None/Isolated Analysts Isolated Analysts Coordinated Analysts Competent Analysts Empowered

14 Maturation Descriptors Beginner Localized Aspiring Capable Leader Basic Healthcare IT systems in Departmental Production Mode Separate Clinical and Business Data Isolated Analytics Departmental Targets Clinical + Business Data Integrated Enterprise Targets Set External Data for Peer Comparison Data Governance Enterprise Targets With Gain-sharing Real Time Predictive Analytics Clinical Mission Leverages Analytics as a Market Differentiator Formal C&BI Strategy & Tactical Plan

15 1 st Cohort Participating Organizations Akron Children s Hospital Blackstone Valley Community Health Care Butler Health System, Inc. Carolinas HealthCare System Centura Health Corporation Cleveland Clinic Dartmouth-Hitchcock Duke University Health System, Inc. Intermountain Healthcare KishHealth System Lakeland Regional Health System Marshfield Clinic Northeast Georgia Health System, Inc. Northshore University Healthsystem Orlando Health, Inc. Seoul National University Bundang Hospital Southwest Kidney Institute, PLC The Stamford Hospital Trinity Health System UAB Health System UC Davis Health System University of Missouri System University of Pittsburgh Medical Center University of Virginia Medical Center

16 1 st Cohort Respondent Profile Total of 1,825 respondents completed the survey in the Fall of 2013 Job Title N % President or CEO 23 1% CXO, Sr or EVP or Board Member 120 7% Division Head, VP or GM % Department, Unit Manager or Director % Manager, Administrator or Supervisor % Non-management position % 3 to 5 years 14% Tenure 6 to 10 years 20% 2 years 9% <1 year 5% 1 year 5% >10 years 47% Copyright 2014 IIA All Rights Reserved 16

17 Importance vs. Effectiveness

18 Impact of Chief Analytics Officer (CAO)

19 Effectiveness by Level in the Organization

20 Maturation Distribution

21 IMPORTANCE Priority Matrix High Importance Low Effectiveness Improve Performance Healthcare organizations say these are important competencies, but demonstrate low effectiveness. This may be due to under investment in the competency or a lack of focus. High Importance High Effectiveness Continued Investment Areas of alignment. Competencies are important and healthcare organizations are mostly effective. Continued investment in these areas. Low Importance Low Effectiveness Identify Surplus Resources Healthcare companies said these competencies are not as important to them at this time and they are also mostly ineffective with these competencies. EFFECTIVENESS GAP Low Importance High Effectiveness Selectively Allocate Resources Healthcare organizations see these as less important competencies, but are more effective at them. This could be due to the competency itself, or the organization may be over-investing. Effectiveness Gap = Overall Importance - Effectiveness Copyright 2014 IIA All Rights Reserved 21

22 Priority Matrix Distribution

23 Priority Matrix Distribution Data Enterprise Analysts Leadership Targets

24 Summary The promise of Big Data requires analytically oriented healthcare organizations. Providers need to know their analytical capabilities. DATA appears to be a bright spot for healthcare organizations but much work yet to be done.