Improving Quality: Theories of Change Pierre Barker MD Senior Vice President IHI “Every system is perfectly designed to achieve the result it gets” Paul Batalden Every System…… What are we trying to solve? The “How” The “What” Basic science Proof of concept Large scale efficacy Study Guidelines, Training & Resources, Reliable “real-life” implementation Scale-up to populations Adaptive Designs that are context sensitive Designs that can be scaledup with the resources at hand Quality Improvement: Bringing Together Two Types of Knowledge – (Deming) Evidence Based Subject Matter Knowledge Protocols/Guidelines, Physical resources, Clinical Training the “what” the “how” Implementation Knowledge Motivation/Leadership Efficient Systems Accurate Reflective Data Context-sensitive learning Improvement: Bringing Together Two Types of Knowledge Evidence-based Subject Matter Knowledge Improvement Implementation Knowledge Dr. Joseph M. Juran’s “Trilogy” QUALITY PLANNING QUALITY IMPROVEMENT QUALITY CONTROL 7 Juran Trilogy: All three elements are needed Sporadic spike Chronic waste (opportunity for improvement) PERFORMANCE SHIFT Source: Juran J, Godfrey AB, eds. Juran’s Quality Handbook: Fifth Edition. New York: McGraw-Hill, 1999. 8 Juran Trilogy: All three elements are needed Sporadic spike Chronic waste (opportunity for improvement) PERFORMANCE SHIFT Source: Juran J, Godfrey AB, eds. Juran’s Quality Handbook: Fifth Edition. New York: McGraw-Hill, 1999. 9 Juran Trilogy: All three elements are needed Sporadic spike Chronic waste (opportunity for improvement) PERFORMANCE SHIFT Source: Juran J, Godfrey AB, eds. Juran’s Quality Handbook: Fifth Edition. New York: McGraw-Hill, 1999. 10 Components of quality: structure, quality control and quality improvement Quality Planning Quality Control Standards/ Guidelines/ protocols Professional oversight Accreditation Checklists Inspection & reward/censure Policy, resources, coordination, accountability, mandates, etc. Quality Improvement Motivation/Leadership IMPROVED OUTCOMES Efficient Systems Reflective Data Context-sensitive learning 11 The “Engine” of Improvement: the Model for Improvement 12 Model for Improvement Aims 1. What are we trying to accomplish? Measures 2. How will we know that a change is an improvement? Changes 3. What change can we make that will result in an improvement? QA and “QI” confusion: both use Model for Improvement and long/short PDSAs Quality Planning Quality Control Quality Improvement Aims Measures Changes Aims IMPROVED OUTCOMES Measures Changes 13 QA and QI: Differences in approach QA Performance goal Perform to standards (controls) across multiple parts of the system QI Aspire to a best performance goal for a focused improvement area Measurement Periodic inspection of past events (large set of measures of inputs and/or processes) Continuous tracking of current activity (few key processes linked to outcome) Data Tracking Before and after change Continuous (e.g. run charts) Data System External: e.g. inspection tools Internal: e.g. registers and tally sheets Changes Standards driven. Normative. Can be linked to frontline system analysis Theory driven. Adaptive. Always linked to frontline system analysis Motivation for change Management led, compliance, incentives, competition Shared governance, internal motivation, “all teach all learn” PDSA Management planning with single “slow” (months) intervention cycle. Can use frontline rapid cycle to respond to defects Frontline planning Rapid cycle (days/weeks) is core activity Putting it together on the QA/QI Spectrum COPE QA QI Accreditation SBM-R Training and Supportive Supervision QA Approach PROBLEM SOLUTION IMPLEMENT SYSTEM BARRIERS Review (Succeed/Fail) QI Approach SYSTEM ANALYSIS PROBLEM GREAT IDEAS PLAN IMPLEMENT SUCCEED/ FAIL ACT DO STUDY Theory of Change: Drivers of Maternal and Newborn Survival Decrease Maternal and newborn mortality by 50% Drills, mentoring Knowledgeable health workers ready to use their skills Decision support, checklists Immediate access to essential commodities Key suppliers part of QI team Supportive Supervision Multi-level leaders promote change Motivation for change Progress celebrated, challenges supported Informed/activated patients & communities Data systems in real time Data for improvement vs data for reporting Data “owned” at every level A Learning System Cross-professional teams meet regularly, review data, test changes, report progress % eligible women received at least one dose of corticosteroids 1 2 % eligible women received at least one dose of corticosteroids Status Quo QI Zone of control 1 Sustainability Hospital 1 2 2 1 1 1 Hospital 2 2 2 1 1 1 Nkhoma 1 Sustainability June 2014 December 2014 February 2014 Role of QI, QA in Scale-up 24 P25 Role of QI, QA in Scale-up QA Set-up (Status Quo) QI Scalable Model for Improvement (innovation) QA Test ScaleUp (Context adaptations) Go to Full-Scale (Spread) Phases of Scale-up Questions Are some methods intrinsically more suitable for different aspects/phases of implementation? Should we ensure that large system improvement designs include all elements of Juran trilogy (QP, QC, QI) How do we ensure that our improvement efforts are sustained into the future?