Adventures in Practice Transformation Perry Dickinson, MD Miriam
Adventures in Practice Transformation Perry Dickinson, MD Miriam Dickinson, PhD, Practice Transformation and Quality Improvement Changing healthcare environment in US and elsewhere Practice redesign and large-scale quality improvement efforts Ongoing need for quality improvement and innovation in all primary care practices Critical to capture data to understand what works and what doesnt and learn from our efforts Need to show improvements in quality of care and patient experience and overall health while decreasing cost Need New Approaches to Rapid
Cycle Learning Rapid change policy decisions, implementation arent waiting on traditional randomized clinical trials Many demonstration projects, natural experiments Support for rapid cycle evaluation & learning Need to develop new methods Combine research, evaluation, & QI approaches Mixed qualitative & quantitative approaches Contextual Factors Practice characteristics and settings can affect the ability of the practice to make and sustain changes Critical to understand the context what is working (or not) in what context Organization/system level factors Structural features of the practice
Setting Practice culture Characteristics of the patient panel Conceptual Frameworks for Practice Change Organizational Theory Steve Shortell Leif Solberg Deborah Cohen RE-AIM framework (Russ Glasgow) Reach, Effectiveness, Adoption, Implementation, Maintenance Practice Transformation Conceptual Framework Change Capacity Practice
Transformation Support Intervention Adaptive Reserve Alignment & Motivation Change Management & QI Process Improved Practice Quality Improvement& Change Management Change Content (Implementation of Comprehensive Primary Care &
Behavioral Integration) Improved Care & Population Health, Reduced Cost Transformation Support
Education, leadership engagement Development of a shared vision Readiness assessment with feedback Data resources and technical support Practice facilitation Practice quality improvement teams Learning collaboratives, learning community Training resources for staff and clinicians Patient advisory groups and other patient engagement efforts Practice Facilitators Implement an ongoing change and quality improvement process improvement teams Specifically, implement the targeted change content Implement sound quality improvement
techniques Keep the team on task ongoing nudge Identify and assist in solving problems Link to transformation resources Cross-pollinate best practices Common Designs Pre/post or longitudinal, no comparison group Quasi-experimental designs with comparison group Cluster randomized trials (CRT) Parallel group design Stepped wedge - practices randomized to an intervention implementation time rather than a group so that ultimately, all practices get the intervention Quantitative Methods Pre/post or
longitudinal with no comparison group Multi-practice quality improvement or clinical demonstration projects Data: practice level and/or patient level outcomes Methods Comparisons of pre-post conditions Growth curve models Statistical process control (e.g. run charts) What can we learn? Quantitative methods can be used to answer the question, did it improve (or get worse) but cant determine whether change was due to the intervention or something else Quantitative Methods Quasiexperimental and experimental with comparison group Methods involve comparison of change in one group vs change in another group, with or without randomization
What can we learn? Quantitative methods can address the question, did the intervention work? Is there greater improvement in the intervention group than in the comparison group? Quasi-Experimental and Experimental Designs with Comparison Group Parallel group design General or generalized linear mixed effects modeling used to accommodate clustering of patients within practices and multiple observations of patients over time Propensity score approaches can be used for quasiexperimental designs to assure comparable patient groups Cross-over or stepped wedge designs involve both within and between practice comparisons Quantitative Methods - Other
Contextual effects Moderators of change (effect modification) What can we learn? For whom did it work? i.e. Did the intervention work better for practices with certain characteristics? Mediators of change (in the causal pathway) What can we learn? How did it work? Did change in some expected intermediate step result in change in the outcome? Cost analysis - Addresses the question, is it worth it? What Do You Learn from Qualitative Methods Qualitative methods help to describe Details about the practice context Details about the implementation of the change
process Qualitative methods help to answer questions about... Why (or why not)? Who? (Patients, clinicians, staff) How? (Details about who does what, in what sequence) Qualitative Methods Practice observation Process or workflow mapping Time-motion studies Key informant interviews Clinicians, staff - all roles Group discussions lessons learned Document reviews Content analysis Comparative case studies
Mixed Methods Quantitative data help answer did the intervention or change work (and sometimes for whom) Qualitative data help answer why and how questions. Describe implementation. Describe roles. Provide insight into office processes. Add context to numeric data. Reach: an essential skill for understanding practice change efforts for practices and evaluators. Tracks who receives the intervention. Process or workflow maps: help identify key operational obstacles or questions; (answers who and what questions). Example, QI Project, No Comparison Colorado Beacon Consortium Evaluation Three year project - practice transformation initiative in 51 practices, with HIT support and quality improvement advisors working onsite with the practices
Goal - to optimize the efficiency, quality, and performance of our health care system, and integrate the delivery of care and use of clinical information to improve community health. Sought to assist practices in accomplishing extraction, reporting, and meaningful use of clinical data to improve care Particular focus on asthma, diabetes, heart disease, obesity, and depression Goals for Beacon Evaluation Describe the Quality Improvement Advisors work and how it contributed to practice IT implementation and practice quality improvement, including lessons learned Determine if there were improvements in quality measures Limitations Not all practices were able to provide consistent data
Practices could choose which measures to report Monthly reporting of outcomes aggregated at practice level, with no individual patient data No comparison group Beacon Mixed Method Evaluation Quantitative approaches Descriptive statistics Growth curve modeling with individual trajectories for practices (random intercept and random slope) and estimates of overall trend Qualitative approaches Content analysis of monthly practice notes, other data sources Interviews of Beacon team members and practice clinicians and staff members Comparative case studies Meta-matrices
Colorado Beacon Collaborative: Results for Selected Adult Measures Beacon Measure Adult BMI 18 to 64 Years Old Rate # of practices reporting data Estimate of 12 p-value # of practices month for that improved
change change significantly # of practices that met target or improved 17 8.39 0.005 11 12 17 10.67 0.002
16 16.56 0.0002 7 13 Adult BMI 65 Years and Older Rate Breast Cancer Screening Diabetes A1c > 9.0% Content Analysis Results Meaningful use of EHR data Aligned with overall practice aims for improving carebut required substantial workflow
reengineering and new training and skills for staff and clinicians Practice facilitators provided essential support, translating MU objectives into practice-specific objectives Collaborative, peer-to-peer learning helped build local/regional expertise for sharing Local technical EHR expertise was vital ingredient Quasi-Experimental and Experimental Designs with Comparison Group Example: the Colorado EPIC study Cluster randomized trial of 3 approaches to using the chronic care model to improve diabetes care in 40 practices CQI Facilitation Arm Onsite practice facilitation to assist with practice
changes to improve diabetes care Facilitator helped form practice improvement teams and provided structure and process for quality improvement and use of the chronic care model for diabetes Underlying theory Continuous Quality Improvement Model using plan-do-study-act (PDSA) cycles guided by quality measurement data in a focused effort to improve diabetes care Duration was up to 18 months, RAP Facilitation Arm Reflective Adaptive Process (RAP) change model Based on complexity theory, focusing on practice capacity to make and sustain change Assumed that improving practice change capacity is primary in implementing
improvements Facilitator formed improvement teams, but allowed each practice to set its targets for change in diabetes care Not driven by quality measures Duration: 6 months Self-directed Arm Given website with information about Chronic Care Model for diabetes Did not receive practice facilitation Methods Study Design: Cluster randomized trial Primary subjects: 40 primary care practices in Colorado 822 chart audits of random sample of patients with type 2 diabetes covering period from 12
months prior to baseline through 18 months post intervention 502 clinician and staff Practice Culture Assessment surveys at baseline, 9, and 18 months Outcome Variables Chart audits - diabetes process of care measures from the ADA Physician Recognition Program Number of up to date guideline concordant elements: HgA1c, eye exam, foot exam, blood pressure, lipids, nephropathy screening, flu shot, self-management activities, nutrition counseling Analytic Methods
Multilevel models Patient included as a random effect: repeated measures within individuals over time Practice also included as a random effect: subjects nested within practices Patient age, gender, race/ethnicity, comorbidities included as fixed effects Colorado EPIC Study Total Process of Care RAP CQI SD Differential change Mean Up-to- Mean Up-to- Mean Up-to- over time date out of 9 date out of 9 date out of 9
R x S: F(2,1838)=3.65, p=.0263 18 months 4.85 4.91 4.39 C x S: F(2,1475)=9.99, p<.0001 * **
** C x R: F(2,1455)=19.27, p<.0001 Moderators Moderators baseline characteristics that are associated with differential improvement in outcomes across intervention groups Potential Moderators Practice Characteristics Rural or urban location Others also looked at, no time to present! Practice Culture Assessment Change Culture and Work Culture Mean scores and variability (SD)
Analytic Methods Moderator analyses Main effects and all two way interactions for time, arm, and practice characteristic Three way interaction (time x arm x practice characteristic) used to determine whether intervention effects differed by arm Moderating Effect of Practice Location Intervention effects
differed by rural location : p<.0001 Greater improvement in rural CQI and RAP practices compared to their urban counterparts (both p<.05) Less improvement in rural SD practices compared to their urban counterparts (p<.05). Moderating Effect of Change Culture
Low=-1SD, Medium=mean, High=+1sd Intervention effects differed by change culture : p<.001 Greater improvement in CQI in practices with higher CC scores (p<.01) Less improvement in RAP practices with higher CC scores (p<.05)
No difference in improvement in SD practices by CC (p=.2236) Moderating Effect of Work Culture Low=-1SD, Medium=mean, High=+1sd Intervention effects differed by Work Culture : p<.0001 Greater improvement
in CQI and RAP practices with higher WC scores (both p<.01) No difference in POC change by WC in RAP practices (p=.1838). Effects of Variability in Practice Change Culture and Work Culture on Diabetes POC Change Culture Work Culture
Greater variability CC associated with less improvement in RAP and SD (p<.05) Greater variability in WC associated with less improvement in CQI and SD (p<.05) Multilevel Model for Assessing Moderation Level 1 model. Outcomes within patients over time (random intercept for patients) Level 2 model. Patients within practices (random intercept for
practices) Level 3 model. Between practices The two intervention arms are indicator variables with SD as the reference group (only model for slopes shown). 10j is the slope for patients in practice j 10j = 100 + 110 (RAP) + 120 (CQI) + 130 (rural) + 140 (RAP x rural) + 150 (CQI x rural) where 100 is the slope in urban SD practices, 110 and 120 are the differences in slope in urban RAP and urban CQI practices, 130 is the difference in slope for rural SD practices (compared to urban SD practices), and 140 and 150 are the differential effects of RAP and CQI in rural practices (e.g. differences in slope for rural practices in these arms compared to their urban counterparts) Summary and Conclusions Practice characteristics can have a differential impact across various practice redesign interventions
Important to assess practice context and culture as part of practice transformation efforts May be important in tailoring interventions Need to improve our understanding of practice context and culture Lessons Learned Keep the research burden as low as possible Primarily or only collect survey data that is useful to the practice or the intervention team Dont rely on registry data unless necessary although that may be better in some places For some projects primary learning may be qualitative, but think mixed methods You can learn a tremendous amount from multiple QI projects involving multiple practices a different type of evidence, but very important
References Dickinson WP, Dickinson LM, Nutting PA, Emsermann C, Tutt B, Crabtree BF, Fisher L, Harbrecht M, Gottsman A, West DR. Practice facilitation to improve diabetes care in primary care: a report from the EPIC randomized clinical trial. Annals of Family Medicine. 12:8-16 (2014). Dickinson LM, Dickinson WP, Nutting PA, Fisher L, Harbrecht M, Crabtree BF, Glasgow RE, West DR. Practice Context Affects Efforts to Improve Diabetes Care for Primary Care Patients: A Pragmatic Cluster Randomized Trial. Journal of General Internal Medicine. In press. Fernald DH, Dickinson WP, Wearner R. Supporting Primary Care Practices in Building Capacity to Use Health Information Data. eGEMS (Generating Evidence and Methods to Improve Patient Outcomes. 2;3:1-7 (2014). Fernald DH, Wearner R, Dickinson WP. The journey of primary care practices to meaningful use: a Colorado Beacon Consortium study. Journal of the American Board of Family Medicine. 26:603-611 (2013).
Technical review meeting on World Programme for the Census of Agriculture 2020 . Volume 2 - Operational. g. uidelines on implementing census of agriculture. Rome, Italy. 30-31 January2017. Naman Keita. Senior Consultant. FAO Statistics Division
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