driving outcomes by enhancing workflow with technology · implementing a population health chronic...
TRANSCRIPT
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Driving Outcomes by Enhancing Workflow with Technology
Session 48, February 20, 2017
Steven J. Atlas, MD, MPHHARVARD MEDICAL SCHOOL
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Speaker Introduction
Steven J. Atlas, MD, MPHDirector, Practice Based Research & Quality Improvement
Massachusetts General Hospital
• Trained in Internal Medicine
• Practicing Primary Care Physician (PCP)
• Health Services Researcher
• Responsible for Quality Measurement and Assessment for Network
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Conflict of Interest
Steven J. Atlas, MD, MPH
• Royalty: Beneficiary of an arrangement between Massachusetts General Hospital and SRG Technology - no payments have been received to date
• Consulting fees: SRG Technology
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Agenda
• Integrating Population Health Management into Primary Care
– Understanding provider workflow and organizational structure
– Ensuring providers are practicing at the “top of their license”
– Role of health IT infrastructure
– Using data to drive decisions
• The above points will be illustrated in 3 case studies:
1. Central Population Health Chronic Disease Management Program
2. Collaborative Team Care Model to Manage Patients with Diabetes
3. Patient Navigators for Comprehensive Cancer Screening
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Learning Objectives
• Analyze what makes an effective combined workflow-informatics
intervention that drives clinical outcomes
• Compare effectiveness of central vs. distributed approaches to
patient outreach
• Measure the impact of patient navigation on patient outcomes
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Value of Health IT STEPSTM
Today’s
presentation will
impact all Health
IT value STEPS
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Population Health Management
• Application of public health principles to the health care delivery system
‒ Well defined populations
‒ Importance of surveillance
‒ Role of prevention
‒ Need to assess outcomes
‒ Focus on those at higher risk for non-adherence: making it easier to do the right thing
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Case Study 1:
Implementing a Population Health Chronic Disease Management Program
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• Objective:
– Develop and implement a chronic disease population health
management (PHM) program with and without central coordination
using an established health IT tool in a large primary care network
• Hypothesis:
– Practices assigned a central population health coordinator (PHC)
will have larger increases in performance on quality measures
compared to practices not assigned a PHC.
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Background
• Prior research evaluated value of population health management
system when added to visit-based care
• Focused on preventive cancer screening
– PCPs using non-visit based IT tool to identify women overdue for
mammogram increased rates compared to usual care*
– Compared an automated PHM reminder system using office staff support
with or without PCP involvement: no difference in screening rates for
breast, cervical or colon cancer†
* Atlas et al, JGIM 2011, AJMC 2012 † Atlas et al, JABFM 2014
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Study Setting and Design
• Setting: Massachusetts General Hospital (MGH) primary care network
– 18 primary care practices, including 6 community health centers
• Design: Before - after evaluation of PHM program over first six months (July 1 – Dec. 31, 2014)
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PHC Allocation
• Central Population Health Coordinator Assignment
– Insufficient resources to implement in all 18 practices
• Non-randomly allocated based on interest from practice leader, baseline
quality scores, practice size, nature and location of practice
• Sought to equitably distribute available resources to get network buy-in, and
maximize impact of the program for all practices
– Pilot Program (n=8): 4 FTE central PHCs
– Non-Pilot: Remaining practices (n=10): support for PHM IT tool training;
existing staff responsible for administrative tasks
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Patient Eligibility
• Validated algorithm identified primary care patients linked to specific PCP or practice
• PHC Focus - Chronic Disease Populations: Diabetes (DM), Cardiovascular Disease (CVD), Hypertension (HTN)
– Patients identified using validated algorithms using billing claims, laboratory testing, problem lists, medications, and/or procedures
• Control Measures - Cancer Screening: Patients eligible for breast, cervical, or colorectal cancer screening
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PHM Health IT Tool
• All practices utilized a PHM health IT tool for cancer screening since 2011
• PHM tool expanded to include registries for DM, CVD, and HTN in 2014
• Algorithms assign patients to chronic disease registries, and tool tracks goal attainment in near-real time
• Implemented clinically relevant outcomes for all patients
– Whether appropriate testing performed (process)
– Test at goal or on maximal therapy (outcomes)
– Ability to document relevant exceptions
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Central PHM Program
• PHCs were non-clinical staff who were integrated into practice workflow
• PHCs regularly “huddled” with physicians
– Review patient lists to manage patients not at goal
– Performed administrative tasks: appointment scheduling, ordering overdue laboratory testing, chart reviews, obtaining home blood pressure values and outside tests/labs
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Process and Outcome MeasuresProcess
Measure*
Outcome
Measure
Population Measure Time period Based on Lab or BP
Values
Maximal Rx
Diabetes LDL
cholesterol
12 months <100 mg/dL Moderate or high dose statin
A1c 6 months <9%
BP 6 months SBP≤140, DBP≤90 or
Age ≥ 60: DBP≤70
≥ 3 meds from different anti-
hypertensive classes
CVD LDL
cholesterol
12 months <100 mg/dL High dose statin
Hypertension BP 6 months SBP≤140, DBP≤90 or
Age ≥ 60: DBP≤70 or
if no diabetes and Age ≥ 60:
SBP<150, DBP≤90
≥ 3 meds from different anti-
hypertensive classes
*Clinical Exception Passes Process Measure: Terminal illness, competing comorbidity, contraindicated
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Cancer Screening Process Outcomes (Control Measures)
• In eligible patients
• Breast: mammogram in past two years
• Cervical:
– Pap smear completed in prior three years
– Five years if 30-65 with a HPV test
• Colorectal:
– Colonoscopy in past ten years
– Sigmoidoscopy, barium enema, or CT colonography in past 5 yrs
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Outcomes and Analysis
• Difference in differences over 6-months follow-up between PHC and non-PHC practices
• Primary Outcome: combined process and outcome measures
• Secondary outcome: process measures for cancer screening
• Analyses:
– Included patients in registries at both time periods
– Logistic regression with time period-by-PHC interaction term and accounting for clustering within patients using generalized estimating equation approach
– Adjusted for age, gender, language, race, insurance, practice type, and patient-physician continuity
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Primary Outcome: Proportion of Patients at Goal for Combined Measures
* All p<0.001
PHC Practices Non-PHC Practices Difference in
Differences*Baseline 6-Months Difference Baseline 6-Months Difference
Diabetes: LDL 62.1% 71.1% 9.1% 67.9% 72.4% 4.5% 4.6%
Diabetes: BP 77.7% 80.6% 2.9% 81.0% 79.0% -2.0% 4.9%
Diabetes: A1c 67.2% 73.2% 6.0% 69.4% 70.7% 1.3% 4.7%
CVE: LDL 69.0% 78.0% 9.0% 70.1% 75.8% 5.7% 3.3%
HTN: BP 74.3% 78.0% 3.7% 76.7% 78.1% 1.4% 2.3%
Ashburner et al, AJMC in press
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Process Measures for Cancer Screening
PHC Practices Non-PHC Practices Difference in
DifferencesBaseline 6-Months Difference Baseline 6-Months Difference
Breast Cancer 90.7% 91.2% 0.5% 92.0% 92.9% 0.9% -0.4%
Cervical Cancer 92.4% 93.0% 0.6% 91.3% 92.8% 1.5% -0.9%
Colorectal Cancer 85.5% 86.8% 1.3% 87.8% 89.5% 1.7% -0.4%
Ashburner et al, AJMC in press
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Limitations
• Practices not randomly assigned to receive a central PHC
• Differences in patient populations or staff willingness to implement PHM
could account for better outcomes in practices with central PHCs
– Adjusted for patient characteristics and practice type in our multivariable
models and all differences persisted
– Differences in cancer screening between PHC and non-PHC practices were
small
• Improvements may level off over time
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Conclusions
• PHM program using health IT significantly increased quality measures for
patients with diabetes, CVD, and hypertension over initial 6 months
• Central population health coordinators working closely with practices led
to greater improvements in outcome measures
• Supports using central personnel working with practice-based staff, but
longer term follow-up is needed
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Case Study 2:
Diabetes Care Redesign
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Breaking Down Traditional Silos
PCP Specialty Behavioral
Health
Other
Services
INTEGRATE PROVIDER SERVICES
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Goals Reflect Evidence-Based Diabetes Care
• Medical therapies:
– Metformin and lifestyle change for T2 Diabetes
– Insulin initiation and titration for those clinically not at goal
– Statin therapy to prevent CVD
– ACE-I or ARB for hypertension control and to prevent ESRD
• Preventive strategies:
– Diabetes education and self management programs (DSME-S)
– Smoking cessation counseling
– Eye screening every 2 year
– Comprehensive foot care for prevention of ulcers
Rui L, Zhang P. Diabetes Care, 2010
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Incorporate Evidence Based Strategies into the Care Process
Medication
• Metformin
• Metformin + Sulfonylurea or Basal Insulin
• Intensive insulin
• ACE-I/ ARB
• Statin
Diabetes Education
• Self management and support
• Nutrition/ exercise
• Smoking cessation
• Foot care
• Referral for eye exam
Lifestyle and behavioral
change
• Weight management programs
• Depression screening
• Social stressors
1
2
3
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Training: • Multidisciplinary conferences
• Nurse competency training
• Population health coordinator training
Staff Resources:• Practice champion team (MD, NP, RN, MA)
• DSME-S
• Diabetes virtual visits
• Nutrition access
• Group visits
• Nurse outreach
• Diabetes peer-to-peer e-consults
• Documentation templates for EHRService coordination:• MGH Diabetes Center
• Population Health
• Patient Centered Med Home
• Nurse leadership council
CONTINUOUS
QUALITY
IMPROVEMENT
Diabetes Collaborative Team CarePractice based teams, facilitating staff and patient engagement around
diabetes-related care, especially for those not at goal
Clinical and Educational Materials: • Insulin handbook
• Diabetes guidebook
• Patient handouts available thru EHR
• Social media resources
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Diabetes Care Team Goals
• Keep care local
• Use all team members at top of licensure
• Link medical prescribing with education, lifestyle change, self management and support
• Improve team communication and documentation
• Encourage multi-disciplinary training, education and learning
• Promote continuous process improvement
• Integrate principles of population health
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Results
• More patients eligible for insulin are now being treated
• Helped practices improve their diabetes team care
• Higher engagement of non-MD staff with diabetes care
• More resources available to PC practices (e.g. diabetes e-consult)
• 6 multidisciplinary conferences, with high provider ratings
• Multiple publications:
– Insulin initiation and titration handbook
– Primary care diabetes guidebook
– Patient education materials
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Proportion of Diabetic Patients with A1c>9%
Eisenstat et al, Am J Med Qual, 2016
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Case Study 3:
Patient Navigation in Cancer Care
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• Patient navigation (PN) can improve rates of cancer screening in
vulnerable populations
• Most cancer PN programs are located in community health centers and
focus on screening for a single cancer
• Many patients who could benefit from PN may receive care in settings
where on-site PN is not practical
• Some patients may be overdue for more than one cancer screening test
Background
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Objective
• To evaluate a patient navigation program to help
– Vulnerable patients obtain comprehensive (breast, cervical, and/or colorectal) cancer screening
– Within a large, diverse academic primary care network
– Using a population-based information technology (IT) system
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Pop Health for Cancer Screening
• Patient navigation implemented as part of a visit-independent, PHM
system to identify adult patients overdue for cancer screening in an
academic primary care network
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Patient Navigation Program Uses Algorithm to Identify High Risk
‒ No-show history
‒ Number of cancer screenings overdue
‒ Non-English speaking
Patient centered navigation
‒ Breast cancer screening
‒ Cervical cancer screening
‒ Colorectal cancer screening
Four part-time navigators
‒ 2 FTEs
‒ Each spoke 2-5 languages
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Methods• Study design: randomized controlled trial
‒ Population health system with or without PN
• Study setting: 18 practice primary care network
• Eligible patients:
‒ Overdue for at least one cancer screening test
‒ High risk for non-compliance using algorithm
• Outcomes: screening completion rates at 9 month
‒ Primary: intention-to-treat
‒ Secondary: as-treated (excluding PN patients not contacted)
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Patient Baseline DemographicsIntervention
(n=792)
Control
(n=820)
P-Value
Age (years), mean (SD) 56.9 (9.3) 57.1 (9.4) 0.68
Gender, female 479 (60.5%) 496 (60.5%) 1.0
Race 0.02
Asian 61 (7.7%) 82 (10.0%)
Black 94 (11.9%) 83 (10.1%)
Hispanic 104 (13.1%) 71 (8.7%)
Other/Unknown 44 (5.6%) 42 (5.1%)
White 489 (61.7%) 542 (66.1%)
Language, English 565 (71.3%) 608 (74.2%) 0.21
Insurance 0.34
Commercial 430 (54.3%) 410 (50.0%)
Medicaid 174 (22.0%) 200 (24.4%)
Medicare 147 (18.6%) 169 (20.6%)
Self-Pay 41 (5.2%) 41 (5.0%)
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Cancer Screening by Study Group
P=0.01
Proportion completing cancer screening during study period among patients
overdue for screening at baseline – Intention to Treat Analysis
0%
5%
10%
15%
20%
25%
Breast Cervical Colorectal
% C
om
ple
tin
g
Sc
ree
nin
g
Intervention Control
P=0.01 P < 0.001
Percac-Lima et al, JAMA IM, 2016
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Limitations
• One academic primary care network
• Established patient navigation program
• Advanced information technology infrastructure to perform population management
• Many patients at high risk for not completing screening could not be reached or were deferred
• Patients might have obtained the screening outside of the practice network
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Conclusions and Implications
• Network-wide patient navigation as a part of visit-independent, PHM
system increased comprehensive cancer screening rates in vulnerable
patients
• PN may help population health efforts decrease disparities in care while
improving overall outcomes
• Efforts to address equity of care should be built into PHM systems
‒ Algorithms to better identify vulnerable patients who could benefit from PN
‒ Improved referral and tracking of high-risk patients
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A Summary of Benefits Realized (1/2)
• Satisfaction:
– Physician survey: 85% said + impact on care provided to their patients
• Treatment/Clinical:
– 3-9% increase in chronic disease outcomes over 6 months
– >1% decrease in patients with A1c>9 after 2 years
– 14.8% increase in cancer screening among underserved patients
• Electronic Data
– > 90% in sensitivity and specificity for PCP-Patient attribution
– Improved data quality via patient feedback reporting
– Data quality improvement via claims/EHR data reconciliation
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A Summary of Benefits Realized (2/2)
• Patient Engagement/PHM:
– Centralized pop health coordination improved chronic disease
outcomes 2.3% to 4.9%
– Increased engagement of non-MD staff with diabetes care
– Multidisciplinary conferences, with high provider ratings
– Patient navigation decreased disparities of care
• Savings (in case study 1):
– Estimated treatment of disease avoided: ~$20/patient
– Cost of initiative: ~$7/patient
– ~$1.6M saved at MGH in first 6 months
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Questions
• Thank you!
• Contact: [email protected]
• Remember to complete online session evaluation
HARVARD MEDICAL SCHOOL