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Care Metrics and Quality Improvement
Jason H. Wasfy, MD MPhil FACC
Medical Director, Massachusetts General Physicians Organization
Assistant Professor, Harvard Medical School
November 7, 2018
Measuring and managing
• Conceptually, hard to improve what you can’t measure
• As such, many of us have been working on developing metrics for improved care
• Payment models are evolving
• Success has been mixed
• Reviewing past experience can help us develop good measures
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HRRP and HF mortality
• Statistical approaches to causal inference depend on methods (linearization, reference group etc)
• Initially, I was skeptical about results linking HRRP and mortality because of ecological inference fallacy
• There is an argument both ways (look at all patients affected by the policy vs. lack of plausible causal mechanism)
• Different results among analyses performed with different methods
• Qualitative methods could strengthen understanding3
Papadogeorgegou 2018 Wasfy 2017
Glass half empty or half full?
• No question some measurement has caused adverse consequences (PCI reporting)
• Sometimes improvement in process measures not translated into outcomes (DTB)
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From Joynt et al. JAMA 2012
Menees et al NEJM 2013
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Half full
• But I still think carefully designed measures have great potential
• Also, not measuring/reporting has produced suboptimal health outcomes
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Some successes -ART
• Fertility Clinic Success Rate and Certification Act
• Mandated national public reporting of outcomes for ART
• Good outcomes (single infant born alive after 37 weeks and weight above 2.5 kg) increased from 18.5% to 26.7% from 2000 to 2010
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cdc.gov
JAMA 2013
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ACOs and patient experience
• CAHPS scores contribute to ACO financial performance
• Average performance of ACOs went from 82nd to 96th
percentile nationally, robust to group differences in trends
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McWilliams et al NEJM2014
Our experience at MGH
• Metrics allow us internal attention and funds to support quality improvement
• Discuss our efforts to show how an organization might respond to quality metrics
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AMI and PCI readmission tactics
• We use mixed-methods more common to health services research
• Quantitative techniques such as adjusting for preexisting trends and including readmissions to other facilities
• Systematic physician chart reviews to inform clinical and operational strategies (avoids anecdotes/personal experiences)
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Low risk chest discomfort and heart failure
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Wasfy et al Circ Intv 2014
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Lots of chest discomfort – but low TLR
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• 9288 PCI patients, 893 (9.8%) readmitted at MGH and BWH within 30 days
• 6.2% complications of PCI, 38.1% suspicion for angina
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Wasfy et al Circ Intv 2014
only 2.6% TLR!
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Variables known before PCI can predict readmission risk prospectively (C-statistic = 0.69)
Wasfy et al Circ Qual Outcomes 2013
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NLP searches refine risk prediction
• Anxiety, frequent ED visits
• Social variables are prominent (homelessness, non English speaking)
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Wasfy et al Circ Cardiovasc Qual Outcomes 2015
Nearly half preventable
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Wasfy et al Journal of the American Heart Association 2014
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We then validated this preventability
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Tanguturi et al. Circ Cardiovasc Qual Outcomes 2017
Including non-index readmissions, slope for MGH patients is 2.5x rest of state
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13%
14%
15%
16%
17%
18%
19%
20%
2006 2007 2008 2009 2010 2011 2012 2013 2014
Rea
dmission Rate W
ithin 30 Days post‐Discharge
MGH PCI ReadmitRate
NON MGH ReadmitRate
Linear (MGH PCI ReadmitRate)
Linear (NON MGH ReadmitRate)
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Tactics
• Risk prediction and patient education
• Two pillars
(1) Mandatory outpatient follow up
(2) NP based electronic notifications when patients are in the ED
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Outpatient follow up
• Standard protocols around 2 week follow up for all MGH patients
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Protocol-based triggers for cardiology consultation in the emergency department
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Presentation Default Recommendations
Early recognition at time of registration
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PCI and AMI do not fully overlap
• Only 184,110 of those readmitted PCI patients (32.7%) had AMI during the index PCI hospitalization
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Wasfy JH, Dominici F, Yeh RW. Circulation 2016
Review of AMI readmissions
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Of 197 AMI readmissions, 111 received no revascularization.
28.4% too high risk11.7% type 2 MI6.6% patient declined interventions
So we included AMI patients in the protocols.
Martin et al. JAHA 2018
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These interventions are correlated with a 40% drop in index-readmission rate
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Associated with CMS penalty reduction
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Our mission is quality care - external incentives only organize efforts and provide funds for important work.
We can use these funds to improve care, expand access and rapid consultative care in the ED.
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Meaningful measures?
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Metrics are most meaningful if:
(1) Risk adjusted without unmeasured confounders(2) Clinical data rather than administrative data (harder to do)(3) There is minimal opportunity to manipulate data by clinicians
(either conscious or unconscious) (4) Some attention to social determinants of health (even if not
included in risk-adjustment)
Wasfy et al Circulation 2015
Thanks!
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@jasonwasfy