The Reducing Readmissions Through Improving Care Transitions - - PowerPoint PPT Presentation

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The Reducing Readmissions Through Improving Care Transitions - - PowerPoint PPT Presentation

Jan 07, 2024 116 likes •336 views

The Reducing Readmissions Through Improving Care Transitions (RRTICT) Quality Improvement Program Ashley Ketterer Gruszkowski, MHA Health System Specialist Veterans Engineering Resource Center (VERC) - Pittsburgh Co-Authors: Michael W.

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Ashley Ketterer Gruszkowski, MHA Health System Specialist Veterans Engineering Resource Center (VERC) - Pittsburgh

The Reducing Readmissions Through Improving Care Transitions (RRTICT) Quality Improvement Program

Co-Authors: Michael W. Kennedy, PhD; Emily Rentschler Drobek, MSPPM; Lior Turgeman, PhD; Aleksandra Milicevic, PhD; Terrence L. Hubert, PhD; Larissa Myaskovsky, PhD; Jerrold May, PhD, Robert Monte, RPh, MBA; Kathryn Sapnas, PhD, RN-BC, CNOR

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VETERANS HEALTH ADMINISTRATION

VA Pilot Sites & Collaborators

  • Albany VA Medical Center
  • Bay Pines VA Healthcare System
  • Gainesville - Malcom Randall

VAMC, NF/SGVHS

  • Miami VA Healthcare System
  • Omaha VA Medical Center
  • Pittsburgh - VA Pittsburgh

Healthcare System

  • San Juan - VA Caribbean

Healthcare System

  • Sioux Falls - Royal C Johnson

Veterans Memorial Medical Center

  • Office of Strategic Integration |

Veterans Engineering Resource Center (OSI|VERC)

  • VA National Program Office of

Patient Care Services (PCS)

  • Center for Health Equity Research

and Promotion (CHERP)

  • University of Pittsburgh Katz

Graduate School of Business

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VETERANS HEALTH ADMINISTRATION

Agenda

  • History of Big Data in the VA
  • Why readmissions are important
  • RRTICT program overview

3

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VETERANS HEALTH ADMINISTRATION

History of Big Data in the Veterans Administration (VA)

  • VHA is the nation’s largest integrated healthcare

system

  • Serves 9 million Veterans in 1,700 healthcare facilities
  • Access to > 1 billion Veteran health data points
  • Ability to connect data analysts with clinical expertise

– Improve clinical quality metrics – Prospectively identify patients for care needs

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1,700 hospitals, clinics, CLCs 6.6 billion lab tests 3.8 billion clinical

  • rders

2.1 billion inpatient &

  • utpatient

encounters 9 million Veterans

Corporate Data Warehouse (CDW) VISTA/CPRS = EHR

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VETERANS HEALTH ADMINISTRATION

Importance of Reducing Readmissions (Private vs VA)

Private Hospitals

  • Affordable Care Act 

Hospital Readmissions Reduction Program

  • Economic incentives and

penalties from Center for Medicare and Medicaid Service (CMS)

– Quality of Care – Outcome Measures

Veterans Administration

  • Not driven by economic

incentives

  • Data-driven performance

measurement

  • Reduce burden on

patients, families and hospital resources

  • Increase inpatient access
  • Quality and safety
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VETERANS HEALTH ADMINISTRATION

30 Day Readmission Rate

  • 30 DRR defined by CMS

– Readmissions after a longer time period may not correlate with care received in the hospital – Financial penalties and reimbursement threshold

  • VA readmission rate data = CMS definition

– Data sources Inpatient Evaluation Center (IPEC) and Strategic Analytics for Improvement and Learning (SAIL)

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VETERANS HEALTH ADMINISTRATION

Reducing Readmissions Through Improving Care Transitions (RRTICT) Pilot

  • Goal: Determine if a bundled strategy approach

reduces 30DRR

  • Background:

– VERC/Patient Care Services (PCS)/VA facilities – Risk predictive model – decision support tool

  • Guided clinicians to target those most at risk

– Evidence-based best practices in bundled options to reduce readmission

  • Provider- and system-level strategies to address major patient

transition failure points

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VETERANS HEALTH ADMINISTRATION

Risk-Stratified Predictive Model

  • VERC  University of Pittsburgh
  • Used at time of admission
  • Addresses limitation of previous models that focused
  • n prediction at discharge or post-discharge
  • Allows clinicians to proactively plan for discharge
  • Tailor specific interventions to patients at greatest

risk for readmission

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VETERANS HEALTH ADMINISTRATION

Model Variables

12/2 1/20 16 10

  • Age, sex, marital status, next of kin

Demographics

  • # recent ED visits, # recent inpatient

admissions, time since last inpatient discharge, number & type of medications

Patient History

  • Source (reason) of admission, lab values,

systolic / diastolic, pulse, respiration, BUN / creatinine, WBC, albumen

Current Hospitalization Information

  • Anemia, asthma, chronic renal failure,

cerebrovascular accident stroke, COPD, diabetes, depression, dementia, hypertension, ischemic heart disease, obesity, PTSD, peripheral vascular disease

Co-morbidities

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VETERANS HEALTH ADMINISTRATION

Risk Prediction Output Example

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Site Name SSN Admit Date Risk Bay Pines Pt 1 P0001 2/19/2016 Low Bay Pines Pt 2 P0002 2/22/2016 Moderate Bay Pines Pt 3 P0003 2/24/2016 High

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VETERANS HEALTH ADMINISTRATION

Table of Bundled Strategies

In-Hospital Strategies Post-Discharge Strategies Appointment Scheduling C-TraC Program Patient Education Hospital In Home Discharge Instructions Home Based Primary Care Discharge Team Telehealth Pharmacist-Led Med-Rec PACT Team Rounding Community Residential Care Daily Huddles PILL Program Discharge Coordinator

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VETERANS HEALTH ADMINISTRATION

Aligning Model with Strategies

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Site Name SSN Admit Date Risk Suggested Strategy Bundle Bay Pines Pt 1 P0001 2/19/2016 Low Attending Preference Bay Pines Pt 2 P0002 2/22/2016 Moderate In-Hospital Bay Pines Pt 3 P0003 2/24/2016 High In-Hospital & Post-Discharge

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VETERANS HEALTH ADMINISTRATION

RRTICT Pilot Procedures

  • 7 Facilities across 3 VISNs volunteered to

participate

  • 6 Month pilot (4/1/2015 – 9/30/2015)
  • Required to collaborate on monthly

virtual learning sessions

  • Implement RRTICT
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VETERANS HEALTH ADMINISTRATION

Analyses

  • Qualitative feedback from all sites
  • Quantitative comparison of pre/post 30DRR
  • Data from VA’s IPEC all-cause 30DRR

– Percent of patient discharges with readmission within 30 days – Derived from CMS definition (exclusion criteria)

  • Two-tailed z-test, with p < 0.01 significance
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VETERANS HEALTH ADMINISTRATION

Feedback from Pilot Sites

  • “Pairing evidenced based interventions with

cost effective interventions”

  • “Developed a strong culture that embraces

change”

  • “Use of the predictive model helped develop

a well established discharge planning team and meeting format”

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VETERANS HEALTH ADMINISTRATION

Readmission Rate and Percent Difference (FY14 vs FY15)

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15.8% 15.2% 15.9% 25.4% 15.2% 16.2% 12.1% 11.4% 13.9% 11.7% 19.8% 9.9% 8.5% 10.0% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% Overall Albany *Bay Pines (+) Gainesville Omaha *San Juan Sioux Falls FY14 (Historical) FY15 (Intervention)

  • 28%

*P ≤ 0.01; (+) 2 pilot sites

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VETERANS HEALTH ADMINISTRATION

Limitations

  • QI based study with limited data points
  • Predictive model limitations:

– Excluded patients new to the VA – Excluded length of stay and admitting diagnosis codes

  • Comparison made with historical group
  • Variation of implementation of bundle
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VETERANS HEALTH ADMINISTRATION

Next Steps

  • VA Leadership interested in OSI|VERC leading a

prospective controlled trial

– HSR&D Merit or QUERI – VA Cooperative Study

  • Monitoring RRTICT’s impact on other variables

– Mortality – Length of Stay

  • Promote RRTICT through presentations and

publications

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VETERANS HEALTH ADMINISTRATION

Thank You! Ashley Ketterer Gruszkowski, MHA Ashley.Gruszkowski@va.gov