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Building A Technical Assistance Program For Hospital Quality Improvement

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Title: Building A Technical Assistance Program For Hospital Quality Improvement


1
Hospital Leadership Quality Assessment
AHQA 2008 Annual Meeting
  • Building A Technical Assistance Program For
    Hospital Quality Improvement
  • Hospital Leadership Collaborative
  • (HLC)

February 27, 2008
Health Benchmarks Inc
2
Outline
  • 1. Overview of HLQAT project
  • 2. Origins Leadership Survey (Short)
  • 3. HLQAT Scope Phases
  • 4. Testing the Model
  • 5. Roles of the QIOs
  • 6. Anticipated value of the project

2
3
1. HLQAT Project (2008-2010 beyond)
  • Purpose
  • Integrate activities of the HLC with the CMS 9th
    SOW to drive hospital quality improvement.
  • Goals
  • Develop and test HLQAT as a tool for measuring
    domains of hospital leadership that drive change.
  • Develop tools to provide support to the QIOs in
    giving technical assistance to low-performing
    hospitals under the CMS 9th SOW.
  • Apply measures of hospital quality performance to
    evaluate effectiveness of technical assistance
    protocols and test HLQAT as an agent of change.

3
4
  • Key Project Staff
  • University of Iowa Barry R. Greene, PhD, Tom
    Vaughn, PhD, Samuel Levey, PhD , Duncan Moore,
    FACHE
  • Brandeis University Chris Tompkins, PhD
  • HBI Judy Chen, Ph.D., Josh Marehbian, MPH
  • OFMQ Dale Bratzler, DO, MPH, Shannon Archer RN,
    CPHQ
  • Premier-CareScience (Penn-LDI) Eugene Kroch, PhD
  • HSAG Andrea Silvey, PhD
  • Dot.Comments Chris Hatcher, MHA
  • CMS Ninth Scope of Work
  • Oversight (and collaboration)
  • AHA Steve Mayfield
  • IHI James Conway (via IHI Impact Network)

4
5
2. Origins Leadership Survey (2005-2006)
  • Identify how hospital leadership is involved in
    quality improvement.
  • Link survey results to hospital quality outcomes
    (Quality Index).
  • Share findings to promote a strategic approach to
    quality improvement in hospitals based on
    empirical findings.
  • Collaborators
  • Centers for Medicare and Medicaid Services
  • Univ. of Iowa College of Public Heath
  • Leonard Davis Institute of Univ. of Pennsylvania
  • Hospital Associations from 9 states

Vaughn et al., Engagement of Leadership in
quality Improvement Initiatives Results from the
Executive Quality Improvement Survey, J Pat
Safety, 2006. Kroch et al., Hospital Boards
and Quality Dashboards, J Pat Safety, 2006.
5
6
Leadership Survey
  • 18-question survey distributed via internet in
    early 2005 to 1,380 hospitals in 9 states AZ,
    CO, IL, IA, MD, NJ, NY, PA, and WI.
  • 438 usable hospital responses (rate 32)
  • CEOs (55), QI execs (25), CMO/CNO (13)
  • Examines hospital QI drivers and impediments,
    reporting methods, board and physician
    participation in QI, and senior executive
    incentives.

6
7
Descriptive Findings
  • 24 of boards interact with the medical staff a
    great amount in setting hospital quality
    strategy.
  • 27 of boards spend more than one fourth of their
    time on quality issues.
  • 66 of hospitals base some type of executive
    compensation on measurable Quality Improvement.
  • BUT only 13 of hospitals tie quality improvement
    to executive base compensation packages
  • 80 of responding hospitals use a formal quality
    performance measurement dashboard for reporting
    to their boards.

7
8
Linking Leadership Survey to CareScience Quality
Index (Qx)
  • Measures the risk-adjusted overall rate of
    adverse outcomes
  • Mortality
  • Morbidity
  • Complications
  • Uses the Corporate Hospital Rating Project
    utility weights to construct an index (Qx)
  • Responses were matched to Qx derived from H.A.
    all-patient data and MedPAR 2004 data

Pauly MV, Brailer DJ, and Kroch EA. Measuring
Hospital Outcomes from a Buyers Perspective
(1996). American Journal of Medical Quality. 11
(3) 112-122.
8
9
Low Quality (bottom third)
High Quality (top third)
9
10
Findings Better outcomes found in
hospitals where...
  • the board spends gt25 of time on quality issues
    (p 0.009)
  • the board receives a formal quality performance
    measurement report (p0.005)
  • there is a high level of interaction between the
    board and the medical staff on quality strategy
    (p0.021)
  • the senior executives compensation is based in
    part on QI performance (p0.008)
  • the CEO is identified as the person with the
    greatest impact on QI (p0.01), especially when
    so identified by the QI executive (plt0.001).

10
11
NB numbers above bars are case counts
12
N 438
Hospitals where the CMO/QI exec identifies the
CEO/Pres as the most influential person are about
three times more likely to be in high performance
group (p-value lt 0.001).
12
13
3. Scope of the HLQAT Project
  • Baseline links between hospital leaderships
    involvement in quality improvement and hospital
    performance
  • Interventions to be carried out at designated
    hospitals
  • Based on outcomes/process gaps
  • Based on HLQAT-identified opportunities for
    change
  • Follow-up analyses will measure changes in
    performance
  • Follow-up HLQAT will assess change of hospital
    leaderships involvement in quality improvement
  • Relative to comparison group

14
HLQAT Project Phases
  • Phase I Preparation for Technical Assistance
  • HLQAT validation and assessment
  • Identify low performers April to Sept 2008
  • Develop TA protocols
  • Phase II Implementation of Technical Assistance
  • TAP training Sept 2008 to Dec 2009
  • Interventions
  • Phase III Empirical Evaluation
  • Hospital Performance evaluation
  • HLQAT re-evaluation Jan 2010 and beyond

14
15
HLQAT Activity/Intervention Flow
15
16
HLQAT Assessment Domains Path to Quality
Improvement

Silvey et al., Components Essential to
Achieving High Performer Status, HSAG, 2005
16
17
4. Testing the Model
  • ASSERTION Technical Assistance Protocols (TAP)
    can strengthen Hospital Leadership (HL)
  • ASSERTION The dimensions of HL are significant
    determinants adherence to Clinical Processes
    (CP).
  • THUS HL improvements can be tested for their
    effects on CP.
  • ASSERTION CP are hypothesized to be
    significantly related to Clinical Outcomes (CO).
  • THUS Causal chain to test
  • TAP? HL? CP ?CO

In a subsequent phase of the project the test is
for the extent to which specific CP improvements
are related to targeted Clinical Outcomes.
17
18
Empirical Method
  • Score hospital performance on a combination of CP
    and CO consistent with CMS priorities.
  • Scoring measures criteria
  • Workable for QIO technical assistance
  • Relevant to quasi-experimental method (Regression
    Discontinuity Design)
  • Choose threshold values such that
  • Hospitals scoring below a chosen threshold will
    be formally assigned to receive QIO technical
    assistance
  • Hospitals scoring above a threshold will be
    assigned as control hospitals, and will not
    receive QIO technical assistance
  • Option Choose both low and high thresholds,
    (i.e.,divide the hospitals into three (versus
    two) groups), and allow for technical assistance
    to be delivered to hospitals falling between the
    thresholds

18
19
Performance Metrics
  • Clinical Process (CP) measures
  • JC-CMS-HQA core measures
  • CMS SCIP measures
  • Other (stroke, asthma, etc.)
  • Clinical Outcomes (CO)
  • Mortality
  • Complication rates
  • ALOS
  • Readmissions
  • AHRQ PSIs

19
20
Surgical Complications Methodology
  • A combination of a data-driven and
    literature-based approach is taken to identify
    about 30 procedures to include in the methodology
  • Complications for each procedure are classified
  • A risk-adjustment methodology accounts for
    patient factors including
  • Age
  • Gender
  • Type of procedure
  • Comorbidity level (Charlson Index)
  • Disease burden
  • Other patient factors

21
Surgical Complications Methodology
  • STEP I Identify the relevant procedures
  • Inpatient data are aggregated across all
    hospitals
  • Frequencies of all procedures are obtained and
    procedures are grouped by clinical team
  • The most commonly performed procedures are
    identified
  • Primary and non-primary diagnosis codes related
    to these patients are identified
  • Literature is searched for complications
  • For each procedure, a comprehensive list of
    complications is developed, driven by literature,
    data, and expert review

22
Surgical Complications Methodology Sample of
procedures included in the model
23
Surgical Complications Methodology
  • Step II Classification of complications and
    comorbidities

24
Surgical Complications Methodology
  • STEP III Run Model on MedPAR Claims Data
  • Identify procedures during our measurement period
  • Identify relevant complications during the 1-30
    day period post surgery
  • Inpatient and outpatient diagnoses
  • Crude complication rates calculated
  • Identify comorbid conditions before and after
    procedure (Charlson Index)
  • Predicted (risk-adjusted complication rates
    calculated)
  • Rates and benchmarks reported

25
Minimizing Limitations of Claims Data
  • If more than one surgery occurs within a 30 day
    window, case will be excluded.
  • Additive effect of two procedures may place
    patient at elevated risk.
  • Most complications are identified at 1-30 days
    post-op. This helps minimize the identification
    of present-on-admission diagnoses as
    complications
  • When POA codes become common practice, these will
    be integrated into methods.
  • Other exclusions in order to elevate precision
  • Eg Diagnosis of hepatobiliary malignancy anytime
    in patients history

26
Other Potential Measures
  • Obstetrical Complication Rates
  • Methods similar to surgical complications
  • Based on nationally endorsed metrics
  • Risk-Adjusted Inpatient Length of Stay and
    Readmission Rates
  • CMS Process/Outcomes Measures
  • Data published quarterly on Hospital Compare and
    Quality Check websites
  • Mostly processes of care and some outcomes of
    care
  • Score and rank based on performance compared to
    national benchmarks
  • HCAHPS Survey
  • Patient satisfaction survey required by CMS for
    all facilities
  • Validated case-adjusted assessment methodology

27
Regression Discontinuity Design
27
28
Specific Hypotheses
  • H1 Hospitals receiving QIO interventions based
    on the TAP will significantly improve in clinical
    quality (measured by CP and CO) relative to
    control hospitals.
  • H2 Hospitals receiving TAP interventions will
    significantly improve in domain scores related to
    hospital structures (measured by HLQAT) relative
    to controls.
  • H3 Prior improvements in hospital structures are
    associated with improved clinical quality
    (measured by CP and CO).

28
29
5. Roles of the QIOs
  • Rationale
  • Under 9th SOW QIOs are the obvious means to
    helping low-performing hospitals.
  • Coordination, engagement, and support across QIOs
    for an national effort will enhance the
    likelihood of success.
  • Specifics
  • Input during validation phase of the HLQAT
  • TAP design based on the HLQAT domains
  • HLQAT and TAP training (CMS support contracts)
  • TAP implementation (CMS support contracts)

29
30
CMS 9th SOW
  • QIOs are to focus on these main themes (Section
    C.6)
  • C.6.1. Beneficiary Protection
  • C.6.2. Patient Safety
  • C.6.3. Prevention.
  • QIOs will be required to offer help to specific
    hospitals that have not recently performed well
    on important quality measures.
  • The requirements of the Patient Safety Theme are
    designed to address areas of patient harm for
    which there is evidence of how to improve safety
    by improving health care processes and systems.
  • Required activities include
  • Administer and collect results of the Healthcare
    Leadership and Quality Assessment Tool (HLQAT)….
  • Source CMS web site

30
31
QIO Interventions
  • Utilize these tools for providing tailored and
    structured interventions to identified
    participant groups that will ultimately assist
    them in reaching the Achievable Benchmarks of
    Care…. The QIO shall administer, collect and
    utilize the results by the 18th month of the
    effective date of contract and then readminister
    between months 18 and 35.
  • Under QIO support contract CMS will provide
    training on the HLQAT via series of meetings
    (not yet finalized)
  • HLQAT domains
  • Culture knowledge seeking, goal setting,
    communication, collaboration, support
  • Policy operations roles responsibilities,
    monitoring, manamgement strategy, incentives,
    resource allocation

31
32
Hawaii Hospital Incentive Program
  • In 2001, the Hawaii Medical Service Association
    (Blue Cross and Blue Shield of Hawaii) initiated
    the Hospital Quality Service Recognition Program.
  • Designed to provide financial bonuses to
    facilities based on performance across several
    measures
  • Hospitals were eligible to receive payments based
    on their total reimbursement from the plan
  • Portion of their eligible sum was paid based on
    their performance across measures

33
Facility, QIO, and Health Plan Collaboration
  • HLQAT will be integrated in the Hawaii hospital
    incentive program. Participation will be tied to
    bonuses.
  • Hospitals will implement the HLQAT tool at
    different levels within the facilities.
  • HBI will measure baseline and follow-up outcomes
    and measures.
  • QIOs will work with hospitals to implement
    interventions.

34
6. Anticipated Value of the Project
  •  1. Creation of an accessible, electronic version
    of the HLQAT, and benchmark data, to support
    hospitals as they assess themselves in terms of
    their shared culture and commitment to improved
    quality.
  •  
  • 2. Development of a technical assistance toolkit
    (TAP) for QIOs and other groups working with
    hospitals that can be used to match interventions
    with specific gaps in perceived commitment to a
    culture of excellence, or with shortfalls in
    organizational capacity to effect change.
  •  
  • 3. Improved quality of care, as measured by the
    metrics described in the proposal, in hospitals
    receiving consultation.

34
35
Questions Comments
  • Building A Technical Assistance Program For
    Hospital Quality Improvement
  • Eugene A. Kroch, PhD ekroch_at_wharton.upenn.edu
  • Josh Marehbian, MPH jmarehbian_at_healthbenchmarks.
    com

Health Benchmarks Inc
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