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Synergizing QUERI Research and Operations Analyses in Monitoring and Improving the Population Health of Veterans with Diabetes D. Aron, L. Pogach, E. Kerr and D. Miller WORKSHOP OBJECTIVES.

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slide1

Synergizing QUERI Research and Operations Analyses in Monitoring and Improving the Population Health of Veterans with DiabetesD. Aron, L. Pogach, E. Kerr and D. MillerWORKSHOP OBJECTIVES

  • (1) address issues surrounding current diabetes quality measures and discuss alternatives and how they can be used in both research and clinical practice; and
  • (2) describe two data sources: the Patient Care Services (PCS)-VSSC Diabetes Cube and Diabetes Epidemiological Cohort (DEPIC) and their potential use in operations and research.
introduction and context for diabetes quality measurement

Introduction and Context for Diabetes Quality Measurement

David C. Aron, MD, MS

Co-Clinical Coordinator, DM QUERI

ACOS/Education, Louis Stokes Cleveland DVAMC

Professor of Medicine

Case Western Reserve University School of Medicine

queri workshop agenda

QUERI WORKSHOP Agenda

Context for performance measurement

Limitations of measurement

Alternative means for cross sectional measurement

Cross sectional measures vs Longitudinal Measures

Individuals versus populations

Sources of Data

how do we use performance measurement
How Do We Use Performance Measurement

Public Accountability (External)

  • External transparency
  • To compare healthcare plans or physicians based upon a metric
  • To financially reward plans and physicians

Quality Improvement (Internal)

  • Internal to plan or practice
  • To guide population based improvement
  • Can be used for internal reimbursement
slide5

Specialty Societies, Big Pharma, Disease Group Advocacy

Encourage Disease Management

Offer better products

Inform product design

↑Sustainability

Quality

Improved

practice

Formulary Management

Drug

Detailing

Payers

Seeking

Value

Provider inbroadsense-PDCA/QI

Re-Assess

Proftability

↑Market

Share

Evidence

QI

Measures

and

Reports

Research

Measures

Identify

Gaps

LowerMedical loss ratio

Reports

Marketing

Marketing

Get

Information

Utilization(Micro-Choice)

Utilization(Macro-Choice)

InformedConsumer

Choice

MakeHealthcarechoices

Chooseamongplayers

Direct to

Consumer

Advertising

NON-TRANSPARENT INFLUENCES ON MEASURES AND PUBLIC REPORTING OF QUALITY DATA

Assess

who are the patient age distribution of va patients 2005
Who are the patient?Age Distribution of VA Patients - 2005

WWII

Korea War

Vietnam War

Post-Vietnam

%

24%

40%

14%

23%

20 30 40 50 60 70 80 90 100

AGE

Dramatic increase in proportion of younger patient recent years.

5 year mortality rates in veterans with diabetes 65 years with co morbidities
5-year Mortality Rates in Veterans with Diabetes <65 Years with Co-morbidities

Pogach et al. AJMC March, 2007

what populations do worse on glycemic control
What populations do worse on glycemic control ?
  • Longer duration of diabetes African Americans and White Hispanic
  • <45 yrs worse than 45-54 than 55-64
  • Mental Health Conditions: Psychoses, Substance Abuse and/or anxiety/PTSD disorders
  • Lower socioeconomic-educational status (buying healthy food, diabetes numeracy?)
  • Less social support
queri workshop 12 09

QUERI WORKSHOP 12-09

Context for performance measurement

Limitations of measurement

Alternative means for cross sectional measurement

Cross sectional measures vs Longitudinal Measures

Individuals versus populations

Sources of Data

technical elements of performance measurement
Technical Elements of Performance Measurement
  • Measurement uncertainty
  • Population at risk
  • Bias (differences in population)
  • Effectiveness in practice
  • Feasibility and cost of data collection
  • Baseline status, patient safety, patient preferences
facility variation in factors impacting glycemic control 1999 2000 maney et al diabetes care 2007
Facility Variation in Factors Impacting Glycemic Control (1999-2000) Maney et al, Diabetes Care, 2007
fy08 performance measures a1c
FY08 Performance Measures A1c
  • A1c FACILITY VARIATION RANGE (730-2270, mean 1560 EPRP charts)
    • <7=46% [range 42-49 VISNS]
    • <8=72% [range 66-72 VISNS]
    • >9=16% [range 15-20 VISNS]
  • LDL-C
    • <120 mg/dl=81%
    • <100 mg/dl=68%
    • Based upon ~32K charts
slide16

Age is Important:

Lifetime Risk for Blindness Due to Diabetic Retinopathy*

Vijan, S. et. al. Ann Intern Med 1997;127:788-795

slide17

Lifetime Risks for End-Stage Renal Disease*

Vijan, S. et. al. Ann Intern Med 1997;127:788-795

slide18
Benefits of Glycemic Risk Reduction (7.9% to 7.0%) over 10 yrs from the UKPDS (Budenholzer et al, BMJ 1245, 2001)
cross sectional good measure options
Cross Sectional “Good” Measure Options
  • Case mix adjustments: What is not under plan control?
    • Socio-positioning
    • Age
    • Duration, Type 1 or 2, others
  • Exclusions:
    • What to exclude? Life expectancy, health risk, side-effects
  • How To Score
    • Pass/Fail? “Partial” credit:
    • How to weight?
slide21

Curvilinear relationship between A1c and Microvascular Disease: Risk of retinopathy and by A1C level

DCCT Research Group NEJM 1993

stringent dichotomous outcome measures
Stringent Dichotomous Outcome Measures
  • Don’t target patients most likely to benefit
    • Ignore the heterogeneity of patient risk factors
  • Don’t help providers do the “right” thing
    • Do not give “partial credit” for actions or improvements that may yield considerable benefits
  • Don’t take into account patient preferences
    • Could mandate care that is contrary to the wishes of a reasonable, well informed patient
  • Could result in unintended consequences
    • Polypharmacy, hypoglycemia, worse outcomes
queri workshop 12 0924

QUERI WORKSHOP 12-09

Context for performance measurement

Limitations of measurement

Alternative means for cross sectional measurement

Cross sectional measures vs Longitudinal Measures

Individuals versus populations

Sources of Data

implementing linked clinical action measures for assessment and improvement

Implementing Linked Clinical Action Measures for Assessment and Improvement

Eve A. Kerr, MD, MPH

PI, Ann Arbor VA Center

for Clinical Management Research

Research Coordinator, DM QUERI

Associate Professor of Internal Medicine

University of Michigan Health System

the paradox of performance measurement
The Paradox of Performance Measurement

"Not everything that can be counted counts, and not everything that counts can be counted."

  • Albert Einstein (1879-1955)

From a sign hanging in Albert Einstein's office at Princeton.

How can we measure what counts?

slide27
What Makes a Good Quality Measure?AHRQ/NIDDK/VA Scientific Conference on Diabetes Quality Assessment
  • Target patients most likely to benefit
  • Help providers do the “right” thing
  • Incorporate (or at least don’t ignore) patient preferences
  • Avoid unintended consequences
  • Acknowledge limitations of current data sources and resulting measures (and motivate collection and use of clinically detailed data)

http://www.ahrq.gov/QUAL/diabetescare/

measuring quality in diabetes
Measuring Quality in Diabetes

Patients with diabetes who are 75 years or younger should have

A1c < 7%

BP < 130/80

Do stringent dichotomous outcomes measure what counts?

do stringent dichotomous outcomes target patients most likely to benefit
Do stringent dichotomous outcomes target patients most likely to benefit?

Lifetime Risk of Blindness due to Retinopathy

Vijan et al. Ann Int Med, 1997

stringent dichotomous outcome measures30
Stringent Dichotomous Outcome Measures
  • Don’t target patients most likely to benefit
    • Ignore the heterogeneity of patient risk factors
  • Don’t help providers do the “right” thing
    • Do not give “partial credit” for actions or improvements that may yield considerable benefits
  • Don’t take into account patient preferences
    • Could mandate care that is contrary to the wishes of a reasonable, well informed patient
  • Could result in unintended consequences
    • Polypharmacy, hypoglycemia, worse outcomes
tightly linked clinical action measures
Tightly Linked Clinical Action Measures
  • Identify high risk populations by diagnosis or by a poor intermediate outcome or other assessment of high risk
  • Evaluate processes of care that are strongly associated with important outcomes for that population
  • Intrinsically identify appropriate quality improvement responses within the measure that are under a health system’s control

- Kerr et al. Am J Manag Care 2001

linked clinical action measures adequate quality for hyperlipidemia treatment
Linked Clinical Action Measures:Adequate Quality for Hyperlipidemia Treatment
  • Tightly Linked Clinical Action Measure
    • LDL <130 mg/dl; or
    • LDL>= 130 mg/dl with appropriate clinical action:

1) were on a high dose statin; or

2) had statin started or dose increase within 6 months; or

3) repeat LDL <130 mg/dl within 6 months; or

4) had contraindications noted to statin treatment

percentage with adequate quality
Percentage with Adequate Quality

Kerr et al. Medical Care 2003

linked clinical action measures
Linked Clinical Action Measures
  • Target patients most likely to benefit by :
    • accounting for patients’ risk factors and benefits of intervention
    • Incorporating exceptions
  • Help providers do the “right” thing
    • Intrinsically incorporate quality improvement response
  • Can take into account patient preferences
    • Incorporate refusals or patient priorities
  • Diminish but don’t eliminate potential for unintended consequences

Kerr et al. Am J Managed Care 2001

measuring what counts
Measuring What Counts
  • Focus on high risk populations and high benefit interventions
  • Consider the costs, burden and safety of the treatments needed to achieve the goals
  • Give at least partial credit for processes under providers’ control
  • Insist on improvements in availability of clinically meaningful data
  • Guard against unintended consequences
slide36
“Everything should be made as simple as possible, but not one bit simpler.”
  • Albert Einstein (1879 - 1955)
accord more deaths in intensive vs standard glycemic control groups
ACCORD: More deaths in intensive vs standard glycemic control groups

Do stringent dichotomous outcomes target patients most likely to benefit?

National Heart, Lung, and Blood Institute. ACCORD telebriefing prepared remarks. February 6, 2008.

the universe of performance measures

Sampled performancemeasures =

SUBSET OF

QUALITY

(+ MORE NOISE)

Universe

of possible

performance measures

= OVERALL QUALITY (+ SOME NOISE)

The Universe of Performance Measures
va compared to community
VA Compared to Community

*

*

Asch et al. Annals of Internal Medicine, 2004

*P<0.01

designing clinically meaningful measures
Designing Clinically Meaningful Measures
  • Tightly-Linked Clinical Action Measures
  • Weighted or QALY-adjusted Measures
weighted measures david aron and len pogach

kerr

Weighted MeasuresDavid Aron and Len Pogach
  • Differentially weighting measures in a composite can reflect the relative contributions of each measure to outcomes of interest
  • Outcomes of interest may be defined specifically: e.g., cardiovascular events, mortality, or QALYs
  • Certain measures can be weighted (or stratified) to reflect the importance of achieving the measure to different populations
ada ncqa diabetes physician recognition weights and measures
ADA-NCQA Diabetes Physician Recognition “Weights and Measures”

Scored Measures Threshold Weight

(% of patients in sample)

  • HbA1c Control <7.0% 40%10.0
  • HbA1c Poor Control >9.0 % 15% 15.0
  • Blood Pressure Control >140/90 mm Hg 35% 15.0
  • Blood Pressure Control <130/80 mm Hg 25% 10.0
  • LDL Control >130 mg/dl 37% 10.0
  • LDL Control <100 mg/dl 36% 10.0
  • Eye Examination 60% 10.0
  • Foot Examination 80% 5.0
  • Nephropathy Assessment 80% 5.0
  • Smoking Status and Cessation Advice or Rx 80% 10.0

Total Points = 100.0 Points to Achieve Recognition = 75.0

what are quality adjusted life years
What Are Quality Adjusted Life Years?
  • Trade off between quality and length of life
  • QALY for a given intervention is the average number of years of life gained by the intervention, multiplied by a judgment of quality of life in those years, summed over a lifetime
  • Can address summary benefits and harms
  • Can address issues of life expectancy at time of intervention
qaly model
QALY Model
  • Trade off between quality and length of life
  • QALYs are the most important and broadly used method for evaluating health quality.
  • Panel on Cost Effectiveness in Health and Medicine (Gold et al. 1996): Medical CE studies should incorporate morbidity and mortality consequences into a single measure using QALYs.
the case for qalys to assess quality
The Case for QALYs to Assess Quality
  • A1c reduction improves QOL by reducing complications, which differ in their impact upon QALYs
  • Prioritization of public health measures requires an assessment of the impact of an intervention (ARR)
  • The relationship of A1c to absolute reduction of complications is log-linear over a wide A1c range and is a function of life expectancy
problems with qalys
Problems with QALYs
  • Numerous studies have demonstrated that the correlation between one’s current health and the time-tradeoff or standard gamble utility for that health state is at best modest. (Tsevat 2000)
  • Maximum endurable time: Subjects can tolerate no more than a particular time in an undesirable health state, beyond which each additional increment of time decreases overall utility. Miyamoto et al (1998)
  • Such behavior cannot be accommodated within the QALY model.
qaly model and extrinsic goals
QALY model and Extrinsic Goals
  • In the QALY model, quality of health is given weight proportional to health duration; It follows that the QALY model cannot directly account for extrinsic goals, whose importance is by definition independent of duration
    • an author might want to complete a book;
    • many individuals seek to have children and raise families.
additional considerations qalys
Additional Considerations (QALYs)
  • Equity considerations
    • interventions for young preferred to interventions for old
      • Young have more life years remaining
    • life extensions for healthy preferred to life extensions to less-healthy
      • Healthy have a higher quality than chronically ill
ce ratios cost qaly
CE Ratios (Cost/QALY)

CDC, JAMA 2002

proportional short fall

Prop. Short Fall = 60%

Prop. Short Fall = 50%

QoL 

Prop. Short Fall = 50%

QALY gain

QALY lost

Now

t 

Prop. Short Fall = 25%

Proportional short fall
  • Compares loss in QALY with expected QALY
    • The higher the proportion
    • The higher the need for equity compensation
slide51

Comparison of Weighted Performance Measurement and Dichotomous Thresholds for Glycemic Control (Pogach, Rajan, Aron, Diabetes Care, February 2006):

  • 141 VA Centers
  • Thresholds <7%, <8%, or QALYsS
  • Incremental lifetime QALYs gained are based on assumptions:
    • Linear relationship between QALY and A1C between 7.0 and 7.9%
    • Lifetime QALYs range by age from 0 to 0.648
    • Maintaining A1c over lifetime
    • No adjustment for comorbid conditions
weighted measures

Top and bottom decile ranking using <7% or QALY weighted measure

Weighted Measures
  • Gives “partial credit” to achieved A1c levels ranging from 7.0 – 7.9%
  • Differential credit based on potential for Quality Life Years Saved (QALYS) in different age groups of moving from 7.9 - 7.0%
  • A1c values ≥7.9% (or not performed) received zero credit; A1c values <7% received full credit

*Pogach, Rajan, Aron. Diabetes Care, 2006.

advantages of weighted a1c measure

kerr

Advantages of Weighted A1c Measure
  • Assesses progress toward achieving thresholds, rather than whether the targets were completely met
  • Motivates movement toward target even if target cannot be fully achieved
  • Takes differential benefit of decreasing A1c by age into account
  • Can be easily calculated using A1c data currently collected
disadvantages of weighted measures

kerr

Disadvantages of Weighted Measures
  • Need underlying QALY information
  • “Credit” given for a narrow range of A1c levels and not for intensity or modification of treatments
  • Maximal QALYs calculations are based only on age strata
slide56

Use of Continuous Weighted Measures and Exclusion Criteria Can Address Possible Unintended Consequences of <7% Measure for Public Reporting

  • Avoids selection of marginal A1cs closest to 7% which may decrease
  • Adverse Selection biases
  • Incorporation of Patient Preferences into target setting
  • Use of additional medications without consideration of actual benefit
  • Adverse events from additional medications
queri workshop 12 0957

QUERI WORKSHOP 12-09

Context for performance measurement

Limitations of measurement

Alternative means for cross sectional measurement

Cross sectional measures vs Longitudinal Measures

Individuals versus populations

Sources of Data

diabetes epidemiology cohorts a resource for quality measures donald r miller

Diabetes Epidemiology Cohorts:A Resource for Quality MeasuresDonald R. Miller

Monitoring and Improving the Population Health of Veterans with Diabetes: Cross-sectional vs. Longitudinal Measures

slide59

DEpiC - Diabetes Epidemiology Cohorts,

VA Epidemiology

A National Registry of Diabetes since 1998

DEpiC and affiliated projects are maintained by over 30 people across U.S.

A Resource for Studying Diabetes in the VA

Donald R. Miller – Epidemiologist - Bedford VA, Boston University

Leonard Pogach – Endocrinologist - East Orange VA, UMDNJ

B. Graeme Ficnke – Internal Medicine - Bedford VA, Boston University

Monika Safford – Internal Medicine – Birmingham VA, U. Alabama

Susan Frayne – Internal Medicine – Palo Alto VA, Stanford Univ.

Cindy Christianson - Statistician - Bedford VA, Boston University

Chin-lin Tseng - Statistician - East Orange VA, UMDNJ

Ann Hendricks – Health Economist - Boston VA, Boston University

Yujing Shen – Health Economist - East Orange VA, Rutgers University

Mangala Rajan – Program Analyst - East Orange VA, UMDNJ

Qing Shao – Program Analyst - Bedford VA

Xi Chen - Program Analyst – Boston University

applications for depic

Applications for DEpiC

  • Distribution of people and diseases Epidemiology
  • Monitoring processes of care Quality assessment
  • Measuring outcomes of care Quality improvement
  • Risk adjustment Costs of treatment
  • Medication safety & effectiveness Evidence based

Broad collaboration; over 20 funded projects

slide61

DEpiC

Diabetes Epidemiology Cohorts

VA National

Patient Care

Databases

VA Pharmacy

Prescriptions

Medicare Claims

CMS

Laboratory

Test Results

VA National

Health Surveys

Mortality

Records

Other data links:

- Vital signs

- Health care costs

- Disease Registries

who has diabetes
Who Has Diabetes?
  • Define explicitly, evaluate rigorously.
  • Working definition of 2 or more diabetes diagnostic codes over a 24 month period or prescribed diabetes medication in the year + other restrictions and conditions.
    • Specificity of 98.3%
    • Positive predictive value of 93.4%
  • Fixed cohorts + linked longitudinal data; or dynamic cohort analysis
diabetes prevalence and incidence in va fy98 fy05
Diabetes Prevalence and Incidence in VA FY98-FY05

499,243 → → → → 117% increase → → → → 1,082,678

23.7%*

23.1%

22.9%

22.0%

20.9%

19.3%

18.1%

16.6%

1.1%

1.0%

2.9% 3.1% 2.6% 2.0%

2.8% 1.9% 1.7% 2.0% 2.2% 2.0% 2.6%

* projected

hemoglobin a1c trends in va
Hemoglobin A1c Trends in VA
  • What are the trends and what do they mean?
  • Is better treatment progressively improving glucose control?
  • Are there differences by race, comorbidities, or other population groups?
  • Can these be used in quality monitoring to improve care?

Questions

cross sectional trends in a1c measures68
Cross Sectional Trends in A1c Measures

27%

32%

36%

39%

Adherence in meeting threshold measures in overall diabetes population is dependent upon diabetes “duration”

longitudinal hba1c trends in va fy99 05
Longitudinal HbA1c Trends in VA FY99-05
  • 13 million measures from 1.2 million patients
  • Growth curve model: longitudinal linear regression with random effects (slopes and intercepts) for individuals nested within facility and year
  • Adjustment for age, sex, race, facility, seasonality

Methods

slide70

Trends in HbA1c in VA FY2000-2004

N= 1,351,551 patients with 9,400,875 A1c values

Mean annual decline of 0.09% per year

Mean HbA1c by month

FY2000 2001 2002 2003 2004

HbA1c declines steadily by nearly 0.5% over 5 years

slide71

Seasonality of HbA1c in VA - FY1999-2003

Average A1c is highest in late winter

Average A1c is lowest in late summer

Monthly mean HbA1c

average hba1c by year fy1999 2003
Average HbA1c by Year FY1999-2003

Highest

ALL diabetes patients -0.09% per year

Lowest

Substantial within year variation.

Industry measures last a1c in year but clinicians treat last value.

slide73

Trends in HbA1c by Subgroup in VA FY2000-2004

N= 1,351,551 patients with 9,400,875 A1c values

Prevalent panel surviving: -0.06% per year

Prevalent panel died: -0.14% per year

Prevalent new to VA: -0.12% per year

Incident: -0.01% per year

FY2000 2001 2002 2003 2004

Lower HbA1c level and little trend with incident diabetes.

Steepest trend if prevalent and near death or new to VA

slide74

Trends in HbA1c in VA FY2000-2004

in National Panel of 248,768 patients

Prevalent panel surviving: -0.06% per year

FY2000 2001 2002 2003 2004

slide75

Trends in HbA1c by Month by Co-morbidity in

National Panel of VA Diabetes Patients FY2000-2004

Sicker patients have lower HbA1c and flatter trends

A1c

%

Without co-morbidities

-0.06%

-0.03%

With stroke, CVD, recent cancer, liver failure, COPD

FY2000 2001 2002 2003 2004

slide77

Trends in HbA1c by Month by Race/Ethnicity in

National Panel of VA Diabetes Patients FY2000-2004

Whites similar to overall

A1c

%

White, non-Hispanic

70.2% 152,352

Standardized to combined age & sex distribution

FY2000 2001 2002 2003 2004

slide78

Trends in HbA1c by Month by Race/Ethnicity in

National Panel of VA Diabetes Patients FY2000-2004

AA have higher A1c but steeper decline & less difference in 2004

A1c

%

African American

20.1% 43,641

White, non-Hispanic

70.2% 152,352

Standardized to combined age & sex distribution

FY2000 2001 2002 2003 2004

slide79

Trends in HbA1c by Month by Race/Ethnicity in

National Panel of VA Diabetes Patients FY2000-2004

Hispanics are intermediate but less steep decline & highest in 2004

A1c

%

African American

20.1% 43,641

Hispanic, non-White

8.6% 18,716

White, non-Hispanic

70.2% 152,352

Standardized to combined age & sex distribution

FY2000 2001 2002 2003 2004

slide80

Trends in HbA1c by Month by Race/Ethnicity in

National Panel of VA Diabetes Patients FY2000-2004

Other racial groups just below AA but similar trends

A1c

%

African American

20.1% 43,641

Pacific Isl./Asian

0.7% 1,426

Hispanic, non-White

8.6% 18,716

Native American

0.5% 1,030

White, non-Hispanic

70.2% 152,352

Standardized to combined age & sex distribution

FY2000 2001 2002 2003 2004

longitudinal hba1c trends in va fy99 04
Longitudinal HbA1c Trends in VA FY99-04
  • Mean HbA1c decreased (0.06% per year) and proportion below target levels increased (53.8%→60.4% -- <7%).
  • This may be due in part to better treatment of diabetes but there are other factors to consider, including:
    • Incident versus prevalent diabetes
    • Medical, psychiatric, and social conditions that contraindicate intensification of glucose control
    • Seasonality and age modification
  • Trends varied by race, age, and comorbidity.
can va longitudinal hba1c trends be used for quality monitoring
Can VA Longitudinal HbA1c Trends be used for Quality Monitoring?
  • Yes, but methodologic issues must be addressed?
  • Standardize data collection and data quality
  • Develop method for more rapid analysis – currently computationally demanding
  • Better understanding of external sources of variation
  • How to interpret and use in improving quality of care
queri workshop 12 0983

QUERI WORKSHOP 12-09

Context for performance measurement

Limitations of measurement

Alternative means for cross sectional measurement

Cross sectional measures vs Longitudinal Measures

Individuals versus populations

Sources of Data

slide84
Two options: Interventions for Everybody or Targeted A moderate intervention for all patients with DM (1 point improvement in A1c)Vs. An intensive intervention for the 20% at the highest risk (2 point improvement in A1c)
of treatment years needed to prevent 1 yr of blindness vijan ann intern med 1997
# of Treatment Years Needed to Prevent 1 Yr of Blindness (Vijan Ann Intern Med 1997)

A1c 9% 7%

Pt Age(Pt Years)

45 yrs 40

65 yrs 180

of treatment years needed to prevent 1 yr of blindness estimates if bp controlled
# of Treatment Years Needed to Prevent 1 Yr of Blindness (Estimates if BP controlled)

A1c 8% 7%

Pt Age(Pt Years)

45 yrs > 400

65 yrs > 6000

possible action steps
Possible Action Steps
  • Identify sub-populations of veterans not doing well (especially <65, MHCs)
  • >9% applies to all; <8% to many; <7% to some
  • Focus on shared care (mental health-primary care)
  • Insulin initiation and management teams (NPs, Pharm Ds) with “graduation”
  • Use of telehealth for patients who may not have access during the day
  • Identify “innovative practices” by comparing like facilities with like facilities.
queri workshop 12 0988

QUERI WORKSHOP 12-09

Context for performance measurement

Limitations of measurement

Alternative means for cross sectional measurement

Cross sectional measures vs Longitudinal Measures

Individuals versus populations

Sources of Data

slide89

VHA Corporate Data Warehouse ArchitectureUnder Development

Source

Systems

Diabetes

Care Management

Data Mart

Diabetes

Care Management

Data Mart

Clinical Care Management Site

Health Data

Repository

Common Query, Reporting,

Analysis, & Data Mining Tools

Data

Warehouse

Data

Warehouse

Administrative

(DSS, PBM, etc.)

Diabetes Research

Data Mart

Diabetes Research

Data Mart

Other

(Medicare, DoD)

Updated continuously

Research site

1

2

3

4

Acquire Populate Create Access Data Warehouse Data Marts Information

vssc diabetes cube history
VSSC Diabetes Cube - History
  • Developed in 2005-2006.
  • Definitions per evidence available at that time.
  • As additional data (example: Vital Signs) are sent to/stored in HDR, additional “Hierarchies” can be added.
  • ProClarity software used.
slide91

What’s a Cube?

A Patient at

A Facility on

A Date

--------Patients-------

Multi-Dimensional Store of Data

--Time--

--------Facilities ------

what can a cube do
What can a Cube Do?
  • Organizes and optimizes data
  • Efficient and fast querying
  • Aggregated or detailed data
  • Numerical analysis
  • Graphical interface
  • Easy to use
hierarchies
Diabetes Definitions

Definite DM

Possible Unrecognized DM

Possible Pre-Diabetes

Admin/Demographic

Employee

Fee Patient

Location, Preferred Location

Admin/Demog, Contd

Age (ranges)

OEF-OIF

SC status

PCP Assigned

Priority Status

FY

Home County

(Race: not reliable)

Hierarchies
hierarchies94
Lab Values (ranges)

A1C

Urine Alb/Cr Ratio

Total Cholesterol

HDL Cholesterol

LDL Cholesterol

Creatinine

Complications (Y/N)

Retinopathy

ESRD

Amputations

Foot Ulcers

IHD

Stroke

Hierarchies
hierarchies95
Glycemice Rx’s (Y/N)

Insulins, any

Glargine

SU’s

Biguanides

TZD’s

Exenatide

AGI’s

Metaglitinide

CV Rx’s (Y/N)

ACEI’s

ARB’s

Statins

Thiazides

Hierarchies
hierarchies96
Comorbidities (codes)

PTSD

Serious Mental Illness

Substance Abuse

Tobacco Use Disorder

Costs

Overall Pharmacy $

Diabetes Pharmacy $

Inpatient/Outpatient costs per encounter, per patient, etc.

Hierarchies
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Point Prevalence of possible pre-diabetes, possible unrecognized diabetes and definite diabetes

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With ProClarity, we can sort patients by any combinations) of hierarchies.

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View of A1c control in patients on insulin versus no insulin (no evaluation oral agents)

view of a1c control on insulin versus no insulin for patients on metformin and sulfonylurea therapy
View of A1c control on insulin versus no insulin for patients on metformin and sulfonylurea therapy
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View of A1c control on insulin versus no insulin for patients on metformin, sulfonylurea and TZD therapy
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TZD Study effectiveness View

A1C changes within 8 months

After first TZD dose

37% have an increase or no change

In the A1C value

diabetes data mart latest results of critical labs
Diabetes Data MartLatest Results of Critical Labs

VISN Service Support Center developmental database

summary where future research is relevant
Summary: Where Future Research is Relevant
  • Defining exclusion criteria – establishing the proper denominator
  • Developing linked and weighted measures
  • Defining risk adjustment
  • Evaluation of outlier status
  • Evaluation of unit of measurement (provider vs clinic vs facility)
  • Time Frame