These data collection instruments were used in the preliminary and full abstraction phases of the Phase1 literature review: "Identifying indicators". Three forms are provided in the following order: 1.) Full article abstraction, 2.) Risk adjustment, and 3.) Initial screening. They provide a structured mechanism for data collection. However, currently most literature available does not provide the level of detail required by these forms. These forms may be useful for future projects once the health care quality literature expands to include more validation studies and detailed reports of potential indicators.

Abstractor's Initials:
Date Abstracted:

Indicator Data Collection Instrument
UCSF-Stanford EPC/AHCPR HCUP QI

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Measure Header
Measure Set Name (e.g., HEDIS, DEMPAQ):
Developer Subset Name
Performance Measure Name:
Measure Code:			System assigned
Type of Measure: (structure/process/proxy-outcome/outcome)
Year Published:
Year Most Recently Updated: ("Update"= change in construction of measure)
Developer    Organization (ie. JCAHO, NCQA)    Last Name, First Name    Affiliation (University, Dept.)    Address    Phone    Email
Contact Person (if different from Developer)    Last Name, First Name    Affiliation (University, Dept.)    Address    Phone    Email
Technical Support Available:		YES	NO		Unclear (For published work, contact author may have information on support by commercial organization).
Measure Detail
Type of Measure: The activity or area of major concentration			Mortality Readmission Complication length of stay avoidable hospitalization potentially overused procedure potentially underused procedure other
Enhancement Area: These are the areas AHCPR has asked us to focus on. You may choose more than one- as many as are applicable.	Chronic medical condition	Avoid. Hospit.	Pediatrics	New Technol.	None
Clinical Domain: One of these options should be chosen based on article. Check all that apply.	Medical	surgical	pediatric	obstetric	psychiatric
Level of Intervention			primary prevention (health promotion, disease prevention) secondary prevention (screening, early detection) tertiary prevention (optimal treatment of established disease to prevent complications/death)
Measure Rationale			.
Scoring method (e.g., percentage, proportion, rank, mean, median, etc.)			rate (events/person-time at risk) ratio (events/events, where the numerator is not a subset of the denominator) mean median proportion/percentage (death/complication "rate") index or score rank or percentile range standard deviation or variance odds ratio or relative risk hazard rate z score (standardized difference) other
Quality Standard: (if suggested a priori by developer or sponsor)			accepted benchmark external comparison institutional comparison
Numerator Statement: A statement that depicts the portion of the denominator population that satisfies the condition of the performance measure to be an indicator event. (ICD-9 diagnosis or procedure codes, DRG codes, MDC codes, or Revenue Center Codes)
Denominator Statement: A statement that depicts the population evaluated.. (ICD-9 diagnosis or procedure codes, DRG codes, MDC codes, CPT or Revenue Center Codes)
Population Inclusions (numerator): Additional information describing the population(s) not contained in the numerator statement.
Population Inclusions (denominator): Additional information describing the population(s) not contained in the denominator statement
Population Exclusions (numerator): Additional information describing the population(s) that should not be included in the numerator
Population Exclusions (denominator): Additional information describing the population(s) that should not be included in the denominator
Longitudinal Data Required: The measure requires the ability to match patient discharges over time			yes no
Additional Data Sources Required: (e.g., area population counts, etc.) Check all that apply, indicate specific data source (e.g. population/ census birth certificate)			population/ census: __________________ chart data: _________________________ lab data: __________________________ pharmacy data: _____________________ outpatient data: _____________________ other: _____________________________
Extent of prior use: (e.g., number/type of organizations)			None Single Site Single organization/ Multiple Sites Multiple organizations
Current Status			measure defined but not pilot tested pilot testing complete but not implemented implemented without pilot testing tested and implemented and still in use tested and implemented but discontinued
Scientific support for measure (e.g., expert panel, published guideline, clinical trials, other empiric research) Check all that apply			published guideline(s) clinician/expert panel(s) review of published literature (especially RCTs or equivalent) application or revision of pre-existing instruments or measures consensus within user group theory/concept only
Reference Citations:			List the publications according to the style from the software program EndNote.
Validity			face validity consensual validity criterion validity (gold standard measure) predictive validity construct validity (correlated with other measures in the absence of a gold standard) other none
Risk Adjustment:			none stratified/subgroup analysis paired/matched data at patient level risk-adjustment using publicly or commercially available software risk-adjustment devised specifically for this measure and condition
Abstractor Comments:

Risk-Adjustment Method Data Collection Instrument
UCSF-Stanford EPC/AHCPR HCUP QI

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Risk-Adjustment Method Header
Method Name:
Method Code:			System assigned
Year Developed:
Year Most Recently Update (" Update"= Change in risk adjustment strategy) (if applicable)
Developer    Organization (ie. JCAHO, NCQA)    First Author    Affiliation (University, Dept.)    Address    Phone    Email
Contact    Name    Affiliation (University, Dept.)    Address    Phone    Email
Technical Support Available: YES NO Unclear (inquire with developer) Public Domain Proprietary
Risk-Adjustment Method Detail
Adjustment Rationale: An explanation of why the adjustment is necessary to reduce or remove the influences of confounding patient factors
Classification or analytic approach (e.g., stratification (number of strata), logistic regression, linear regression, etc.) Check all that apply			Stratification Logistic regression Linear regression Other
System development method (e.g., empirical model, score based on empirical model, clinical judgment, etc.) Check all that apply			Logistic regression Score based on empirical model A priori/ Clinical Judgement Other
Published performance: discrimination (e.g., the extent to which the model predicts higher probabilities of an event for those who experience the event)
Published performance: calibration (e.g., does the mean of the predicted equal the mean of the actual for the entire population and for population subgroups)
Co-morbidities: Pre-existing diseases or conditions
Severity of Illness Classification: (AJCC staging, ASA-PS classification for surgical patients)
Patient Demographics (e.g., age and gender)
Longitudinal Data Required: The measure requires the ability to match patient discharges over time			Yes No
Additional Data Sources Required: (e.g.,chart review, registry data) Check all that apply, indicate specific data source (e.g. population/ census birth certificate)			population/ census: __________________ chart data: ________________________ lab data: __________________________ pharmacy data: _____________________ outpatient data: _____________________ other: ____________________________
Extent of prior or current use: (e.g., number/type of organizations)			None Single Site Single organization/ Multiple Sites Multiple organizations
Reference Citations:			List the publications according to the style from the software program EndNote.
Abstractor Comments:

Screener's Initials:
Date Screened:

Indicator Data Screening Instrument
UCSF-Stanford EPC/AHCPR HCUP QI

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Measure Header
Lead Author:
Article Title:
Year Published:
Measure Detail
Type of Measure: The activity or area of major concentration			Mortality Readmission Complication length of stay avoidable hospitalization potentially overused procedure potentially underused procedure other
Type of Measure: (structure/process/proxy-outcome/outcome)
Enhancement Area: These are the areas AHCPR has asked us to focus on. You may choose more than one- as many as are applicable.	Chronic medical condition	Avoid. Hospit.	Pediatrics	New Technol.	None
Clinical Domain: One of these options should be chosen based on article.	Medica l	surgical	pediatric	obstetric	psychiatric
Measure Rationale
Longitudinal Data Required: The measure requires the ability to match patient discharges over time			yes no
Additional Data Sources Required: (e.g., area population counts, etc.)			population/ census chart data lab data pharmacy data outpatient data other