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**AHRQ Quality Indicators Mapping Tool**

**Documentation and User Guide**

**Contents**

*Page*

Introduction: What Is the Quality Indicators (QI) Mapping Tool? 2

Who Can Benefit From Using This Tool? 2

What Kinds of Questions Does This Tool Address? 3

Which Quality Indicator Sets Does the Tool Use? 3

Prevention Quality Indicators (PQIs) 3

Inpatient Quality Indicators (IQIs) 4

Patient Safety Indicators (PSIs) 4

Pediatric Quality Indicators (PDIs) 5

What Are the Primary Outputs/Reports? 5

Where Do I Get the Necessary Data To Run the Tool? 5

What Are the Technical Requirements? 6

What Steps Are Required To Use This Tool? 8

How Do I Change the Map Size? 12

Is There Any Known Problem Area? 14

Example: Sample Analysis of Hypothetical State Population Data 14

How Do I Interpret and Present Findings? 19

Displaying Major Findings/Opportunities 19

Preparing a Narrative Summary 21

Limitations of the QI Data Sets 21

Prevention Quality Indicators 21

Inpatient Quality Indicators 22

Patient Safety Indicators 22

Case Study: Using the QI Mapping Tool 23

AHRQ Pub. No. 07-0031-EF

April 2007

**Introduction: What Is the Quality Indicators (QI) Mapping Tool?**

The QI Mapping Tool is a software program that:

-   Maps selected AHRQ Quality Indicators (QIs) for a State, by county.

-   Estimates the cost savings associated with reducing the level of
    potentially avoidable hospitalizations.

> This tool assists health care decisionmakers in identifying
> communities for future interventions---such as programs to improve
> preventive services, primary care, or patient safety---and in tracking
> the impact of such interventions over time. By overlaying residence
> data, the program shows where interventions can have the biggest
> impact for certain health plan enrollees or other target populations.

The QI Mapping Tool uses a set of programs written in the SAS^®^
programming language that read in user-supplied SAS^®^ datasets of
hospital discharge records. The program calculates the county-level
rates for each of the area-level indicators in AHRQ's four QI modules:

-   [Prevention Quality
    Indictors](http://www.qualityindicators.ahrq.gov/pqi_overview.htm)
    (PQIs).

-   [Inpatient Quality
    Indicators](http://www.qualityindicators.ahrq.gov/iqi_overview.htm)
    (IQIs).

-   [Patient Safety
    Indicators](http://www.qualityindicators.ahrq.gov/psi_overview.htm)
    (PSIs).

-   Pediatric Quality Indicators (PDIs).

The QI Mapping Tool will:

-   Produce maps displaying the county-level QIrates.

-   Overlay user-defined population data, assuming the user can supply
    these additional datasets. This tool can calculate the expected cost
    savings for a 10- to 50-percent reduction in cases for each county.
    With this function, the user supplies the supplementary charge
    information for inputted discharge records.

-   Display county-level rates and cost-reduction data in SAS^®^ as well
    as in Microsoft Excel^®^ format.

**Who Can Benefit From Using This Tool?**

The QI Mapping Tool was designed to help health care policymakers and
administrators analyze their own hospital inpatient discharge data in
order to assess the magnitude of problems they face and identify
interventions to reduce potentially preventable hospitalizations and
medical errors. Therefore, the tool would be useful for employers,
employer coalitions, Medicaid programs, health departments, hospitals,
health systems, health plans, and researchers interested in
population-based quality measures.

Examples of how certain groups of users might apply the QI Mapping Tool
to address pressing policy questions include the following:

-   **Hospitals and hospital systems** could use the tool to assess how
    hospital cesarean section rates compare across the State or identify
    which hospitals have similar mortality rates following hip
    replacement.

    -   **State data organizations and community health partnerships**
        could use the tool to provide initial feedback about clinical
        areas appropriate for further, more in-depth analysis, such as
        creating a comparison of hysterectomy rates across the State.

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-   **Managed care organizations, business-health coalitions, and State
    data organizations** could use the analysis to design community
    interventions that reduce hospital admission rates in areas with
    high rates of diabetes complications.

-   **State policymakers** could use the tool to track health care
    quality in the State over time. For example, to assess whether
    health care quality is improving they can look at how the rate of
    coronary artery bypass graft procedures varies over time and across
    the State.

If you have a working understanding of the SAS^®^ program software and
past experience with Microsoft Excel^®^, you can use the QI Mapping
Tool. However, you must also input hospital administrative data to
SAS^®^ in a prespecified way using the step-by-step guidance provided in
this guide.

Although you do not need expertise in data manipulation to use the QI
Mapping Tool, a localized understanding of the hospitals and the
surrounding communities in your area will serve to heighten the
presentation and application of the findings.

**What Kinds of Questions Does This Tool Address?**

The AHRQ QIs can be used to address a variety of issues. Building upon
the intrinsic value of these measures, the QI Mapping Tool helps
aggregate and display this information in new ways to address the
following kinds of questions:

-   Which counties have the highest rates of admissions for short-term
    complications for diabetes that could potentially have been avoided
    through improved outpatient services?

-   Which counties have the highest rates of infections due to medical
    care in their hospitals?

-   Which counties consistently have poorer indicators or consistently
    better indicators?

-   In which counties would there be the largest potential payoff for
    reducing admissions by 10 percent, 20 percent, 30 percent, 40
    percent, or even 50 percent?

-   Which counties have the highest density of my target population?

**Which Quality Indicator Sets Does the Tool Use?**

As noted above, the AHRQ QIs are organized into four \"modules,\" each
of which measures the quality associated with processes of care that
occurred in an outpatient or an inpatient setting. These modules,
discussed below, rely solely on hospital inpatient administrative data.
The QI Mapping Tool uses only population-based QI measures, so the rates
are not applicable to individual hospitals.

More information on each module and documentation for all QI modules can
be downloaded from the AHRQ Quality Indicators Web site at
http://www.qualityindicators.ahrq.gov/downloads.htm. Detailed technical
reports are based on projects conducted for AHRQ by the University of
California at San Francisco (UCSF)-Stanford Evidence-based Practice
Center.

**[Prevention Quality
Indictors](http://www.qualityindicators.ahrq.gov/pqi_overview.htm)
(PQIs).**[^1] This set of measures can be used with hospital inpatient
discharge data to identify \"ambulatory care sensitive
conditions\"---those conditions for which good outpatient care can
potentially prevent the need for hospitalization, or for which early
intervention can prevent complications or more severe disease.

Even though these indicators are based on hospital inpatient data, they
provide insight into the quality of the health care system *outside* the
hospital setting. For example:

-   Patients with diabetes may be hospitalized for diabetic
    complications if their conditions are not adequately monitored or if
    they do not receive the patient education needed for appropriate
    self-management.

-   Patients may be hospitalized for asthma if primary care providers
    fail to adhere to practice guidelines or to prescribe appropriate
    treatments.

-   Patients with appendicitis who do not have ready access to surgical
    evaluation may experience delays in receiving needed care, which can
    result in a life-threatening condition---perforated appendix.

The PQIs consist of the following 14 ambulatory care sensitive
conditions, which are measured as rates of admission to the hospital:

-   Diabetes, short-term complication admission rate (PQI 1)

-   Perforated appendicitis admission rate (PQI 2)

-   Diabetes, long-term complication admission rate (PQI 3)

-   Chronic obstructive pulmonary disease admission rate (PQI 5)

-   Hypertension admission rate (PQI 7)

-   Congestive heart failure admission rate (PQI 8)

-   Low birth weight (PQI 9)

-   Dehydration admission rate (PQI 10)

-   Bacterial pneumonia admission rate (PQI 11)

-   Urinary infection admission rate (PQI 12)

-   Angina admission without procedure (PQI 13)

-   Uncontrolled diabetes admission rate (PQI 14)

-   Adult asthma admission rate (PQI 15)

-   Rate of lower extremity amputations among patients with diabetes
    (PQI 16)

(PQI 4 and PQI 6 relate to children and have been moved to the Pediatric
Quality Indicators module. All PQIs now apply only to adult
populations.)

Although other factors outside the direct control of the health care
system, such as poor environmental conditions or lack of patient
adherence to treatment recommendations, can result in hospitalization,
the PQIs provide a good starting point for assessing quality of health
services in the community. Because the PQIs are calculated using readily
available hospital administrative data, they are an easy-to-use and
inexpensive screening tool. They can be used to provide a window into
the community---to identify unmet community heath care needs, to monitor
how well complications from a number of common conditions are being
avoided in the outpatient setting, and to compare performance of local
health care systems across communities.

**[Inpatient Quality
Indicators](http://www.qualityindicators.ahrq.gov/iqi_overview.htm)
(IQIs).** These measures reflect the quality of care inside hospitals,
focusing on the utilization of procedures for which there are questions
of overuse, underuse, or misuse and on the volume of procedures for
which there is evidence that a higher volume of procedures may be
associated with lower mortality. The IQIs are:

-   Coronary artery bypass graft (IQI 26)

-   Percutaneous transluminal coronary angioplasty (IQI 27)

-   Hysterectomy (IQI 28)

-   Laminectomy or spinal fusion (IQI 29)

**[Patient Safety
Indicators](http://www.qualityindicators.ahrq.gov/psi_overview.htm)
(PSIs).** This set of measures can be used with hospital inpatient
discharge data to provide a perspective on patient safety. Specifically,
PSIs screen for problems that patients experience as a result of
exposure to the health care system and that are likely amenable to
prevention by changes at the system or provider level. These are
referred to as complications or adverse events.

Area-level PSIs capture all cases of the potentially preventable
complications that occur in a given area (e.g., metropolitan service
area or county) either during hospitalization or resulting in subsequent
hospitalization. Area-level indicators are specified to include
principal diagnosis, as well as secondary diagnoses, for the
complications of care. This specification adds cases where a patient's
risk of the complication occurred in a separate hospitalization. The
PSIs are:

-   Foreign body left during procedure (PSI 21)

-   Iatrogenic pneumothorax (PSI 22)

-   Selected infections due to medical care (PSI 23)

-   Postoperative wound dehiscence (PSI 24)

-   Accidental puncture and laceration (PSI 25)

-   Transfusion reaction (PSI 26)

-   Postoperative hemorrhage or hematoma (PSI 27)

**Pediatric Quality Indicators (PDIs).** This set of measures can be
used with hospital inpatient discharge data to provide a perspective on
the quality of pediatric health care. Specifically, PDIs screen for
problems that pediatric patients experience as a result of exposure to
the health care system and that may be amenable to prevention by changes
at the system or provider level.

Development of quality indicators for the pediatric population involves
many of the same challenges associated with the development of quality
indicators for the adult population. These challenges include the need
to carefully define indicators using administrative data, establish
validity and reliability, detect bias and design appropriate risk
adjustment, and overcome challenges of implementation and use.

However, the special population of children invokes additional, special
challenges. Four factors---differential epidemiology of child health
care relative to adult health care, dependency, demographics, and
development---can pervade all aspects of children's health care; simply
applying adult indicators to younger age ranges is insufficient.

These PDIs focus on potentially preventable complications and iatrogenic
events for pediatric patients treated in hospitals, and on preventable
hospitalizations among pediatric patients. The five area-level
indicators are:

-   Asthma admission rate (PDI 14)

-   Diabetes short-term complications admission rate (PDI 15)

-   Gastroenteritis admission rate (PDI 16)

-   Perforated appendix admission rate (PDI 17)

-   Urinary tract infection admission rate (PDI 18)

**What Are the Primary Outputs/Reports?**

SAS^®^ allows to you customize data inputs in multiple ways. The QI
Mapping Tool can produce area-level maps and Microsoft Excel^®^ data
tables. A walk-through of all these resources is provided. They are:

-   **Maps.** Maps of the State present each indicator's rate (e.g., PQI
    1, PQI 2) within quintiles for each year. Maps can also show the
    location of the presenting hospital(s), as well as a symbol
    indicating the number of patients within each county.

-   **Indicator rates.** Microsoft Excel^®^ spreadsheets contain the
    rates of each PQI and area-level IQIs, PSIs, and PDIs organized by
    individual indicators.

-   **Indicator costs.** Microsoft Excel^®^ spreadsheets detail the
    average (mean) cost per discharge for each indicator by county.
    These also display the number of discharges per year, total costs,
    and the potential cost savings if the number of discharges is
    reduced by a set percentage. The represented costs are hospital
    facility costs that do not include the costs for physicians who bill
    separately. The costs also do not represent the health plans\'
    reimbursements.

**Where Do I Get the Necessary Data To Run the Tool?**

You may acquire the relevant hospital discharge data from individual
hospitals, State associations or data organizations, or State agencies
that make discharge data available. As of June 2006, 38 States are part
of the [Healthcare Cost and Utilization
Project](http://www.ahrq.gov/data/hcup) (HCUP), an ongoing
Federal-State-private sector collaboration to build uniform databases
from administrative hospital-based data
(<http://www.hcup-us.ahrq.gov/home.jsp>). The National Association of
Health Data Organizations (NAHDO) also provides information on how to
access State datasets ([http://www.nahdo.org](http://www.nahdo.org/)).

**What Are the Technical Requirements?**

In order to use this module, you must have version 9 or greater of
SAS^®^ ([http://www.SAS®.com](http://www.sas.com/)) and an input SAS^®^
dataset containing the hospital discharge records to be processed. The
Base SAS^®^ is required to produce all the rates and to export all the
data to Microsoft Excel^®^; SAS/Graph^®^ is required to generate the
maps of the rates. You must also have a copy of Microsoft Excel^®^ on
the computer where SAS^®^ is installed, and Microsoft Excel^®^ must be
turned off before running the SAS^®^ programs[^2]. An additional
user-supplied SAS^®^ dataset of population values is required only if
you wish to overlay your data on to the maps showing the county-level
rates.

Table 1 describes the required and optional fields on the user's input
dataset of hospital discharges. The order in which the variables appear
in the input dataset is irrelevant. Note however, that if any of the
required fields are absent, the module will not run at all; and if any
of the optional fields are absent, the cost-savings portion of the
module will be skipped.

+-----------+----------------------------------------------------------+
| *         |                                                          |
| *Table 1. |                                                          |
| Structure |                                                          |
| of        |                                                          |
| user      |                                                          |
| -supplied |                                                          |
| SAS**^®^  |                                                          |
| **dataset |                                                          |
| of        |                                                          |
| hospital  |                                                          |
| discharge |                                                          |
| records** |                                                          |
+===========+==========================================================+
| *         | **Description**                                          |
| *Variable |                                                          |
| name**    |                                                          |
+-----------+----------------------------------------------------------+
| HOSPSTCO  | This field is required. It should contain a numeric FIPS |
|           | county code (\[ss\]\[ccc\], where \[ss\] corresponds to  |
|           | a 1-digit or 2-digit State FIPS code followed by a       |
|           | 3-digit county FIPS code) corresponding to the area on   |
|           | which all rates and optional cost savings will be        |
|           | calculated. For example,                                 |
|           |                                                          |
|           | 01001 or 1001=Autauga, Alabama                           |
|           |                                                          |
|           | 16001 = Ada, Idaho                                       |
|           |                                                          |
|           | This field may correspond to the county where the        |
|           | hospital is located or to the county where the patient   |
|           | resides. In either case, the field must be called        |
|           | HOSPSTCO.                                                |
+-----------+----------------------------------------------------------+
| HOSPID    | This field is optional. If available, it should contain  |
|           | the numeric SID hospital identifier. Like TOTCHG, it is  |
|           | used to calculate the cost-savings which is estimated to |
|           | be achieved from a 10-50% reduction in the cases in an   |
|           | area. If users are not interested in the cost-savings    |
|           | component of these programs or otherwise do not have     |
|           | access to the hospital's SID identifier, this field      |
|           | should be left off the input dataset.                    |
+-----------+----------------------------------------------------------+
| TOTCHG    | This field is optional. Like HOSPID, it is used to       |
|           | calculate cost savings for a 10-50% reduction of cases.  |
|           | If users are not interested in the cost savings          |
|           | component of these programs, this field should be left   |
|           | off the input dataset.                                   |
+-----------+----------------------------------------------------------+
| SEX       | This field is required. It should contain a numeric      |
|           | value denoting the patient's sex coded:                  |
|           |                                                          |
|           | 1 = male                                                 |
|           |                                                          |
|           | 2 = female                                               |
|           |                                                          |
|           | Any discharge record with an unspecified sex of patient  |
|           | (any code other than 1 or 2) will not be utilized in any |
|           | analysis.                                                |
+-----------+----------------------------------------------------------+
| AGE       | This field is required. It should contain a numeric      |
|           | value denoting the age of the patient in whole years.    |
|           | Discharges where the age of the patient is unknown or    |
|           | otherwise unspecified (any code less than 0) will not be |
|           | utilized in any analysis. Any value greater than 0 is    |
|           | taken to be the patient's age.                           |
+-----------+----------------------------------------------------------+
| DX        | DX1, the field containing the 1^st^ listed or principal  |
| 1-DX\[n\] | diagnosis is required. Users may have as many \[n\]      |
|           | secondary diagnoses fields as they wish. All of them     |
|           | will be utilized. If they exist, the secondary diagnosis |
|           | fields must be called DX2-DX\[n\].                       |
+-----------+----------------------------------------------------------+
| PR        | PR1, the field containing the 1^st^ listed or principal  |
| 1-PR\[n\] | procedure is required. Users may have as many \[n\]      |
|           | secondary procedure fields as they wish. All of them     |
|           | will be utilized. If they exist, the secondary procedure |
|           | fields must be called PR2-PR\[n\].                       |
+-----------+----------------------------------------------------------+
| MDC       | This field is required. It should be a numeric code      |
|           | denoting the major diagnostic category in effect on the  |
|           | date of the discharge.                                   |
+-----------+----------------------------------------------------------+
| ATYPE     | This field is required. It should contain a numeric      |
|           | value for the admission type.                            |
|           |                                                          |
|           | ATYPE = 4 should denote a newborn.                       |
+-----------+----------------------------------------------------------+
| ASOURCE   | This is a required field that contains a numeric value   |
|           | for the uniform admission source. ASOURCE = 2 or ASOURCE |
|           | = 3 should denote a transfer from another institution.   |
+-----------+----------------------------------------------------------+

Users may have additional variables on their input dataset of hospital
discharge records. All of those variables will not be utilized by any of
the programs.

Table 2 describes the required fields on the optional secondary dataset
of population values that you may wish to overlay onto the maps showing
the rates for each area-level quality indicator. This dataset need not
be provided for the programs to work. If you wish to overlay your data,
the dataset with the population values must have the following required
variables:

+-----------+----------------------------------------------------------+
| **Table   |                                                          |
| 2.        |                                                          |
| Structure |                                                          |
| of        |                                                          |
| user      |                                                          |
| -supplied |                                                          |
| dataset   |                                                          |
| of        |                                                          |
| p         |                                                          |
| opulation |                                                          |
| values to |                                                          |
| be        |                                                          |
| overlaid  |                                                          |
| on maps** |                                                          |
+===========+==========================================================+
| *         | **Description**                                          |
| *Variable |                                                          |
| name**    |                                                          |
+-----------+----------------------------------------------------------+
| COUNTY    | This field should contain a numeric FIPS county code     |
|           | (\[ss\]\[ccc\], where \[ss\] corresponds to a 1-digit or |
|           | 2-digit State FIPS code followed by a 3-digit county     |
|           | FIPS code). For example,                                 |
|           |                                                          |
|           | 01001 or 1001=Autauga, Alabama                           |
|           |                                                          |
|           | 16001 = Ada, Idaho                                       |
+-----------+----------------------------------------------------------+
| SEX       | Sex                                                      |
|           |                                                          |
|           | 1 = male                                                 |
|           |                                                          |
|           | 2 = female                                               |
+-----------+----------------------------------------------------------+
| AGE       | Age group                                                |
|           |                                                          |
|           | 1 = age 0-17                                             |
|           |                                                          |
|           | 2 = age 18-39                                            |
|           |                                                          |
|           | 3 = age 40 and greater                                   |
+-----------+----------------------------------------------------------+
| POP       | Population (that is, population of interest) in the      |
|           | county by sex and by age cell.                           |
+-----------+----------------------------------------------------------+

The program will select the population closest to the at-risk population
for each indicator and overlay that on the maps. For instance, on the
map displaying pediatric asthma admission rates (PDI 14), the user
population age 0-17 will be displayed; for low birth weight (PQI 9), the
population of women age 18 to 39 will be displayed. For other
indicators, the total population or other population subgroups will be
displayed.

**What Steps Are Required To Use This Tool?**

1.  You should already have Base SAS^®^ and SAS^®^/Graph installed and
    configured.

> (Refer to the SAS^®^ documentation for help on this).

2.  If you have not already done so, create a directory (e.g.,
    c:\\myUserData), and store your SAS^®^ dataset of hospital discharge
    records and your optional dataset of population values to this
    directory.

3.  Also create a directory (e.g., c:\\myPrograms) for the program files
    and auxiliary files needed by these programs. Extract the contents
    of the downloaded archive into this directory.

4.  You should now have a directory with your hospital discharge
    dataset; for example,

> c:\\myUserData\\myHospitalDischarges.sas7bdat
>
> (optional) c:\\myUserData\\myPopulation.sas7bdat
>
> and an additional directory with all programs and auxiliary
> information from the downloaded archive, for example,
>
> c:\\myPrograms\\control.sas
>
> c:\\myPrograms\\includes\\macros\\\*.sas \[these are the supplied
> SAS^®^ programs\]
>
> c:\\myPrograms\\includes\\txtfiles\\\*.txt \[these are the supplied
> auxiliary text files\]

5.  After configuring your directories, launch SAS^®^ and open the
    control.sas program in the SAS^®^ program editor.

> (You can select the control.sas file from Windows Explorer to do
> this).

6.  You will need to modify the parameters shown in Table 3 in
    control.sas (each line must end with a semicolon):

+------------------+---------------------------------------------------+
| **Table 3.       |                                                   |
| Parameters in    |                                                   |
| the CONTROL.SAS  |                                                   |
| program that     |                                                   |
| users should     |                                                   |
| modify**         |                                                   |
+==================+===================================================+
| **Parameter**    | **Description and example**                       |
+------------------+---------------------------------------------------+
| includes         | This specifies the location of the includes       |
|                  | directory, i.e., the directory where all the      |
|                  | supplied SAS^®^ programs needed to process the    |
|                  | discharge records, calculate all rates and        |
|                  | optional cost savings and draw the maps are       |
|                  | located. Specify the fully qualified path,        |
|                  | without quotes, for example,                      |
|                  |                                                   |
|                  | %let includes = c:\\myPrograms\\includes;         |
+------------------+---------------------------------------------------+
| user_dir         | This specifies the *directory* location where the |
|                  | user's input dataset of discharge records is      |
|                  | stored, for example,                              |
|                  |                                                   |
|                  | %let user_dir = c:\\myUserData;                   |
|                  |                                                   |
|                  | (Note: this is also the directory where SAS^®^    |
|                  | will write its final dataset and all Excel^®^     |
|                  | workbooks).                                       |
+------------------+---------------------------------------------------+
| data_in          | This specifies the *filename* of the user's input |
|                  | dataset (without the file extension). For         |
|                  | example, to specify the file                      |
|                  | myHospitalDischarges.sas7bdat, use:               |
|                  |                                                   |
|                  | %let data_in = myHospitalDischarges;              |
|                  |                                                   |
|                  | Do not use %let data_in =                         |
|                  | myHospitalDischarges.sas7bdat;                    |
+------------------+---------------------------------------------------+
| data_yr          | This specifies the 4-digit year of the discharge  |
|                  | data. If the discharges are from 2002, then use:  |
|                  |                                                   |
|                  | %let data_yr = 2002;                              |
|                  |                                                   |
|                  | (Note: you may chose any year from 1995 to 2006.) |
+------------------+---------------------------------------------------+
| mapsize          | This specifies the dimensions, in inches, of the  |
|                  | graphic image of the maps that you want SAS^®^ to |
|                  | produce. For example, to instruct SAS^®^ to       |
|                  | produce maps 4 inches wide by 7 inches tall,      |
|                  | specify:                                          |
|                  |                                                   |
|                  | %let mapsize=4x7;                                 |
+------------------+---------------------------------------------------+
| gdevice          | This instructs SAS^®^ to produce maps in either   |
|                  | PDF or EMF format.                                |
|                  |                                                   |
|                  | PDF is Adobe^®^ Portable Document Format. It      |
|                  | should be used to produce hard copy pictures;     |
|                  | that is, images that can be sent directly to a    |
|                  | printer.                                          |
|                  |                                                   |
|                  | EMF is Microsoft^®^ Enhanced Meta File format. It |
|                  | should be used to produce pictures that can be    |
|                  | inserted into Microsoft applications[^3], e.g.    |
|                  | Microsoft Word^®^ documents, Microsoft Excel^®^   |
|                  | or Power Point files.                             |
|                  |                                                   |
|                  | To produce PDF images, specify %let gdevice =     |
|                  | PDF;                                              |
|                  |                                                   |
|                  | To produce EMF images, specify %let gdevice =     |
|                  | EMF;                                              |
+------------------+---------------------------------------------------+
| palette          | This specifies the palette[^4] (color scheme) to  |
|                  | be used in the maps. Four palettes are supplied   |
|                  | -- "gyr," "red," "blu," and "gry."                |
|                  |                                                   |
|                  | "gyr" (green-yellow-red) is the only diverging    |
|                  | color scheme that is supplied. It shades counties |
|                  | with average rates in yellow, those with lower    |
|                  | rates in green and those with higher rates in     |
|                  | red.                                              |
|                  |                                                   |
|                  | "red," "blu," and "gry" are the sequential color  |
|                  | schemes that are supplied. They display counties  |
|                  | with average rates in a medium shade, those with  |
|                  | lower rates in a lighter shade, and those with    |
|                  | higher rates in a darker shade \[of red, blue or  |
|                  | gray, respectively\].                             |
|                  |                                                   |
|                  | To specify the gyr palette, specify:              |
|                  |                                                   |
|                  | %let palette = gyr;                               |
+------------------+---------------------------------------------------+
| userPopData      | This tells SAS^®^ the name (without the file      |
|                  | extension) of the optional SAS^®^ dataset with    |
|                  | the user's population data. If you do not have    |
|                  | this dataset or otherwise do not want it          |
|                  | displayed, specify:                               |
|                  |                                                   |
|                  | %let userPopData = None;                          |
|                  |                                                   |
|                  | To use the optional population dataset, called    |
|                  | myPopulation.sas7bdat, specify:                   |
|                  |                                                   |
|                  | %let userPopData = myPopulation;                  |
+------------------+---------------------------------------------------+
| userPopLabel     | This tells SAS^®^ the label that you want written |
|                  | in the legend of the user-supplied values. If the |
|                  | user population contains the number of Medicare   |
|                  | beneficiaries, then the label can be specified    |
|                  | with                                              |
|                  |                                                   |
|                  | %let userPopLabel = Medicare beneficiaries;       |
+------------------+---------------------------------------------------+
| map_Ho           | This tells SAS^®^ to display the SID hospitals on |
| spital_Locations | each map (shown as blue dots). To specify that    |
|                  | hospitals should be displayed, use:               |
|                  |                                                   |
|                  | %let map_Hospital_Locations = Yes; (use = No to   |
|                  | suppress them)                                    |
+------------------+---------------------------------------------------+
| m                | This tells SAS^®^ to display the names of the     |
| ap_County_Labels | counties on the map. To specify that county names |
|                  | should be written on the map, use                 |
|                  |                                                   |
|                  | %let map_County_Labels= Yes; (use = No; to        |
|                  | suppress the labels)                              |
+------------------+---------------------------------------------------+

7.  Before running the control.sas program, make sure Microsoft Excel^®^
    is not open.

8.  Once all the parameters have been correctly set, should submit this
    program by using the run program button
    ![](media/image2.png){width="0.25in"
    height="0.21597222222222223in"}on the SAS^®^ application toolbar.
    The program will examine your input dataset to make sure it has all
    the required and optional variables. If any of the required
    variables are missing, SAS^®^ will skip all the processing of the
    records. The example in Figure 1 shows the log when it reads a
    dataset that does not have the required variable, sex.

Figure 1. A screen shot of a SAS® software log that indicates that the
inputted dataset does not have the required variable, "sex"

![](media/image3.png){width="5.9631944444444445in"
height="4.145833333333333in"}

> If all the required variables are present, SAS^®^ will process all the
> records, calculate all the rates, draw the maps and export the final
> dataset to Excel^®^ workbooks. If the optional variables are present,
> SAS^®^ will additionally calculate the cost savings from 10-50%
> reduction of cases and add that to the final dataset and to the
> Excel^®^ workbooks. If the optional variables are not present, SAS^®^
> will skip just the cost-savings portion of the program.
>
> (Note: depending on the age of your computer, the amount of available
> memory, and the number of hospital discharge records, this program
> should complete in 3 to 4 minutes per million discharge records. So,
> for instance, if the input dataset has 1 million discharge records,
> the program should complete in 3 to 4 minutes, and if it has 10
> million discharge records, it should complete in about 30 to 40
> minutes).

9.  When the program completes, you will have the following additional
    files in the dataset specified by the user_dir parameter:

-   A SAS^®^ dataset called rates.sas7bdat which will contain the one
    record per indicator per county (plus one more for all counties
    combined). Figure 2 gives an example of the SAS^®^ dataset run with
    fabricated discharges.

Figure 2. A screen shot of a SAS® dataset run with fabricated discharges

![](media/image4.png){width="5.998611111111111in"
height="4.236805555555556in"}

-   Four Microsoft Excel^®^ workbooks, one for each QI module, named
    PQI.XLS, IQI.XLS, PSI.XLS, and PDI.XLS. Each workbook will contain
    one sheet for each indicator -- 14 indicators for the PQIs, 4 for
    the IQIs, 7 for the PSIs, and 5 for the PDIs. Each sheet will
    contain the same information as the rates.sas7bdat dataset. Instead
    of the flag indicating whether the county level rate is higher or
    lower than the overall rate, the cell with the rate will be shaded
    green (low) or red (high). Below is an example of an Excel^®^
    workbook output from running the discharges on the fabricated
    dataset.

**Figure 3. A screen shot of an Excel® workbook spreadsheet that the
user receives after running discharge data**

![](media/image5.png){width="5.989583333333333in"
height="3.748611111111111in"}

**Note**: The Excel spreadsheet will display county rates statistically
lower than the overall rate in green and rates statistically higher than
the overall rate in red.

-   Thirty maps in either PDF or EMF format (depending on how the
    gdevice parameter was set) showing the risk-adjusted rate (observed
    rate for the PSI indicators) optionally overlaid with the
    user-defined population.

    -   The 14 PQI maps in EMF format will be named PQI1.EMF---PQI16.EMF
        (there is no longer an indicator 4 and 6)

    -   The 4 IQI maps will be named IQI26---IQI.EMF

    -   The 7 PSI maps will be named PSI21---PSI27.EMF

    -   The 5 PDI maps will be named PDI14---PDI17.EMF

**How Do I Change the Map Size?**

-   You may have to experiment a little with the map dimensions before
    you are satisfied. The entire program need not be rerun to do this.
    Provided the rates have all been calculated, you may change the map
    dimensions or turn on or off the county names and rerun just the
    mapping portion.

    1.  In the control.sas program, change the parameters as required.

> For example, to change the dimensions from 8x6 to 6x7, change the line
>
> %let mapsize = 8x6; to %let mapsize = 6x7;

2.  Highlight this single line with the curser and submit it using
    ![](media/image2.png){width="0.25in"
    height="0.21597222222222223in"}.

3.  Then highlight the command to draw all maps, %drawAllMaps and submit
    it using ![](media/image2.png){width="0.25in"
    height="0.21597222222222223in"}.

> The two screen shots in Figures 4 and 5 show the steps.

Figure 4. A screen shot highlighting the steps that users must take to
change the SAS® output map dimensions

![](media/image6.png){width="5.752777777777778in"
height="4.172916666666667in"}

Figure 5. A screen shot highlighting the steps that users must take to
turn on or off the county names

![](media/image7.png){width="5.752777777777778in" height="4.16875in"}

-   The maps with the new dimensions will overwrite the maps with the
    old dimensions and should take only a minute or two to complete. If
    you wish to save maps with both the new and the old dimensions,
    rename or save to a different location the old maps before running
    the program with the new dimensions.

**Is There Any Known Problem Area?**

There is a known bug in the Microsoft^®^ DDE protocol which may affect
users of this module. The first time you run this program on your
computer, SAS^®^ may report a DDE error to the effect that the Microsoft
Excel^®^ files (the Excel^®^ files to which SAS^®^ is attempting to
write) do not exist. Should you experience this error, the solution is
to highlight and resubmit the line in the control.sas program
instructing SAS^®^ to save data to Excel^®^, specifically,

> %saveAllData2Xl

The error will not appear again and SAS^®^ will write the dataset
correctly.

**Example: Sample Analysis of Hypothetical State Population Data**

*Note: The following hypothetical situation was created to provide an
example of how to use the QI Mapping Tool.*

You are a public health analyst working for the Holmes State Department
of Health and Human Services. Your boss has asked you to identify areas
where the State (represented in Figure 6) could achieve substantial
health care cost savings over the next few years. Your aim is to
identify possible areas for improving health care quality while reducing
potentially avoidable hospital admissions and procedures.

Figure 6. A map of the fabricated State of Holmes by county

![](media/image8.png){width="4.879861111111111in"
height="4.945138888888889in"}

As a State employee, you obtain access to the 2003 Holmes SID (State
Inpatient Data), which contains administrative data on all discharges in
the state in 2003. After processing these data using the QI Mapping Tool
with SAS^®^, you receive the following outputs:

1.  One SAS^®^ dataset, called rates.sas7bdat file*,* with county-level
    summaries of the number of discharges flagged for each quality
    indicator, the corresponding at-risk population and observed rate,
    the risk-adjusted rate (this is the rate assuming a standard casemix
    of discharges), and estimates of the cost-savings that may be
    achieved if the number of flagged cases was reduced by 10% to 50%.
    (Go to Figure 7 for a display of the contents of the the first 18
    records in the rates.sas7bdat file*.)*

2.  Four Microsoft Excel^®^ workbooks, called PQI.XLS, IQI.XLS, PSI.XLS
    and PDI.XLS, each containing the same information as the SAS dataset
    but organized into separate worksheets for each indicator. (Go to
    Figure 8 for a display of the contents in the SAS® produced output
    file labeled "IQI.XLS.)

3.  Fourteen maps in EMF or PDF format (depending on how the gdevice
    parameter was set in control.sas), named PQI1.EMF -- PQI16.EMF or
    PQI1.PDF -- PQI16.EMF. (Note: there is no PQI4 and PQI6).

4.  Four maps in EMF or PDF format corresponding to the area-level IQI
    indicators, named IQI26.EMF---IQI29.EMF or IQI26.PDF---IQI29.PDF.

5.  Six maps in EMF or PDF format corresponding to the area-level PSI
    indicators, named PSI21.EMF---PSI26.EMFor PSI21.PDF---PSI26.PDF.

6.  Four maps in EMF or PDF format corresponding to the area-level PDI
    indicators, named PDI14.EMF---PDI17.EMF or PDI14.PDF---PDI17.PDF.

**Figure 7. A screen shot of the contents in the SAS® produced output
file labeled "rates.sas7bdat" (first 18 records only)**

![](media/image9.png){width="6.622222222222222in"
height="2.3965277777777776in"}

**Understanding the Excel^®^ Table Displayed in Figure 7:**

-   **qimod:** the "Quality Indicator Module." IQ is "Inpatient Quality
    Indicator." Other values are PQ "Prevention Quality Indicator", PS
    "Patient Safety Indicator" and PD "Pediatric Quality Indicator"

-   **n:** the indicator number within the module. Indicator 26 within
    the IQI module is "Coronary Artery Bypass Graft Area Rate".

-   **hospstco:** the FIPS county code upon which the rate is based. The
    first record with no code is the overall rate, the rate based on all
    the discharges in all the counties.

-   **countynm:** the name of the county, or Overall.

-   **numer:** the "Numerator" number of flagged discharges in the
    county. This number is summarized from the user's input dataset of
    hospital discharge records.

-   **denom:** the "Denominator" number of at-risk persons. Depending on
    the indicator, the at-risk population might be persons age 0-17,
    persons age 18 and older, persons age 40 and older, number of live
    births, or something else. This number is summarized from the
    discharge records or comes from the population files supplied with
    the programs.

-   **rate:** the observed rate, calculated as numer over denom. The
    rate is per discharge or per person in the population. Users can
    convert the rate to per 100 discharges or per 1,000 population, eg,
    by multiplying the rate by 100 or 1,000, respectively.

-   **rarate:** the "Risk-adjusted Rate" is the rate assuming a standard
    case-mix of patients. Please refer to the User Guide to each QI
    module
    ([www.qualityindicators.com](http://www.qualityindicators.com/)) for
    more information about how this rate is calculated.

-   **serarate:** the "standard error" of the risk adjusted rate. Please
    refer to the User Guide to each QI module
    ([www.qualityindicators.com](http://www.qualityindicators.com/)) for
    more information about how this is calculated.

-   **flag:** a flag indicating whether the county's rate is "hi" or
    "lo" relative to the Overall rate. This is calculated using a
    z-test. Counties not flagged with either "hi" nor "lo" are
    statistically similar to the overall rate.

-   **meancost:** the mean cost per flagged discharge in the county.
    This is calculated if the user's dataset has the proper variables
    (HOSPID, the hospital ID that links back to the SID, and TOTCHG, the
    total charge on the discharge record).

-   **save10 to save50:** the savings estimated by reducing the number
    of cases 10 to 50 percent. This is calculated if the user's dataset
    has the proper variables (HOSPID, the hospital ID that links back to
    the SID, and TOTCHG, the total charge on the discharge record).

**Figure 8. A screen shot of the contents in the SAS® produced output
file labeled "IQI.XLS"**

![](media/image10.png){width="5.988888888888889in"
height="3.251388888888889in"}

**Note**: The table displayed in Figure 8 contains the same information
from the SAS^®^ database of IQI rates (IQI.XLS). There are no qimod and
n variables. Instead, all the records from rates.sas7bdat with qimod
equal to "IQ" have been sent to this workbook and placed into the
worksheet IQI26 if n was equal to 26, in IQI27 if n was equal to 27,
etc. Each worksheet has a title, and the flag variable was replaced with
shading on the cell containing the risk-adjusted rates (red for high and
green for low).

**Analysis Tips:**

-   After generating the data, you may have questions such as these:

> \(1\) What is a reasonable risk-adjusted rate for this indicator?
>
> \(2\) Is a higher or lower rate a sign of better quality?

\(3\) What rates are either too high or too low to signal a significant
quality concern?

> Although the QI Mapping Tool does not automatically benchmark the
> county rates against the State or national standard, you can easily
> access this information from one of AHRQ's many online quality tools.

-   HCUPnet, a free online query system based on HCUP data, presents the
    PQIs at the national level. The HCUPnet Web site provides national
    trend statistics as well as other breakouts. Also reported are the
    IQIs and PSIs that provide national trend information. Go to
    http://hcupnet.ahrq.gov.

-   The National Healthcare Quality Report (NHQR) and National
    Healthcare Disparities Report (NHDR) also include similar national
    level statistics for selected PQIs and the PSIs. Go to
    http://www.ahrq.gov/qual/measurix.htm.

-   You can also access the User Guides for each indicator set (PQI,
    IQI, PSI, and PedQIs) that contain information on national rates for
    the current definitions of each indicator at
    <http://www.qualityindicators.ahrq.gov/downloads.htm>.

-   State benchmarks can also be found in the State Snapshots derived
    from the NHQR. Go to <http://statesnapshots.ahrq.gov/2005>.

```{=html}
<!-- -->
```
-   The calculated risk rates can also be used to track hospital
    outcomes and primary care access over time, or across counties.

-   The denominators for these indicators are mostly population based,
    and therefore, the rates do not directly apply to individual
    hospitals.

-   The cost savings categories represent hospital facility costs only,
    and do not include the costs for physicians who bill separately.
    They also do not represent the health plans\' reimbursements.

-   To achieve the greatest cost savings look for counties where the ten
    percent reduction of cases will produce the largest decrease in
    spending. Be sure to assess both cost savings and risk-adjusted
    rates benchmarked to the State rates in tandem. While you may be
    able to achieve potentially high cost savings through reduced
    admissions for a particular condition in one county, if that same
    county already has rates significantly below the State average it
    may be hard to justify an increased intervention. Ideally, if you
    can identify counties with both high rates of avoidable
    hospitalizations and high procedural average costs they will signify
    areas ripe for intervention.

-   Be sure to also look for counties where the admissions rates are
    substantially below State or national benchmarks as well. A possible
    action step might be to create a collaborative charged with
    identifying the county's best practices and opportunities, in an
    effort to capture and expand these effective practices to a broader
    population across the State.

Figure 9 displays in EMF format the risk-adjusted area-level rate for
coronary artery bypass graft (CABG) with overlaid SID hospitals and an
overlaid special population of interest (age 40 and over)

Figure 9. A SAS® produced map of the State of Holmes highlighting the
variations in oronary artery bypass graft rate by county

![](media/image11.png){width="5.747916666666667in"
height="4.651388888888889in"}

**Understanding the Map Displayed in Figure 9:**

-   The county-level risk-adjusted rates (multiplied by 100,000 for the
    map displayed in Figure 9) have been ranked into five categories
    with roughly an equal number of counties in each group. The counties
    with the lowest risk adjusted rate for CABG are displayed in dark
    green, those with the highest rate are displayed in dark red, and
    those with an average rate are displayed in cream (average), light
    green (slightly lower than average), or orange (slightly higher).

```{=html}
<!-- -->
```
-   Overlaid onto the maps are two symbols: (1) SID hospitals and (2) a
    special population of interest. The blue dots represent SID
    hospitals in the area. You may suppress this symbol if you wish. The
    person-symbols represent the number of persons of the population of
    interest in the same age category as the at-risk population as this
    indicator (age 40 and older for this indicator). You can display
    these symbols only if you have such a dataset of population values
    formatted in the correct manner and you request they be displayed on
    the maps.

**Analysis Tips:**

-   The overlaid population can be used to identify areas of particular
    interest to users. For instance, in the map displayed above, the
    population of interest is primarily clustered in the bottom left
    corner whereas the areas with the highest rates are in the bottom
    right corner. Only one of these counties has a large population of
    interest (Montgomery), and one has an average number (Jackson).
    Users may conclude that these two counties are in need of greater
    inspection.

**How Do I Interpret and Present Findings?**

The previous example analysis presented a sample of the SAS^®^ program
and Microsoft Excel^®^ outputs that the QI Mapping Tool produces and
also suggested some tips for further analysis. After running the data,
the user is likely to ask: how do I present this data in a meaningful
way? Stakeholders have divergent needs and so may want to consider
presenting data in variety of ways. Therefore, consider reconstructing
some of the data tables to eliminate information not needed for your
purposes. It may also be useful to create an accompanying narrative
summary of the data to share with interested parties.

**Displaying major findings/opportunities**. As generated, the SAS^®^
and Microsoft Excel^®^ tables do not produce dynamic data displays. To
make the data more visually appealing and to highlight the most
significant findings, you can cull the most valuable data from across
the numerous data outputs. You may also choose to display only the most
pertinent data illustrating the greatest potential for improvements.

Figure 10 provides an example of an alternative presentation of data
from the Rates.SAS7bdat file for CABG rates (IQI 26). A new category for
the "Total Potential Savings from a 10% Reduction" in CABG for the first
10 counties was created (although the SAS^®^ and Microsoft Excel^®^
tables do not have a column for total savings, you can create your own
by adding up all the individual county savings rates). Also, a new level
of analysis was added to the county risk-adjusted rates column by
benchmarking these rates to the Holmes State average. The Holmes State
risk-adjusted average for CABG is 0.000217673.[^5] Figure 10 displays an
Excel^®^ spreadsheet highlighting which counties are statistically
above, below, or equal to that rate, so which counties have the greatest
quality issues can be easily identified.

The example spreadsheet presents:

-   Number of discharges per year.

-   Average cost of each discharge.

-   County risk-adjusted rate (with a color-coding system based on
    whether the county rate was above, below, or near similar to the
    State average).[^6]

-   Potential savings from a 10% reduction in admissions per county.

-   Total savings with a 10% reduction in cases across 10 counties.

This example more clearly illustrates how CABG is affecting these 10
counties in Holmes. Eliminating information such as the FIPS codes,
adding in a color-coded comparison of the county rates to the State
rate, and creating a new total savings column helps to synthesize the
bottom line and make the data more user friendly.

**Figure 10. Excel**^®^ **spreadsheet highlighting the potential savings
by county for reductions in coronary artery bypass graft rates by 10
percent**

  ---------- -------------- ------------ ------------ --------------- ------------- ------------- ------------------
  **IQI      **Inpatient    **County     **Number of  **County        **Average     **Potential   **Total Potential
  Number**   Quality        Name**       Discharges   Risk-adjusted   Cost Per      Savings with  Savings with 10%
             Indicators**                2003**       rate\***        Discharge**   10% Reduction Reduction**
                                                                                    in Admissions 
                                                                                    Per Year**    

  **26**     **Coronary     Washington   2            0.000215629     \$114,307     \$22,860      **\$329,400.00**
             Artery Bypass                                                                        
             Graft (CABG)                                                                         
             area rate**                                                                          

                            Jefferson    1            0               \$54,680      \$5,470       

                            Franklin     4            0.000273631     \$75,798      \$30,320      

                            Jackson      4            0.000375303     \$123,649     \$49,460      

                            Lincoln      2            0.000462683     \$53,003      \$10,600      

                            Madison      2            0.00045455      \$71,414      \$42,850      

                            Clay         4            0               \$98,508      \$39,400      

                            Montgomery   2            0.000008139     \$68,957      \$20,690      

                            Union        7            0.000218253     \$77,089      \$53,960      

                            Marion       5            0.000172798     \$107,575     \$53,790      
  ---------- -------------- ------------ ------------ --------------- ------------- ------------- ------------------

.

  --------------- -----------------------------------------------------------------
  ***\**Key to    
  County          
  Risk-Adjusted   
  Rates:**        

                  County rate is lower than Holmes State average

                  No statistical difference between county and State

                  County rate is higher than State average
  --------------- -----------------------------------------------------------------

**Note**: Figure 10 shows that county risk-adjusted rates are lower than
Holmes State average in Jefferson, Clay, and Montgomery counties and
higher than Holmes State average in Jackson, Lincoln and Madison
counties.

**[P]{.smallcaps}reparing a narrative summary.** You may also consider
creating summaries for each QI that concisely explain the data findings,
while also suggesting opportunities for intervention. The following
sample narrative is presented as an example of how data from the map
illustrated in Figure 10 might be summarized for presentation to
policymakers and other health care leaders considering implementing
community collaboratives or other efforts to reduce potentially
preventable CABG hospitalizations. The summary:

-   Highlights which counties have CABG rates that are statistically
    higher than the State average.

-   Presents the degree of variation in costs associated with the
    procedure.

-   Offers a clinical observation that CABG procedures are often
    overused.

-   Presents the total cost savings associated with a 10% reduction in
    cases for the region.

-   Suggests which counties might be the most beneficial for
    intervention.

**Sample Narrative Summary of Coronary Artery Bypass Graft Admission
Rates** **in 10 Counties**

Considerable variance exists in the number of admissions for CABG, as
well as the average cost per case in the Dillweed region. Madison,
Lincoln, and Jackson counties each had rates significantly higher than
the State average. Jefferson, Clay, and Montgomery counties had rates
lower than the State average, while the rates in Washington, Franklin,
Union and Marion counties were not significantly different. The average
cost per case varied from \$54,680 (Jefferson) to \$123,649 (Jackson)
per case. CABG is a procedure that may be overused. Reducing the rate of
CABG admissions by just 10 percent offers a potential cost savings of
nearly \$330,000 across the 10 counties. Jackson County presents the
greatest opportunity for realized cost savings because it has a CABG
rate that is statistically higher than the Holmes State average, and it
has one of the highest cost-per-discharge rates in the region.

**Limitations of the QI Data Sets**

**Prevention Quality Indicators**. The PQIs are valuable tools for
identifying potential quality problems in outpatient care that help to
set the direction for more in-depth investigation. Because the PQIs are
based on readily available data---hospital discharge
abstracts---resource requirements are minimal. With uniform definitions
and standardized programs, the PQIs will allow comparisons across
States, regions, and local communities over time. Despite the unique
strengths of the PQIs, there are several issues that should be
considered when using these indicators:

-   First, for some PQIs, differences in socioeconomic status have been
    shown to explain a substantial part---perhaps most---of the
    variation in PQI rates across areas. The complexity of the
    relationship between socioeconomic status and PQI rates makes it
    difficult to delineate how much of the observed relationships is due
    to true access to care difficulties in potentially underserved
    populations or due to other patient characteristics, unrelated to
    quality of care, that vary systematically by socioeconomic status.
    For some of the indicators, patient preferences and hospital
    capabilities for inpatient or outpatient care might explain
    variations in hospitalizations. In addition, environmental
    conditions that are not under the direct control of the health care
    system can substantially influence some of the PQIs. For example,
    the COPD and asthma admission rates are likely to be higher in areas
    with poorer air quality.

-   Second, the evidence related to potentially avoidable hospital
    admissions is limited for each indicator because many of the
    indicators have been developed as parts of sets. Only five studies
    have attempted to validate individual indicators rather than whole
    measure sets. A limitation of this literature is that relatively
    little is known about which components represent the strongest
    measures of access and quality. Most of the five papers that did
    report on individual indicators also used a single variable, such as
    median area-specific income or rural residence, for construct
    validation. All but one of these papers included adjustment only for
    demographic factors (such as age, sex, and race).

-   Third, despite the relationships demonstrated at the patient level
    between higher quality ambulatory care and lower rates of hospital
    admission, few studies have directly addressed the question of
    whether effective treatments in outpatient settings would reduce the
    overall incidence of hospitalizations. The extent to which the
    reporting of admission rates for ambulatory care sensitive
    conditions may lead to changes in ambulatory practices and admission
    rates also is unknown. Providers may admit patients who do not
    clinically require inpatient care, or they may do the
    opposite---fail to hospitalize patients who would benefit from
    inpatient care.

**Inpatient Quality Indicators.** The IQIs represent the current state
of the art in assessing quality of care using hospital administrative
data. However, these indicators must be used cautiously because the
administrative data on which the indicators are based are not collected
for research purposes or for measuring quality of care, but for billing
purposes. While these data are relatively inexpensive and convenient to
use---and represent a rich data source that can provide valuable
information---they should not be used as a definitive source of
information on quality of health care. At least three limitations of
administrative data warrant caution:

-   First, there are coding differences across hospitals. Some hospitals
    code more thoroughly than others, making "fair" comparisons across
    hospitals difficult.

-   Second, there is a level of ambiguity about when a condition occurs.
    Most administrative data cannot distinguish unambiguously whether a
    specific condition was present at admission or whether it occurred
    during the stay (that is, a possible complication).

-   Third, there are limitations in ICD-9-CM coding. The codes
    themselves are often not specific enough to adequately characterize
    a patient's condition, which makes it impossible to perfectly
    risk-adjust any administrative data set; thus fair comparisons
    across hospitals become difficult.

**Patient Safety Indicators.** Many important concerns cannot currently
be monitored well using administrative data, such as adverse drug
events, and using these data tends to favor specific types of
indicators. For example, the PSIs evaluated in this software contain a
large proportion of surgical indicators, rather than medical or
psychiatric, because medical complications are often difficult to
distinguish from comorbidities that are present on admission. In
addition, medical populations tend to be more heterogeneous than
surgical populations, especially elective surgical populations, making
it difficult to account for casemix. It is often expressed that
indicators are more applicable to patient safety when limited to
elective surgical admissions. However, the careful use of administrative
data holds promise for screening to target further data collection and
analysis. The ability to assess all patients at risk for a particular
patient safety problem, along with accessing the relative lowest cost,
are particular strengths of these indicators. Two broad areas of concern
also hold true for these data sets:

-   First, there are questions about the clinical accuracy of
    discharge-based diagnosis codes leading to concerns about the
    interpretation of reported diagnoses that may represent safety
    problems. Specifically, administrative data are unlikely to capture
    all cases of a complication, regardless of the preventability,
    without false positives and false negatives (sensitivity and
    specificity). When the codes are accurate in defining an event, the
    clinical vagueness inherent in the description of the code itself
    (for example, "hypotension") may lead to a highly heterogeneous pool
    of clinical states represented by that code. Incomplete reporting is
    an issue in the accuracy of any data source used for identifying
    patient safety problems, as medical providers might fear adverse
    consequences as a result of "full disclosure" in potentially public
    records such as discharge abstracts.

-   Second, the information about the ability of these data to
    distinguish adverse events in which no error occurred from true
    medical errors is limited. A number of factors contribute to the
    difficulty in identifying complications that represent medical error
    or may be at least in some part preventable. These factors include
    the heterogeneity of clinical conditions included in some codes,
    lack of information about event timing available in these data sets,
    and limited clinical detail for risk adjustment.

**Case Study: Using the QI Mapping Tool**

AHRQ approached the General Motors (GM) Corporation in early 2004 in an
effort to connect large employers with the AHRQ Quality Indicators. At
the time, GM was looking for ways to reduce health care costs without
compromising patient safety or quality. For GM, the QIs and QI Mapping
Tool offered an important opportunity to identify areas for improving
health care quality and reduce potentially avoidable hospital admissions
and procedures. In addition, a QI Mapping Tool analysis could identify
communities for future interventions and track the impact of such
interventions over time.

AHRQ provided GM with data and guidance to utilize the tool
successfully. As a result of the project, GM has been able to use
generated maps and tables to mobilize action on regional health
priorities. The AHRQ analysis also highlighted communities with high
concentrations of GM employees, dependents, and retirees, enabling GM to
focus their efforts.

[^1]: PQI rates can be calculated using the modified Federal Information
    Processing Standards (FIPS) State/county code. A list of codes is
    available at: <http://www.census.gov/popest/geographic/codes02.pdf>
    .

[^2]: Microsoft Excel^®^ should be on the same computer because SAS®
    will use DDE to turn Microsoft Excel^®^ on and to create Microsoft
    Excel^®^ workbooks and transfer the data to them. Unexpected results
    will occur if Microsoft Excel^®^ is open when the application
    starts.

[^3]: To insert an EMF picture into a Microsoft Word^®^ document, please
    see your Microsoft Word software instructions.

[^4]: See ColorBrewer
    (http://www.personal.psu.edu/faculty/c/a/cab38/ColorBrewer/ColorBrewer_intro.html)
    for more information on these or other color schemes.

[^5]: This rate is different from the statistical significance rates
    produced in the SAS^®^ and Excel^®^ data tables. The Holmes State
    average is a more robust than the number produced by the QIMT
    software because it is an average across the entire State, not just
    the counties for which the user may have available data. Users
    should feel free to utilize the overall average for the calculated
    counties or another quality benchmark for comparison as well.

[^6]: The State risk-adjusted significance rates are not calculated by
    the QIMT. These numbers were derived from the AHRQ National
    Healthcare Quality Report State Snapshots. The Holmes State
    risk-adjusted average for CABG is 0.000217673. The color-coding is
    based upon retrieving the State average rate for CABG and comparing
    it to the individual county rates. Where the county rate is higher
    than that of the State, the number is highlighted in red. Where the
    county rate is below the State rate, the number is highlighted in
    green. If there is little variation between the county and State
    rates the cell is left white. These numbers can be obtained at
    <http://statesnapshots.ahrq.gov/2005>.