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Journal for Healthcare Quality

Quartile Dashboards: Translating Large Data Sets into Performance Improvement Priorities Diane Storer Brown, Carolyn E. Aydin, Nancy Donaldson

Abstract: Quality professionals are the first to understand chal- lenges of transforming data into meaningful information for

frontline staff, operational managers, and governing bodies.To understand an individual facility, service, or patient care unit's

comparative performance from within large data sets, priori- tization and focused data presentation are needed.This article

presents a methodology for translating data from large data sets into dashboards for setting performance improvement

priorities, in a simple way that takes advantage of tools readily available and easily used by support staff.This methodology is illustrated with examples from a large nursing quality data set,

the California Nursing Outcomes Coalition.

Key Words benchmarking

dashboard prioritization

radar diagrams

Dashboards have transformed the way that healthcare professionals and senior leaders iiionitor organizational perfonnance and pri- oritize the design of improvement interven- lions (Donaldson, Brown, Aydin, Bolton, 8c RLiilcdgc, 2005; Rosow, Adam, Coulombe, Race, 8c Anderson, 2003). Dashboards provide data on structure, process, and outcome variables; report cards provide final leporLs on (jutcomes and are often intended for external audiences (Gregg, 2002). Recent public reporting initia- tives and tJie pay-for-perforinance demonstra- tion project funded by tbe Centers for Medicare and Medicaid Semces represent tbe report card strategy in whicb liospital performance isjudged by external constituents incoiporating incentives for performance improvement {Lindenauer, Remus, 8c Roman, 2007). In order to improve performance on public report cards, hospitals construct internal dashboards to review perfor- mance and identify areas in need oí change. Benchmarking with similar hospitals in a confi- dential context is an important clement in this proce.ss (Brown, Donaldson, Aydin, Sc C^aiison, 2001; Gregg, 2002).

Understanding Performance Data Traditionally, large quality data sets have been

for Healthcare Quality summarized using descriptive statistics such as

rcQuality

frequencies, averages, and standard deviations placed in tables, bar graphs, or line graphs to track key metrics over time. Those operationally accountable to improve patient care quality and saiety depend on quality professionals to trans- late data into usable information, which is then used to determine performance thresholds foi' (Irilkiown analyses oi" benchmarks and perfor- mance goals to understand relative comparative performance. This article uses common defini- tions for perlbrmance metrics as follows from Merriam Webster Online Dictionary (2007): Goal is the end toward which effort is directed (where you want your perfonnance to be) and is synony- mous with target, a goal to be acbieved; threshoùl is a level, point, or value above which something will take place, and below which it will not (the point where performance has declined and you need to drill down further to understand why); a benchmark is something that sei'ves as a standard by which others may be measured or judged (a best practice that you strive to meet or exceed).

Those new to the field of healthcare quality must learn how to translate data for benchmark- ing endeavors based on the data set undei review. Raw data reported out as frequencies (the count or number of occurrences) has liitk- use in performance monitoring, with the excep- tion of monitoring rare events. When monitoi- ing patient safety indicators that occur rarely, monitoring days between occurrences may be an important metric for frontline st;iff watcli- ing zenKolerance indicators such as falls witli major injury. Tbe mean or average, calculated as tbe sum of all occurrences divided by tbe lumi- ber of occurrences, is a statistic likely reported in all numerical data sets. However, the mean is known to be sensitive to extreme values or outliers, especially when sample sizes are small (Dawson 8c Trapp, 2004). This means that one patient with an extreme value can pull the mean for tlie datii set and leave the wrong impies- sion about performance for all patients, which could lead to unnecessary improvement efforts. The median or middle value may be a bettei'

Vol. ;ÎO N O . 6 November/December 20Ü8

reference point for data sets when there are extreme values. The median reflects the middle point of all observations—half the observations are larger than the median, and half are smaller. The median is also more appropriate to use for ordinal data—data where there is an inherent order to the values, but the values themselves may not have meaning. An example of ordinal tiata consists of the numeric response choices on a satisfaction survey where I may represent dissatisfaction and 5 may represent complete satisfaction. The average of these data (response choices of 1-5) may be distorted or skewed by sui'vey respondents selecting complete satisfac- tion (5), and those interpreting the results may not clearly see the distribtition of the patients or statï responses.

Understanding how the data acttially spread out is important for determining per- formance goals and benchmarks from data sets. Traditionally, the average may have been used as a goal. However, in today's competitive heallhtiue industi"y, sttiving to be average may not be the benchmark that senior leaders wish to target. Quality professionals have the task of interpreting the spread of the data to help establish ti.seful benchmarks from the daui set so that leaders can establish realistic targets. Healthcare qtiality data are often sknoed data— dat;i that are not symmetrically distributed (bell- shaped or normally distributed) in such a way that hail* the data are above the mean and half are belo\v. hi symrnelmal data, the mean and the median are numerically equal. This is important information to confirm when using a mean for a target—when the mean is pulled by extreme values, it may not be represen ta uve. The range may be included In reports to show where the mean sits in the data set. The range describes the data spread from the highest to the lowest luimbeis and is calculated by subtracting the minimimi value from the maximum (Dawson Sc Trapp, 2004). The same infonnation is available il d a t a sets provide the minimum and maximum \alues.

Most data sets report standard deviations when means are reported. The standard dnnaiion mathematically descrihes how the data spread (lut around the mean by representing the aver- age distance of obsei"vations from the mean (Dawson & Trapp, 2004). You might recall from statistics cla.sses that if the observations are symmetrical or normally distributed (in a hell-shaped ciii-ve). then 67% are between the mean and plus or minus I standard deviation;

95%, between the mean and plus or mintis 2 standard deviations; and 99.7%, between the mean and plus or minus 3 standard deviations. By taking the mean and adding or suhtracting 1, 2, and 3 standard deviation values from it, you will see the distribution of the data and will bet- ter understimd the usefulness of the mean to set performance metrics.

An example of setting perfonnance met- lics with semce times (minutes of waiting) follows. When meastiring mintites of waiting, negative values would not be possible (mintites below zero), and if the mean minus 1 standard deviation produces negative ntimbers, consider whether ihere were patients with extremely long wait times that ptiUed tlie group average up (resulting in a large standard deviation). The average tor this data set may not be use- ful for performance metrics. Consider pulling the outliers otit of the data set after re\1ewing the individual datii points. A scattergram is an easy way to see tlie outliers. By looking at the actual data and pulling otit extreme values (e.g., more than 3 standard deviations), the average for these data would be lower and would better reflect actual patient experiences.

As benchmarking data sets become more sopbisticated, reporting percentiles is emerg- ing as another way tí) understand the spread of the data and to pro\ide more specificity for estahlishing performance metrics. Percentiles aie easier to explain to those who operationally use the data, and it is easier to set benchmarks or targets with percentiles. A percentile is the per- centage of a distribution (responses or \~alues) that are equal to or below that number (Dawson &: Trapp, 2004). Percentiles are commonly repoited in healthcare with growth charts for children and in academia wilh test scores. For example, in a growth chart, if 60 pounds is the 90th percentile, that ntimber tells us that 90% of the children at that age weigh (30 pounds or less, and 10% of the children weigh more. It is easy to understiuid that this child is heavier than 89% of the other children the same age.

When percentiles are available, quartiles and interquartile ranges describe how the data spread out and tluis are extremely valuable for establishing performance metrics. Quartiles divide the data set into four quarters, with the 25th percenlile as the firet or lower quartile; the 50th percentile as the median or middle, which separates the second and third qnartiles; and the 75th percentile as the upper quartile (Figure 1). The interquartile range is the spread

UM Journal for Healthcare Quality

Figure 1. Data Distribution with Percentiles and Quartiles

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i)t daia between ihc 25th and 75th qiiatiiles— the middle values llial represeiu 50% of" ilie data set. Quartiles demonsti'ate performance relative to oihers in the data set and are used to set uieauiugful metrics. For example, if senice satisfaction scores are being compared, and your unit or lio.spital falls in the hiwer quartile, this means that 75% of those conipaied have higher satisfaction. A meaningiul goal might be to reacli the 50th pereentile for performance. Setting tlie 75tli percendle or upper quartile as the goal may be a stretch goal and diñicult to achieve, creating frustration for those account- able to implement improvements. The 50th percendle, or median, could be a short-term goal; and the 75th pereentile, a long-term goal. Another hospital might already be in the upper quartile at the 85th percendle; quality profes- sionals at tliat hospital may wish to set the 75th pereentile as the tlneshold indicating that their performance has declined (or indicating that the competition has gotten better).

Use of percentiles and quartiles f(ir bench- marking expands the toolbox for qnality profes- sionals for data display beyond traditional pie charts, bar graphs, and trend or line graphs. Today, qualit)' prt)fe.ssionals can nse the follow- ing guidelines in deciding which measure of

data spread may be most appropriate for a given data set (Daw.son &• Trapp, 2004):

1. Standard deviations are appropriate when Ihe mean is tised and the data are synnnetrical numerical data.

2. Percentiles and the interquardle range are appropriate when the median is used for ordinal data or the nutnerical data are skewed.

3. Interquartile ranges can be used to describe the middle 50% of the data dis- trihiuion regardless of its shape.

4. Ranges are used with uumerical data when the purpose is to understand extreme valttes.

Where does the quality professional begin to translate data sets into dashboards and set performance t;irgets, thresholds, and bench- marks? Armed with a basic understanding of the statistics described earlier, quartiles may provide a more sophisticated mcthodolog)' to establish e\idence-based performance metrics. Quartiles or percendles can be .selected as goals for performance, ÍLS thresholds for drilUiown analyses if perfonnance is already at the desired level, or as benchmarks for best practices from high perfomiers.

Vol. M) No. 6 Novcniber/üecember 2008

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Understanding Data Set Reports UiiUibasi's pro\'idc iiilbrinaüoii to users in a variety of formats. Selecting which format to iise may he ovei'whelniiiig for new quality profession- als. Keeping tlic purpose of ihe dala review in mind will help make the selection easier. Typical reports include suiniiuiries of multiple indicators at a point in time, compiuison of peifonnance against outside henchmarks, comparison of per- formance on an individual or multiple indicators with a pictuie, and uiouiLoiing performance on individual indicators over time. To illustrale reports that arc commonly availahlc, examples from the (California Nursing Oulcouics Coalition (CalNOC) data set are described, with discussion on hi)w to iLse the reports to meet the reviewer's iutcuded ptupose.

CalNOC, a regional nursing quality mea- surement database, is a collaborative effort of the American Nursing Association-California (.AJVA/C) and the Association of Clalifornia Nui^c Leaders lo advance improvements in patient care by sustaining a valid and reliable statewide outcomes databa.se. Voluntaiy tnem- beiship is available to all acute care hospitals in the state of California, as well as selected hospital groups in other states in the western

region of the United States. In 2()()7, more than 180 of CJalifornia's H60 acute care hospitals par- ticipated in CalNOC, with additiotiai hospitals from Nevada, Arizona, (Oregon, and Hawaii. Nuim'-seusitive qtiality indicators are collected at tlie patient care utiit level and clustered into categories of variables related lo muse staffing (houi-s of care, skill tnix, tise of contiacl stalf, staff tutuover, and bed turnover); registered nurse (RN) education level, certification, and years of experience; ¡jatienl falls; pressure ulcer (FU) prevalence; restraint prevalence; central line-associated bloodstream infections; and medication administration accuracy. Hospitals access Web-ha-sed customized reports generated directiy fri)m the data set to compare their own performance with thai of like hospitals, CalNOC hospitals develop their own facility dashboards, combining reports from the Web site with those from other dala sources to display indicators on a single document (Don;üdson et al., 2005). The CalNOC prí)ject has been described in de-tail elsewhere (Aydin et al,, 2004; Brown et al., 2001).

Siimniaiy statistic rt^xrrt.s provide a quick ref- etence for aggregaled data at a given point in time (e.g., the curieut quarter) to populate

Journal for Healthcare Quality

dashboards or view indicatoi"s tracked over time. These reports often provide columns ol' aggre- gated numeric data without graphs, and they usually include averages and mea.sures of data spread such a.s standard deviations or mini- mum and maximum values and may provide quaitiles. CalNOC summar)' statistics reports provide member hospitals with aggregated sta- tistics for all CalNOC hospitals on all variahles. Figure 2 shows an example of summaiy statistics for stafTing and falls bv' unit type and hospital average daily census.

Graph irporLs provide a visual comparison of performance on select indicators at a point in time (e.g., the current quarter). Graphs provide a visual representation of comparative hospital performance, which may quickly provide perfor- mance information. Graphs should not be used to summarize «/idata, only those prioritized for performance monitoring. Wiien reports iiii hide pages and pages of graphs, the key messages and analyses from the data set are lost on those reviewing the teports. Figure 3 shows a sample comparison graph for falls per 1,000 patient days for all medical/surgical units in hospitals with an average daily census under 100 patients. This graph gives hospitals a visual reprcscntit- tion of the variation amotig hospitals, followed by a report that lists the actual performance for each hospital (not included).

Trmd reports provide the ability to monitor prioritized indicators over time. These reports often include graphs as well as a data table for monitoiing. Using trend charts can heip hospi- tals understand their ongoing performance over time by watching tbe slope of the line or bars to uuderstand vvbcther performance is improv- ing, declining, or stable compared to the same hospital (your hospital) each month or quarter. Figure 4 provides an example f)f a hospital trend teport for falls per 1,000 patient days for one ho.spital. Both the facilit)' average and CalNOC average for tbe .selected time period are shown by Unes across the gi-aph. The report includes the graph sbown, followed by a table listing the actual numeric fall rates ibr each montb (not included).

Be careful when monitoring only trend reports. Even if perfonnance remains stable (i.e., flat slope), comparison to others is still important to see whetlier the bar rises. As the group prioritizes improvement over time, the group average may raise the bar or benchmark. Even if individual performance is stable, relative performance may decline—for example, from

tbe 90th percentile to the 80th percentile— sitTipl} because tlie rest of the group in the data set improved. It would be a mistake to monitor only individtial performance over time.

Monitoring uends over time for prioritized indicators is very importiint in determining whether gains are held. Wiien data are being viewed over time, it is usually better to use line graphs to better visualize trends. Figure 5 pro- vides an example of the same data using verti- cal bai graphs and line graphs. Although both graphs clearly demonstrate die spike in restraint use in 2005, the trend of decrease over time is much clearer in the line graph.

Henchmarking rcpart.s provide a succinct sum- mary of performance, together with the per- formance of like groups. These teports may be helpful to setiior leaders such as the chief officers or the board of directors when data are at the facility level, and ihey may be helpful to individual unit managers when data are at the unit level of analysis. These reports are usually nimieric data in columns and provide compari- sous for Uic individual perlbrmance with other groups such as state or national averages, or averages of other like facilities based on criteiia from the given database. Data may be similar to summary statistics with averages and data spread infonnation and may include percentiles or quartile itiformatioti. GalNOC's facilitv-level benchmarking leports show stunmary data for tbe total facility and by unit type (i.e., critical care, step-down, and medical/surgical ttnits). Figure 6 shows a facility-level benchmarking report for prevalence studies. Unit-level data allow managers to compare their performance within the facility as well as externally. Unit man- agers can examine imit perfonnance in detail, including botb PU prevention process variables and patient oiUcomes. These statistics track the actttal number of patients with ulcers in addi- tion to tbe percent. Actual ntimbei's may be meaningful to fiontlinc unit suilf wbcu tracking rare events by days between occurrences. Also included arc statistics useful for performance metrics such as the facility' mean by unit type, like ho.spital mean by unit type, and CalNOC mean by unit type. Taken togetlier, the statistics on this uuit-Ievel report provide a valuable drill- down into both patient outcomes and the PU prevention process.

Translating Data into Quartile Dashboards A six-step process has been developed to guide quality professionals through the translation

Vol. 30 No. 6 November/December 2008

Figure 3. Graph Report

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Journal for Healthcare Quality

Figure 5. Comparison of Data Using Bar and Line Graphs

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process. Continuing with the C âlNOCl example, and using the definition lor dashboards present- ed earlier, prioritized indicators representiiif» stnictnre, process, and ontcomes were selected

to demonstrate a simple method to translate (¡nartile information troni siiiiiniar}- reports using readily availahle tools in software prochiets sitch as Microsoft Excel or PowerPoint.

Vol. 30 No. 6 November/December 2008

r- Figure 6. Benchmarking Report

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Step 1: Príorítizatíon After reviewing all the reports available to qual- ity professionals in databases, tbe next cbal- Icnge is one of syntbesizing tbe information to narrow the focus to indicators that are impor- tant to tnonitor compared to benchmarks. Prioritization should come from tbe key stake- bolders wbo manage operations associated with the data set. Indicators should be limited to the "vital few" and should represent structure, pro- cess, and outcomes. Tbe prioritized indicator list will need to be placed into a .spreadsbeet to create tbe dasbboard.

Step 2: Translating Performance into Quartiles Performance on tlie prioiitizt-d indicators will next need to be translated into quartiles. Gather the reports tbat provide bencbmark quartile values witb facility performance. For eacb indicator, identify tbe numeric value tbat defines tbe range of values for eacb i|uartile in tbe data set. Next, identify tbe facility's individual performance and wbere tbat value falls witbin tbe identified quartile range (this can be done concurrently or as individual steps). Transfer this information

into tbe spreadsheet. Tbis abstraction from summary reports can be completed by support staff after training on tbe specific reports tbat will be used atid the ftmdamentals of quartile metrics. Figure 7 sbows a very simple worksheet for capluring performance by indicating wbich quartile the hospital fell into for each indicator. Pereentile numbers (25, 50, 75) were assigned in the last colmTin of the worksheet, which will be used to generate dashboard grapbs.

As a practice example for translating quartile infonnation, refer back to Figure 2, Summary Statistics, as a reference. Tolal hours per patient day in medical/surgical units bas tbe following quartiles: the lower quartile is 7.44 {1st to 25tb percentiles), the median value is 8.56 (50th pereentile), and tbe upper quartile begins at 9.75 (75th to lOOtb pereentile). Next, identify the individual hospital's performance on tbe same indicator. If the value is 7.44 or less, it is in tbe lower quartile; if it is 7.45 to 8.56 (tbe median value), it is below the median but above tbe lower quartile; if it is 8.57 to 9.74, it is above tbe median btit below tbe tipper quartile; and if it is 9.75, it is in the upper quartile.

Journal for Healthcare Quality

Figure 7. Worksheet for Capturing Performance by Indicator

Worksheet 1: CalNOC Indicator Performance from Summary Statistics Quarter 1 2008

Structure (Staffing):

Below Above Facility Lower Upper Performance

Quartile Below Above Quartile (number from 25 Median 50 Median 75 100 column to left)

% RN Hours of Care %LVNHour^ofCare % Other Hours of Care % Contract Hours of Care Total Hours Per Patient Day # Patients Per RN Licensed Hours PPD Sitter Hours Bed Turnover RN Voluntary Turnover LVN Voluntary Turnover Total Voluntary Turnover

50 75 25 100 75 100 100 25 100 100 25 100

Process: % PL) Risk Assess in 24 hours % At Risk for PU % At Risk PL) Prevention % Restrained % Restrained Vest or Limb

50 25 25 100 100

Outcomes: Palls Falls with Iniury %Hosp Acquired Ulcer % Stage II+ HAPU % Stage III+ HAPU

100 25 100 100 75

Note. lA'N = licensed vocational nurse; RN = registered nurse; PPD = per patient day; PU = pressure ulcer; ! = hospital-acquired tilcer.

Step 3: Creating the Dashboard The next step in Úiv translation process is to use Uie quartile data to create a picture that will show perfomiance priorities tisitig the data in the la.st column of the worksheet and a readily available software application, Microsoft Excel or PowerPoint. Again, support staff will he ahle to accomplish this translation once the indicators have heen selected and the worksheet has been set up.

Figure 8 shows a traditional way to look at these data using horizontal bar graphs. The

quartiles are demarcated numerically hy the percentiles tiiat define them. A more poweritil picture may be available for quartiles using radar or spider diagrams. Rgure 9 provides the same information, but the picture is more powerful \isvially. Similar to the bar graph, the quartiles are demarcated numerically by the percentiles that define them. Ttie center of the diagram represents the lower quartile, with each quartile moving away from the center progressively, so that the upper qnartile is the ()Uter ring of the diagram, which resembles a spider web.

Vol. 80 Nt>. fi Novcmber/Deccmbei 2008

Performance is identified by coloring of ibe (liagrani—^wilb more color Indicaling perfor- mance reacbing out from the center and lower quartile.

Step 4: Consolidation to a One-Page Dashboard Clusici ibc i^iaplis on a one-page document so tbal all infonnation is readily a\ailable at a glance. Two examples are provided in Figure 10 and Figure 11. sbowing the boiizontal bar gia[)h.s and (hi- radai" fliagrams, respeclively, using stiiic- ture, process, and outcome indicators from ihe worksheet. Because all the data are on one page, (be end user can quickly visualize comparative performance on prioritized indicators.

Step 5: Supporting Documentation Creation of an appendix or stipporting docti- menl for tbe dasbboaril is based on die end user's need for additional information. A t;ible <»f indicator definitions may be inchided, whicb also could provide data sources and time frames for tbe data set. Wben quartiles arc used ;LS bencbmarks, it is also belplul to identily tbe desired direction for perfonnance. For example. using the indicator data in these d;\shboaids for

PUs, process data related to asse.ssment for PU risk or prevention inteiTention perfonnance in tbe uftfjn quartiles would be desirable, and outcome perfonnance related lo acquiring PUs in the toiCíT quartiles would be desirable. AITOWS indicating tlie desired direction can be placed on tbe dasbboard as one helpful tool, as shown in Figure 10. Anolber option, one tequiring liu tber explanation to the users, is to rescale tbe dasbboard so that low performance is always in the Icjwer quartile and desired performance is always in the upper quartile. For ihe infonnation on PUs, this would require transposing actual quartile perfonnance data for acquiring ulcers— in this case, being in the lower {|uarlile is good— and representing that as the upper quartile on the da.shboard. Tbe dasbboard must be clearly labeled witb ibotnotes so Íl is clear to those using the dashboard tbat good perfonnance is always bigh, even though intuiiively you wisb it to achieve low pre\alence.

Step 6: interpretation Tbe final step in the translation process involves analysis or interpretation of comparative perfor- mance to otbers in the data set. The key opera- tional stakeholders wbo prioritized tlie itidicator

Figure 8. Quartile Performance Using Horizontal Bar Graphs

Staffing Performance in Quartiles

Total Voluntary Turnover

LVN Voluntary Turnover

RN Voluntary Tumover

Bed Tumovef

Sitter Hours

Licensed Hours PPD

# Patients Per RN

Total Hours Per Patient Day

% Contract Hours of Care

% Other Hours of Care

% LVN Hours of Care

% RN Hourï of Care

Senes1

% RN Hours of Caie

% LVN Hours of Care

Vo Olfier Hours of Care

% Contract Hours ot Care

100

Total Houfs Per Palieni

Day

i Palienls Per RN

100

Licensed Hours PPD

100

Sitter Hours

Bed Turnover

10Û

RN Voluntary Turnover

100

LVN Voluntary Turnover

100

Total Voluntary Turnover

100

Quartiles

Journal for Healthcare Quality

Figure 9. Quartile Performance Using Radar Diagrams -

Staffing Quartile Performance

%RN Hours of Care

Total Voluntary T u m o v e r ^ . . - ^ - ^ ~~--~-.-_^ % LVN Hours of Care

LVN Voluntary Tumover

RN Voluntary Turnover

Bed Tumover

%aher Hours of Care

% Contract Hours of Care

Total Hours Per Patient Day

Sitter Hours i' Patients Per RN

Licensed Hours PPD

set mnst be involved in this process. Key conclu- sions must be summarized ioi senior leadersbip.

Continuing with tbe CalNOC example, the following interpretation might be drawn by tbosi- with operational accountability. {Note diat this dashboard was not rescaled for desired perfonnance placement in tbe upper quartile.) Looking at tbe structure data, one sees ibat tbis bospital bas more licensed vocational nurse (LVN) boms tban tbe median and has little L\TS' tumover olthcstaíí (lower quai lile). Unlicensed support stafFuse is low (lower quartile) although RN lumrs of care are at the median, but the number of patients lor each RN is bigb (upper quartile). Tbe ntimber of patients in a bed (bed turnover) on a given day is bigh (indicat- ing many adtnissions, discbarges. or transfers), whicb would require a lot of RN time. RN ttirn- over on tbe workforce is also bigb (perbaps the unit is too bu.sy), and staüing is accomplished with contract or registry staff (upper quartile). Tbis luiit likely would examine its sUiffing pat- terns because the siiuation appeals to be a dif- ficult one for the RN workforce.

Next, looking at tbe process and outcome data wilbin die context of ibese structure data, one might make tbe following interpretation.

Restiaint use is higb (upper quarlile), altbongb usf t)f sittfis to prevent resuaint or falls is in tbe lower quardle. Patients at risk for PUs are not getting prevention intei-ventions (lower quarlile), and tbe risk assessments ibr PU devel- opment are only at the median. Ri.sk assessments and detennination ()f appropriate intci-veniions may not be gelling accomplisbed, given die RN patterns just identified. Although tbe percent of patients at risk for hijspital-acqiiired PUs (FL'VPL's) is low (lower quartile), this bospital is in the upper quartile for HAPU develoi> ment. This b(xs]>ital will want to invesiigiue tbese outcomes further by drilling down inlo tbe dala to better understand performance. This hospital may be doing well wilb fall prevention, however: falls with injtir)' are in the lower quarlile. Note thai "all falls" are higb (upper quartile), whicb could be inter¡5reied as good reporting or as a bigb rate tbat needs fut ther investigadon. If this bospital bas been working on a culture of saiety and respi)nsible reporting, a high fall rate may indicate success in tbis area (good reporting).

Based on this dasbb(iard, quality profes- sionals at tbis bospital would likely prioritize perfonnance improvement around PU develop- ment and use of resti'aint; tbey may wisb to set

Vol. 30 No. 6 November/December 2008

Figure 10. Bar Graph Dashboard starling P t r t B t m i R U In Q m r l l l a i

Turnover

lumovw

RN Volunlary TumovB

Bed T.ii-i,vi.i

Sitlei MuuFi

Ucaraad Houn PPO

f Patœms P« RN

ToW Moura Pw PaWnl Day

%Olhe( Hours otCsre

% LVN Hours oí Cars

%RNHauraorCare

. j -

NHtilng Procsas Ouaillle PiilormancB Anat^sls F i l l i • I d P n u H r a Ulcsr Q i i r l l i a Psrlotmanc» Analytic

Figure 11. Radar Diagram Dashboard

Desired Periormance Direction

SlaHIng auirllte PsrlDimancs

LVN Houn of C m

c a n i a Hum ol C m

Tov H o c P H Pdiint Or,

DutEomai QuaMlli Analysis Rertortninee Knaiysls

Journal for Healthcare Quality

performance targets oí being below the 75th percentile as a short-term goal, and below the 50th percentile or median as a long-tenn goal. Given that they are doing well with injuiy falls, they may wish to set the median as a thresh- old for further analyses should the hospital's performance decline to that level. They would also likely investigate fnrther staffing patterns to support the higli volume of patients that are admitted, discharged, or translerred Into this uiiii dailv. Given the high RN staff turnover, they may also wish to coudnct a survey or focus grotip to better undeistand the stafFs perspec- ti\e on the work cn\iroument. They may wish to set a performance target to be below the median for total voluntary stafT tumover.

Summary This article provides tools for the quality pro- fessional to translate data sets into dashboards and (o .set performance tingets, thresholds, atid benchtnarks. Armed widi a basic understatiding of the statistics described, quartiles may provide a more sophisticated mt'thodolog)' lor bench- maiking. Depending on how data are reported, quartiles or petcentiles can be selected as goals for performance, as thresholds for drill-down analyses if performance is already at the desired level, or as the benchmarks for best practices from high performers. Graphs cati be used to create powerful \isual UJOIS to quickly inlbrm froiuline staff, operational leaders, and gcnern- ing bodies on piioritized metiics.

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lindenaner, l\ K,, Remas, I)., Sc Roman, S. (20()7). Public rc|xin- ing and |7ay for perlbmiance in hospital qiuüity inipit)veineiit. Neil'F.n^and Jmmmlof Mfilkive. 556(5), 486—4%.

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Authors' Biographies Diane Stotrr Jhowii. PhD fîN FNA¡Í(¿, is the California Nursing (hitcomrs Coalition (CalNOC) ayfniniipal iiix'es- tigatM' and has heen part tif the CalNOC research learn for more than 10 years. She is nirrmtly the dinical practice lead- er for hospital accreditation pmgranis at Kaiser P/rmanente Ntirlherri California Region in Oablan/l. CA.

Carolyn E. Aydin, IViD, is a California Niunirig (hitcoines Coalition. {CalNOC} coinvestiga tor and has been tlie CalNOC {fata manager for the f)a.st ¡O^ears. Shf is currently a research scientist nt Ceilitr.s-Sinai Health System. Bums antl Allen Research Institute in. ¡.os Angeles, (A.

Nancy Domildsori, DNSc RN FAAN, is Ihe CaUfmnia Nursitig Outcomes Coalition (CalNOC) cof/rincifial inves- tigattrr and has aLw hem part of tlw CalNOl. research team for imiif than 10 years. She is the .Ameritan Nurses Associâtiini-Catif(/rnia (ANA/(^) CJIINOC pmje/t direclrrr and coprinrifuil investigator as well as the dim tor far the Cinder for Research and ¡nnoi'ation in Patient (Jam al University of Cakfomia-San Frandvo Stnnfi/rd Health (jitv through the University of California—.San Francisco SchiM>l tif Nursing.

For more informatitm on this article, rontact Diane Storer Bwuni at [email protected].

Joimial for 11 et ill h rare (¿uality is pleastd [o olí( r the opportunity to earn continuing edticatioii (CE) credit to those who read this article and take tlie online posttest at www.nahq. org/Journal/ce. This contitiuing edtication offering, J H Q 209, will prinidi' I contact liDtn to those who toiuplete it ap|}ropriately.

Core CPHQ Examination Content Area 111. Perlorinaiicf ltnpro\ctuciil