Back to the Basics! Dashboards, Quartiles, and Setting ... and Public Files/CalNOC_Public... · Dashboards, Quartiles, and Setting Priorities Presented on Behalf of the CALNOC TEAM

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member hospitals only

Back to the Basics! Dashboards, Quartiles, and

Setting Priorities

Presented on Behalf of the CALNOC TEAM by

Diane Storer Brown PhD, RN, CPHQ, FNAHQ, FAAN

Co-Principal Investigator, Collaborative Alliance for Nursing Outcomes (CALNOC)

Clinical Practice Leader for Hospital Accreditation Programs Kaiser Permanente Northern California Region, Oakland, CA



(2008). Volume 30(6), 18-30.



Objectives1. To illustrate one methodology for translating

benchmark data into dashboards. – Understand percentiles and quartiles as

benchmarks – Illustrate simple/available tools to summarize

benchmarks from large data sets2. To illustrate setting performance improvement

priorities from benchmark dashboard data.– Examples include traditional methods and

radar/spider diagrams.– Illustrate the methodology using individual

hospital’s data from the Collaborative Alliance for Nursing Outcomes (CALNOC).



Healthcare Environment

Challenged to balance efficiency goals which assure patients receive exactly the care they need in systems without waste, with highly reliable care that is consistently safe and clinically effective (high quality). – Greatly impacted by the economic downturn– Facing escalating health care costs and changing

reimbursement models – Growing scrutiny over issues that erode public trust which are

highlighted in the media – Public demands for transparency in both cost and quality data

have increased – Growing lists of payers who will no longer reimburse hospitals for

preventable hospital-acquired conditions



Benchmarking Importance• Leaders are challenged to identify

appropriate benchmarks for comparative data.

• Benchmarking is an indispensable tool to gauge progress with strategic priorities.

• Benchmarking with other similar organizations in a confidential context is an important component of improving performance on public report cards.



Dashboards• A tool used by leaders to monitor organizational

performance over time compared to benchmarks.• Provide data on structure, process, and outcome.• Support efforts to achieve good outcomes on

external reports and publicly reported report cards.

• Designed to incorporate those metrics deemed most important by leadership (not all data).

• Versus report cards which are often intended for external audiences and final reports of outcomes.



Traditional Data Use• Reports include ALL variables and are

overwhelmingly large (versus a prioritized dashboard with key metrics).

• Use of frequencies, means/averages, standard deviations, tables, bar graphs, tracking everything over time.

• Performance thresholds and benchmarks are set haphazardly (90% commonly used) or to be above average (striving to be above average won’t set you apart from the competition!).



Efficient Data Use• Consideration given for each data set to

determine key metrics to prioritize.• Goals, benchmarks, and thresholds based on

data rather than “standard targets” like 90%.• Dashboards created at the facility to monitor

performance and prioritize further actions.• Drill-down data utilized to investigate

performance on prioritized metrics and to drive appropriate improvement interventions.



Data Definitions• Goal: “the end toward which effort is directed” (where

you want your performance to be)• Target: “a goal to be achieved” (where you want your

performance to be)• Thresholds: “a level, point, or value above which

something is true or will take place and below which it is not or will not” (the point where performance has declined and you need to drill down further to understand why)

• Benchmarks: “something that serves as a standard by which others may be measured or judged” (best practice that you strive to meet or exceed)

Merriam Webster Online Dictionary: http://www.merriam-webster.com/dictionary, October 15, 2007



Understanding Data Description

Raw Data: • Frequencies (the count or number of

occurrences)• Useful to monitor rare events using days

between occurrences for zero-tolerance indicators (falls, pressure ulcers, infections)

• OTHERWISE -- little use for most other performance monitoring




Average or Mean:• Arithmetic mean is found by adding the occurrences and

dividing the sum by the number of occurrences in the list. • A value between the extreme members of the data set. • Skewed (pulled or distorted) by extreme values or outliers.• Likely included in all dataset reports.• Example: 10 average people cluster around 135 pounds --

some higher, some lower, but average is 135. Exchange one average person for a heavy person of 350 pounds, the average is now 156.5 pounds – which no longer describes the average weight of the group.




Median:• The middle value when numbers are ordered

from smallest to largest (50% are above and 50% are below).

• A better reflection of the middle of data if there are extreme values or outliers.

• Appropriate to use for ordinal data (data with an inherent order to the values but the values themselves may not have meaning – like the numeric response on satisfaction surveys).



Understanding Data Spread

Data Spread:• Understanding the spread helps establish useful

benchmarks or thresholds.• Understand if the data are distorted (skewed, not

symmetrical, data with extreme values or outliers-- dataset won’t have half of the data above the average and half below).

• Important to understand if using the average for goals or benchmarks or thresholds.



Averages Can be Skewed!Acquired Pressure Ulcers Stage II+

N=110 Hospitals

35.7 37.5

15.2

5.8 3.70 0 0

8.3

05

10152025303540

CC SD MS

Maximum Mean Minimum

Outliers included



SKEWED DATAVariance in Falls per 1000 Patient Days

N=117 Hospitals

16 15.43

28.88

3.28 3.590 0 01.170

5

10

15

20

25

30

35

CC SD MS

Maximum Mean Minimum

Medians: CC=0, SD=2.89, MS=3.16 Outliers included

Performance Example: How hospital data varies.



Being “average” is not where your leadership wants to be!

• Interpreting the data spread is necessary to establish useful benchmarks and realistic targets.

• Healthcare quality data are often skewed data –not symmetrically distributed (bell-shaped or normally distributed) with half of the data above the average or mean and half below.

• In symmetrical 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 representative.




• Range: how the data spread from the highest to the middle to the lowest numbers; datasets may provide the minimum and maximum values, or the numeric range calculated by subtracting the minimum value from the maximum.

• Standard Deviation: a calculated measure of the spread of the numerical values about their arithmetic mean (the average distance of data from the mean).




Standard Deviation: If the observations are symmetrical or normally distributed (bell shaped curve) – 67% are between the mean and plus/minus 1 standard deviation, – 95% between the mean and plus/minus 2 standard deviations, – 99.7% are within plus/minus the mean and 3 standard deviations.

• Draw your own picture: add and subtract one, two, and three standard deviation values from the mean, line up the values, and connect the dots to see the distribution.

• By understanding the possible values from a data set (spread), you will be able to understand the usefulness of the mean as a benchmark, goal, or threshold.



Understanding Data SpreadPercentiles: Provide more specificity for

establishing goals and benchmarks.• Easier to understand the spread of data.• Easier to explain to those that

operationally use the data reports than SD.

• Easier to use to set benchmarks or targets from a dataset.



Understanding Data SpreadPercentile: • The percentage of a distribution (responses

or values) that are equal to or below that number.

• A value on a scale of 100 • Example: a score in the 75th percentile

means 75% of the scores are equal to or below that score

• Common in growth charts and testing scores.



Understanding Data SpreadQuartiles: Uses percentiles to divide the data into

4 equal sections (fourths). – Listed as three values (25th, 50th, 75th) that

divide the data distribution into four sections each containing one fourth of the total data.

– The middle value in the data is the median (50th percentile).

– Inter-quartile range describes the spread of the data between the 25th and 75th percentiles.



Percentiles & Quartiles

0% 20% 40% 60% 80% 100%

Lower Quartile Below MedianAbove Median Upper Quartile

25% of data 25% of data 25% of data 25% of data

Quartiles

Percentiles

50th Percentile is the Median

Inter-Quartile Range



Percentiles & Quartiles: Set Meaningful Goals or Thresholds

• If your service satisfaction scores are in the lower quartile (below 25th percentile) -- 75% of those you are comparing with have higher satisfaction. – A meaningful goal might be the 50th percentile (lower middle

quartile) for performance – setting the 75th percentile or upper quartile may be a stretch goal or difficult to achieve creating frustration for those accountable to implement improvement.

– The 50th percentile could be a short term goal, and the 75th percentile a long term goal.

• Another hospital might already be in the upper quartile or know they are in the 85th percentile.– The 75th percentile could be set as a threshold to indicate

performance decline (or the competition is better) and time to take a closer look might be appropriate.



Which Measure of Spread to Use?Guidelines on which measure of data spread to use: • The standard deviations can be used when the mean is

used and the data are symmetrical numerical data.• Percentiles and the inter-quartile range can be used

when the median is used for ordinal data or with skewed numerical data.

• The inter-quartile range can be used to describe the middle 50% of the data distribution regardless of its shape.

• Simple ranges (the difference between the largest and the smallest observation) are used with numerical data when the purpose is to understand extreme values.

Dawson, B., & Trapp, R.G. (2004). Basic & Clinical Biostatistics.

Lange Medical Books.



Data Spread with Quartiles: Falls per 1000 Patient Days (N=163 California Hospitals)

0

4.8 4.8

2.43.1

0 0.0

1.8

00

1

2

3

4

5

6

CC SD MS

Q3 (75th %ile) Median (50th %ile) Q1 (25th %ile)



Translating Reported Data Into Quartile Dashboards



Six Step Process1. Prioritization2. Translating Performance into Quartiles3. Creating the Dashboard4. Consolidating to a 1-Page Dashboard5. Supporting Documentation6. Interpretation



Step 1: PrioritizationNarrow the focus to important indicators to monitor

compared to benchmarks.• Prioritization decisions should come from key

stakeholders that manage operations associated with the dataset.

• Indicators should be limited to the “vital few”.• Indicators should represent structure, process and

outcomes.• The prioritized indicator list will need to be place

into a spreadsheet to create the dashboard.


member hospitals only Total Voluntary Turnover

LVN Voluntary Turnover

RN Voluntary Turnover

Bed Turnover

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 Hours of Care

Structure (Staffing):

CALNOC Indicator Performance from Summary Statistics

% Stage III + HAPU

% Stage II + HAPU

% Hosp Acquired Ulcer

Falls with Injury

Falls

Outcomes:

% Restrained Vest or Limb

% Restrained

% At Risk PU Prevention

% At Risk for PU

% PU Risk Assess in 24 hours

Process:

Step 1: Prioritization



Step 2: Translating Performance into Quartiles

• Gather the data -- gather the reports that provide benchmark quartile values and facility performance for the prioritized indicators.– For each indicator, identify the numeric value that defines

the range of values for each quartile in the dataset.– For each indicator, identify the facility’s individual

performance and where that value falls within the quartile ranges just identified.

– This can be done concurrently or individually.• Transfer this information into a worksheet that will be used

to create the dashboard.• This abstraction from summary reports can easily be

completed by support staff after training on reports that will be used and the fundamentals of quartile metrics.



Statistics to Create Your Dashboard Means, Standard Deviations, and Quartiles are listed

as actual values (not percentiles)



Median

Lower

Quartile

Upper

Quartile

For indicators of interest, identify quartile numeric ranges.For indicators of interest, identify quartile numeric ranges.

Lower quartile ends at 7.44 (1st to 25th percentiles), the median value is 8.56 (50th percentile), and the upper quartile begins at 9.75 (75th to 100th percentile).



Median

Lower

Quartile

Upper

Quartile

For indicators of interest, identify facility performance.For indicators of interest, identify facility performance.

If facility value is 7.44 or less it is in the lower quartile; if it 7.45 to 8.56

(the median value) it would be below

the median but above the lower quartile; if it is

8.57 to 9.74 it is above the median

but below the upper quartile; and if it is 9.75 it is in the upper quartile.



Worksheet 1: CalNOC Indicator Performance from Summary Statistics

Below Lower

Quartile 25

Below Median

50

Above Median

75

Above Upper

Quartile 100

Structure (Staffing):% RN Hours of Care x% LVN Hours of Care x % Other Hours of Care x% Contract Hours of Care xTotal Hours Per Patient Day x# Patients Per RN xLicensed Hours PPD xSitter Hours xBed Turnover xRN Voluntary Turnover xLVN Voluntary Turnover x Total Voluntary Turnover x


Once quartile numeric

ranges are identified, plot the facility's performance

on each indicator into

the worksheet.




Worksheet 1 is a very simple method of capturing performance by indicating which quartile the hospital fell into for each indicator.

Percentile numbers (25, 50, 75) were assigned in the last column of the worksheet which will be used to generate dashboard graphs.



Worksheet 1: CalNOC Indicator Performance from Summary Statistics

Below Lower

Quartile 25

Below Median

50

Above Median

75

Above Upper

Quartile 100

Performance (number from

column to left)

Structure (Staffing):% RN Hours of Care x 50% LVN Hours of Care x 75% Other Hours of Care x 25% Contract Hours of Care x 100Total Hours Per Patient Day x 75# Patients Per RN x 100Licensed Hours PPD x 100Sitter Hours x 25Bed Turnover x 100RN Voluntary Turnover x 100LVN Voluntary Turnover x 25Total Voluntary Turnover x 100

Process:% PU Risk Assess in 24 hours x 50% At Risk for PU x 25% At Risk PU Prevention x 25% Restrained x 100% Restrained Vest or Limb x 100

Outcomes:Falls x 100Falls with Injury x 25% Hosp Acquired Ulcer x 100% Stage II + HAPU x 100% Stage III + HAPU x 75

Assign a percentile number in the last column of the worksheet which will be used in the next step to

generate dashboard graphs.




Step 3: Creating the Dashboard• Use the data in the last column of the worksheet• Create graphs using readily available software programs,

Microsoft Excel or PowerPoint as examples. • Support staff could accomplish this translation once the

indicators have been selected and the worksheet set up.• Horizontal bar graphs are a traditional way to look at these

data. – The quartiles are demarcated numerically by the

percentiles that define them. – Note that performance for each quartile is easily visible.



Staffing Performance in Quartiles

0 25 50 75 100

% RN Hours of Care

% LVN Hours of Care




# Patients Per RN

Licensed Hours PPD

Sitter Hours

Bed Turnover



Total Voluntary Turnover

QuartilesSeries1 50 75 25 100 75 100 100 25 100 100 25 100

% RN Hours of

Care

% LVN Hours of

Care

% Other Hours of

Care

% Contract Hours of

Total Hours Per

Patient

# Patients Per RN

Licensed Hours PPD

Sitter Hours

Bed Turnover






Step 3: Creating the DashboardRadar or spider diagrams may be a more powerful

visual picture for quartiles.• The quartiles are demarcated numerically by the

percentiles that define them. • The center of the diagram represents the lower

quartile, with each quartile moving away from the center progressively so that the upper quartile is the outer ring of the diagram (or spider web).

• Performance is identified by coloring of the diagram – more color indicating performance reaching out from the center and lower quartile.



Staffing Quartile Performance

0

25

50

75

100% RN Hours of Care

% LVN Hours of Care




# Patients Per RN

Licensed Hours PPD

Sitter Hours

Bed Turnover






Step 4: Consolidation to 1-page Dashboard

• Cluster the graphs into a one page document so that all information is readily available to the end user.

• By placing all the data together on one page, the end user can quickly visualize relative or comparative performance on prioritized indicators.

• Multiple pages lose your audience!



Staffing Performance in Quartiles

0 25 50 75 100

% RN Hours of Care

% LVN Hours of Care




# Patients Per RN

Licensed Hours PPD

Sitter Hours

Bed Turnover




Quartiles

Falls and Pressure Ulcer Quartile Performance Analysis

0 25 50 75 100

Falls

Falls with Injury

% Hosp Acquired Ulcer

% Stage II + HAPU

% Stage III + HAPU

Quartile

Nursing Process Quartile Performance Analysis

0 25 50 75 100

% PU Risk Assess in 24 hours

% At Risk for PU

% At Risk PU Prevention

% Restrained


Quartile

Step 4: Consolidation

to 1-page!




0

25

50

75


% LVN Hours of Care




# Patients Per RN

Licensed Hours PPD

Sitter Hours

Bed Turnover




Outcomes Quartile Analysis

0

25

50

75

100Falls

Falls with Injury

% Hosp Acquired Ulcer% Stage II + HAPU

% Stage III + HAPU

Process Performance Analysis

0

25

50

75

100% PU Risk Assess in 24 hours

% At Risk for PU

% At Risk PU Prevention% Restrained


Step 4: Consolidation

to 1-page!



Step 5: Supporting Documentation

End users may need additional information for the dashboard:

• A table of indicator definitions may be included, which also could provide data sources and timeframe for the dataset.

• Arrows indicating the desired direction can be placed on the dashboard as the lower quartile can be a good or bad thing!




0

25

50

75


% LVN Hours of Care




# Patients Per RN

Licensed Hours PPD

Sitter Hours

Bed Turnover




Outcomes Quartile Analysis

0

25

50

75

100Falls

Falls with Injury

% Hosp Acquired Ulcer% Stage II + HAPU

% Stage III + HAPU

Process Performance Analysis

0

25

50

75

100% PU Risk Assess in 24 hours

% At Risk for PU

% At Risk PU Prevention% Restrained


Desired performance Direction



Understanding Quartile DirectionFor pressure ulcers: process data related to assessment

for pressure ulcer risk or prevention intervention performance would be desired to be in the upper quartiles, while outcome performance related to acquiring pressure ulcers would be desired to be in the lower quartiles.

If high numbers are better– Lower Quartile is under the 25th percentile = bottom

quarter of performance – Between 25th and 50th percentile or Median = below

average performance– Between 50th or Median and 75th percentile = better

than average performance– Upper Quartile is above the 75th percentile = in the

top quarter of performance



Understanding Quartile Direction

If low numbers are better• Lower Quartile is under the 25th percentile = in

the top quarter of performance • Between 25th and 50th percentile or Median =

better than average performance• Between 50th or Median and 75th percentile =

below average performance • Upper Quartile is above the 75th percentile =

bottom quarter of performance



Step 5: Supporting Documentation

– Another option is to rescale the dashboard so that low performance is always in the lower quartile and high performance is always in the upper quartile.

– Using the pressure ulcer example, this would require transposing actual quartile performance data for acquiring ulcers – if in the lower quartile (good), representing that as the upper quartile on the dashboard.

– When doing this, the dashboard must be clearly labeledwith foot notes so those using the dashboard are clear that good performance is always high even though intuitively, you wish it to be low prevalence. Frame the dashboard as “performance” rather than “indicator performance”.



Step 6: InterpretationThe final step in the translation process

involves analysis or interpretation of comparative performance to other hospitals in the dataset.

• The key operational stakeholders who prioritized the indicator set MUST be involved in this process.

• Key conclusions MUST be summarized for senior leadership.



Step 6: InterpretationUsing the CALNOC example, the following interpretation might be drawn

(note that this dashboard is NOT rescaled for desired performancealways being in the upper quartile).

• Structure data:– More LVN hours than the median, and little LVN turnover on the staff

(lower quartile). – Unlicensed support staff use is low (lower quartile) while RN hours of

care are at the median, but the number of patients for each RN is high (upper quartile).

– The number of patients in a bed (bed turnover) on a given day is high (lots of admissions, discharges, or transfers) which would require a lot of RN time (they are very busy).

– RN turnover on the workforce is also high (perhaps the unit is too busy) and staffing is accomplished with contract or registry staff (upper quartile).

This unit likely would examine their staffing patterns as it appears to be a difficult situation for the RN workforce.



Step 6: InterpretationProcess and outcome data within the context of

these structure data:• A lot of restrained patients (upper quartile) and the

use of sitters (to prevent restraint or fall injuries) is in the lower quartile.

• Risk assessments for pressure ulcer development are only at the median, and patients at risk for pressure ulcers are not getting prevention interventions (lower quartile).

Risk assessments and determination of appropriate interventions may not be getting accomplished given the RN patterns identified.



Step 6: Interpretation• Although the percent of patients at risk for hospital acquired

pressure ulcers is low (lower quartile), this hospital is in theupper quartile for hospital acquired pressure ulcer development. These are outcomes this hospital will want to investigate further by drilling down into their data to better understand performance.

• This hospital may be doing well with fall prevention work –falls with injury are in the lower quartile. – Note that all falls are high (upper quartile) – this could be

interpreted as good reporting or as a high rate to investigate further. If this hospital has been working on a culture of safety and responsible reporting, a high fall rate may indicate success in this area (good reporting).



Step 6: Example Priorities• Opportunity to improve performance around pressure ulcer

development and use of restraint.– Use these data to set performance targets of being below the

75th percentile as a short term goal, and below the 50th percentile or median as a long range goal.

• Doing well with injury falls but set the median as a threshold for further analyses should their performance decline.

• Investigate further staffing patterns to support the high volume of patients that are admitted, discharged, or transferred into this unit daily.

• High RN staff turnover -- conduct a survey or focus group to better understand the staff’s work environment perspective. – Set a performance target to be below the median for total

voluntary staff turnover.



Using Real Data!

CALNOC Hospital Benchmarks &

3 Hospitals’ Priorities



Table 3: Outcome Benchmarks (Percentiles) Mean SD 10% 25% 50% 75% 90% All Unit Types Combined HAPU Stage 2+ 3.84 2.2 1.41 2.48 3.62 5.04 6.02 Falls per 1000 patient days 2.94 0.9 1.99 2.32 2.87 3.33 4.19 Injury Falls per 1000 patient days 0.10 0.2 0.01 0.04 0.07 0.12 0.19 Medical Surgical Units HAPU Stage 2+ 3.16 2.2 0.81 1.87 2.87 4.13 5.30 Falls per 1000 patient days 3.28 1.06 2.12 2.52 3.20 3.87 4.82 Injury Falls per 1000 patient days 0.12 0.18 0.00 0.04 0.08 0.14 0.23 Step Down Units HAPU Stage 2+ 4.30 3.4 0 1.98 3.95 5.73 7.89 Falls per 1000 patient days 2.98 1.22 1.43 2.24 2.80 3.78 4.57 Injury Falls per 1000 patient days 0.11 0.20 0.00 0.01 0.02 0.16 0.32 Critical Care Units HAPU Stage 2+ 7.79 6.7 1.9 3.92 7.11 10.17 13.89 Falls per 1000 patient days 1.06 1.25 0.06 0.5 0.8 1.27 2.22 Injury Falls per 1000 patient days 0.03 0.1 0.0 0.0 0.0 0.0 0.10

Percentiles or Quartile Data to Use



Table 4 Medical Surgical Benchmarks: Staffing Variables and Patient Characteristics (Percentiles) Mean SD 10% 25% 50% 75% 90% Staffing Total Hours of Care per Patient Day 9.21 1.3 7.72 8.37 9.16 9.93 10.90 RN Hours of Care per Patient Day 6.55 1.1 5.05 5.88 6.61 7.19 7.81 Licensed Hours of Care per Patient Day 7.03 1.0 5.76 6.42 6.98 7.68 8.28 Ratios Number of Patients per RN 3.81 0.7 3.08 3.37 3.66 4.09 4.87 Number of Patients per Licensed Staff 3.52 0.5 2.94 3.15 3.46 3.77 4.20 Skill Mix Percent of Care Hours by RN 71.51 9.5 58.84 66.11 71.80 77.69 82.05 Percent of Care Hours by LVN 5.21 6.1 0.0 0.55 3.57 7.60 12.83 Percent of Care Hours by Other Staff 23.28 8.2 13.48 18.90 22.99 29.08 33.50 Percent of Care Hours by Contract Staff 6.25 6.9 0.13 2.19 4.39 8.20 12.83 Sitter Hours as Percent of Total Care Hours 3.55 4.1 0.0 0.55 2.46 5.23 8.07 Unit & Patient Characteristics Workload Intensity as Pct of Total Pt Days 53.36 13.8 38.95 44.39 50.37 61.92 69.28 RN Voluntary Turnover 1.13 1.1 0.34 0.48 0.84 1.37 2.30 Total Voluntary Turnover 1.13 1.3 0.26 0.47 0.81 1.35 2.23 Percent Medical Patients 70.95 12.1 55.77 65.79 72.62 79.62 84.33 Patient Age 63.75 4.47 57.60 61.25 64.15 67.18 69.25 Percent Male (patient gender) 47.22 10.3 40.00 42.40 45.11 48.89 53.42



Real Hospital #1Hospital Level

05

1015

Q1 200

7Q2 2

007

Q3 200

7Q4 2

007

Q1 200

8Q2 2

008

HAPU 2+FallsInjury Falls

Medical Surgical

0

1

2

3

4

5

6

Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008


SD

012345

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


CC

010203040

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


Looks like HAPU should be the focus??? (Blue Line)



Hospital Level 10th or <

> 10th, < 25th

> 25th, < 50th

> 50th, < 75th

> 75th, < 90th

at or > 90th

Structure (Staffing):Total Hours PPD XRN Hours PPD XLicensed Hours PPD X# Patients/RN X# Patients/Licensed X% RN Hours of Care X% LVN Hours of Care X% Other Hours of Care X% Contract Hours of Care XSitter Hours XBed Turnover XRN Voluntary TurnoverTotal Voluntary TurnoverPatient Descriptors% Medical XPatient Age X% Male X

Outcomes:Falls XFalls with Injury X% Stage II + HAPU X

HAPU are fine! Falls with Injury are the issue!!!!!



Hospital Level

0

25

50

75

100% Medical

Patient Age% Male

Hospital level

0

25

50

75

100Falls

Falls with Injury% Stage II + HAPU

Falls with Injury

Older

Patients



Hospital Level

0

25

50

75

100Total Hours PPD

RN Hours PPD

Licensed Hours PPD

# Patients/RN

# Patients/Licensed

% RN Hours of Care% LVN Hours of Care



Sitter Hours

Bed Turnover



Medical/Surgical

10th or <> 10th, <

25th> 25th, <

50th> 50th, <

75th> 75th, <

90that or > 90th

Structure (Staffing):Total Hours PPD XRN Hours PPD XLicensed Hours PPD X# Patients/RN X# Patients/Licensed X% RN Hours of Care X % LVN Hours of Care X% Other Hours of Care X% Contract Hours of Care XSitter Hours XBed Turnover XRN Voluntary TurnoverTotal Voluntary TurnoverPatient Descriptors% Medical XPatient Age X% Male X




Med Surg

0

25

50

75

100% Medical

Patient Age% Male

Med Surg

0

25

50

75

100Falls


Med Surge

0

25

50

75

100Total Hours PPD

RN Hours PPD

Licensed Hours PPD

# Patients/RN

# Patients/Licensed

% RN Hours of Care% LVN Hours of Care



Sitter Hours

Bed Turnover

Med Surg uses more registry than the rest of the hospital, doesn’t use sitters, and is in the lower quartile for “other” hours to assist a

mostly RN staff.



Hospital Level

012345

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


SD

02468

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


MS

02

46

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


Real Hospital

#2

Fall rates seem to be an issue??????



Real Hospital

#2

Hospital Level 10th or <

> 10th, < 25th

> 25th, < 50th

> 50th, < 75th

> 75th, < 90th

at or > 90th

Structure (Staffing):Total Hours PPD XRN Hours PPD XLicensed Hours PPD X# Patients/RN X# Patients/Licensed X% RN Hours of Care X% LVN Hours of Care X% Other Hours of Care X% Contract Hours of Care XSitter HoursBed TurnoverRN Voluntary TurnoverTotal Voluntary TurnoverPatient Descriptors% Medical XPatient Age X% Male X

Outcomes: 10th or <> 10th, <

25th> 25th, <

50th> 50th, <

75th> 75th, <

90that or > 90th

Falls XFalls with Injury X% Stage II + HAPU X



SD

0255075

100Falls


SD

0255075

100% Medical

Patient Age% Male

SD

0255075

100Total Hours PPD

RN Hours PPD

Licensed HoursPPD

# Patients/RN

# Patients/Licensed% RN Hours ofCare

% LVN Hours ofCare

% Other Hours ofCare

% Contract Hoursof Care

Hospital fall rates impacted by Step Down Unit in 90th. Also in 90th for

registry use, LVN hours, # pts/licensed!

Drill down on the structure of care suggested.



Real Hospital #3

Hospital Level

02468

10

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


MS

05

1015

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


SD

05

1015

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


CC

02468

10

Q12007

Q22007

Q32007

Q42007

Q12008

Q22008


Falls stable. HAPU looks like the place to focus????



Step Down

10th or <> 10th, <

25th> 25th, <

50th> 50th, <

75th> 75th, <

90that or > 90th

Structure (Staffing):Total Hours PPD XRN Hours PPD XLicensed Hours PPD X# Patients/RN X# Patients/Licensed X% RN Hours of Care X% LVN Hours of Care X% Other Hours of Care X% Contract Hours of Care XSitter Hours XBed Turnover XRN Voluntary Turnover XTotal Voluntary Turnover XPatient Descriptors% Medical XPatient Age X% Male X


Falls stable but in the 90th . HAPU are also an issue.



Step Down

0

25

50

75

100Falls


Step Down

0

25

50

75

100% Medical

Patient Age% Male

Step Down

0

25

50

75

100Total Hours PPD

RN Hours PPD

Licensed Hours PPD

# Patients/RN

# Patients/Licensed

% RN Hours of Care

% LVN Hours of Care% Other Hours of Care


Sitter Hours

Bed Turnover



Poor outcomes. Focus on care delivery structure: 90th in registry use! High use of “others” and “efficient” use of Total hours

of care, RN hours of care and # pts/licensed.



Summary• You have the benchmark data AND the tools to translate

datasets into dashboards and to set performance targets and thresholds.

• Armed with the basic understanding of quartiles and percentiles, you can help provide your facility a sophisticated methodology for benchmarking: – goals for performance, – thresholds for drill-down analyses if performance is

already at the desired level, and – benchmarks for best practices from high performers.

• Dashboards can be used to create powerful visual tools to quickly inform frontline staff, operational leaders, and governing bodies on prioritized metrics.



Bibliography• Aydin C.E., Burnes B. L., Donaldson, N., Brown, D.S., Buffum, M.,

& Sandhu, M. (2004). Creating and analyzing a statewide nursing quality measurement database. Journal of Nursing Scholarship, 36(4), 371-378.

• Brown, D., Aydin, C. & Donaldson, N. (2008). Quartile Dashboards: Translating Large Datasets into Performance Improvement Priorities. Journal for Healthcare Quality, 30(6), 18-30.

• Brown, D.S., Donaldson, N., Aydin, C.E., & Carlson, N. (2001). Hospital nursing benchmarks: The California Nursing Outcome Coalition project experience. Journal for Healthcare Quality, 23(4), 22-27.

• Dawson, B., & Trapp, R.G. (2004). Basic & Clinical Biostatistics. Lange Medical Books.

• Donaldson, N., Brown, D. S., Aydin, C. E., Bolton, M. L., & Rutledge, D. N. (2005). Leveraging nurse-related dashboard benchmarks to expedite performance improvement and document excellence. Journal of Nursing Administration, 35(4), 163-172.

Documents

Back to the Basics! Dashboards, Quartiles, and Setting ... and Public Files/CalNOC_Public... · Dashboards, Quartiles, and Setting Priorities Presented on Behalf of the CALNOC TEAM