Back to the Basics! Dashboards, Quartiles, and Setting Priorities


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1 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 CoPrincipal Investigator, Collaborative Alliance for Nursing Outcomes (CALNOC) Clinical Practice Leader for Hospital Accreditation Programs Kaiser Permanente Northern California Region, Oakland, CA
2 (2008). Volume 30(6),
3 Objectives 1. 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 sets 2. 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).
4 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 hospitalacquired conditions
5 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.
6 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.
7 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!).
8 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. Drilldown data utilized to investigate performance on prioritized metrics and to drive appropriate improvement interventions.
9 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: October 15, 2007
10 Understanding Data Description Raw Data: Frequencies (the count or number of occurrences) Useful to monitor rare events using days between occurrences for zerotolerance indicators (falls, pressure ulcers, infections) OTHERWISE  little use for most other performance monitoring
11 Understanding Data Description 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 pounds which no longer describes the average weight of the group.
12 Understanding Data Description 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).
13 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.
14 Averages Can be Skewed! Acquired Pressure Ulcers Stage II+ N=110 Hospitals CC SD MS Maximum Mean Minimum Outliers included
15 SKEWED DATA Variance in Falls per 1000 Patient Days N=117 Hospitals Performance Example: How hospital data varies CC SD MS Maximum Mean Minimum Medians: CC=0, SD=2.89, MS=3.16 Outliers included
16 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 (bellshaped 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.
17 Understanding Data Spread 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).
18 Understanding Data Spread 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.
19 Understanding Data Spread Percentiles: 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.
20 Understanding Data Spread Percentile: 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.
21 Understanding Data Spread Quartiles: Uses percentiles to divide the data into 4 equal sections (fourths). Listed as three values (25 th, 50 th, 75 th ) 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 (50 th percentile). Interquartile range describes the spread of the data between the 25 th and 75 th percentiles.
22 Percentiles & Quartiles 50 th Percentile is the Median Quartiles 25% of data 25% of data 25% of data 25% of data InterQuartile Range Percentiles 0% 20% 40% 60% 80% 100% Lower Quartile Above Median Below Median Upper Quartile
23 Percentiles & Quartiles: Set Meaningful Goals or Thresholds If your service satisfaction scores are in the lower quartile (below 25 th percentile)  75% of those you are comparing with have higher satisfaction. A meaningful goal might be the 50 th 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.
24 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 interquartile range can be used when the median is used for ordinal data or with skewed numerical data. The interquartile 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.
25 Data Spread with Quartiles: Falls per 1000 Patient Days (N=163 California Hospitals) CC SD MS Q3 (75th %ile) Median (50th %ile) Q1 (25th %ile)
26 Translating Reported Data Into Quartile Dashboards
27 Six Step Process 1. Prioritization 2. Translating Performance into Quartiles 3. Creating the Dashboard 4. Consolidating to a 1Page Dashboard 5. Supporting Documentation 6. Interpretation
28 Step 1: Prioritization Narrow 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.
29 Step 1: Prioritization CALNOC Indicator Performance from Summary Statistics Process: % PU Risk Assess in 24 hours Structure (Staffing): % RN Hours of Care % LVN Hours of Care % Other Hours of Care % Contract Hours of Care % At Risk for PU % At Risk PU Prevention % Restrained % Restrained Vest or Limb Total Hours Per Patient Day # Patients Per RN Licensed Hours PPD Sitter Hours Bed Turnover RN Voluntary Turnover LVN Voluntary Turnover Total Voluntary Turnover Outcomes: Falls Falls with Injury % Hosp Acquired Ulcer % Stage II + HAPU member hospitals % Stage III + HAPU only
30 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.
31 Statistics to Create Your Dashboard Means, Standard Deviations, and Quartiles are listed as actual values (not percentiles)
32 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
33 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. Median Lower Quartile Upper Quartile
34 Step 2: Translating Performance Once quartile numeric ranges are identified, plot the facility's performance on each indicator into the worksheet. into Quartiles 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 x Total Hours Per Patient Day x # Patients Per RN x Licensed Hours PPD x Sitter Hours x Bed Turnover x RN Voluntary Turnover x LVN Voluntary Turnover x Total Voluntary Turnover x
35 Step 2: Translating Performance into Quartiles 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.
36 Worksheet 1: CalNOC Indicator Performance from Summary Statistics Step 2: Translating Performance 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 100 Total Hours Per Patient Day x 75 # Patients Per RN x 100 Licensed Hours PPD x 100 Sitter Hours x 25 Bed Turnover x 100 RN Voluntary Turnover x 100 LVN Voluntary Turnover x 25 Total Voluntary Turnover x 100 into Quartiles Assign a percentile number in the last column of the worksheet which will be used in the next step to generate dashboard graphs. 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 100 Falls with Injury x 25 % Hosp Acquired Ulcer x 100 % Stage II + HAPU x 100 % Stage III + HAPU x 75
37 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.
38 Staffing Performance in Quartiles 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 % RN % LVN % Other % Total Licensed RN LVN Total # Patients Sitter Bed Hours of Hours of Hours of Contract Hours Per Hours Voluntary Voluntary Voluntary Per RN Hours Turnover Care Care Care Hours of Patient PPD Turnover Turnover Turnover Series Quartiles
39 Step 3: Creating the Dashboard Radar 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.
40 Staffing Quartile Performance Total Voluntary Turnover % RN Hours of Care % LVN Hours of Care LVN Voluntary Turnover 50 % Other Hours of Care 25 RN Voluntary Turnover 0 % Contract Hours of Care Bed Turnover Total Hours Per Patient Day Sitter Hours # Patients Per RN Copyright CALNOC. Licensed Hours For internal PPD use by
41 Step 4: Consolidation to 1page 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!
42 Staffing Performance in Quartiles Total Voluntary Turnover LVN Voluntary Turnover Step 4: Consolidation to 1page! 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 Quartiles Nursing Process Quartile Performance Analysis Falls and Pressure Ulcer Quartile Performance Analysis % Restrained Vest or Limb % Stage III + HAPU % Restrained % Stage II + HAPU % At Risk PU Prev ention % Hosp Acquired Ulcer % At Risk for PU Falls with Injury % PU Risk Assess in 24 hours Quartile Falls Quartile
43 Step 4: Consolidation to 1page! Staffing Quartile Performance % RN Hours of Care 100 Total Voluntary Turnover % LVN Hours of Care 75 LVN Voluntary Turnover % Other Hours of Care RN Voluntary Turnover 0 % Contract Hours of Care Bed Turnover Total Hours Per Patient Day Outcomes Quartile Analysis Sitter Hours # Patients Per RN 100 Falls Licensed Hours PPD 75 Process Performance Analysis % Stage III + HAPU Falls with Injury % PU Risk Assess in 24 hours % Restrained Vest or Limb 25 % At Risk for PU 0 % Stage II + HAPU % Hosp Acquired Ulcer % Restrained % At Risk PU Prevention
44 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!
45 Staffing Quartile Performance Total Voluntary Turnover % RN Hours of Care 100 % LVN Hours of Care Desired performance Direction LVN Voluntary Turnover % Other Hours of Care RN Voluntary Turnover 0 % Contract Hours of Care Bed Turnover Total Hours Per Patient Day Sitter Hours # Patients Per RN Licensed Hours PPD Outcomes Quartile Analysis Process Performance Analysis 100 Falls % PU Risk Assess in 24 hours % Stage III + HAPU 25 Falls with Injury % Restrained Vest or Limb 25 % At Risk for PU 0 0 % Stage II + HAPU % Hosp Acquired Ulcer % Restrained % At Risk PU Prevention
46 Understanding Quartile Direction For 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 25 th percentile = bottom quarter of performance Between 25 th and 50 th percentile or Median = below average performance Between 50 th or Median and 75 th percentile = better than average performance Upper Quartile is above the 75 th percentile = in the top quarter of performance
47 Understanding Quartile Direction If low numbers are better Lower Quartile is under the 25 th percentile = in the top quarter of performance Between 25 th and 50 th percentile or Median = better than average performance Between 50 th or Median and 75 th percentile = below average performance Upper Quartile is above the 75 th percentile = bottom quarter of performance
48 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 labeled with 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.
49 Step 6: Interpretation The 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.
50 Step 6: Interpretation Using the CALNOC example, the following interpretation might be drawn (note that this dashboard is NOT rescaled for desired performance always 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.
51 Step 6: Interpretation Process 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.
52 Step 6: Interpretation Although the percent of patients at risk for hospital acquired pressure ulcers is low (lower quartile), this hospital is in the upper 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).
53 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.
54 Using Real Data! CALNOC Hospital Benchmarks & 3 Hospitals Priorities
55 Table 3: Outcome Benchmarks (Percentiles) Mean SD 10% 25% 50% 75% 90 % All Unit Types Combined Percentiles or Quartile Data to Use HAPU Stage Falls per 1000 patient days Injury Falls per 1000 patient days Medical Surgical Units HAPU Stage Falls per 1000 patient days Injury Falls per 1000 patient days Step Down Units HAPU Stage Falls per 1000 patient days Injury Falls per 1000 patient days Critical Care Units HAPU Stage Falls per 1000 patient days Injury Falls per 1000 patient days
56 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 RN Hours of Care per Patient Day Licensed Hours of Care per Patient Day Ratios Number of Patients per RN Number of Patients per Licensed Staff Skill Mix Percent of Care Hours by RN Percent of Care Hours by LVN Percent of Care Hours by Other Staff Percent of Care Hours by Contract Staff Sitter Hours as Percent of Total Care Hours Unit & Patient Characteristics Workload Intensity as Pct of Total Pt Days RN Voluntary Turnover Total Voluntary Turnover Percent Medical Patients Patient Age Percent Male (patient gender)
57 Real Hospital #1 Hospital Level Medical Surgical HAPU 2+ Falls Injury Falls HAPU 2+ Falls Injury Falls Q Q Q Q Q Q Q Q Q Q Q Q Looks like HAPU should be the focus??? (Blue Line) SD CC Q Q Q Q Q1 Q HAPU Falls 10 Injury Falls 0 Q1 Q2 Q3 Q Q Q HAPU 2+ Falls Injury Falls
58 Hospital Level > 10th, < > 25th, < > 50th, < > 75th, < at or > 10th or < 25th 50th 75th 90th 90th Structure (Staffing): Total Hours PPD X RN Hours PPD X Licensed 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 X Sitter Hours X Bed Turnover X RN Voluntary Turnover Total Voluntary Turnover Patient Descriptors % Medical X Patient Age X % Male X HAPU are fine! Falls with Injury are the issue!!!!! Outcomes: Falls Falls with Injury % Stage II + HAPU X X X
59 Hospital level Falls Falls with Injury Hospital Level % Stage II + HAPU Falls with Injury % Medical Older Patients % Male Patient Age
60 Hospital Level Bed Turnover Total Hours PPD RN Hours PPD Sitter Hours Licensed Hours PPD % Contract Hours of Care 0 # Patients/RN % Other Hours of Care # Patients/Licensed % LVN Hours of Care % RN Hours of Care
61 Medical/Surgical 10th or < > 10th, < 25th > 25th, < 50th > 50th, < 75th Outcomes: Falls X Falls with Injury % Stage II + HAPU member X hospitals only > 75th, < 90th Structure (Staffing): Total Hours PPD X RN Hours PPD X Licensed 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 X Sitter Hours X Bed Turnover X RN Voluntary Turnover Total Voluntary Turnover Patient Descriptors % Medical X Patient Age X % Male X at or > 90th X
62 Med Surge Med Surg Sitter Hours % Contract Hours of Care Bed Turnover Total Hours PPD RN Hours PPD Licensed Hours PPD # Patients/RN % Medical % Other Hours of Care # Patients/Licensed % Male Patient Age % LVN Hours of Care % RN Hours of Care 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. Med Surg Falls % Stage II + HAPU Falls with Injury
63 Hospital Level Real Q Q Q Q Q Q HAPU 2+ Falls Injury Falls MS Hospital #2 Fall rates seem to be an issue?????? SD Q Q Q Q Q Q HAPU 2+ Falls Injury Falls Q Q Q Q HAPU 2+ Falls Injury Falls Q1 Q Copyright 2008 CALNOC. For internal use by
64 Hospital Level > 10th, < > 25th, < > 50th, < > 75th, < at or > 10th or < 25th 50th 75th 90th 90th Structure (Staffing): Total Hours PPD X RN Hours PPD X Licensed 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 X Sitter Hours Bed Turnover RN Voluntary Turnover Total Voluntary Turnover Patient Descriptors % Medical X Patient Age X % Male X Real Hospital #2 Outcomes: 10th or < Falls Falls with Injury % Stage II + HAPU X > 10th, < 25th > 25th, < 50th > 50th, < 75th > 75th, < 90th X X at or > 90th
65 SD % Stage II + HAPU % Male 100 Falls SD % Medical Falls with Injury Patient Age % Contract Hours of Care % Other Hours of Care % LVN Hours of Care % RN Hours of Care SD Total Hours PPD RN Hours PPD Licensed Hours PPD # Patients/RN # Patients/Licensed Hospital fall rates impacted by Step Down Unit in 90 th. Also in 90 th for registry use, LVN hours, # pts/licensed! Drill down on the structure of care suggested.
66 Real Hospital #3 Hospital Level MS Q Q Q Q Q Q HAPU 2+ Falls Injury Falls Q Q Q Q Q Q HAPU 2+ Falls Injury Falls Falls stable. HAPU looks like the place to focus???? SD CC Q Q Q Q Q Q HAPU 2+ 8 Falls 6 4 Injury Falls 2 0 Q1 Q2 Q3 Q4 Copyright CALNOC. For 2007 internal 2007use 2007 by 2007 Q Q HAPU 2+ Falls Injury Falls
67 Step Down > 10th, < 25th > 25th, < 50th > 50th, < 75th > 75th, < 90th 10th or < Structure (Staffing): Total Hours PPD X RN Hours PPD X Licensed 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 X Sitter Hours X Bed Turnover X RN Voluntary Turnover X Total Voluntary Turnover X Patient Descriptors % Medical X Patient Age X % Male X Falls stable but in the 90 th. HAPU are also an issue. at or > 90th Outcomes: Falls X Falls with Injury % Stage II + HAPU X X
68 Step Down Falls 100 Step Down Total Voluntary Turnover Total Hours PPD 100 RN Hours PPD RN Voluntary Turnover 50 Licensed Hours PPD 25 % Stage II + HAPU Falls with Injury Bed Turnover 0 # Patients/RN Step Down % Medical 100 Sitter Hours # Patients/Licensed % Contract Hours of Care % Other Hours of Care % LVN Hours of Care % RN Hours of Care % Male 25 0 Patient Age Poor outcomes. Focus on care delivery structure: 90 th in registry use! High use of others and efficient use of Total hours of care, RN hours of care and # pts/licensed.
69 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 drilldown 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.
70 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), Brown, D., Aydin, C. & Donaldson, N. (2008). Quartile Dashboards: Translating Large Datasets into Performance Improvement Priorities. Journal for Healthcare Quality, 30(6), 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), 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 nurserelated dashboard benchmarks to expedite performance improvement and document excellence. Journal of Nursing Administration, 35(4),
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