Analyzing Performance Improvement

Question 1

data aggregation

Answer 1

pool data in 1 place, collect performance indicators, helps see big picture

Question 2

benchmarking

Answer 2

comparison of data, common expectations/standards

Question 3

internal benchmarking

Answer 3

org performance against itself over time, stricter

Question 4

external benchmarking

Answer 4

compare one org to a group or org collecting data on the same measures in the same way- same scale

Question 5

types of data

Answer 5

qualitative: nominal, ordinal
quantitative : discrete, continuous

Question 6

nominal data

Answer 6

categorical data, values assigned to name specific category
ex: gender, inpt/outpt
bar/pie charts

Question 7

ordinal data

Answer 7

ranked data
compare evaluation of various characteristics and value relative to each other
ex: pain scale, stages of disease
how respondents feel about issue
bar/pie charts

Question 8

discrete data

Answer 8

numerical values represents whole numbers
ex: # of children in family

bar graphs

Question 9

continuous data

Answer 9

assumes infinite number of possible values, decimal values
ex: weight, BP, temp
histogram

Question 10

sampling

Answer 10

sufficient # of observations can be predictive of overall configuration of data
not efficient to collect every single occurrence - too frequent
ex:
30 pts pop –>use all cases
pop greater than 596 –> use 120 cases

Question 11

key performance indicators

Answer 11

types of data most important for org, balanced look
clinical quality –> adverse events, mortality
financial viability –> net revenues, growth
customer loyalty –> staff loyalty, pt satisfaction
operational effectiveness –> staff efficiency, readmission rate,

Question 12

data sources

Answer 12

MR, admin database, pt surveys, adverse drug rxn reports, incident reports, performance evaluations, infection surveillance, JC surveys

Question 13

when data aggregated, what happens

Answer 13

data loses context and usefulness
ex: average of test is high, but there could be individuals issues overlooked

used as report card, won’t pinpoint what needs to be fixed

Question 14

bundled data

Answer 14

overall report, lose context, begin to pinpoint issue

Question 15

unbundled data

Answer 15

break down data to show the differences, and pinpoint where action needs to be

Question 16

Qualitative QI tools

Answer 16

brainstorming
set priorities
maintain direction
determine problem causes
clarify process
present ideas in useful form

Question 17

quantitative QI tools

Answer 17

measure performance
collect, display data
monitor performance
present performance measurement in useful form

Question 18

fishbone/ishikawa diagram

Answer 18

breaks down problem, does not determine root cause, gives options

Question 19

5 whys

Answer 19

asking multiple times will identify root cause, the more whys, the harder it gets to answer

Question 20

run chart

Answer 20

view performance trends over time
ex: breast pts in imaging center

reveal interesting structure present
averages can help avoid overreacting

Question 21

bar graph

Answer 21

compares relative size of various data categories

Question 22

histogram

Answer 22

graphical representation that is used to describe single set of continuous data- bell curve

Question 23

scatter diagram

Answer 23

graphical representation used to determine relationships between quantitative variables of interest
ex: correlation

Question 24

pie charts

Answer 24

relationship of each part to the whole

Question 25

pareto chart

Answer 25

80-20 rule (focus on 20%, improve by 80%)
prioritize problem solving on vital few
assumes quantity = importance (not always true)
prevent shifting problem to other causes
ongoing measurement of progress

Question 26

radar chart

Answer 26

display importance categories of performance
defines full performances for each category
shows gaps between current and full performance

Question 27

where do dashboards pull data from

Answer 27

EHR

Question 28

variation

Answer 28

how it currently works vs how it should work

Question 29

process inputs

Answer 29

ppl, method, machine, measurement

source of variation, always changing, reflected in output

Question 30

types of statistics

Answer 30

descriptive, inferential

Question 31

why is it better to use median than mode

Answer 31

outliers can skew the mean

Question 32

numerical methods under descriptive analytics

Answer 32

measures of central location - mean, median, mode
measures of variability - range, SD, interquartile range

Question 33

control chart

Answer 33

determine unstable/stable process
contains median of upper control, low control (3 SD away from mean)

turn bell curve on its side

distinguish special and common causes of variation

Question 34

predictive analytics

Answer 34

make future prediction about key performance measures
AI

Question 35

SD

Answer 35

spread of values, how far off from mean

Question 36

bell curve

Answer 36

1 SD- 68%
2 SD- 96%
3 SD- 99.7

tall- tight process
wide- unstable process

Question 37

absolute frequency

Answer 37

number of times that a score or value occurs int he data set, denoted by f1

Question 38

relative frequency

Answer 38

percentage of time that the characteristic score appears in data set

Question 39

considered a special cause when

Answer 39

one or two points outside of UCL or LCL
or
two out three successive values are on same side of centerline/3 SD away from mean