Calculating & Reporting Healthcare Statistics

Front 1

Find 63% of 94

Back 1

59.22

.63 x 94

convert % to decimal then multiply

Hint 1

tape 6

Front 2

Changing

Fraction to %

Back 2

divid 1 by 2 x 100

1/2 = .05 x 100 = 50%

Front 3

Changing

Decimal to %

Back 3

multiply decimal by 100

0.29 x 100 = 29%

Front 4

Changing % to a Fraction

Back 4

eliminate % sign x the # by 1/100.

5% - 5 x 1/100 = 5/100

15% - 15 x 1/100 = 15/100

Front 5

Changing % to a Decimal

Back 5

76% = .76
4% = . 04
104% = 1.04

eliminate the % & place decimal pt 2 places to the left.

Front 6

Descriptive Statistics

Back 6

used to explain data in manageable and easily understood ways.

Hint 6

CH 10 - tape 7

Front 7

Rank

Back 7

Denotes a scores position in a group relative to others.

Letting u know ur position in a group

Front 8

Percentile

Back 8

dividing data into 100 = parts

Hint 8

Put in order 1rst
find out what score u were asked to look @, find out where it ranks what # it is in the list.
-take & divide by the total # of scores.

Front 9

3 Measure of Central Tendency

Back 9

Mean, Median, Mode

Front 10

Mean

Back 10

Average

used most often

Front 11

Median

Back 11

Midpoint of the values

*if u have a even # of scores & not a true middle score (Ex. on pg 145) u add then up divide by 2

Front 12

Mode

Back 12

most reoccuring value

Front 13

Census

Back 13

indicates the # of IP present in a healthcare facility @ any given time.

It is a important indicator for the volume of activity in a healthcare facility.

Front 14

Daily IP Census

Back 14

the # of IP present @ census-taking time ea. day, Plus any patients who were both admitted after the previous census-taking time & discharged before the next census taking time.

Front 15

The difference btwn the census & the IP daily census is that any paitents admitted & discharged the same day are added to:

Back 15

a. The census to compute the daily census

b. The midnight (or other designated time) head count to dompute the daily census.

Front 16

Because they are a unit of measure, the data used for most census computation are:

Back 16

Total inpatient service days

Front 17

The time for taking the IP census must always be

Back 17

consistent

Front 18

At census taking time, a patient who has been transferred into a unit is

Back 18

Counted where he or sh came from

Front 19

The inpatient census at midnight is 50. one patient was admitted @ 1 pm & died @ 3:15. The IP service days for that day are

Back 19

51

Front 20

Bed Count

Back 20

The # of IP beds both occupied & vacant on any given day.

Front 21

Bed Count Day

Back 21

a unit of measure denoting the presence of 1 IP bed (either occupied or vacant) set up & staffed for use in 24 hr period

Front 22

Total Bed count Days

Back 22

The sum of IP bed count days for ea. of the days in the period.

Front 23

Bed Occupancy Ratio

Back 23

Important Indicater for hosp financial position

Hint 23

if beds r not full they r not functioning @ their capacity.

Front 24

Length of Stay LOS

Back 24

The total # of patient days for an IP episode, calculated by subtracting the date of admission from the date of discharge

This is a judge of how efficient a Institution is.

Hint 24

Ex. for a patient admited on Jan 30 & discharged on Feb 4

subtract Jan 30 from Jan 31 & add 4 days in Feb

(31 - 30 = 1 + 4 days in Feb = 5 days

Front 25

Utilization Management

Back 25

A program that evaluates the healthcare facility's efficiency in providing necessary care to patients in the most effective manner.

Its all about efficiency

Hint 25

tape 9

Front 26

Avg. LOS

Back 26

...

Front 27

Discharge Days

Back 27

...

Front 28

Total Length of Stay

Back 28

is the sum of the days stay of any group of IP discharged during a specific period of teim

Front 29

DRG - Diagnosis Related Group

Back 29

they look @ the LOS perdiagnosis

Front 30

Death Rate

Back 30

The proportion of IP hospitalization that end in death.

___ is indication of Quality u have.

Usually in percentage

Hint 30

CH 6

Mortality synomous w / Death

Front 31

Death is a type of

Back 31

discharge or disposition

Front 32

Postoperative Death Rate

or Surgical DR

Back 32

# of death occurring after an operation has been performed.

within 10 days

total # of deaths x 100
divided
total # of patients who were operated on for the period

Front 33

Categorical

2 types

Back 33

Nominal

Ordinal

Front 34

Categorical

Nominal

Back 34

types of data that fall into

Unordered Categories

Ec. Male/female, true/false, blood type

Front 35

Categorical

Ordinal

Back 35

represents values that can be

Ordered Categories, can be put in order categories.

Front 36

Numerical Data

2 types

Back 36

Discrete Date

Continuous Data

Front 37

Numerical Data

Discrete

Back 37

Finite #'s that represent distinct values, Its restricted to whole #'s.

is always classified as Quantitative Data cause its #'s

Ex. # of children, # of women

Front 38

Numerical Data

Continuous Data

Back 38

continuous of values, can be in a franctional value, decimals.

Is always classified as Quantitative Data cause its #'s.

Ex. temperature 101.5, Age, Height 5 ft 10 1/2 inches.

Front 39

Constant

Back 39

its value that never changes

Hint 39

Ex. Patients that were discharged w/diagnosis of MI, that is the Constant that will never change, my study, Im only going 2 look @ people that have the diagnosis of MI, that stays the same.

Front 40

Variable

Back 40

factor that assumes different values

Hint 40

Ex. Take that same study of patients, ur only going to look @ patients w/diagnosis of MI, the variable could be their sex,if the smoke or not, age. What stays the same is the MI, what changes is what is different about the patients.This is what makes the study important, especially something like the smoking that is something that always sticks out.

Front 41

Qualitative data

Back 41

data that does not have numerical values. U are looking @ the quality

Ex. Could b ur diagnosis, age, sex

Front 42

Quantitative Data

Back 42

data that has numerical values. Quanity

Ex. # of births or deaths, weight,

Front 43

Population

Back 43

the set of all possible causes to be studied.

Front 44

Sample

Back 44

a group selected from the population.

It has to be fair, there has to be enough to show the true picture.

Hint 44

U are doing a study w/a lot of patients U will do a sample, no hosp or healthcare institution has the time staff, or the money to take a look @ every case. Theory is if U look @ a good same U will find out if there are any problems vs look @ entire bit. There are issues w/samples, U have to b careful U get a wide selection of the population, cant just pick everyone that was diagnosed or discharged in 1 month. U cant just look @ the same Dr. make sure U are not just looking @ 1 set or age group depending on ur study.

Front 45

Randon Sample

Back 45

is a selection from the population in which all have an equal chance of being selected.

Hint 45

This can be done in many ways, ask ur computer to spit out all the patients #’s of everybody that has diagnosis of MI. U would randomly take every 5th case. Put #’s in hat & pick out. If U do this way what might happen is U have 2 many of 1 Doc & not enough of somebody else. U got to make sure U pick from all possible. If U do a study U got 2 make sure it is fair sample, random.

Front 46

Data

Back 46

reveals patterns & behavior

presented in a form of table or graph

Front 47

Table

Back 47

summarizes data

an organized arrangement of values that groups data in columns & rows

almost any kind of # info can be grouped..

More info can be presented

Front 48

Graph

Back 48

present data fro a quick visualization of relationships