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48 Cards in this Set
- Front
- Back
- 3rd side (hint)
Find 63% of 94
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59.22
.63 x 94 convert % to decimal then multiply |
tape 6
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Changing
Fraction to % |
divid 1 by 2 x 100
1/2 = .05 x 100 = 50% |
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Changing
Decimal to % |
multiply decimal by 100
0.29 x 100 = 29% |
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Changing % to a Fraction
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eliminate % sign x the # by 1/100.
5% - 5 x 1/100 = 5/100 15% - 15 x 1/100 = 15/100 |
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Changing % to a Decimal
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76% = .76
4% = . 04 104% = 1.04 eliminate the % & place decimal pt 2 places to the left. |
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Descriptive Statistics
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used to explain data in manageable and easily understood ways.
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CH 10 - tape 7
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Rank
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Denotes a scores position in a group relative to others.
Letting u know ur position in a group |
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Percentile
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dividing data into 100 = parts
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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. |
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3 Measure of Central Tendency
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Mean, Median, Mode
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Mean
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Average
used most often |
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Median
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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 |
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Mode
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most reoccuring value
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Census
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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. |
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Daily IP Census
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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.
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The difference btwn the census & the IP daily census is that any paitents admitted & discharged the same day are added to:
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a. The census to compute the daily census
b. The midnight (or other designated time) head count to dompute the daily census. |
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Because they are a unit of measure, the data used for most census computation are:
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Total inpatient service days
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The time for taking the IP census must always be
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consistent
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At census taking time, a patient who has been transferred into a unit is
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Counted where he or sh came from
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The inpatient census at midnight is 50. one patient was admitted @ 1 pm & died @ 3:15. The IP service days for that day are
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51
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Bed Count
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The # of IP beds both occupied & vacant on any given day.
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Bed Count Day
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a unit of measure denoting the presence of 1 IP bed (either occupied or vacant) set up & staffed for use in 24 hr period
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Total Bed count Days
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The sum of IP bed count days for ea. of the days in the period.
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Bed Occupancy Ratio
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Important Indicater for hosp financial position
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if beds r not full they r not functioning @ their capacity.
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Length of Stay LOS
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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. |
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 |
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Utilization Management
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A program that evaluates the healthcare facility's efficiency in providing necessary care to patients in the most effective manner.
Its all about efficiency |
tape 9
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Avg. LOS
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...
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Discharge Days
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...
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Total Length of Stay
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is the sum of the days stay of any group of IP discharged during a specific period of teim
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DRG - Diagnosis Related Group
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they look @ the LOS perdiagnosis
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Death Rate
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The proportion of IP hospitalization that end in death.
___ is indication of Quality u have. Usually in percentage |
CH 6
Mortality synomous w / Death |
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Death is a type of
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discharge or disposition
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Postoperative Death Rate
or Surgical DR |
# 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 |
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Categorical
2 types |
Nominal
Ordinal |
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Categorical
Nominal |
types of data that fall into
Unordered Categories Ec. Male/female, true/false, blood type |
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Categorical
Ordinal |
represents values that can be
Ordered Categories, can be put in order categories. |
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Numerical Data
2 types |
Discrete Date
Continuous Data |
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Numerical Data
Discrete |
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 |
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Numerical Data
Continuous Data |
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. |
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Constant
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its value that never changes
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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.
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Variable
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factor that assumes different values
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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.
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Qualitative data
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data that does not have numerical values. U are looking @ the quality
Ex. Could b ur diagnosis, age, sex |
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Quantitative Data
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data that has numerical values. Quanity
Ex. # of births or deaths, weight, |
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Population
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the set of all possible causes to be studied.
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Sample
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a group selected from the population.
It has to be fair, there has to be enough to show the true picture. |
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.
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Randon Sample
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is a selection from the population in which all have an equal chance of being selected.
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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.
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Data
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reveals patterns & behavior
presented in a form of table or graph |
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Table
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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 |
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Graph
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present data fro a quick visualization of relationships
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