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06 - absite > chapter 44: statistics and patient safety > Flashcards

chapter 44: statistics and patient safety Flashcards

1
Q

rejects null hypothesis incorrectly -> falsely assumed there was a difference when no difference exists

A

type 1 error

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2
Q

rejects null hypothesis incorrectly -> falsely assumed there was a difference when no difference exists

A

type 1 error

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3
Q

accepts null hypothesis incorrectly because of small sample size -> the treatments are interpreted as equal when there is actually a difference

A

type 2 error

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4
Q

hypothesis that no difference exists between groups

A

null hypothesis

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5
Q

p value that rejects the null hypothesis

A

p

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6
Q

p value: > 95% likelihood that the difference between the populations is true

A

p

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7
Q

likelihood that the difference is not true and occurred by chance alone with p

A
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8
Q

spread of data around a mean

A

variance

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9
Q

population

A

parameter

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10
Q

most frequently occurring value

A

mode

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11
Q

average

A

mean

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12
Q

middle value of a set of data

A

median

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13
Q

prospective study with random assignment to treatment and non treatment groups

A

randomized controlled trial (avoids treatment biases)

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14
Q

prospective study in which patient and doctor are blind to the treatment

A

double-blind controlled trial

- avoids observational bias

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15
Q

prospective study -> compares disease rate between exposed and unexposed groups (nonrandom assignment)

A

cohort study

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16
Q

retrospective study in which those who have the disease are compared with a similar population who do not have the disease; the frequency of the suspected risk factor is then compared between the 2 groups

A

case-control study

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17
Q

combining data from different studies

A

meta-analysis

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18
Q

2 independent groups and variable is quantitative -> compares means (mean weight between 2 groups)

A

student’s t test

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19
Q

variable is quantitative; before and after studies (e.g. weight before and after, drug versus placebo)

A

paired t tests

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20
Q

compares quantitative variables (means) for more than 2 groups

A

ANOVA

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21
Q

compare categorical (qualitative) variables (race, sex, medical problems and diseases, medications)

A

nonparametric statistics

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22
Q

compares 2 groups with categorical (qualitative) variables (number of obese patients with and without diabetes versus number of non obese patients with and without diabetes)

A

chi-squared test

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23
Q

small groups -> estimates survival

A

Kaplan-Meyer

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24
Q

incidence in exposed / incidence in unexposed

A

relative risk

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25
Q

probability of making the correct conclusion = 1 - probability of type 2 error

  • likelihood that the conclusion of the test is true
  • larger sample size increases power of a test
A

power of test

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26
Q

number of people with disease in a population (Eg number of patents in US with colon CA)
- long-standing disease increases prevalence

A

prevalence

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27
Q

number of new cases diagnosed over a certain time frame in a population (e.g. number of patients in the US newly diagnosed with colon CA in 2003)

A

incidence

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28
Q

ability to detect disease = true-positives/(true-positives+false-negatives)
- indicates the number of people who have the disease who test positive

A

sensitivity

with high sensitivity, a negative test result means patient is very unlikely to have disease

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29
Q

ability to state no disease is present = true-negatives/(true-negatives + false-positives)
- indicates the number of people who do not have the disease who test negative

A

specificity

with high specificity, a positive test result means patient is very likely to have disease

30
Q

true-positives / (true-positive + false-positive)

- likelihood that with a positive result, the patient actually has the disease

A

positive predictive value

31
Q

true-negatives / (true-negatives + false-negatives)

- likelihood that with a negative result, the patient does not have the disease

A

negative predictive value

32
Q

depends on disease prevalence

A

predictive value

33
Q

depends on disease prevalence

A

predictive value

34
Q

accepts null hypothesis incorrectly because of small sample size -> the treatments are interpreted as equal when there is actually a difference

A

type 2 error

35
Q

hypothesis that no difference exists between groups

A

null hypothesis

36
Q

p value that rejects the null hypothesis

A

p

37
Q

p value: > 95% likelihood that the difference between the populations is true

A

p

38
Q

likelihood that the difference is not true and occurred by chance alone with p

A

less than 5%

39
Q

spread of data around a mean

A

variance

40
Q

population

A

parameter

41
Q

most frequently occurring value

A

mode

42
Q

average

A

mean

43
Q

middle value of a set of data

A

median

44
Q

prospective study with random assignment to treatment and non treatment groups

A

randomized controlled trial (avoids treatment biases)

45
Q

prospective study in which patient and doctor are blind to the treatment

A

double-blind controlled trial

- avoids observational bias

46
Q

prospective study -> compares disease rate between exposed and unexposed groups (nonrandom assignment)

A

cohort study

47
Q

retrospective study in which those who have the disease are compared with a similar population who do not have the disease; the frequency of the suspected risk factor is then compared between the 2 groups

A

case-control study

48
Q

combining data from different studies

A

meta-analysis

49
Q

2 independent groups and variable is quantitative -> compares means (mean weight between 2 groups)

A

student’s t test

50
Q

variable is quantitative; before and after studies (e.g. weight before and after, drug versus placebo)

A

paired t tests

51
Q

compares quantitative variables (means) for more than 2 groups

A

ANOVA

52
Q

compare categorical (qualitative) variables (race, sex, medical problems and diseases, medications)

A

nonparametric statistics

53
Q

compares 2 groups with categorical (qualitative) variables (number of obese patients with and without diabetes versus number of non obese patients with and without diabetes)

A

chi-squared test

54
Q

small groups -> estimates survival

A

Kaplan-Meyer

55
Q

incidence in exposed / incidence in unexposed

A

relative risk

56
Q

probability of making the correct conclusion = 1 - probability of type 2 error

  • likelihood that the conclusion of the test is true
  • larger sample size increases power of a test
A

power of test

57
Q

number of people with disease in a population (Eg number of patents in US with colon CA)
- long-standing disease increases prevalence

A

prevalence

58
Q

number of new cases diagnosed over a certain time frame in a population (e.g. number of patients in the US newly diagnosed with colon CA in 2003)

A

incidence

59
Q

ability to detect disease = true-positives/(true-positives+false-negatives)
- indicates the number of people who have the disease who test positive

A

sensitivity

with high sensitivity, a negative test result means patient is very unlikely to have disease

60
Q

ability to state no disease is present = true-negatives/(true-negatives + false-positives)
- indicates the number of people who do not have the disease who test negative

A

specificity

with high specificity, a positive test result means patient is very likely to have disease

61
Q

true-positives / (true-positive + false-positive)

- likelihood that with a positive result, the patient actually has the disease

A

positive predictive value

62
Q

true-negatives / (true-negatives + false-negatives)

- likelihood that with a negative result, the patient does not have the disease

A

negative predictive value

63
Q

true-positives + true-negatives / true-positives+true-neg+false-pos+false-neg

A

accuracy

64
Q

depends on disease prevalence

A

predictive value

65
Q

are independent of prevalence

A

sensitivity and specificity

66
Q

seeks to collect outcome date to measure and improve surgical quality in the US. outcomes are reported as observed vs expected ratios

A

National Surgery Quality Improvement Program (NQSIP)

67
Q

JCAHO prevention of wrong site/procedure/patient protocol

A
  • preop verification of patient and procedure
  • operative site and side
  • time out before incision is made (verifying patient, procedure, position site + side, and availability of implants or special requirements)
68
Q

promoting culture of safety

A
  • confidential system of reporting errors
  • emphasis on learning over accountability
  • flexibility in adapting to new situations or problems
69
Q

risk factors for retained object after surgery (MC sponge)

A

emergency procedure, unplanned change in procedure, obesity, towel used for closure

70
Q

unexpected occurrence involving death or serious injury, or the risk thereof; hospital undergoes root cause analysis to prevent and minimize future occurrences (Eg wrong site surgery)

A

sentinel event (JCAHO)

71
Q

GAP protection technique

A

gaps in care (shift change, etc) can lead to loss of information and error. prevention - structured handoffs and checklists (face to face if possible; standardizing orders; reading back orders if verbal

06 - absite (44 decks)

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