Week 2 Flashcards
CQL
clinical quality language
CQL stands for Clinical Quality Language, an open-source standard that allows users to describe clinical quality and rules in a way that can be understood by both humans and machines. CQL is used to communicate complex healthcare information, such as patient conditions, interventions, and eligibility. It’s designed to help health professionals make informed decisions by bridging the gap between raw healthcare data and meaningful insights
eCQM
electronic clinical quality measures
tools that use data from electronic health records (EHRs) and health information technology (IT) systems to assess the quality of healthcare.
CDS(S)
clinical decision support (systems)
value stream mapping
a visual guide that shows all components needed to deliver a product/service
process mapping
a high-level flowchart which showsactions anddecisions through time.
Swimlane mapping
performed when you want to illustrate a single process that involves more than two roles simultaneously across time.
a type of flowchart that delineates who does what in a process. Using the metaphor of lanes in a pool, a swimlane diagram provides clarity and accountability by placing process steps within the horizontal or vertical “swimlanes” of a particular employee, work group or department.
Institute of Medicine’s (IOM) definition of quality healthcare
The Institute of Medicine (IOM) defines quality of healthcare as the extent to which health services improve the likelihood of desired health outcomes and are consistent with current professional knowledge. The IOM also identifies six domains of healthcare quality, which are used to evaluate and improve healthcare services:
Effectiveness, Efficiency, Equity, Patient centeredness, Safety, and Timeliness.
RCT
randomized control trial
A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied.
systematic review
the overview of several randomized trials of the same intervention or treatment for the same situation or condition; this overview systematically and critically reviews and combines all the studies, providing a better answer than the results from just one study
Low acuity
a medical term that refers to a condition or injury that is urgent but not immediately dangerous and doesn’t require emergency care.
Fast track in primary care / emergency room
A fast track system in an emergency room treats patients who require less care, which can help reduce wait times and overcrowding. Fast track patients are those who don’t need urgent care or devices like monitors in their treatment rooms.
Scarlatiniform rash
The scarlet fever rash first appears as tiny red bumps on the chest and abdomen that may spread all over the body. Looking like a sunburn, it feels like a rough piece of sandpaper, and lasts about two to five days.
Vividness effect heuristic
A heuristic where probability is increased based on how vividly it is described or emotionally evocative recalling it is.
Cognitive Heuristic
A mental shortcut for solving a problem, producing an aproxímate solution
aka Rule of Thumb
Availability heuristic
The availability heuristic is a mental shortcut that influences people’s perceptions of reality by causing them to overestimate the likelihood of events that are easy to recall. It’s also known as availability bias.
Representativeness heuristic
A mental shortcut that introduces estimate bias by increasing probability based on the target appearing to be representative of a group.
Representativeness heuristics are biased judgments made in everyday life. An example of a representativeness heuristic is thinking that because someone is wearing a suit and tie and carrying a briefcase, that they must be a lawyer, because they look like the stereotype of a lawyer.
Test Sensitivity of a diseased population
Sensitivity of a diseased population is the percentage of that population that will test positive
p[positive test|D] = the probability of a positive test given the patient is diseased.
Test specificity in a diseased population
The test specificity of a diseased population is the percentage of that population without the disease that will test negative.
p[negative test|no disease] = the probability that there is a negative test result for a patient that does not have the disease.
aka TNR - true negative ratio
Prevalence (aka prior probability)
The probability of a disease before a test.
aka the frequency of a disease/condition in a population
Positive predictive value, PPV, posterior probability
The PPV is the probability of an disease after a positive test is observed.
Negative predictive value, NPV, or posterior probability of no disease
The negative predictive value (NPV) is the probability of no disease after a negative test is observed
Liklihood ratio (LR)
LR+ vs LR-
A calculation that estimates how much a test result changes the probability of a patient having a disease
LR+ = Seleectivity / (1-Specificity)
LR- = (1-Selectivity) / Specificity
selectivity / (1 - specificity) =
LR+ aka the positive likely ratio
Odds of disease
Can be expressed by the formula
Odds(prior) = prevalence / (1- prevalence)
For diseases with lower prevalence there are lower odds of disease.
For diseases with higher prevalence there are higher odds of disease.
Odds ratio of Baye’s formula
Odds(post) = odds(prev) x LR+
Or the odds of a positive test given the presence of disease is the odds of disease times the positive liklihood ratio which is selectivity / (1 - specificity). Similarly the negative likelihood ratio, corresponding to the negative predictive value is specificity / (1-selectivity)
prior / pretest probability
The estimated probability before more information is obtained. Sometimes called Prevalence
conditional probability
The probability that event A occurs given event B = p(A|B)
anchoring and adjustment heuristic.
A type of heuristic error where a clinician fails to correctly adjust probability of a diagnosis hypothesis favoring staying closer to the anchor or initial probability than is statistically appropriate..
clinical subgroup
clinical subgroups can help set probabilities for disease in a population and are defined by symptoms / findings
clinical prediction rules
sets of definitions on how clinicians can use combinations of clinical findings to estimate probability from systematic studies of patients with a specific diagnosis.
referral bias
this effects published studies done in specialists office and occurs because patients often do not end up in a specialists office without referral, and a referral comes from a primary care physician who suspects the patient of having an issue that the specialist deals with.
CEA
the carcinoembryonic antigen
A blood test released in the early 70’s that claimed to have high sensitivity and specificity in detecting colon cancer but was later proved to be useless.
spectrum bias
a bias that is introduced in testing when the study population includes only people on the far end of the spectrum (aka very sick) and healthy patients.
This often occurs in the early stages of developing a test.
test referral bias
This occurs when a positive test is required to approve a gold standard test. This means that TN, and FN populations will not receive the gold standard test. This will increase the TPR and lower the TNR of the gold standard test.
ROC vs Summary ROC curve
Receiver Operating Characteristic curve
The plot of sensitivity by 1 - specificity results in this curve. The diagonal line represents random chance and the area under the curve to the diagonal line represents the quality of the test. A test with more area under the ROC curve is a more accurate test. A standard ROC curve uses data from 1 study.
A summary ROC curve is a result of meta-analysis of multiple studies that quantitatively combines the estimates from individual studies.
Bayes’ theorem for a positive test
p(D|+) = (PrevalenceSensitvity) / ((PrevalenceSensitivity)+((1-Prevalence)*(1-Specificity)))
Bayes’ theorem for a negative test
p(-D|-) = (Specificity(1-Prevalence)) / (Specificity(1-Prevalence) + (Prevalence* (1-Selectivity)))
