There is a MOOC (Massive On-line Open Course) focused on patient safety
taught by Peter Pronovost and colleagues-it is free.
https://www.coursera.org/#taught by Peter Pronovost and colleagues-it is free.
Wednesday, May 29, 2013
Tuesday, May 28, 2013
Clinical Research Oriented Workshop (CROW) Meeting: May 23, 2013
Present: Abby Crocker, Kairn Kelley, Rodger Kessler, Ben
Littenberg, Connie van Eeghen
1.
Start Up: Ben’s
dad is turning 89 – hooray!
2.
Journal Article
Review Kairn: Discussion of binomial model in 1978 journal article by Thornton
Raffin “Speech Discrim Scores Modeled As Binomial Variable”
a. Article
focuses on a specific test: W22; Kairn uses a similar test (single syllable
words): NU6
b. One
part of the article is focused on adjusting the confidence limits of this use
of a binomial distribution. There are
several ways to do this transformation; the various methods aren’t in complete
agreement with each other but they’re close.
c. The
article does not use a standard binomial distribution; it uses a transformation
to account for distributions that can’t be normally shaped because their means
are close to extreme values (i.e. close to 0 or 100). Per Abby, the appendix does a good job
illustrating how the transformation variable theta is derived.
d. These
adjusted confidence intervals estimate a range of uncertainty but don’t
indicate whether 2 scores are different.
To figure this out, we need to know the proportions of the two scores (#
right/total), the difference between them, and the standard deviation of the
difference. Abby explained this as the
combined, pooled estimate, which is described here as the square root of the
sum of each “pooled” proportion complement, each divided by sample size. Or, SQRT((PQ/N1)+(PQ/N2)) as an Excel
formula.
e. We
tested this out manually and then validated with a chi square and Fisher’s
Exact on Stata. It worked! The point of the article, though, was that
the binomial distribution needs to be adjusted for test results at the extreme
(when a tester gets all or none of the responses right). In these cases, the distribution is skewed
because the number of wrong responses can’t be less than 0 or greater or 100.
f. Abby
went on to show us the shape of the binomial distribution and how it changes
using our proportion estimates, mirroring the distribution we had been working
on. The distribution will look like a
normal model for probabilities that are not close to the extreme ends of the
scale.
g. Abby
pointed out that when the small proportions are very small (i.e. the denominator
is very large or incalculable), the preference is to use the Poisson
distribution, rather than a very skewed binomial distribution.
h. The
reason this discussion is important is because many audiologists don’t have a
strong understanding of probability and it could be possible to interpret
changes in test scores incorrectly. This
work can both help develop strong research methods and help develop a method
for communicating the results clearly to audiologists. Use the chi square for the difference between
two results and use a transformation (a table or sin arc formula) to the normal
model to create confidence intervals.
a.
Thursday, May
30: (no Abby) LAST THURSDAY MEETING Ben:
Journal article by Feldman, 2013, “Impact of Providing Fee Data on Laboratory
Test Ordering, JAMA
b.
Wednesday, June
5: NOTE: New summer schedule will start: Wednesdays, 11:30 – 1:00. Abby: Journal article (no Connie)
c.
Wednesday, June
12: Marianne: Feedback on ideas for web site that will help CTS students
(faculty, and fellows too) find/know /access, evaluate/apply content and
literature they need for courses and research
d.
Thursday, June
13: 2:00 – 3:30 Wilson Pace, Director & Dave West from the largest practice
based research networks from American Academy of Family Physicians; access to
Medicaid data base. Kairn, Abby, Charlie
can all make it.
e.
Wednesday, June
19:
f.
June 26:
g.
July 3:
h.
July 10: Marianne: Review of literature review
i.
July 17:
j.
July 24:
k.
July 31:
l.
August 7
m.
August 14
n.
August 21
o.
August 28
p. Future
agenda to consider:
i.
Abby and Charlie: data analysis of Exploration of
analytical plan for Natural History of Acute Opioid Use
ii.
Peter Callas or other faculty on multi-level modeling
iii.
Charlie MacLean: demonstration of Tableau; or Rodger’s
examples of Prezi
iv.
Journal article: Gomes, 2013, Opioid Dose and MVA in
Canada (Charlie)
v.
Ben: Tukey chapter reading assignments, or other book
of general interest
vi.
Summer plan: each week, one person will send out an
article or prezi ahead for review or discussion by all. Alternatively,
if a participant is working on a key document for their professional
development, this is also welcome (e.g. K awards, F awards, etc.)
Tuesday, May 21, 2013
Clinical Research Oriented Workshop (CROW) Meeting: May 16, 2013
Present: Marianne Burke, Abby Crocker, Kairn Kelley, Rodger
Kessler, Ben Littenberg, Charlie MacLean, Connie van Eeghen
1.
Start Up: Project
review for GIM-Research – to be posted on SharePoint, when we set up a new
site.
2.
Presentation: Connie:
Feedback on poster presentation on “Quick Turns in Tight Places: Implementing
Change in Small Practices,” to be presented at Academy Health at the end of
June in Baltimore. Comments:
a. Focus
on the audience: predominately researchers rather than clinicians. They will be more interested in the method of
implementation than in the practice strategies for managing opioid
prescriptions.
b. Organize
the poster to visually support this: the process flow steps go top and center,
not the strategies. Shrink the
strategies, remove the “universal” part of the title as this is not
self-explanatory to this group and does not add to the discussion.
c. Change
the color scheme in the process flow: using yellow on the one least used step
makes it look like the most important step.
d. Add
an “Intervention” section to highlight the process flow; remove this language
from Methods
e. Modify
the objectives to match this focus.
f. Streamline
the discussion to focus on the points about the intervention.
g. Adjust
the title to reflect all of the above.
Yes, it’s been accepted under the current title, but as long as the
first five words stay the same, it won’t confuse the audience. Add the
department name.
h. (Took
a half hour – Efficiency is Us!)
3.
Manuscript Review:
Connie: Introduction and Methods sections of manuscript with working title:
“Integrating Behavioral Health using Workflow (Lean)”
a. As
with the above poster, it is important to emphasize the key purpose of the
study. In this case, with submission
planned for JGIM, the readers are clinicians with a keen interested in the
topic of integrating behavioral health (BH), with less interest/prior
understanding of Lean. So this is a
story about integration, its impact, and what the participants (providers and
staff) thought about the method of integration, which happens to be Lean.
b. Lots
of helpful rewording, with clearer language; not listed here.
c. Discussion
of whether we should add 18 months of data and re-analyze: the P value is about as good as it can get,
so more data are not needed to improve power.
And in the 3-4 months it would take to re-do, we likely wouldn’t have
gained much. Keep as is.
d. Debated
identifying the practice. At this point,
no.
e. Next
step: rewrite, with Results & Discussion, and ask for more comments. Thank you!
a.
May 23: Kairn: Discussion of binomial model in 1978 journal
article by Thornton Raffin “Speech Discrim Scores Modeled As Binomial Variable”
(no Marianne)
b.
May 30: (no
Abby)
c.
June 5: NOTE: New summer schedule will start: Wednesdays,
11:30 – 1:00. Abby: Shared learning on
the NAMCS, DAWN, NHANES and KID databases?
d.
June 12: Marianne: Feedback on ideas for web site that
will help CTS students (faculty, and fellows too) find/know /access,
evaluate/apply content and literature they need for courses and research
e.
June 19:
f.
June 26:
g.
July 3:
h.
July 10: Marianne: Review of literature review
i.
July 17:
j.
July 24:
k.
July 31:
l.
4 Wednesdays in August
m. Future
agenda to consider:
i.
Abby and Charlie: data analysis of Exploration of
analytical plan for Natural History of Acute Opioid Use
ii.
Peter Callas or other faculty on multi-level modeling
iii.
Charlie MacLean: demonstration of Tableau
iv.
Journal article: Gomes, 2013, Opioid Dose and MVA in
Canada (Charlie)
Wednesday, May 15, 2013
Clinical Research Oriented Workshop (CROW) Meeting: May 9, 2013
Present: Marianne Burke, Abby Crocker, Kairn Kelley, Amanda
Kennedy, Ben Littenberg, Charlie MacLean, Connie van Eeghen
1.
Start Up: Connie
reported back from a poster presentation in Denver; great opportunity to meet
new colleagues
2.
Presentation: Charlie
MacLean: Exploration of analytical plan for Natural History of Acute Opioid Use
a. Key
points from the 2009 Boudreau article that Amanda found in her lit review: Trends in De-facto Long-term Opioid Therapy
for Chronic Non-Cancer Pain (CNCP)
i.
CONSORT study of adults enrolled in two health plans serving
> 1% of US population (4m)
ii.
Automated health plan data to construct episodes of
opioid use 1997-2005; incidence & prevalence; rates of change
iii.
Long term episode: >90 days with 120+ days’ supply or
10+ opioid rx in one year
iv.
Results: incident long term use increased from 8.5 to
12.1/1000 (Group Health) with 6% annual change (PCA) and 6.3 to 8.6/1000
(Kaiser Permanente) with 5.5 PCA
1. Small
increases observed in overall use of opioids: 2.2 PCA and 0.83 PCA
2. Prevalent
long term use almost doubled: 8.1 PCA and 8.6 PCA
3. Incident
long term use lasted > 1 year for 75% of episodes
v.
Non-Schedule II opioids most common and increased in
long term use
1. MEq
dose was stable for incident long term opioid users
vi.
Regular users of sedative hypnotics: 28.6% (GH) and
30.2% (KPNC)
vii.
Conclusion: increasing prevalence; need to study
benefits and risks. Need to study
concurrent use of opioids and sedative-hypnotics, which were unexpectedly
common
b. Amanda
provide a draft introduction and her search strategy.
i.
Target population: adults getting their first dose of
opioids with the intention of short term management.
c. Research
Question: what proportion of opioid naïve adult patients started on an opioid
become long term users? What are the
characteristics of use? What are the
predictors of long term use?
i.
Previous study (Boudreau, above) provides definitions
for “long term,” although we may not be able to determine “days’ supply.” Can be estimated and evaluated with a
sensitivity analysis. We can look only at
first to last claim, not scripts.
ii.
Can be constructed as a survival analysis, with the
“survivors” being those who continue to generate additional claims.
d. Ben
& Abby sketched out statistical analyses
i.
Kaplan-Meier survival analysis
ii.
Logistic regression based on whether or not patient
became long term/chronic, with variables of interest:
1. Should
cancer-related opioid use be included?
Literature is separate. We cannot
easily determine patients considered terminally ill and long term consequences
of opioid use for this population is not a great concern. May be identifiable
by provider or treatments. Retain the
data and examine: does the inclusion of cancer patients make a difference? Plan
to stratify on cancer patients.
2. Eliminate
non-opioid patients.
3. Eliminate
non-opioid medications as inclusion criteria; keep as covariates. Include all opioids, including non-Schedule
II.
4. Classify
each episode as acute or chronic.
5. Collect
predictive data: age, sex, nearness of provider (zip code), prior episodes,
prior rx, prior chronic episodes, recent diagnoses (low back pain, headache),
other med use, prescriber (specialty: PCP, ED, other; buprenorphine
prescribers; methadone prescribers; chronicity index-preference for prescribing
opioids), length of days of dose, dosage, insurer, co-morbid mental health
conditions (anti-depressant use, diagnosis within past six months… but keep
benzo’s separate), history of substance use disorder (previous SA claims), zip
code linked to median income and education; out-of-state pharmacy (mail or
picked up) based on pharmacy zip code
6. Drug
index: was the initial drug a long or short acting formulation?
7. Hospital
initiation: Will not see hospital prescriptions but will include hospital
claims. 96 hours post-hospital filled rx
is likely to be a hospital-initiated drug.
iii.
Charlie: how do the prescribers cluster in prescribing
patterns or preferences
e. Specification
for data report
i.
One record for each claim, linking medication data and
patient data: Charlie and Abby over the next two weeks (on May 17th)
1. Steve
Kappel this morning released a 5 gig data base for Charlie to review: all
prescriptions from all prescribers; not the other claims: Charlie will import
and clean
ii.
Test data for face validity BEFORE excluding any claims
from the study
1. Clean
in STATA: Abby and Charlie
2. Run
descriptive analyses – in CROW: Abby and Charlie
iii.
Meet with Neil S on validation plan (Abby to join
biweekly Tuesday meetings)
1. Request
IRB permission for validation process
2. Validation
process needed to confirm that patient identifiers are unique (not shared with
other patients ) and not duplicated (only one per patient). May need to cross-validate with other data
sources, under supervision by the IRB.
a. Use
four years of data to build predication model; test model on data from year 5 –
or hold out 20% of data, to avoid problems with a time trend – or both
b. One
time related issue is the policy decision to avoid prescribing NSAIDs to
elderly (over 65) in order to avoid stomach and kidney problems, resulting in
greater use of opioids
iv.
Analysis – CROW session TBD
3.
Summer sessions:
consider Wed 11:30 – 1:00 starting June 5.
Will ask Sylvie for help in figuring this out.
a.
May 16: Connie: Feedback on poster presentation on
“Quick Turns in Tight Places: Implementing Change in Small Practices” and the
Introduction and Methods sections of manuscript with working title: “Integrating
Behavioral Health using Workflow (Lean)”
b.
May 23:
c.
May 30:
d.
June 5: New summer schedule will start
e. Future
agenda to consider:
i.
Christina Cruz, 3rd year FM resident with
questionnaire for mild serotonin withdrawal syndrome?
ii.
Peter Callas or other faculty on multi-level modeling
iii.
Charlie MacLean: demonstration of Tableau
iv.
Journal article: Gomes, 2013, Opioid Dose and MVA in
Canada (Charlie)
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