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@enricocoiera
The rhythms of the hospital
How data analytics is helping us to understand the drivers of
clinical processes
:: time and care
•Temporal patterns are core to clinical medicine
•We use these patterns to disambiguate differential
diagnoses, detect co-morbidities, predict most likely next
event in a sequence
•Patient level patterns:
Time ordering of events in a patient history
Dynamic signals e.g. ECG, arterial pressure
•Population level patterns:
Unfolding rates of infectious outbreaks, seasonal
mortality.
(AIHW Bulletin, 2, 2002)
(AIHW Bulletin, 2, 2002)
:: health services have temporal
patterns too
Why study patterns of health service delivery?
Allocation of scare resources:
•Optimise day to day resource allocation
•Assist in longer term workforce and resource planning
Improve safety and quality of care:
•Identify when we are not providing the services our
patients need
•Minimize avoidable harms due to mismatch between
allocation and need
Three data detective stories …..
6
:: Case study 1 - Follow-up of
test orders
•Not every test that is ordered is followed up
•In US, between 20-61% of inpatient tests not followed up(Callen et al. BMJ Qual Saf. 2011;20(2):194-9)
•Failure to follow-up test results accounts for 45% of US
diagnosis-related malpractice cases.(Gandhi et al. Ann Intern Med. 2006;145(7):488-96.)
•Many results are clinically significant.
•Why? Poor systems? Busy clinicians? Poor training?
:: study: patterns in failure to
follow up
•Study setting: 370-bed metropolitan teaching hospital. Lab
order entry implemented for all path tests.
•Data: All 664,643 inpatient path and micro tests between
Feb and June 2011. Time stamps for test orders, posting,
and first test result view.
•Internal medicine and surgery accounted for 63.4% and
33% respectively of all inpatient tests. ED 3%.
:: results
•Of 664,643 tests, 3.2% not reviewed at the time of
discharge (n=21,141), 1.5% 2 months post discharge (n =
10,166).
•40.3% of inpatients had one or more results not reviewed
at discharge (n=2717) , 28.7% 2 months post discharge
(n=1932).
•Of unreviewed tests, 20.5% outside normal range at
discharge, 10.6% 2 months post discharge.
•Unreviewed tests had a similar level of abnormal results
as the reviewed tests
:: But why?
Possible hypotheses:
Unnecessary tests ordered, so not followed up (efficiency
problem, $ wasted)
Necessary tests ordered but then somehow missed (clinical
quality and safety problem) e.g.
A failure to be notified of test’s existence?
Too many clinical tasks?
Information received by other channels?
…..
:: Testing the hypotheses
• What can we do with observational data that helps
hypothesis testing?
•Look for temporal signatures that support one hypothesis
over another e.g. forever ignored unnecessary tests that
are part of an admission panel c.f. necessary tests with
delayed follow up
•The workflow hypothesis: test follow up rates are a
function of the way work unfolds over time
•Test follow up thus should be a function of time available
for review during an admission i.e. p(follow-up) = f (LOS)
46.8% of all unreviewed tests ordered on
the day of discharge
At day of discharge, 21.4% of tests ordered not
followed up compared to 1.9% of tests ordered
on other days (p<0.001).
Archives of Internal Medicine, 2012;172(17):1347-1349
:: how can we improve test follow-up?
•Tests ordered on the day of discharge seem to have
very limited chance of review.
•Temporal pattern suggests a focus on discharge
planning and post-discharge follow up:
- Discharge planning should shape test ordering
- Better use of electronic alerts:
at time of order (e.g. n days before result
available)
to trigger post-discharge follow-up
- Enabling patients to assist follow up by:
Informing them of pending tests e.g. alert, PHR
Encourage them to seek GP follow up
14
:: Case study 2 – cause of the
weekend effect
A higher rate of death following weekend admission to
hospital compared to weekday admission.
Possible causes:
•Selection bias: cohort of patients admitted on weekends
different (e.g. sicker and older) compared to weekdays.
•Quality of weekend services: lower staffing levels, locum
staff, unavailability of tests or procedures.
•In many studies ED and ICU in major hospitals seem
relatively protected from the weekend effect as many run a
similar service across all days.
:: study: weekly patterns in
death rates
•Study setting: Emergency department admissions to all
501 hospitals in New South Wales, Australia, between 2000
and 2007 were linked to the Death Registry and analysed.
•Data: There were a total of 3,381,962 admissions for
539,122 patients and 64,789 deaths at 1 week after
admission.
•We computed excess mortality risk curves for weekend
over weekday admissions, adjusting for age, sex,
comorbidity (Charlson index) and diagnostic group.
:: results
•Weekends accounted for 27.1% of all admissions (917
257/3 381 962) and 28.2% of deaths (18 282/64 789).
•Adjusted mortality rates: weekday 1.85% (95% CI 1.85%
to 1.85%), weekend 2.12% (95% CI 2.12% to 2.12%)
(difference 0.27%, p<0.001).
•Sixteen of 430 diagnosis groups (DRGs) had a
significantly increased risk of death following weekend
admission. They accounted for 40% of all deaths.
F70 (Major Arrhythmia and
Cardiac Arrest).
E61 (Pulmonary Embolism)
E64 (Pulmonary Oedema and
Respiratory Failure)
F65 (Peripheral Vascular
Disorders)
I65 (Connective Tissue
Malignancy, including
Pathological Fracture)
R60 (Acute Leukemia)
R61 (Lymphoma and Non-Acute
Leukaemia)
B02 (Craniotomy)
B67 (Degenerative Nervous
System Disorders)
B70 (Stroke and Other
Cerebrovascular Disorders)
E71 (Respiratory Neoplasms)
F62 (Heart Failure and Shock)
G60 (Malignancy)
H61 (Malignancy of Hepatobiliary
System, Pancreas)
J62 (Malignant Breast Disorders)
L60 (Renal Failure).
:: results (2)
s
d w
c
s = severity effectc = care effectd = delay in care effectw = washout of care effect
excess(t) = pweekend(t) − pweekday(t)
Exce
ss R
isk
of
dea
th
t
Care Same Care Different
Co
ho
rt D
iffe
ren
t
Co
ho
rt S
ame
t
Ris
k o
f d
eath
H0 H1
H2 H3
t
Ris
k o
f d
eath
t
Ris
k o
f d
eath
t
Ris
k o
f d
eath
Major Arrhythmia and Cardiac Arrest
Pulmonary Embolism,Pulmonary Oedema and Respiratory Failure, Peripheral Vascular Disorders
Connective Tissue Malignancy, Acute Leukemia, Lymphoma and Non-Acute Leukaemia
Malignant Breast Disorders, Respiratory Neoplasms, Malignancy of HepatobiliarySystem, Pancreas, Craniotomy, Stroke, Heart Failure and Shock, Renal Failure
:: what weekend effect patterns
say about causation
•Pure care effect for Myocardial infarction i.e. may be due
to variation in care e.g. unavailability of specialist staff,
imaging or stenting services.
•Risk washout e.g. PE, pulmonary oedema. Acute events
requiring access to high quality immediate care, but with
less abrupt risk of mortality. Those who survive first 48 hrs
fare better when re-exposed to weekday care.
•Cancer patients dominated the steady risk pattern.
Possibly cancer patients with more severe illness are
admitted on the weekend e.g. when community care can
no longer manage them.
The end of the story?
• Temporal signatures seem a promising tool in exploring
causation in observational data.
• Classic biomedical signal analysis methods and skills,
long used in patient monitoring, have a clear role in
health analytics
• Classic errors in temporal signal analysis may however
lead to missing the big picture e.g. aliasing
25
26
t
t
Samplerate=2f
Samplerate=4f
f
t
Samplerate=f
t
Samplerate=1.8f
The rate at which a
continuous signal is
sampled determines
the frequency and
shape of the
waveform we recover.
The sampling rate
needs to be twice the
highest frequency
component of a signal
to avoid recovering a
lower frequency alias
(Nyquist rate).
Decision Support at the point-
of-care
Automated ToolsComputed in real-timeUpdated in real-time
Routinely collected DataCollected in real-timeUpdated in real-time
:: Case study 3 – predicting
in-hospital disposition
Forecasting patient trajectoriesWill a patient be: in hospital, at home or dead in the next week?
We simultaneously predict the probability of discharge, readmission and
death for each of the next 7 days, throughout the patient’s hospitalisation.
Average AUC per day per outcome class=0.8 (Death AUC=0.9)
ED
87 years old male
Arrives by ambulance
Triage: Urgent
ED
4 hours in hospital
8 panels of tests
High Bilirubin
Low Albumin
Low Sodium
Low Chloride
High Creatinine
Low eGFR
High CRP
High APTT
ICU
7 hours in hospital
12 panel tests
Low RBC
Low Haemoglobin
Low Haematocrit
Low Platelets
High WBC
High Neutrophils
High Creatinine
Low eGFR
Geriatrics
52 hours in hospital
16 panel tests
Low RBC
Low Haemoglobin
Low Haematrocrit
High WBC
High CreatininePatient arrives to ED
Initial prediction
New information available
Updated prediction
Forecasting patient trajectoriesWill a patient be: in hospital, at home or dead in the next week?
0
0.2
0.4
0.6
0.8
1
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Pro
ba
bil
ity
B. Expected Discharge
Home
Hospital
Death
Discharge
Cai et al, JAMIA 2015, DOI: http://dx.doi.org/10.1093/jamia/ocv110
Forecasting patient trajectoriesWill a patient be: in hospital, at home or dead in the next week?
0
0.2
0.4
0.6
0.8
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Pro
bab
ilit
y
D. Expected Death
Home
Hospital
Death
Death
Cai et al, JAMIA 2015, DOI: http://dx.doi.org/10.1093/jamia/ocv110
Some conclusions
•Data analytics is often a detective story – we can’t solve the
crime without having hunches to test against data.
•Temporal signatures help our thinking move from ‘mere’
association, to exploring causation in the observational data.
•We should be very careful not to jump to decisions that change
services, because it is still observational data, and the system it
comes from is complex and dynamic.
•Predictive models are surprisingly accurate for variables such as
in hospital death.
•Analysis is not decision making. Just because we calculate a
probability for an event does not mean we know how to act on that
information.
33
References:
Ong M et al. Last Orders - Follow-up of tests ordered on the day of
hospital discharge. Archives of Internal Medicine, 2012;172(17):1347-
1349.
Perez-Concha et al. Do variations in hospital mortality patterns after
weekend admission reflect reduced quality of care or different patient
cohorts? A population-based study. BMJ Quality and Safety,
2014;23:215-222.
Coiera E et al. Predicting the cumulative risk of death during
hospitalization by modeling weekend, weekday and diurnal mortality
risks. BMC Health Services Research 2014, 14:226
Cai X, Perez-Concha O, Coiera E, Martin-Sanchez F, Day R, Roffe D,
Gallego B. Real-time prediction of mortality, readmission, and length of
stay using electronic health record data. JAMIA (2015).
