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Predictive analytics for patient wait-time management, and on-time clinical
workflow
Oleg S. Pianykh, PhD
Medical Analytics Group Department of Radiology, Massachusetts General Hospital
Harvard Medical School
Chief Analytics Officer October 2017, Boston
Outline
• Assessing the importance of patient wait-time management and predictability.
• Creating wait-time models.
• Lessons learned in predicting wait-time and impact on hospital efficiency. Queuing bottlenecks.
• Implementation challenges: culture changes, best interventions, and organic growth of data-driven solutions.
Oleg Pianykh [email protected] https://www.chiefanalyticsofficerforum.com/seminar/keynote-presentation-harnessing-analytics-personalisation-precision-health-applications/
Big Data market hype
Oleg Pianykh [email protected]
Big Data in healthcare: Hype and reality
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PubMed publications with "Big Data"
Big Data on paper: exponential growth (wow!)
Big Data in reality: “business as usual”
Oleg Pianykh [email protected]
Predicting healthcare workflow
Oleg Pianykh [email protected]
Important timelines to predict in healthcare
• Length of stay
• Discharge time
• Patient wait
• Survival time, clinical prognosis
• Recurrence time (cancer etc.) and probability
… and many more
Oleg Pianykh [email protected]
Benefits of predictability
• One can run hospital operations much better knowing what to expect. Resources can be allocated more appropriately and ahead of time, expected bottlenecks can be avoided or dealt with more efficiently.
• All workflow participants (patients, staff, management) are less stressed and more satisfied when events are predictable and everything goes “as planned”
• Workflow predictability, in essence, is synonymous to successful workflow management. You cannot manage unpredictable.
Oleg Pianykh [email protected]
Problem: Patient wait time
• Patient wait time was shown to be the #1 satisfaction factor for patient experience in a hospital.
Oleg Pianykh [email protected] http://www.hhnmag.com/articles/6417-the-push-is-on-to-eliminate-hospital-wait-times
Problem: Patient wait time
Oleg Pianykh [email protected]
Wait time W Exam length EL
Time on the floor F
Mining wait time data
• Q: How do we find patient wait time ?
Oleg Pianykh [email protected]
W = Tb - Ta
Patient Accession Arrival Ta Begin Tb Complete Resource Exam Tech
123456 E33445 2016-08-15 14:23:45
2016-08-15 15:03:09
2016-08-15 15:19:45
MR1 MRKNEE John Smith
… … … … … … … …
Patient wait line size
• Q: How do we find the number of patients waiting for exams at any given time T ?
Oleg Pianykh [email protected]
Number L of patients such that Ta < T < Tb
Patient Wait Time: Changing patterns
Oleg Pianykh [email protected]
Afternoon drop, Th & Fr
Lunch drop
Worst hour 10:30-11:30 AM
Number of patients waiting in line: average (black) and
worst (blue) cases
“The doctor will be with you momentarily”…
Oleg Pianykh [email protected]
Patients waiting for different exam types
• Facing the reality: noise/randomness, temporal trends, different patterns in W values…
• It is obvious that static prediction (“10 minutes on average”) won’t work
Oleg Pianykh [email protected] 0
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Patient timeline
Wait times for the first 500 X-ray patients
“The doctor will be with you momentarily”…
How do we predict the future wait?
Oleg Pianykh [email protected]
Main goal: let’s develop a practical solution
• Scenario: • A new patient walks into a waiting room. The most frequently question
he/she will ask when checking in would be “How long will it take?”. Can we answer it intelligently?
• Most frequent (and the most misleading) answer: “The doctor will be with you momentarily”. Then, after some 47 minutes of waiting...
• We know that the wait for the patient arriving at T=Ta time can be found as W=Tb-Ta. We can also use BD to find some other information about the patients, exams, waiting lines. Can this help?
Oleg Pianykh [email protected]
How would you predict patient wait?
• Q: What predictors would you use? • How many?
• Which ones are the best?
• Q: What model would you prefer?
Oleg Pianykh [email protected]
Features = ?
Model = ?
Predicting with basic linear models
• Predictors: anything we can get from our HIS! p1, p2, p3, p4, p5, …
• Model: Linear regression W = c0 + c1p1 + c2p2 +c3p3 +c4p4 + c5p5 + … = [I P] × C
(where I is a vector of 1, “intercept”)
• Optimal/important predictors – let the model decide!
Oleg Pianykh [email protected]
“State of the Art”: very simple predictions
1. p1 = “previous patient wait time”, c0 = 0, c1= 1, c2 = 0, then
- “waiting the same as the previous patient”
2. p1 , p2 = “previous patient wait time”, c0 = 3 (time to fill patient form), c1 = c2 = 1/2, then
- “filling the form, then waiting the same as the average of the previous two patients”
Oleg Pianykh [email protected]
W = c0 + c1p1 + c2p2 + … = p1
W = c0 + c1p1 + c2p2 + … = 3 + (p1+p2)/2
Can we do better than this?
Predictors: The more, the merrier
Oleg Pianykh [email protected]
Let the model decide!
Oleg Pianykh [email protected]
Prediction error
Predictor count (model size) Highest
impact Highest accuracy
Small models – great for understanding Large models – might provide better accuracy
Let the model decide!
Oleg Pianykh [email protected]
Small model size: understanding
Best model: W = c0 + c1L1 + c2L2 +c3L3, where L1 , L2 and L3 are the line sizes – number of patients waiting now, 5 minutes ago, and 10 minutes ago
Use stepwise regression to reduce model size
Predicting patient wait time
• Not bad for a simple model:
Oleg Pianykh [email protected]
No filtering!
ML vs simple linear model: X-Ray case
Oleg Pianykh [email protected]
ML vs simple linear model: CT case
Oleg Pianykh [email protected]
ML in predicting patient wait
• More complex model:
Oleg Pianykh [email protected]
No filtering!
Real wait
Predicted wait Patient timeline
Del
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Patient wait time: Action
• Patient wait time displays in waiting rooms
• Patient satisfaction
• Staff paying more attention to workflow
88% of patients: “We love those displays, you should have them in all hospital rooms”
Lessons learned
Oleg Pianykh [email protected]
Principal lessons learned
• Own your data
• Stay engaged
• Value negative feedback
• Look for impact
Oleg Pianykh [email protected]
Data is everything
10-min bins
5-min bins
1-min bins
What’s going on??
Oleg Pianykh [email protected]
Challenges
Oleg Pianykh [email protected]
Randomness
STD = 0.27×Average Longer scans are harder
to schedule !
If anything can go wrong it will
Oleg Pianykh [email protected]
Uneven workload
Exams completed, hourly pattern
Oleg Pianykh [email protected]
What operational decisions can you make based on this chart?
Data silos
Oleg Pianykh [email protected]
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ScheduledToFirstImageTime
Scheduling discipline • Let’s visualize time from the scheduled exam start (when we want it
to start) to the first scanned image: d = Ti-Ts
Oleg Pianykh [email protected]
We start late
It gets only worse We run
completely behind
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Scheduling discipline: Starting the first exam on time
• Trying to start on time:
Oleg Pianykh [email protected]
We start on time
We start late
Using the right metrics: Old approach
M: “Average MRI scan should take 45 minutes”
D: “Currently, it takes 50 minutes”
C: “Shorten MRI protocols by 10%”
Metrics Data Compliance
Using the right metrics: New approach:
P: “Patients have to wait 3 weeks for MRI scans”
Da: “Poor scheduling and lack of resources”
S: “Improve scheduling, optimize resources”
Problem Data analysis Solution
metrics (distance to success)
Putting Big Data to work
Action
Big Data
Patterns
Logic
Oleg Pianykh [email protected]
Iterate until proven success!
Conclusions
• Patterns are everything
• You have to identify the underlying logic which creates certain patterns
• You have to convert this logic into actions to improve your system
• You can use data to verify the success of your actions
Oleg Pianykh [email protected]
Action
Data
Patterns
Logic
Roll up your sleeves!
Oleg Pianykh [email protected]
Oleg Pianykh [email protected]
Extra slides
Oleg Pianykh [email protected]
Big Data in Healthcare applications
• We all are getting extremely used to the “Big Data” buzzwords. But what does it mean, practically?
• Larger data volume?
• Better data?
• More diverse data?
• … ?
Oleg Pianykh [email protected]
Big vs. Small: Patterns !
Oleg Pianykh [email protected]
Exam Duration Time
• Pattern: Unusually frequent exam durations
• Logic: Invalid input (rounding time to multiples of 5 or 10). Can we confirm this?
• Action: What can we do?
Oleg Pianykh [email protected]
1-min bins
Data validity analysis: Action
Oleg Pianykh [email protected]
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Tech Bias Ratio in CT and MRI
MRI CT
Honest
Zero-bias
Tempted
Champs
Data validity analysis: Results
Oleg Pianykh [email protected]
Predicting healthcare
• Q: Can we use BD to predict the future? How? Examples: Stock market predictions, finance, weather forecasting etc. heavily rely on dynamic predictive models. How about healthcare?
• Q: Why and what would one like to predict in healthcare?
Oleg Pianykh [email protected]
Example: Google Flu project
• Predicting flu outbreaks in real time from… search engine data:
Oleg Pianykh [email protected] Source: Detecting influenza epidemics using search engine query data Jeremy Ginsberg1, Matthew H. Mohebbi1, Rajan S. Patel1, Lynnette Brammer2, Mark S. Smolinski1 & Larry Brilliant1
Google-predicted
Real
ILI – “Influenza-like illness”