Chief Analytics Officer Fall USA 2017 - Oleg Pianykh - Massachussets General Hospital

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 opiany@gmail.com https://www.chiefanalyticsofficerforum.com/seminar/keynote-presentation-harnessing-analytics-personalisation-precision-health-applications/

Big Data market hype

Oleg Pianykh opiany@gmail.com

Big Data in healthcare: Hype and reality

1980 1985 1990 1995 2000 2005 2010 2015

PubMed publications with "Big Data"

Big Data on paper: exponential growth (wow!)

Big Data in reality: “business as usual”

Predicting healthcare workflow

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

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.

Problem: Patient wait time

• Patient wait time was shown to be the #1 satisfaction factor for patient experience in a hospital.

Oleg Pianykh opiany@gmail.com http://www.hhnmag.com/articles/6417-the-push-is-on-to-eliminate-hospital-wait-times

Problem: Patient wait time

Wait time W Exam length EL

Time on the floor F

Mining wait time data

• Q: How do we find patient wait time ?

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 ?

Number L of patients such that Ta < T < Tb

Patient Wait Time: Changing patterns

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”…

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 opiany@gmail.com 0

1 51 101 151 201 251 301 351 401 451 501

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?

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?

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?

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!

“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”

W = c0 + c1p1 + c2p2 + … = p1

W = c0 + c1p1 + c2p2 + … = 3 + (p1+p2)/2

Can we do better than this?

Predictors: The more, the merrier

Let the model decide!

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!

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:

No filtering!

Predicting patient wait time: ML approach

No filtering!

ML vs simple linear model: X-Ray case

ML vs simple linear model: CT case

ML in predicting patient wait

• More complex model:

No filtering!

Real wait

Predicted wait Patient timeline

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

Principal lessons learned

• Own your data

• Stay engaged

• Value negative feedback

• Look for impact

Data is everything

10-min bins

5-min bins

1-min bins

What’s going on??

Challenges

Randomness

STD = 0.27×Average Longer scans are harder

to schedule !

If anything can go wrong it will

Uneven workload

Exams completed, hourly pattern

What operational decisions can you make based on this chart?

Culture

10-min bins

5-min bins

1-min bins

What’s going on??

Data silos

Data quality

6 8 10 12 14 16 18 20 22 24

Scheduled to First Image, minutes

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

We start late

It gets only worse We run

completely behind

6 8 10 12 14 16 18 20 22 24

Scheduled to First Image, minutes

ScheduledToFirstImageTime ScheduledToFirstImageTime_14

Scheduling discipline: Starting the first exam on time

• Trying to start on time:

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

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

Action

Patterns

Roll up your sleeves!

Extra slides

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?

• … ?

Big vs. Small

Patterns!

Avg. length = 2

STD=0.3

Couple of stats

Big vs. Small: Patterns !

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?

1-min bins

Data validity analysis: Action

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83

Tech Bias Ratio in CT and MRI

MRI CT

Honest

Zero-bias

Tempted

Champs

Data validity analysis: Results

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?

Example: Google Flu project

• Predicting flu outbreaks in real time from… search engine data:

Oleg Pianykh opiany@gmail.com 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

ILI – “Influenza-like illness”

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