Evaluating Predictive Analytics for Capacity Planning

HIC 2015Andrae Gaeth

Predictive analytics is the practice of extracting information from existing data sets, and then applying various techniques (eg, statistical, modelling) in order to determine patterns and predict future outcomes and trends.

How can we practically evaluate and use predictive analytics solutions for capacity planning within health?

What is predictive analytics?

Paradigm Shift Required for Analytics Maturity

Sources:

Competing on Analytics, Tom Davenport

Gartner IT Glossary

What happened?

What will happen?

What should I do?

Why did it happen?

What is happening?

Descriptive

Predictive

Prescriptive

Real Time

Descriptive

What to look for with Capacity Planning

1. Manages multiple planning horizons

– Multi-year, annual, 6-8 week scheduling periods, weeks, days and hours

2. Continuously updates forecasts

3. Forecasts patient demand with consistently high absolute accuracy vs. stated accuracy % aggregated over time

4. Forecasts volumes for door-to-door patient flow vs. preset intervals and departments

5. Converts forecasts automatically into capacity and staffing needs

6. Supports user insight, input and adjustment as part of the planning process

Strategic & Annual Planning

• Discuss “what-if” options, regional plan

• Budget and physical capacity decisions

• Set targets and assumption (linking plans)

Monthly & Weekly Planning

• Manage “current” variation to plan

• Update forecasts & roster staff

• Informed decision making

Daily Planning

• Unit focus: manage current & projected pts.

• Focus on relieving immediate flow issues

• Replace sick leave? Book casual

Forecasting Methods Used for Operational Planning

Predictive modeling

Algorithmic modeling

Pattern identification

Scenario modeling

Simulation

Optimization

Predictive

Determining, mathematically, the relationship between the explanatoryvariables and the predicted variable, based on historical data

Examples:

Insurance: relationship between certain characteristics of a person and lifestyle and predicted outcome of a certain claim

Capacity Planning: relationship between status of ED at a given time of day and the impact on Inpatient beds tomorrow

Pattern Identification

‘Time series forecasting’ identifies different patterns within the predicted variable itself, such as trend, seasonality and day of the week. Various combinations of those patterns can then be used to derive a forecast

AlgorithmicTake recent averages and distributions of past activity for certain locations, services and day of the week or special event days. Then apply an algorithm to utilize these to create a projection

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145,201 150,721 151,870 28,384 5.6 5.3 5.0

122,548 130,929 133,984 23,016 6.1 5.7 5.3

22,653 19,792 17,887 5,368 4.1 3.7 3.5

4,229 4,984 5,546 764 7.6 6.5 7.0

4,113 4,509 5,304 727 7.6 6.2 7.0

115 475 242 37 6.8 12.8 7.8

2,732 2,764 2,536 1,156 2.4 2.4 2.2

1,510 1,630 1,506 615 3.0 2.6 2.5

1,222 1,134 1,030 541 2.0 2.1 1.8

14,197 14,147 14,058 3,488 4.3 4.1 4.0

9,128 10,111 10,041 2,314 4.6 4.4 4.3

5,068 4,036 4,017 1,174 3.9 3.4 3.3

43,161 43,830 41,924 6,800 6.5 6.4 6.0

31,140 33,538 32,961 4,549 7.3 7.4 6.7

12,021 10,292 8,963 2,251 5.0 4.6 4.4

Emergency 4,265 4,884

Scheduled 2,410 2,059

IP Surgery 6,675 6,943

Emergency 1,968 2,354

Scheduled 1,302 1,200

IP Cardiac 3,270 3,554

Emergency 543 756

Scheduled 17 31

ICU 560 787

Emergency 511 606

Scheduled 614 564

Cardiac ICU 1,125 1,170

25,266

Scheduled 5,555 5,042

Annual Patient Turn

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Hospital Total 25,756 30,308

Emergency 20,201

Annual Census Annual Patients In

Scenario

Bed days 4% Activity 16% ALOS 12%

• Analyze which services were driving these trends?

• What do we expect this year? Sustain or continue trends?

• What change initiatives are we investing in to increase volumes and

reduce ALOS?

Allow users to input their own assumptions or ‘what ifs’ into the prediction to assess impacts (vs. based purely on patterns of past activity)

SimulationCoded logic which emulates the actual individual patient ADT activity of planned patient admissions/bookings

Using either a mathematical or algorithmic approach to derive the optimal

outcome, based on an ‘objective function’.

‘Business as usual’ would have

required 50 – 60 beds staffed;

using an optimised activity plan

resulted in 40 - 50

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Optimisation

Patient pathways leveraged to simulate and/or optimise any impact on OR’s and inpatient units.

Pathways

Develops base patient demand based on current trends seen from historical data.

Historical Data

Algorithms applied to forecast increasing accuracy of patient demand.

Algorithms

Combining Approaches

Customers clinical expert input of areas that may impact any patient demand added.

Clinical Input

Aggregate vs. Patient-level

Static vs. Dynamic

Short term vs multi-horizon

Top down vs. Bottom up

Comparing Planning Approaches