SRS Project - EUMASS · 2016-09-05 · SRS Project the use of Big Data in the Swedish sick leave process EUMASS Scientific program 2016-03-04. Stöd för rätt sjukskrivning - ett

Stöd för rätt sjukskrivning- ett samverkansprojekt mellan Försäkringskassan

och Sveriges Kommuner och Landsting

SRS Project the use of Big Data in the Swedish sick

leave process

EUMASS Scientific program

2016-03-04



Utveckling i nära samarbete

• To present how Big data analysis can be used

– to support decisions in the Swedish sick leave process, and

– the possible benefits of such a decision support system

• To invite to collaboration

Aim of presentation




• About decision support systems

– Knowledge versus statistical based

– Predictive models from Big Data

• Project idea

• Results so far

• Work this year

• EUMASS survey

Content of presentation



About decision support systems




• Assist practitioner to make a better analysis

• Combine practitioner knowledge and experience with support

from the DSS

• Improved and knowledge based practitioner performance

• Normative effect, reduce disparities

Decision support systems (DSS)




• Knowledge based (”expert systems”)

– Based on explicitly described human knowledge

– Ex drug interaction: IF drug X is prescribed AND drug Y is

prescribed THEN alert doctor

– In Swedish sick leave process: Guidelines (FMB), since 2008.

• Statistically (”Big Data”)

– Based on ”patterns” in observed ”Big Data”

– Ex: trading systems, weather forecast

– In Swedish sick leave process: SRS decision support system

Two types of DSS




Knowledge based Statistically based

Pros

+Precise knowledge,

clear hypotesis

+Mostly understandable

+High precision

+Easy to explain

+Easy to build

+Feed-back learning

included

+Dynamic

+High coverage

+Easy to include new

knowledge

Cons

- Expensive to develop

- Expensive to maintain

- Static

- Do not cover bad data or data

in between

- Requires a lot of data

- Difficult to explain

- If low quality or ”noisy

data” => bad result

Pros and Cons




Predictive models from Big data

Electronic sick

leave certificate

Registers

Medical Health records

Data sources

Collect and

structure data

and text

Structured data Predictive models

Statistic analysis

Analyzing,

finding patterns



Project idea




• A statistically based decision support system to

– Give predictions for length of sick leave

– Give early identification of individuals with needs of specific and

coordinated interventions

– Propose interventions to increase RTW

• The system shall also

– Increase knowledge of effective interventions

– Serve as pedagogical support in dialogue with patient

– Increase cooperation between parties/actors

Project idea – a common support system for several parties




Parties involved

The Government of Sweden

20 County Councils

and Regions 290 Municipalities

The National Board of Health and Welfare

Swedish Association of Local

Authorities and Regions (SALAR)

The Swedish Social Insurance Agency

Agreement - Sick leave and rehabilitation




Big data Collection and use in SRS

Evaluation of predictive factors1

Supports the dialogue with the patient

2 Interventions, statistically most likely to increase positive outcomes

3

Outcome data collected in database

4Outcome datalooped

5




Vision: A common decision support system




• Increased RTW by a correct intervention at the right time

• Increased knowledge of effect of risk factors and interventions

on RTW, common to all parties

• Increased conditions for equal care despite region and

experience, ”rookies” are brought to a higher level

Expected benefits




• Engagement, committment and cooperation needed from

several stakeholders

• Legal aspects

• Ethical aspects

• Operational (from the end user’s point of view)

• Technical and architectural

• Big Data analysis and predictive model accuracy

Challenges



Results so far




• Pre-study investigated if project idea was possible to realize

– Legal

– Technical and architectural

– Predictive models

– User needs

• Report to government in October 2015.

– Project idea is possible to realize, in theory.

– Decision: Proceed, more practical investigation needed.

Pre-study completed




Patterns in Big data – sick leave for osteoarthritisGrowth trajectory models

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13

No

of

da

ys

wit

h s

ick

le

ave

pa

y w

ith

in 3

0 d

ays

Months, after day 21

Högt, sedansjunkande(8 %)

Sjunkande,sedan stigande(12 %)

Sjunkande(noll vid 10 mån)(15 %)





Work this year




• Proposal on organization and responsibilities for

development, maintenance and operation of SRS DSS

• Cost-benefit analysis

• Operational analysis

– User needs

– Ethical aspects

– Testing prototypes

• List of interventions including codes

Work this year



EUMASS survey




Question:

Does your country have, or have plans for, decision support

systems or other tools to support decisions of sick leave,

rehabilitation or other similar questions?

EUMASS survey




• No system (3 countries)

• Semi-automatic data analyses, for fraud detection etc. (3)

• Knowledge based decision support systems (6)

• Statistically based decision support systems (3)

• No answer (10)

Results so far



Thank you!

Anne Snis

Project manager SRS-Project

[email protected]

mailto:[email protected]

Documents

SRS Project - EUMASS · 2016-09-05 · SRS Project the use of Big Data in the Swedish sick leave process EUMASS Scientific program 2016-03-04. Stöd för rätt sjukskrivning - ett