value based steering on a multi-regional level in...

Value Based Steering on a multi-regional level in Sweden

2

Introduction

• Jonas Wohlin

• PhD candidate Karolinska Institutet

• CEO Ivbar Institute

Increase efficiency

(outcomes in

relation

to cost)

Major potential

(given proven variation in

methodology, outcomes and

cost)

Fundamental challenge in Health

Care faced by OECD Countries

Potential actions by

healthcare policy

makers Increase Health

Care spend

Feasibility long/short

term Not sustainable long

term • High and growing cost for

healthcare

+24%

7,2% 8,9%

2000 2013

HC Cost, % of GDP

Prioritization and

rationing Variation

ILLUSTRATIVE

+X%

Ø

Predikterad

Observerad Ethically and politically

complex

Macro challenges: There are three main line of

actions that policy makers can pursue in order to

meet health care challenges

• Major variations in methodology,

outcomes and costs

• Slow diffusion of best practice

Value= Health outcomes

Resources (cost)

Patient relevant health outcomes, examples:

• Survival

• Quality of life

• Pain level and functional ability

• Depression

• Incontinence/impotence

(not HBa1c level)

Costs through the full cycle of care related to

achieving outcomes (cost on provider level,

not reimbursement)

What is new?

• Incorporates most other goals

• Focus on what is important to

patients

• Focus on cost of treatment

instead of cost of

organizational unit

• Works at all levels (provider,

region, municipality, state)

Theory outlook: Efficiency in healthcare can be

understood by the Value-based healthcare

framework

Care process Complication

Regular care

contact/event

• How much health care do we get from our resources?

Vs.

• How much health do we get from our resources?

Front-line challenges: We have systems to analyse

and monitor productivity but not value and efficiency

Sweden a has unique potential to analyse healthcare

and understand performance

Unique, high quality data sources

>100 National quality registries

Drug registries

Cost-per-patient data

Cause of death registry

Patient Administrative systems

(including DRG-data)

Municipality data registries

Social insurance fund registries

Socio-demographic data

registries

…

Personal ID-Numbers

Culture of measuring quality

Prerequisites

Value= Health outcomes

Resource usage (cost)

• Challenges to measure value due to

• Definitions of patient relevant

outcomes

• Data fragmentation

• Lack of methodology

• Lack of IT-infrastructure and software

?

• Development of methodology and IT-

systems to enable analysis and

monitoring of value in Swedish Health

care

• > 50 participating organizations

• Financed by MoH & 7 regions 2013 to

2017

• Initially covering 8 pilot patient groups and

>25 databases

Program objectives and scope Contribution to health care

system New analysis

platform

A new tool for professionals on all levels of the health

care system to identify potential to improve

performance!

In 2013, the Sveus program was established to

realize this potential

8

A broad approach was taken with both “general

components” and “patient group” specific

components

Descriptive

analytics

How are we

performing (in

terms of

value?)

Diagnostic

analytics

How can we

explain our

performance?

Prescriptive

analytics

How can we

improve our

performance?

New analysis capacity

…

Spine surgery

Logic for monitoring

of stroke care

Patient group components

Legal aspects

Health care informatics

Technical infrastructure

Method for analysis

General components

9

Sveus has involved over 50 stakeholders to

achieve its objectives

Participating regions/county

councils

• Stockholms läns landsting

• Region Skåne

• Region Östergötland

• Landstinget Dalarna

• Västra Götalandsregionen

• Region Jämtland och

Härjedalen

• Landstinget i Uppsala län

Medical quality registries

• SHPR

• SKPR

• BOA-registret

• SWESPINE

• Graviditets-registret

• PNQ

• MFR

• SOReg

• Riksstroke

• WebRehab

• NDR

• Bröstcancerregistret

Patient organisations

• STROKE-riksförbundet

• Svenska Diabetesförbundet

• Riksföreningen för

osteoporotiker

• Överviktigas riksförbund

• Bröstcancerföreningarnas

Riksorganisation (BRO)

Medical speciality associations

• Svensk Ortopedisk Förening

• Svensk Ryggkirurgisk förening

• Svensk Förening för Obstetrik & Gynekologi

• Svensk Barnmorske-förening

• Barnläkar-föreningen

• Svensk förening för anestesi & intensivvård

• Dietisternas Riksförbund

• Svensk Förening för Obesitaskirurgi

• Svensk förening för rehabiliteringsmedicin

• Fysioterapeuterna

• Förbundet Sveriges Arbetsterapeuter

• Svenska Neurologföreningen

• Svensk Geriatrisk förening

• Svensk förening för Diabetologi

• Svensk förening för Sjuksköterskor i

Diabetesvård

• Dietisternas riksförbund

• Svenska Osteoporos-sällskapet

• Svensk Onkologisk Förening

• Svenska Bröstcancergruppen

Other participating

stakeholders

• Karolinska Institutet

• Försäkringskassan

• Statistiska Centralbyrån

• Ivbar Institute AB

10

The program organisation was set up with 12 sub-

projects with project ownership distributed between

the counties and regions

Local groups within county councils/regions

1- Expert group

Hip & knee (SLL)

Steering group consist of representatives of:

• Ministry of Health

• Each participating county council/region

• Swedish Association of Local Authorities and Regions (SKL)

• Karolinska Institutet

• Ivbar Institute AB

9- Technical implementation

Ad

jace

nt

rese

arch

3- Expert group

Child Birth (SLL)

5- Expert group

Stroke (Skåne)

7- Expert group Osteoporosis (Östergötland)

2- Expert group

Spine surgery (SLL)

4- Expert group

Bariatric surgery (VGR)

6- Expert group

Diabetes (Jämtland,

Dalarna)

8- Expert group

Breast Cancer (Uppsala)

Program office

10- FK/SCB

11- Trans. maintenance

12- Analysis patient groups

Functional network groups

Law &

Information security Communication

Health Care informatics

Reimbursement systems

11

Each patient group was treated as a

separate research project

System specification for configuration of analysis platform

Benchmarking report

Publications

1. Define patient group and sub

groups

2. Define patient relevant health

outcomes and other KPIs

3. Identify KPIs in historic data

4. Develop prediction models for

selected KPIs

5. Perform benchmarking on historic

data

1- Expert group

Hip & knee (SLL)

Output from each

group

12

Example: analysis of stroke care as presented in

expert group report (Region Östergötland)

Explanatory comments

• ”First year”, ”year 1” and ”after 1 year” refers to the first year after the initial stroke occurred

• Green line corresponds to average for the remaining regions

• Grey circles indicate the scale; inner circle corresponds to deviation from average with odds ratio of 0,5

(binary variables) or 50 % lower level (continuous variables), outer circle corresponds to odds ratio of

1,5 or 50 % higher level

Change in sick leave year 1

Share with good functional outcome after 1 year

Share with good general health after 1 year

Share moving to nursing home year 1

Number of new home service hours year 1

Net days of sick leave year 1

Outpatient-care visits year 1

Inpatient-care days year 1

Survival rate 1 year

Share with no recurrent stroke during first year

Municipal data

Soc. Insurance

Adm.

Soc. Insurance

Adm. Stat.

Swede

n

Region/ PAS

Region/ PAS

Quality registry

Health

Resource

utilization

Observed average Sveus

(c)/(b) Outcome variable type – continuous or binary

Not all sources of data available for initial version of monitoring system

(c)

(b)

(b)

(c)

(b)

(b)

(b)

(c)

(c)

(c)

Statistically significant casemix-

adjusted deviation from average

for remaining regions (p < 0,05)

Casemix-adjusted deviation for

Region Östergötland compared to

average for remaining regions (not

statistically significant)

13


expert group report (Region Jämtland Härgedalen)







Outpatient-care visits year 1

Inpatient-care days year 1



Health

Resource

utilization




• Grey circles indicate the scale; inner circle corresponds to deviation from average with odds ratio of 0,5 (binary

variables) or 50 % lower level (continuous variables), outer circle corresponds to odds ratio of 1,5 or 50 % higher

level

(c)

(b)

(b)

(c)

(b)

(b)

(b)

(c)

(c)

(c)

14


expert group report (Stockholm County)







Outpatient care visits year 1

Inpatient care days year 1



Health

Resource

utilization

(k)

(b)

(b)

(k)

(b)

(b)

(b)

(k)

(k)

(k)




• Grey circles indicate the scale; inner circle corresponds to deviation from average with odds ratio of 0,5 (binary

variables) or 50 % lower level (continuous variables), outer circle corresponds to odds ratio of 1,5 or 50 % higher

level

15

Childbirth

Stroke

Hip/knee

arthoplasty

Spine

surgery

Obesity

surgery

Diabetes**

Improved health outcomes, # (%) Cost/ resource

use; MSEK

Reduction

perineal tears

# of patients

Reduced

Infections

# of patients

~1 600

(-30%)

~2 300

(-30%) length of stay 230

Ceasarion sections >800 MSEK

520

~830

(-40%)

Apgar<7

at 5 min

# newborns

Reduced

complications

(30 days)

# of patients

Red. severe

complication

(30 days)

# of patients

Reduced

complication

(31-730 days)

# of patients

~500

(-45%)

~160

(-80%)

~300

(-50%)

70

inpatient care

due to comp.

75

length of stay ~220

>350 MSEK

Pain relief # of patients

Back to

work (1y)

# of patients

Patient satisfaction

# of patients,

thousands

~2 100

(+25%)

~4 800

(+50%) ~1800

(+20%)

length of stay

Other direct

100

~1.500

sick-leave >1 600 MSEK

Direct

Indirect*

~250

(-40%) ~130

(-20%) 25

>400 MSEK

length of stay 375

Outpatient

visits Orthopedic hip

complications

# of patients

Orthopedic

knee

complications

# of patients

Non-orthopedic

complications

# of patients

~400

(+10%) ~240

(-30%) ~70

length of stay

>650 MSEK

140

Outpatient

visits

425

High level of

activity ability

# of patients

Re-occurrence

of stroke

# of patients

Good state

of health

# of patients

>400 MSEK

Other

Doctor visits 400

~370

(-50%)

~500

(+10%)

T Estimates. Detailed calculations with case-mix adjusted analysis available in Sveus reports at www.sveus.se

* Estimated sick-days for some patient groups (~1200 SEK/day)

** Examples based on type 2 diabetes

*** 1 ppt equals ~4000 SEK in avoided hospital care over 3 future years, when costs is measured as DRG points

~1,2ppt

(-7%) ~4 400

(-20%) Five-year risk

of heart

disease***

Absolute risk

Cardiovascular

hospital

admissions

# of patients Sick leave

# of days

~787 000

(-8%)

What if identified variation did not existT (not exhaustive)

Example of potential identified during development

of Sveus monitoring systems

Harmonization and calculation is performed centrally Information is pushed back locally

Not: Each provider owns and is responsible for its own data

to enable decision support on all levels of

the health care system to enable benchmarking and prediction

PAS-data

QR-data

Other data (i.e.

Insurance fund)

Integration

platform

Information model

Calculation

engine

Structured

data

Calculated

data

Technical specifications

(1 per patient group)

Providers BI-systems

For research

Other plattforms

Payers BI-systems

Sveus web-plattform

In the Sveus analytics platform, data is harmonized and

calculated centrally, and thereafter pushed back to local

BI-systems

In the new platform, performance analysis is

adjusted for case-mix (patient characteristics)

Importance of case-mix adjustment – Likelihood of successful

treatment in spine surgery

43%

84%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Patient 1 Patient 2

PA

IN G

ON

E O

R M

UC

H

IMP

RO

VE

D 2

YE

AR

S P

OS

T

SU

RG

ER

Y

• High school

• Outside EU

• Yes

• Yes

• University

• Within EU

• No

• No

Education

level

Country of

birth

Smoker

Co-

morbidities

Patientegenskap/

Casemix

Almost 50 difference in

expected outcomes

18

Demo of platform using historic bariatric surgery

data in Region Uppsala as example

• Question No 1 – How is Uppsala performing compared to other regions?

• Question No 2 – Where is potential to improve value?

• Question No 3 – How is patient characteristics affecting performance?

Should Uppsala focus improvement efforts on specific patient groups?

• Question No 4 – Can identified potential be addressed without expected

negative effects for patients

Caveat: System still under development

19

Live Demo

20

Demo of platform using historic bariatric

surgery data in Region Uppsala as example

• Question No 1 – How is Uppsala performing compared

to other regions?

– Well on health outcomes but potential to reduce resource use

• Question No 2 – Where is potential to improve value?

– For example by reducing Length of Stay (LoS)

• Question No 3 – How is patient characteristics

affecting performance? Should Uppsala focus

improvement efforts on specific patient groups?

– Uppsala has average case-mix and performance is not

different for any subgroups

• Question No 4 – Can identified potential be addressed

without expected negative effects for patients

– Yes, there does not seem to be any correlation between

shorter LoS and better outcomes

Potential to reduce

LoS for Bariatric

Surgery in Uppsala

Main challenges

• Coordination and engagement of 50+ organisations

– Keeping excitement from all stakeholders over time

• Data

– Harmonization and connection of many data sources

– Missing data

– Legal aspects of patient data analysis

Next steps

• Plan to move Sveus to permanent organization from 2018

(probably within SKL)

• Work led by Västra Götaland Region

• Adding more patient groups

• Breast cancer is piloted in 5 weeks (compared to 2 years)

• Connect all interested counties

Relevance for other countries: Denmark

performed a feasibility study in 2015 to evaluate

opportunity to move towards value based steering

and support models

1) Assess starting point and general prerequisites for Denmark to adopt

value-based steering and support models (based on learnings from

Sweden)

2) Assess potential and ‘ease of implementation’ through deep-

dives in selected patient groups (medical conditions)

Project objectives

Relevance for other countries: Potential and ease of

implementation was studied for selected patient

groups

Po

ten

tia

l

“Ease of implementation”

Reumathoid arthritis Stroke

Childbirth

Diabetes

Breast cancer

Prostate cancer

Multiple sclerosis

Obesity (surgery)

Knee-replacement

Hip-replacement

Approach (see appendix for details)

Summary of potential by disease

area

A

1 A

2

A

B

Burden of disease

Variation (cost and/or outcomes)

Synthesis “Ease of

implementation”

• Organizational complexity

• Data availability

INDICATIVE

Complex ‘Easy’

Hig

h

Low

Relevance for other countries: Danish Regions

decided to launch a nation-wide program in value

based steering

• Denmark has the general pre-requisites to deploy value based steering

models, with respect to:

Organisational readiness (interview-based)

Technical / data landscape (Swedish algorithms tested on Danish data)

Legal framework

Danish Regions decided in 2015 to initiate a nationwide program in value

based steering

Thank you!

[email protected]

+46-708 55 37 00

www.sveus.se

mailto:[email protected]

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