Big data - Healthcare Financial Management Association · Implementation of a Value Driven Outcomes Program to identify high variability in clinical costs and outcomes in association

Big data:Comparing apples with apples to support improvement in

healthcare

20 April 2018

Dr Gail Prileszky General Manager

Non-profit membership groupHonour CodeOver 90 Health ServicesOver 160 FacilitiesShare problemsShare solutionsProvides informal networkNon-political

The Health Roundtable …An Innovation Clearinghouse since 1995

© 2016 Confidential Draft Discussion Document 2

Focus on Benchmarking for Innovation: Screening toolSearch for differencesIdentify exemplars

Health Roundtable: A ‘knowledge’ company, empowering potential

Data Collection & Standardisation

Analysis & Inquiry

Insight &

Networking

Innovation,

Knowledge….. Enable Action

• Software• AI + • Data

science• Integration

of broad data scope

• Visualisation• Clinical /

Health informatics

• Research support

• Data story telling

• Industry expertise

• Collective intelligence – virtualised networking

• Accelerating innovation & uptake

• Libraries & communities of knowledge

• Action oriented services.

Health Roundtable: Big data


Why is benchmarking useful?


Why is benchmarking useful?


Big data – searchlights not spotlights


Big data shows variance but does not verify cause and effect

Randomised controlled trials and audits give cause and effect (probably)

How do we support member hospitals?

On site and webinar support from Relationship Managers

Analytics, Insights & Workflow – Activity Bar Coding, Codecheck

Innovation & Collaboration – Innovations Awards, Library and Roadshow

Improvement Programs – Financial Improvement Group

My Patients are sicker!How do we risk adjust your data?


“Relative Stay Index” (RSI) : a risk adjusted measure of length of stay standardised across

– 700 Diagnosis Related Groups (using DRG 7.0)– Age group – 0, 1-16, 17-34, 35-49, 50-64, 65-79, 80+– Admission type - same day emergency / overnight emergency / elective– Arrival source : transfer in /normal admit– Discharge destination : home, died, statistical discharge, transfer up, transfer

down– Comorbidity level: 3+ co-morbidities from separate ICD10 chapters– Caretype

“Expected” length of stay calculated for each episode using average length of stay from index period Jul 2013 – Jun 2016 (16M episodes)

Example of RSI for Gall Bladder Surgery (H08A)

© 2015 Confidential Draft Discussion Document

10

My Patients are sicker!How do we risk adjust your data?


“Relative Cost Index” (RCI) : a risk adjusted measure of cost of stay standardised across

– 700 Diagnosis Related Groups (using DRG 7.0)– Age group – 0, 1-16, 17-34, 35-49, 50-64, 65-79, 80+– Admission type - same day emergency / overnight emergency / elective– Arrival source : transfer in /normal admit– Discharge destination : home, died, statistical discharge, transfer up, transfer

down– Comorbidity level: 3+ co-morbidities from separate ICD10 chapters– Caretype

“Expected” cost of stay calculated for each episode using average cost of stay from index period Jul 2013 – Jun 2016 (5M episodes)

www.healthroundtable.org


Anyone from a member health service can register on our website using their

health service email address!

http://www.healthroundtable.org/

Drill down through reports to understand safety, effectiveness of care and efficiency


Highlight improvement opportunities andlearn from exemplars


Big data helps identify variance andexemplars


High quality healthcare costs less


Comparision of cost of not delivering‘perfect’ care


1. Vivian S Lee et al. Implementation of a Value Driven Outcomes Program to identify high variability in clinical costs and outcomes in association with reduced cost and improved quality. JAMA 2016:316 (10): 1061-1072

Risk adjustment Pricing for Complications 2018/19


National Efficient Price Determinationhttps://www.ihpa.gov.au/publications/national-efficient-

price-determination-2018-19


Major Hospital Acquired Complications

List derived from ACsQHC Hospital Acquired Complications v1.1

Profile: Major Hospital Acquired Complications

45 Hospitals 2016/17 Data

• Total Episodes with HAC 51,267• Total HAC Incidents 90,616• Cost $2.01 billion• Revenue $1.75 billion• Profit / Loss $260 million (13%)

Highest Volume HAC: Cardiac Complications – DeliriumTotal Episodes: 10,488Total Loss: $42 million


HACs cost more than they earn in revenue –2016/17


Questions?

[email protected]+64 21 072 1869

Dr Gail Prileszky General Manager

+64 21 072 1869

mailto:[email protected]

Documents

Big data - Healthcare Financial Management Association · Implementation of a Value Driven Outcomes Program to identify high variability in clinical costs and outcomes in association