Design & Implementation of a Learning Health System in Australia

Data Dissect Pty Ltd (datadissect.com.au)Tom Cundy, Stefan Court-Kowalski, Andrew Feutrill, Hilary Boucaut, Francois Duvenage, Tim Boucaut, Peter Hewett, Sanjeev Khurana

Health Data Analytics 2019, Sydney, October 16-17 2019

Disclosures

www.datadissect.com.au

Health technology company

Collaborative of Surgeons, Mathematician, IT Consultant, Computer Scientists, Business Manager

Limitations with current EBM

• ‘Status quo’ – Linear process

– Start and end dates of study/trial

– Strict inclusion/exclusion criteria

– Generalisability of results not guaranteed

– Expensive

– Inefficient

– Translation to practice not guaranteed

• Health Knowledge creation is an industry in

itself and not by-product of clinical care

• Disconnect between administrators,

researchers and clinical work force

Medical research landscape is changing

• Scientific method based on reductionist science– Isolating outcome measures or variables to investigate

causes or effects

• “Research is changing from a hunter/gatherer

mode, where huge amounts of effort is invested

to associate data with rare events, to a harvest

mode in which huge amounts of data are used

more efficiently to give insight.”– Embrace multi-dimensional, multi-disciplinary data with

human-computing symbiosis

1. http://www.learninghealthcareproject.org/publication/5/66/dr-paul-wallace-interview

Value-based healthcare

Value = (Quality + Outcomes + Safety)

Cost

Ureteric reimplantation rate

(per 100,000)

NSW 1.0

VIC 0.8

QLD 2.5

SA 6.5

WA 10.8

TAS 0

Variation in care

• E.g. surgical management of urinary reflux in children

Learning Healthcare System

• Facilitated by the Electronic Medical Record paradigm– 2007 first description

– 2013 Institute of Medicine definition

• “Any type of healthcare delivery system that combines research, data science, and quality

improvement, yielding knowledge as a by-product of the patient-clinician interaction and focused on

improving patient health and system outcomes”

• Health sector slow to adopt from concept to action– Immense and rapidly changing volume of medical information, complexity of decision making, limited

capacity to evaluate decisions

• Only 13 publications reporting actual implementation (2016)

1. Deans KJ, et al. Learning health systems. Semin Pediatr Surg. 2018 Dec;27(6):375-378.

2. Forrest CB, et al. Development of the Learning Health System Researcher Core Competencies. Health Serv Res. 2018 Aug;53(4):2615-2632.

3. Budrionis A , et al The Learning Healthcare System: Where are we now? A systematic review. J Biomed Inform. 2016 Dec;64:87-92.

4. Kwon S, et al. Creating a learning healthcare system in surgery: Washington State's Surgical Care and Outcomes Assessment Program (SCOAP) at 5 years. Surgery.

2012 Feb;151(2):146-52.

What is a Learning Healthcare System?

• Continuous improvements in quality, outcomes & efficiency

– Cycle begins and ends with clinician-patient interaction

– Improving rather than proving

– Afferent (blue) and Efferent (red) arms

– Research influences practice, and practice influences research

• Distinguishing features

– Patient/family engagement through self-reported outcomes

– LHS researchers embedded at point-of-care

– Leverages evidence about “what works” in context of own

setting

1. Deans KJ, et al. Learning health systems. Semin Pediatr Surg. 2018 Dec;27(6):375-378.

2. Greene SM, et al. Implementing the learning health system: from concept to action. Ann Intern Med. 2012 Aug 7;157(3):207-10.

3. http://www.learninghealthcareproject.org

http://www.learninghealthcareproject.org/

Differences between LHS & Clinical Registry/Audit

Clinical information

Learning Health System Medical Record Registry or Audit

• Equally stringent data acquisition and storage

• Multi-user cloud based platform

• All data formats including images and videos

• Timely insight into outcome and process of care

• Single platform to store patient info leaflets and

consent, etc

• Less amenable to point of care data entry

• Expensive

• Often requiring salaried data entry staff

• Lag phase in outcome reporting

• Limited potential for actionable insight for

quality improvement

Clinicians

Context

Data

Data scientists

Interpretation

Visualisation

Tech platform

Acquisition

Collation

Storage

Analytics

Fundamental building blocks

Socio-technological system dependent on technical underpinnings

Fundamental building blocks

Human factors Technological factors

Socio-Technological System

• Motivated stakeholders with desire to

continuously improve system

• Willingness to be vulnerable and

transparent

• Clinical leadership

• Domain experts

• Administrative support

• Ability for patient to actively participate in

their data collection via QoL

assessments that populate platform

• System-wide accessibility allowing

learning to permeate organization

Washington example

• SCOAP (Surgical Care & Outcomes Assessment Program)

– Launched 2006

– 60 out of 65 hospitals with surgical service

– Surgeon designed, grassroots, voluntary, peer-based QI collaborative

• Creates value proposition for surgeons and hospitals to join

• Initially appendicitis, colorectal surgery, bariatric surgery– SCOAP OR Checklist

– Decreased negative appendicectomy

– Decreased UTI in epidural patients

– Decreased anastomotic leak rate in colorectal surgery

– Decreased blood transfusions (only if Hb < 7 g/dL)

– Improved nutritional pre-habilitation for elective surgery

– Appropriate use of neoadjuvant therapy for rectal cancer

1. Kwon S, et al. Creating a learning healthcare system in surgery: Washington State's Surgical Care and Outcomes Assessment Program (SCOAP) at 5

years. Surgery. 2012 Feb;151(2):146-52.

Introducing LHS ‘culture’ for quality improvement

2014

1. Health Roundtable national audit data. https://www.healthroundtable.org/

Introducing LHS ‘culture’ for quality improvement

15

Appendicitis

“Uncomplicated” “Complicated”“Advanced”

• Paper based data collection and machine learning – Fast track protocols

– Identification of a subset of patients with complicated appendicitis that could be safely discharged earlier

Introducing LHS ‘culture’ for quality improvement

2014 2017

1. Health Roundtable national audit data. https://www.healthroundtable.org/

2. Cundy TP, et al. Fast-track surgery for uncomplicated appendicitis in children: a matched case-control study. ANZ Journal of Surgery.

2017 Apr;87(4):271-276.

$143,803 saving p/a

https://www.healthroundtable.org/

Paper-based v1 Digital LHS platform

• Bespoke LHS digital platform – Customised user interface

– Browser and smart-phone functionality

– Hosted on off-site University of Adelaide server

– Approval for satisfying patient privacy and data security

protocols

Clinical implementation

• Integrated into day-to-day practice– n = 180 consecutive patients in 9 months

– Multi disciplinary teams gather data that is not currently being

captured at the point of care

• Structured and unstructured data (including media)

Future work

• Scale-up

• Patient reported outcomes

• Data visualization

• Growing demand for applications

Conclusions

1. Established first active LHS in Australia

2. Continual flow of actionable cloud-based data for analysis– Research influences practice, and practice influences research

3. Healthcare services stand to benefit– Rapid access quality improvement

– Improved patient outcomes with reduced cost of care

Thank you

www.datadissect.com.au

Health technology company

Collaborative of Surgeons, Mathematician, IT Consultant, Computer Scientists, Business Manager

NSQIP Data Dissect

Main use Designed to gather highly accurate clinical outcome data on patients undergoing surgery

and produce benchmarking reports.

An effective, affordable means of collecting info required to drive efficiency of care

delivery in conditions that have already been identified as not meeting benchmarks.

Mode of collection Specially trained senior nurse (1.0 FTE) will need accreditation by American college of

surgeons (ACS)

Point of care data entry by all treating team members; overseen by unit clinical nurse

consultant. No extra FTE

Type of patients on whom data

collected

Only those having an operation Any condition – medical or surgical

Limitation on data points Limited to max 80 clinical variables Unlimited variables including digital photos.

Data storage Two separate databases:

1. To store identifiable data locally. Generates unique ID that is used for storing

clinical data on second database.

All clinical data stored anonymously on ACS database in USA.

All data stored locally on uni Adelaide servers. Data kept encrypted and identifiable info

visible only to authorised wch staff. (See attached doc on tech specs)

Methodology Dedicated Data collector has to retrospectively collect prescribed dataset for a certain

minimum number (38) cases per cycle. The case mix priority is determined by ACS.

1.

A Customised Data set is collected at point of care by various members of the treating

team for all relevant cases. Any number of conditions can be audited simultaneously

Pricing model 25000 AUD per annum + 1.0 FTE senior nurse salary

Data sharing All clinical data is shared with ACS No clinical data sharing required. However, deidentified data can be easily exported for

multi centre studies