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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
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