How Australia’s National Health Services Directory Improved Data Quality

Healthdirect Australia delivers telehealth and digital health services in partnership with the Australian federal, state and territory governments. Healthdirect helps address key priorities and challenges within the health, aging and social service sectors. In Australia, we run the National Health Services Directory (NHSD) that stores every Healthcare Organisation, Health Practitioner, their Physical and Virtual Locations, Health Care Services and eHealth Secure Messaging Endpoints. This is forecast to need to scale to handle over 10 terabytes of data covering time-driven activity-based healthcare transactions. Globally we share similar problems with Healthcare data such as incomplete integration across the Health Sector participants, data supply chain inefficiency and duplication. At Healthdirect we use Apache Spark and Databricks Delta’s fine-grained table features and data versioning to solve duplication and eliminate data redundancy. This has enabled us to develop and provide high-quality data through federation and interoperability services whilst providing the analytics to improve Health Services demand forecasting and clinical outcomes in service lines, such as Aged Care and Preventative Health.
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2.How Australia’s National Health Services Directory Improved Data Quality, Reliability, and Integrity with Databricks Delta and Structured Streaming Peter James, Healthdirect Australia #UnifiedAnalytics #SparkAISummit

3.About Us Healthdirect Australia designs and delivers innovative services for governments to provide every Australian with 24/7 access to the trusted information and advice they need to manage their own health and health-related issues. #UnifiedAnalytics #SparkAISummit 3

4.Our Impact #UnifiedAnalytics #SparkAISummit 4

5.National Health Services Directory The National Health Services Directory (NHSD) holds core information about healthcare organisations, services and practitioners. • Hospital, EMR, Registry Data sharing Arrangements • Clinical Pathway Applications • Telehealth and Contact Centre Services • Support eHealth Secure Messaging Delivery • API Integration using Fast Healthcare Interoperability Resources (FHIR) • Consumer API’s and Web Widgets • Sector Analytics and Reporting #UnifiedAnalytics #SparkAISummit 5

6.Supporting Health Planning NHSD is used widely for analysis & planning healthmap.com.au Royal Flying Doctors Service’s, SPOT platform #UnifiedAnalytics #SparkAISummit 6

7.Landscape Changes A Strategic Assessment defined a long-range vision of success • Australian Health Sector promotes adoption of HL7/FHIR, Provider Directory Federation and Open Data standards • Business drivers pushing for Cost reduction and operational efficiencies • Architecture needed to scale to meet the objectives defined for future success • Need to develop new Analytics capabilities in support of Sector Health Planning • Improve Information Security to support with broader usage scenarios and regulatory changes #UnifiedAnalytics #SparkAISummit 7

8.Challenges Data Quality and Governance • Availability and access to Systems Of Record • Often no Single Source Of Truth • High variety of data sources from Federal, State, Public/Private Hospitals, EMR, and other Commercial Vendors • Health Domains are complex - Ontologies, Taxonomies, Thesauri, Code sets (e.g. MeSH, SNOMED-CT, ANZSIC) • Record Linking Issues, identity harmonisation, quasi-identifiers • Disjoint Data Governance (Network of Data Governance participants) #UnifiedAnalytics #SparkAISummit 8

9.Challenges Data Silos • Data stored in multiple subsystems – File Systems, ETL and workflow transient storage systems – Multiple RDBMS, Multiple Schemas – Search Engine Indexes • Read Inconsistency – Data out-of-synch between Search, Databases and Analytics storage – Large batch updates of low quality data leading to high error rates and process inefficiencies – Transactional boundaries per Data Silo no holistic end-to-end consistency • Data Access – High operational overheads processes – Incompatible with Security boundaries #UnifiedAnalytics #SparkAISummit 9

10. Data Silos to Data Aggregation Single point of access to the component pieces AHPRA Secure (Registries) Message Vendors Desktop Medicare Vendors Hospital Jurisdictions Networks Aged NHSD Care Others #UnifiedAnalytics #SparkAISummit 10

11.Challenges Data Scale • Processes need to scale to ~1 Billion Data Points • New demands include Bookings, Appointments, Referrals, Pricing and eHealth Transaction Activity – est. 1TB p.a. • Support Data federation and Interoperability requirements with requests growing > 58% p.a. • Existing systems were already under pressure with batch overruns and significant administration overheads #UnifiedAnalytics #SparkAISummit 11

12.Architecture Overview #UnifiedAnalytics #SparkAISummit 12

13.Architecture Improvements Databricks DELTA to create Logical Data Zones • LANDING, RAW, STAGE, GOLD (i.e. Bronze, Silver, Gold) • Store data ‘as-is’ either structured and/or unstructured data in DELTA Tables and Physical Partitions • Read Consistency, Data integrity - ACID transactions • Used DELTA Cache for frequent queries with transparent, automated cache control • Databricks Operational Control Plane for Cluster Administration, Management functions like, Access Control, Jobs and Schedules • Data Control Plane runs within our AWS Accounts under our Security Policy and regulatory compliance #UnifiedAnalytics #SparkAISummit 13

14.Architecture Improvements SPARK Structured Streaming for Continuous Applications • Create a Streaming versions of existing batch ETL jobs. • Move to Event based Data flows via Streaming Input, Kinesis, S3, and DELTA • Running more frequently lead to smaller and more manageable data volumes • Automation of release processes through Continuous Delivery and use of Databricks REST API • Recoverability through Checkpoints and Reliability through Streaming Sinks to DELTA tables #UnifiedAnalytics #SparkAISummit 14

15.Databricks Cluster Continuous Applications – Continued • Keeps 'in-memory’ state of object sets def appendAttributeStats( including comparable versions for object self, sourceDF, changes attribute_name, • Stateful transformations metrics = ['mean', 'min', (flatMapGroupsWithState) 'max', 'median', • Aggregations for Data Quality 'num_missing', measurements 'num_unique']): • Built User-defined libraries for Data Cleansing, Validation and complex logic #UnifiedAnalytics #SparkAISummit 15

16.Data Plane & Pipeline #UnifiedAnalytics #SparkAISummit 16

17.Architecture Improvements Unified Analytics and Operations • Data cleaning & matching algorithms reliable, predictable and measurable • Data Quality Analytics calculated at runtime, attached to object attributes, populate analysts dashboards • Track Data Lineage and Lifecycles providing Operational visibility of where our data is at any point • Complex processes easily broken down into simpler steps with ‘checks and balances’ prior to execution • Significant cost reduction through decommissioning complex Administration UI in favour of notebook environment #UnifiedAnalytics #SparkAISummit 17

18.Improved Algorithm Development & Usage Data Sharing Agreements Data Integration Algos & Processes Algorithms • sound design, goal-focused • verified against trusted data • version-controlled, documented Data Quality • archived with training & validation Auditing data Data • permanent, versioned Data Quality • use provenance to enhance algos, Reporting revise confidence estimate #UnifiedAnalytics #SparkAISummit 18

19.Algorithm Evaluation FPR TPR threshold 0.014433 0.800855 1.00 0.014433 0.801709 0.99 0.014433 0.831054 0.98 0.014433 0.855840 0.97 0.016495 0.895726 0.95 0.026804 0.950997 0.90 • interpretation – the algorithm is appropriate to the domain – the data is of good quality – un-matchable names are rare exceptions – a matching threshold of 0.95 gives TPR = 95.1% FPR = 2.7% ROC curve for the set-based, fuzzy name-matching algorithm #UnifiedAnalytics #SparkAISummit 19

20.Improved Data Processing • 95% fuzzy name matches vs. <80% and dependent on manual verification • ~30,000 Automated Updates / mth vs. ~30,000 bi- annual & Manual • 20 mins full load vs. > 24 hrs • 20 million records @ ~1 million/min #UnifiedAnalytics #SparkAISummit 20

21.Improved Data Security • Databricks Platform provided foundational Security Accreditations • Able to cross-walk these security compliance frameworks and localize to Australian Regulatory frameworks • This provided significant cost/effort reduction of GRC • Continuous Data Assurance - Security Guard-Rails monitor changes to access privileges, e.g. role elevation/conditional access, data security metadata data and auditing against data leakage and spills. #UnifiedAnalytics #SparkAISummit 21

22.Securing it ALL #UnifiedAnalytics #SparkAISummit 22

23.Other Benefits • Don’t have to be big to see benefits, strategic value and a new business model • Strategic momentum through realizing business vision with technology transformation • Dataset Transparency and Explainable Models with well-documented lineage and quality • Broader participation - no one holds a single parts of knowledge anymore, we extract more value in our data when everyone was able to access it • Governance transitioned focus on operational improvements • Inked Agreement in August 2018 completed migration to Databricks December– Live March 2019 #UnifiedAnalytics #SparkAISummit 23

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