Alcidion’s Miya Precision enables an integrated view of patient data for researchers at the Kolling Institute

The Kolling Institute aimed to improve research and patient outcomes by linking public and private health datasets. It now has an integrated view of the data across different systems which helps the institute clearly assess variations in care and make more informed decisions about patient care during its episodes of care.
  • 44%
    in minimal trauma patients identified
  • 50%
    in time to identify eligible patients
  • 27%
    in clinical time availability for patient screening

The customer

The Kolling Institute is a recognised world leader at the forefront of research over the past 100 years – turning scientific discoveries into medical realities.  The longest running research organisation in NSW, the Institute links the Northern Sydney Local Health District and the University of Sydney.

Today, hundreds of researchers are part of the team. Given its location on the campus of Royal North Shore Hospital, the institute is in the unique position of being able to directly incorporate scientific breakthroughs into clinical practice.  The teams embrace innovative research and improving the lives of people living with disease by engaging with latest technologies and strong partnerships.

The team is driven by a common goal – to help diagnose, prevent and treat disease – and improve the care their community receives.  

The situation

The Australian healthcare system generates large amounts of data from public and private clinical and non-clinical sources.  The lack of connectivity across healthcare services has resulted in significant impacts on the quality of care and patient safety.  Consumers, who often assume that information is shared, cannot understand why this is not done and why they have to repeat their personal details when attending public and private health providers across the same campus.

The overarching research hypothesis that was undertaken by the Kolling Institute is that integration of data can improve the appropriateness, specificity and efficiency of healthcare delivery through interdisciplinary research.

Alcidion (formerly MKM Health), was engaged to extract data from multiple sources (public and private) and to create an SQL data store to link the extracted data items.

The objective for one group of stakeholders was to improve patient care by answering clinical questions based on integrated data. For others, the goal was to provide a dashboard of linked data to support multi-disciplinary meetings by consolidating all relevant data for a set of nominated patients.

Data was been collected and integrated across three exemplars:

  • Head and Neck Cancer
  • Maternity
  • Cardiology

The clinicians and Kolling Institute research staff now have an integrated view of the data across different systems which helps them to see variation in care and make informed decisions about their patients during their episode of care.

Technical and solution skills have been provided by Alcidion to link the information available across several sources of patient information. The source systems included Cerner’s eMR, McKesson’s Horizon Cardiology System, DXC’s i.Pharmacy, the ObstetriX maternity system and Access and Excel databases for Oncology and Cardiology patient data.  Pathology results have also been incorporated by linking to private pathology providers including Douglas Hanley Moir and this data is matched with existing data sets.

The solution

The Alcidion team used their knowledge of the Cerner Millennium application and Cerner Command Language (CCL) skills to extract data from the eMR solution for the three nominated exemplars.  This data was stored in a central Microsoft SQL repository and matched to data that had been extracted from other systems. Extracts were created using a combination of direct SQL calls, SSIS load routines and real time HL7 messaging.

The integrated datasets were provided to the clinical stakeholders via QlikView applications developed by Alcidion. The Alcidion consultants worked closely with clinicians and user groups to define the data requirements and visualisation techniques.  The applications were then provided to the user base via a web browser using a comprehensive security and access model.

The result

A research database was created for the three exemplars containing more than 900,000 patient episodes and 500,000 associated pathology/radiology results.  This data supports Multi-Disciplinary Team (MDT) meetings to answer specific clinical questions.  For example:

  • How many patients are anaemic just prior to birth and who are they?
  • Of the patients who are discharged how, many are not breast feeding and where do they live?
  • How many patients represent following birth?
  • How great is the variation in care or women in their first pregnancy?

The clinicians and Kolling Institute research staff now have an integrated view of the data across different systems which helps them to see variation in care and make informed decisions about their patients during their episode of care. Individual dashboards and search criteria have been created to suit each exemplar.

The future

The project continued to work with other interested parties to integrate additional datasets, for example, external ultrasound providers and private hospital providers.

An ongoing operational model was implemented to enable the agreed datasets to be refreshed from the production systems.  It is expected that alternative analytical tools will be investigated and other research groups will become involved.  For example, a group at the Institute has been engaged to conduct natural language processing on the textual patient results.  It is expected that when successful, the results of this research project will be extended to include data from other Local Health Districts.

  • Ageing & Pharmacology
  • Allied Health
  • Cancer
  • Cardiology
  • Dementia
  • Dermatology
  • Haematology
  • Musculoskeletal
  • Neuroscience
  • Nursing & Midwifery
  • Pain
  • Pregnancy and Reproduction
  • Rehabilitation
  • Renal
  • Integrated view of patient data across different systems
  • Research database used to support Multi-Disciplinary Team (MDT) meetings and to answer specific clinical questions

Customer Success Stories