In Conversation… with Dr Rebecca Laborde
In Conversation provides a glimpse into the life of an ‘outlier’ — an exceptional person going above and beyond to improve outcomes in their field. In this issue, our guest is Dr Rebecca Laborde, Oracle Health Scientist and Strategist.
How did you come to be in your current role?
I have a scientific background in translational research and precision medicine and was a bench scientist for 20 years before coming into IT. My PhD was in genomics, and I did my advanced training at Mayo Clinic.
I became interested in the IT space and understanding how a smart application of technology can move outcomes and improve patient care faster. So I transitioned to a scientific-based role in an IT environment, to bridge the gap between the technology and the end users.
What exactly does a scientist and strategist do?
I function as a data scientist and spend all my time working with current and prospective customers — for instance, medical centres, hospitals and clinics — helping to identify solutions that will support their initiatives and result in improved patient outcomes, costs and efficiencies. I also work with biotech and pharmaceutical companies.
How is Oracle technology assisting with data use and management?
We’re providing technology solutions that institutions can use to create environments for aggregating and managing their diverse data. Some of the data is highly structured, for example, medical records data. For this we have developed relational data models that can aggregate that data, bringing together clinical, laboratory, genomics and financial data.
Other data types that may benefit a particular patient in the future are less structured, such as clinical notes dictated by a clinician, written down or typed up. It’s easy to miss important information when it’s captured that way, and almost impossible to use across a population to learn anything about outcomes or trends. These unstructured text sources may be converted to useful data elements through the application of natural language processing (NLP). This work often occurs in a data lake environment, with valuable data insights then being aggregated with structured data. Oracle supports all of these approaches. Implementing this type of environment allows for enterprise-wide use of data from a trusted source to drive analytics in clinical care, cost efficiency and precision medicine.
What is Oracle doing to protect data privacy and its ethical use?
This is a very complicated area. Just look at the landscape: for example, let’s say I have a patient who comes into the clinic and provides a sample under clinical care. This represents clinical data that can be used to treat that patient. Often times they will also agree to sign a research consent. So now that clinical data also becomes research data, but it can’t be used for research without an approved project, which means that data can only be used downstream in a controlled setting.
Oracle’s tools can pick up that information about data permissions and consent and only provide the data elements to the approved people who have the right to access it. And more importantly, in the correct form of identification. Having the tools to de-identify data is very important. Our tools remove the effort and uncertainty of having a person manually de-identifying data.
How are healthcare facilities using technology to reduce costs and improve outcomes?
An interesting example is The University of Texas MD Anderson (MDA) Cancer Center in the US, which runs their Cancer Moon Shots program based largely on the Oracle platform. This program aims to reduce mortality and suffering from 13 cancer types, using a variety of tools including multiple Oracle-based research solutions.
When MDA implemented the program, we assumed they would use it primarily for translational research and genomics, but the first area they focused on was pharmacy analytics to answer the question “how do we improve patient outcomes and save money in pharmacy?”
They wanted to align their multiple compounding pharmacies, their clinicians and their large patient population — some of whom were travelling from as far as Europe for treatment — to ensure all were available at the same time. This challenge was impacting the cost of the product and staffing, the keeping of patients on protocol and patient satisfaction.
MDA pulled in all the data sources related to these different pieces and performed analytics to understand where the issues were in the pipelines. It then standardised processes and ensured all the critical pieces came together in the same location, at the same time. The result was not just improved efficiencies and cost savings, but an improved clinical outcome — those patients were receiving their treatment when they were meant to.
This project significantly improved efficiencies for MDA — and provided them a rapid return on their investment in Oracle platforms, which they reinvested into other projects such as precision medicine. So the cost savings derived from the technology are now driving patient outcome improvement, because if you’re doing everything cost-effectively you can offer a greater level of service.
What’s on the horizon in terms of technology?
Machine learning is a hot topic. Healthcare organisations are treading cautiously, as they should, and essentially moving it into the research space first, finding smart ways to integrate it.
Another big trend is natural language processing. This is already heavily in use in health care. Clinical notes and unstructured text hold a goldmine of data for institutions, both in terms of clinical value and cost value, and having the technology to access that in an automated way is really critical.
The third trend is genomics, seeing diversification of data for clinical use. We’re starting to see full exome and genome sequencing, because it’s cheaper to generate, store and manage, and we know more about it. But we’re also starting to see other genomic types, like sequencing RNA. So clinically, we’re seeing examples where we’re generating more than one genomics type to diagnose a patient.
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