Accurate, timely data can literally be the difference between life or death in the healthcare sector. As a healthcare patient myself, my own health outcomes have been directly impacted from a lack of robust analytics within the field. That’s part of the reason I am passionate about enabling future change within the provider, pharmaceutical, research and biotech industries.
While the industry as a whole has been slow to adopt new technologies, the possibilities offered by today’s modern data stack are causing dramatic shifts in the way we deliver and receive care.
We asked several leaders in healthcare and life sciences how new business intelligence innovations are transforming their sector. Here’s what they told us:
Business analytics in healthcare involves using BI tools and technologies to convert raw data into actionable insights. The insights gleaned from the data can help you improve operational efficiency, patient care, and decision-making within the healthcare industry. You can combine data from disparate sources and preprocess it to explore hidden insights and discover improvement opportunities.
Healthcare is notoriously cautious when it comes to data; after all, this is very sensitive personal information protected by HIPPA privacy laws. On The Data Chief podcast, Todd Crosslin, the Global Industry Principal Healthcare and Life Sciences at Snowflake, spoke of the difficulties of working at the intersection between healthcare and data. “It's a struggle. You have to be a little bit crazy to do this to yourself,” he said.
Of course, data privacy and governance aren’t the only factors that make healthcare so challenging for BI and data analytics professionals. Jon Osborn, a software technology executive and the former Chief Technical and Data Officer at Ensemble Health Partners, told The Data Chief, “I think one of the things that holds back healthcare is that some of the data is relatively complex and the relationships are hard to build.”
The healthcare and life sciences ecosystem is composed of multiple stakeholders: patients, healthcare providers, pharmaceutical companies, and insurance firms—each have different priorities. This complex landscape contributes to an excessive amount of data governance, as Crosslin explains:
“The provider wants the best patient care. The patient only wants to share their private data as far as it helps their outcome. The payer wants to control the costs as much as possible. So, how do you get the conflicting priorities of these different pieces to play nicely together as a cross-functional team?”
In summary, healthcare data is complex, highly sensitive, owned by multiple competing stakeholders, hard to share, and heavily siloed—small wonder that healthcare BI has lagged behind other industries; however, things are looking up. Evolving technology has made it easier to protect, exchange, and analyze healthcare and life sciences data’. The insights this data produces help healthcare professionals improve patient care, predict health outcomes, and literally save lives.
COVID-19 accelerated the shifts already underway in the healthcare sector. By applying new BI and healthcare analytics technologies, teams can quickly analyze billions of rows of data from a variety of sources including biometrics, inventory levels, patient results, even health sensors. Here are a few examples of healthcare innovation powered by BI:
It’s no secret that data analysts aren’t usually doctors. Using traditional BI or data analytics tools, the journey from data to insight involves multiple back-and-forth processes between the analyst and the physician. This process is cumbersome and both parties are busy. When it comes time to start prioritizing workload, this insight-to-action process is often cut from the equation, leaving invaluable, life-saving insights trapped in data silos.
If, for example, a cancer researcher has a question about a specific trend that they’re seeing in patient data, they would need to request a report from the data team taking days or even weeks to prepare—in turn, blocking the researcher from immediate and actionable insights. Given that large healthcare organizations can utilize multiple EHR systems, the data team might also need to perform data cleansing and transformation functions in order to analyze the various data sources.
The full data pipeline from preparation to visualization to analysis is completed by data teams who might not know what anomalies to look out for. These data professionals usually don’t have the medical expertise to anticipate the follow-up questions. So, the medical professional receives a one-size-fits-all report that lacks context.
In comparison, self-service analytics creates direct access between technically skilled medical professionals—doctors, researchers, hospital managers or pharmaceutical reps—and the data. By deleting the middle person, you’re empowering real-time analysis by the decision maker which is proven to have real-world impacts.
“You put them down in front of ThoughtSpot, they start asking questions, and... they get lost in it and they don't come back up for four hours. Then suddenly [they share] these massive insights, because of their background–maybe they are a physician or a nurse; you need to pull those people together and collaborate.”
When healthcare professionals are able to explore and analyze the data, they’re more likely to notice patterns and derive unexpected insights that can inform their work. This is the power of physician-led data insights.
By making it faster and easier to analyze clinical trials results and historical genomics data, today’s BI tools are decreasing the time to market for life-saving drugs. For Crosslin, one of the most exciting developments is the ability to put aggregate data querying into the hands of medical researchers. In our conversation with him on The Data Chief podcast, he shared how the idea resonated with a practitioner:
“I was talking about genomic data and a practitioner said, ‘Hey, I get a couple of variants inside of Epic. Now I can see a patient has several variants for their genomic data.’ And I said, ‘So imagine that you actually had all of their genomic data and you could run a query against all of their data against all of their variants and have that present as testing moves forward.' And they were like, ‘Ohhh!’”
Crosslin is adamant that democratizing analytics and enabling researchers to interact with aggregate queries, instead of interacting with a long list of data points, will transform the pace of medical research.
The challenge with health data, of course, is that it’s rarely easy to combine. Major privacy issues and incompatible formatting made it impossible to unify diverse data sources and derive actionable insights; however, when you combine a cloud data platform like Snowflake with a self-service BI tool like ThoughtSpot, you unlock unprecedented levels of data unification.
“We're in these conversations now with companies that know genomics really well and companies that know patient data really well. Before, they were not able to have this conversation to say, 'What if I could just join these datasets? I don't want to move them and forklift them from point a to point B. I just want to join them.' And now they can do that."
- Todd Crosslin, Global Industry Principal Healthcare and Life Sciences at Snowflake
This isn’t only of interest to researchers. Improved data sharing and analytics tools will also have significant benefits for patients. For example, with better data analytics tools, hospitals can cross-reference patient electronic health records with nation-wide statistics to surface likely causes of ailments based on symptoms and pre-existing conditions. Data sharing helps guide doctors on how to treat patients more accurately and consistently.
It’s not only the healthcare providers and researchers that stand to benefit from better access to data. Self-service BI tools that prioritize accessibility and ease of use also make it far easier for patients to access, monitor, and control their own data. Dr. Victoria Gamerman shared more on this topic in a recent episode of The Data Chief podcast.
“The idea is that I, as a patient, would be able to have one place where I can go, and I see all of my connected healthcare information.”
The lack of data unification and the issues with data sharing have made this something of a pipedream until now; however, Gamerman believes that soon, every patient will be able to interact with their own health data–not just medical information, but also data about their wellness levels.
Real-life applications of this are already available. You might interact with examples of this kind of data sharing through your healthcare providers’ My Chart or healthcare portal, but this is only the start. There are ample opportunities to improve the user experience and expand the data that is captured in these patient-facing applications, such as including biometric data from a heart-rate monitor or wearable fitness tracker.
Healthcare and clinical development has always been driven by data, but modern analytics and BI tools make it easier to tell a story with data. By combining medical data with powerful data visualizations, all users, regardless of their level of data literacy, can draw insights and interact with data in a meaningful way.
Whether you’re a researcher seeking funding or a pharmaceutical company hoping to capture greater market share, data visualizations help you identify opportunities and aid in building a convincing argument..
Mahmood Majeed is a Managing Partner and the Global Leader for Digital and Technology Business at ZS Associates. Majeed told us on The Data Chief podcast that the recent developments in business intelligence AI and machine learning technologies are making predictive healthcare possible, and giving rise to scenarios that start to sound like science fiction.
“You actually can predict the likelihood of a prescriber writing a script before a script is being written,” he said. “You can actually predict a patient dropping a therapy before they drop it. You can actually predict a plan changing their formulary status before it actually happens.”
For hospitals and healthcare providers, the financial implications of AI-powered analytics are huge. Augmented analytics platforms like ThoughtSpot provide instant answers to searched questions, along with answers to additional questions employees didn’t think to ask. As a result, users can spot savings and bottlenecks in key operational areas, like hospital staffing, operational costs, patient turnover, and wait times.
BI and healthcare analytics tools put health data in the hands of those that need it most—the physicians, nurses, researchers, and health operations staff at the frontline of patient care.
For healthcare providers, pharmaceutical companies and patients, these capabilities can help lower operational costs and build greater market share, but for many healthcare leaders, the most exciting aspect of this development is the impact it will have on patient care.
“It will allow us to better serve the patients of today, and also the patients of the future,” Dr. Gamerman said.
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