3 data trends that will drive the future of healthcare

The past few years have been ones of radical change in the healthcare industry. The pandemic accelerated the transformation to digital, and it made everyone take a closer look at how to use data to make that transition faster and easier, but also to find new ways to improve outcomes.

Multiple opportunities have arisen for every organization to use data to find new solutions for survival, adaptation, refocusing, and growth. In fact, 85% of healthcare executives recognize that technology is an inextricable part of the human experience, according to an Accenture report (July 2020). Using data to help spur and support every area of growth makes sense: It enables life-saving solutions for patients, more agile responses and action in managing disease and emergencies, and improves patient care by providing more options online. Every area in healthcare can benefit from a data-driven mindset.

We reached out to a panel of healthcare experts and asked them where they see data having the biggest impact. As we emerge from this global pandemic, three areas stand out: Health equity, telemedicine, and operational agility. Read on for their input on how data can and will impact the future of healthcare. 

How are you seeing data applied to address healthcare inequities – and how can it be used in the future to make it more equitable?

Evan Kasof, VP, National Healthcare Providers, Tableau: Social determinants of health’s (SDOH) vision will continue to impact the future of care delivery, with data and analytics being critical to success. We’ve seen it through the pandemic where analytics went from a nice-to-have to being mission-critical.

Dynamic data and visualizations will aid providers in taking a holistic approach to wellbeing in care models, including integration of SDOH data. Analytics are being leveraged to segment the patient population to understand which members are at risk of falling behind on care plans and proactively act.

Through predictive AI/ML capabilities, additional insights can be gained on recommended next best actions. An example would be reducing patient no-shows by predicting, based on data, how to improve the chances that the patient makes their appointment. The future of population health and SDOH will be based on a provider’s ability to govern and connect to the data in near real-time, scale its usability and act on the insights gained through predictions.

Chris Winquist, Area Vice President, Healthcare Payer/Summit/SMB: The collection and analysis of social determinants of health have provided immeasurable value in guiding traditional and upstart healthcare organizations on ways in which they can close gaps and provide improved access to care. We have just begun the process of using data to help solve healthcare inequities, and there is much opportunity in this space.

Jeremy Racine, Director of Healthcare, Salesforce/Tableau: Before the pandemic, health equity simply did not command the level of interest it always deserved. While there has existed immense opportunities to employ data as a powerful catalyst for changing the narrative on health equity, we need only look at the realities of COVID-19, including significant racial health disparities, which serve as a sobering reminder of the lack of historical progress including a glaring gap in maturing data and analytic solutions to help support change.

However, there is a bright light here. The pandemic brought an elevated hyper-focus on health inequities and the ever-important role data and analytics play. Healthcare provider systems and payers are looking at improving the collection, integration, and application of SDOH data. SDOH data is an absolute necessity for the effective analysis of potential health inequities and associated mitigation strategies. Healthcare organizations are also working to mature their data quality and management solutions to ensure they have fully integrated, high-quality, trusted, accurate, complete, and standardized SDOH data.

We expect to see organizations continue to rapidly mature their health equity efforts and invest more in data and analytic initiatives. Health equity is not a single solution area, though. If organizations expect to drastically move the needle, we must face the reality that nearly every aspect of a healthcare provider and payers business may necessitate health equity analysis. We expect to see healthcare organizations embrace this reality and look at enterprise-wide health equity data and analytic efforts as part of their strategic blueprints. 

How does data improve operational agility in healthcare?

EK: The use of data and analytics to scale, streamline operations, and quickly adapt to changing patient needs across staffing, supply chain, and revenue cycle management helps. You can also leverage analytics to track care outcomes, detect care gaps, measure standards of care adherence, and other operational measures of effective care.

Data also helps care teams identify staffing, facility, supply or equipment concerns that would limit the facility’s ability to meet clinical demand. It helps them gain insights to reduce readmissions, claim denials, redundant billing, duplicated supply orders, or managing value-based contracts. You can again leverage AI/ML Predictions to improve KPIs around LOS, costs, PSAT scores and more.

CW: Supply-chain and revenue-cycle management are use-case scenarios where data analytics is highly used and valuable. Healthcare organizations run on slim margins and need to maximize revenue and billing. Maintaining the right equipment, devices, and supplies is critical to clinical outcomes. In both cases, analytical insights create opportunities to improve revenue and patient satisfaction.

JR: Data and analytics also provide crucial insights into operations across the enterprise, enabling key stakeholders with the ability to not only identify potential issues but also assess potential future impact and model and optimize policies, programs, and other interventions. More organizational leaders have realized the importance of fusing and analyzing individual and cross-organizational data.

What role does data play in advancing telemedicine?

EK: Providers can leverage operational visualizations to effectively plan and manage virtual care, recapture revenue, predict and optimize care delivery and set new expectations for care with providers and patients. It helps create holistic views of patient data and risk stratification protocol for predicting and identifying which patients should have virtual referrals or visit a facility for future care.

CW: As telemedicine continues to increase in usage and popularity, the data surrounding efficiency, cost, and patient satisfaction will be critical in determining how best to scale and execute telemedicine strategies.

JR: Data can play a significant role in advancing remote patient monitoring and engagement. As on-person and at-home medical devices develop at warp speed, real-time data will also grow at an exponential pace. Patients have an expectation for quick and relevant answers to their pressing questions.

This is where the intersection with telemedicine perfectly aligns. More medical device data requires robust data and analytics systems to ingest, cleanse and validate the data. The data then needs to be passed on for remote patient monitoring analysis and for supporting encounters, especially those telemedicine-based.

What are top lessons we’ve learned out of the pandemic that can help us going forward?

EK: Data and analytics are now mission-critical to improved patient outcomes, increased operational efficiency, and a competitive advantage. Transforming an organization to becoming data-driven involves having the right governance model, your data in a single source of truth, and enabling your clients with self-service capabilities.

CW: Retrospective data analysis isn't sufficient. Access to high-performance, real-time data is critical to ensuring optimal care whether we’re in a pandemic or not.

JR: We are not nearly as mature in our data and analytic systems as we thought. Many organizations struggled to capture, analyze and share data in the midst of the pandemic. While we have invested much in modernizing healthcare IT systems, we have a ways to go.

And interoperability was a glaring gap. Many healthcare systems were unable to timely and effectively share trusted, basic data around hospital operations and supply chain during a time of extreme need. It was relatively shocking to see how many organizations resorted to legacy approaches like phone, fax, or email to share data. We have some of the most advanced data and analytics systems available in the world, and, hopefully, we will see more organizational investment in these systems. Additionally, more is needed in terms of cross-industry collaboration to improve data-sharing efforts.

Where can data be applied to improve healthcare where it isn’t being used to its fullest potential?

EK: Leveraging a platform like Tableau enables people to explore insights from any data source in the workflow of their apps while also improving efficiency and adoption. This includes the EMR, which could include a tool like Slack, Workday, or a patient portal.

Insights leading to action is the idea that the insights and predictions will kick off an automated workflow in a CRM Tool like Salesforce. And the ability to leverage Natural Language (NLP) to ask questions of your data, whether in Tableau or in your app, is another area of evolution.

JR: I see two areas ready for massive change—the use of data/AI to support medical-imaging initiatives, from radiological analysis to dermatological analysis and many areas in-between. There have been great gains made in the domain, but there is significant room for expansion, especially with the evolution of at-home devices. New data is flowing in and expected to grow vastly, and AI assistance can provide service providers with quick and reliable analysis that they otherwise might have never received or would have taken extensive time to gather.

Genomic data continues to make gains, but we appear only to be scraping the surface of its potential. Integrating more genomic data into the clinical setting may provide frontline providers with a new set of data points to consider in treating their patients. Some providers are leveraging such data, but largely it is quite limited in its everyday application. I imagine a day soon when having genomic data captured as often as many familiar labs will be the norm.

In conclusion

Every day around the world, organizations across the healthcare and life sciences ecosystem use Tableau for the critical insights they need to make data-driven decisions that help save lives and maintain business viability. Check out our Healthcare and Life Sciences Analytics Solutions page to see how Tableau can help your organization improve the patient experience and care outcomes.

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