One of the glaring lessons COVID-19 has taught us is how valuable tailored data are for understanding and tracking trends in public health, while also leveraging these data to optimize care and effectively streamline resources.
As the crisis emerged, a primary challenge many organizations faced was data availability. Whether it be data fragmentation within the supply chain or new diseases requiring data models to be trained and scored from only a few confirmed cases, so many teams didn’t have access to these crucial data sets due to disparate information sources and data silos when trying to create solutions amidst the pandemic. Even some of the most highly regarded research institutions were reduced to scaling their data sets through web-based symptom surveys when building models and had access to limited technology infrastructure in place to share findings.
Healthcare data analytics is a critical resource that hospitals and healthcare systems tap to offer insights into patient management, hospital operation, cost management and more. Unfortunately, most hospitals and healthcare systems have opted for canned, prepackaged analytics—when in reality, the best solutions to understand the data and trends that drive the most valuable return need to be built from scratch.
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Crucial elements of a successful program include building data infrastructure from the inside, so just as data science is critical to most every industry today to gain a competitive edge, healthcare should value this intelligence and designate data analysts and scientists as essential employees. From there, teams can build robust, advanced programs with technology partners to leverage observational analytics tools like video streaming, real-time analytics for emergency management of large medical centers, web touch and digital front door workflows that understand behaviors and patterns.
Now that we’ve been through round one with COVID-19, it’s time to prepare for the next surge of cases by creating data-driven decisions and forecasting future public health threats.
Below are four predictions on how healthcare analytics programs will need to evolve based on what we have learned from the pandemic:
- ‘Coopetition’ partnerships will continue to emerge: A silver lining in all of this is the amount of collaboration that has taken place. We’re witnessing typically competitive markets come together to mobilize and exchange data quickly. Collectively, they are disrupting regional case preparation and predictions, generating unbiased therapeutic outcomes analysis and ventilator availability analysis. One shining example of this is how Rush Medical and neighboring Chicago acute care centers are collaborating to share bed capacity data. This partnership has added reaction time for both case and emergency management to mitigate ambulance diversions and inform on-the-ground staff of abnormal fluctuation in patient flows.
- Cross-functional cooperation will increase: A few organizations have begun to oversee collaboration among emergency management and patient safety teams to open their doors safely and efficiently to non-COVID-19 patients. These partnerships have created innovative strategies like using emerging technologies units to repurpose video camera analysis (which is often utilized for retail analytics) to now ensure physical distancing is enforced and at-risk populations remain safe on large campuses.
- Expect even more transparency to be created in the healthcare supply chain regionally: The data that prevent stock-outs within an organization are largely the same principles that prevent stock-outs regionally. Anticipate seeing more collaboration and incentives to prevent these critical supplies from diminishing at both regional and national levels for the greater good of the healthcare workforce.
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- Hedging bets on barrier analytics: If a baseline understanding is not established as to why certain individuals are or are not responding to support programs, even achieving incremental success will be limited to supporting patient behavior on preventative care initiatives. After COVID-19, there is a need to double-down on “barrier analytics,” or analytics that remove barriers to improved outcomes. Organizations that fine-tune their outcomes programs to alleviate barriers to access to equity care, add self-care compliance, drive telemedicine insights and prioritize team collaboration will thrive.
With financial, technical and workforce constraints influencing decisions, as well as the cultural shift needed to view data analytics as critical infrastructure and assets for crisis management, the challenge is real for leaders to create viable healthcare data analytics programs to meet the needs for COVID-19 recovery and beyond.
However, it’s more critical than ever to build platforms and create expert data teams which can quickly synthesize models to forecast patient volume and supply availability—that can be adjusted in real-time—to create safe spaces for patients to seek care and for healthcare workers to provide care.
Daniel Ulatowski is an epidemiologist and Healthcare & Life Sciences Advisor at Teradata, a leader in healthcare analytics.