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A World of Siloed Data and its Implications on Modern Healthcare in the Post COVID Era

April 7, 2021Category : Blog Healthcare
A World of Siloed Data and its Implications on Modern Healthcare in the Post COVID Era

Looking back, there’s no denying that the healthcare industry has changed forever in the past twelve months. While the vaccine rollout promises to fix much of what’s broken in the coming months, many facets of the healthcare world will never revert to pre-pandemic ways. The pandemic has taught the healthcare industry a lot of difficult lessons. The collective understanding of the vital role that high-quality clinical data plays has changed. Healthcare data extraction or harvesting is necessary, not only as a field of study in information science, but also in practice.  

Data collection efforts have been largely manual, leading to incomplete data that’s often missing key demographic information. As we reflect on a year of unexpected challenges, it has never been more evident that healthcare organizations worldwide need to focus on data even more.

With trends changing not daily but hourly, during the pandemic, healthcare authorities struggle to monitor larger intensive care capacities, track staff safety/fatigue, and optimize every available resource. Simultaneously, decision makers must assimilate new research findings, adjust policies, and do it all in real-time because acting quickly is a matter of life and death. During this crisis, it’s no wonder that healthcare leaders turn to data to make data-informed decisions rapidly.

While the urgency of the pandemic may be pushing the healthcare industry to adopt data more rapidly for decision-making, no one knows what the new normal will look like. To get a better idea of where we’ll go from here, it may help look at where the industry is with data overall.

healthcare data

New Sources Of Healthcare Data

To ensure information is quality and up-to-date, many in the healthcare industry have sought to collect real-world data during the pandemic. This information, collected from sources other than traditional clinical trials, can help stakeholders identify patterns and make critical decisions.

As data-as-a-service technologies have proliferated throughout industry, leading healthcare firms are trying to ensure this data is harnessed to achieve patients’ best outcomes. These IoT technologies include everything from sensors that monitor patient health and the condition of machines to wearables and patients’ mobile phones. The network of these machines means that clinicians have an overview of everything happening in the hospital and can be alerted in real-time should an anomaly in the data reveal changes that need urgent attention.

This radical shift further toward data can support decisions made by doctors and ultimately improve patient outcomes. With the help of artificial intelligence and advanced algorithms, medical professionals will soon see their capabilities advanced by data, in everything from the logistics of prioritizing which patients to treat to how best to support them through diagnosis and treatment. These technologies are changing the way society manages healthcare – leading to healthier citizens with a longer life expectancy. 

Adopting New Technologies

Healthcare providers and clinicians have never been slow to use technology to improve patient outcomes. They have, naturally, sometimes held back because of cost implications. But they always have been quick to see the potential of new technology to help improve patient care. AI, however, has been slower to take off. Somehow, many healthcare providers do not seem to be ready for decision-making supported by algorithms. Perhaps it’s a change of culture and a concern about the explainability of decisions supported by a “black box.” Perhaps staff simply do not yet have the necessary skills and experience to take advantage of the insights locked in the data. Whatever the reason, it’s been a fairly slow start.

New data extraction technologies promise care that is available nearby or at home, support continuous self and autonomous care, and reduces friction costs between supporting stakeholders. These shifts create an imperative for stakeholders to move toward an ecosystem-based model of care enabled by key industry forces driving technological innovation:

    • High rates of healthcare technology investment are being realized. From 2016 to 2020, there have been more than 580 healthcare technology deals in the United States, each more than $10 million, for a total of more than $83 billion in value. They have been disproportionately focused on data and analytics and new care models.
    • Technology giants are locked in a trillion-dollar battle to win share in the public cloud and to retain consumer “mindshare” and engagement. As a result, they are investing billions of R&D dollars into their platforms to create services easily usable by a range of customers and for a range of applications 
    • Healthcare industry incumbents increasingly are making large bets in acquiring capabilities that could advance their ecosystems. Payers, providers, healthcare services, and technology firms are acquiring assets to extend their data and analytics capabilities and engage with patients longitudinally, driving almost $40 billion in healthcare technology deals from 2020 to 2025.

 

But even before the push of the pandemic, a groundswell toward data-driven decision-making was beginning. Several leading healthcare organizations have started to embrace AI and data. They’ve often begun with small-scale projects, but there’s growing recognition that the future lies in personalized healthcare – and that personalized medicine depends on advanced data.

A Shift In Culture Will Take us Into The Future

Now that the COVID-19 crisis is ushering in higher levels of data use, it seems likely that more and more healthcare providers will become data-driven organizations over the next three to five years. This will, in most cases, require a culture change. Providers must move toward using data to generate insights that then drive decisions. This acceptance will likely grow as organizations see what the early impact can be.

A Successful Healthcare Data Strategy Starts Now

Successful data-driven healthcare providers need to centralize their data strategy for business operations and care. This means that healthcare providers must develop strong data and model governance. Staff and managers alike need to be sure that data quality is high and the outputs from models remain appropriate. 

Without reliable data, it’s impossible to generate the necessary impact. A robust data strategy – covering collection, assurance, preparation, and use – will go a long way to help. Throughout history, advancements in healthcare have been met with varying degrees of skepticism by their contemporaries. While the pandemic crisis pushes the healthcare industry to make that cultural shift more quickly, it will be interesting to see what happens during pandemic recovery and potential future outbreaks. 

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